in charge of thinking learning emotions problem solving

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Emotions in Problem Solving

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• Mathematics, Psychology, Education

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Frustration in mathematical problem-solving: a systematic review of research, gnostic emotions of students in solving of thinking tasks, roles of emotions induced by immediate feedback in a physics problem-solving activity, affect in mathematical problem posing: conceptualization, advances, and future directions for research, relating students’ emotions during argumentative discourse to their learning of real-life functional situations.

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Positioning and Emotions in Tackling Algebra Problems with Technology: The Case of High- and Middle-Achieving Students

Non-routine problem posing and prospective middle school mathematics teachers: an emotional perspective, a research on professional tour guides emotional intelligence and problem-solving skills, collaborative learning to improve problem-solving skills: a relation affecting through attitude toward mathematics, an overview of the growth and trends of current research on emotions and mathematics, 76 references, affective issues in mathematical problem solving: some theoretical considerations., affective pathways and representation in mathematical problem solving.

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“Accepting Emotional Complexity”: A Socio-Constructivist Perspective on the Role of Emotions in the Mathematics Classroom

Students’ self-regulation of emotions in mathematics: an analysis of meta-emotional knowledge and skills, all alone with your emotions : an analysis of student emotions during effortful problem solving activities, affect and self-regulation, motivation in mathematics: goals reflected in emotions, affect and meta-affect in mathematical problem solving: a representational perspective, mother and child emotions during mathematics homework, achievement emotions: a control-value approach, related papers.

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Emotions: Functions and Effects on Learning

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Emotions serve to initiate and coordinate individual reactions to important events and objects. They consist of a number of interrelated component processes, including affective, cognitive, physiological, motivational, and expressive components (Kleinginna and Kleinginna 1981 ). For example, a student’s anxiety before an exam can be comprised of nervous, uneasy feelings (affective); worries about failing the exam (cognitive); increased physiological activation (physiological); impulses to escape the situation (motivation); and anxious facial expression (expressive).

As compared to intense emotions, moods are of lower intensity and lack a specific referent. Some authors define emotion and mood as categorically distinct (see Rosenberg 1998 ). Alternatively, since moods show a similar profile of components and similar qualitative differences as emotions (as in cheerful, angry, or anxious mood), they can be regarded as low-intensity emotions (Pekrun 2006 ). Different positive and...

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This paper presents a framework for describing the development and organization of emotional knowledge. Our primary aim is the construction of a theory that explains how people represent, understand, and use knowledge about emotion. This theory includes a description of the knowledge acquired about the conditions that elicit emotional responses, the way in which emotion organizes and regulates cognitive planning and overt action, and the decision making and problem solving processes that occur during and subsequent to emotion experiences.

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REVIEW article

The influences of emotion on learning and memory.

• Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi Petronas, Seri Iskandar, Malaysia

Emotion has a substantial influence on the cognitive processes in humans, including perception, attention, learning, memory, reasoning, and problem solving. Emotion has a particularly strong influence on attention, especially modulating the selectivity of attention as well as motivating action and behavior. This attentional and executive control is intimately linked to learning processes, as intrinsically limited attentional capacities are better focused on relevant information. Emotion also facilitates encoding and helps retrieval of information efficiently. However, the effects of emotion on learning and memory are not always univalent, as studies have reported that emotion either enhances or impairs learning and long-term memory (LTM) retention, depending on a range of factors. Recent neuroimaging findings have indicated that the amygdala and prefrontal cortex cooperate with the medial temporal lobe in an integrated manner that affords (i) the amygdala modulating memory consolidation; (ii) the prefrontal cortex mediating memory encoding and formation; and (iii) the hippocampus for successful learning and LTM retention. We also review the nested hierarchies of circular emotional control and cognitive regulation (bottom-up and top-down influences) within the brain to achieve optimal integration of emotional and cognitive processing. This review highlights a basic evolutionary approach to emotion to understand the effects of emotion on learning and memory and the functional roles played by various brain regions and their mutual interactions in relation to emotional processing. We also summarize the current state of knowledge on the impact of emotion on memory and map implications for educational settings. In addition to elucidating the memory-enhancing effects of emotion, neuroimaging findings extend our understanding of emotional influences on learning and memory processes; this knowledge may be useful for the design of effective educational curricula to provide a conducive learning environment for both traditional “live” learning in classrooms and “virtual” learning through online-based educational technologies.

Introduction

Emotional experiences are ubiquitous in nature and important and perhaps even critical in academic settings, as emotion modulates virtually every aspect of cognition. Tests, examinations, homework, and deadlines are associated with different emotional states that encompass frustration, anxiety, and boredom. Even subject matter influences emotions that affect one’s ability to learn and remember. The usage of computer-based multimedia educational technologies, such as intelligent tutoring systems (ITSs) and massive open online courses (MOOCs), which are gradually replacing traditional face-to-face learning environments, is increasing. This may induce various emotional experiences in learners. Hence, emotional influences should be carefully considered in educational courses design to maximize learner engagement as well as improve learning and long-term retention of the material ( Shen et al., 2009 ). Numerous studies have reported that human cognitive processes are affected by emotions, including attention ( Vuilleumier, 2005 ), learning and memory ( Phelps, 2004 ; Um et al., 2012 ), reasoning ( Jung et al., 2014 ), and problem-solving ( Isen et al., 1987 ). These factors are critical in educational domains because when students face such difficulties, it defeats the purpose of schooling and can potentially render it meaningless. Most importantly, emotional stimuli appear to consume more attentional resources than non-emotional stimuli ( Schupp et al., 2007 ). Moreover, attentional and motivational components of emotion have been linked to heightened learning and memory ( Pekrun, 1992 ; Seli et al., 2016 ). Hence, emotional experiences/stimuli appear to be remembered vividly and accurately, with great resilience over time.

Recent studies using functional neuroimaging techniques detect and recognize human emotional states and have become a topic of increasing research in cognitive neuroscience, affective neuroscience, and educational psychology to optimize learning and memory outcomes ( Carew and Magsamen, 2010 ; Um et al., 2012 ). Human emotions comprise complex interactions of subjective feelings as well as physiological and behavioral responses that are especially triggered by external stimuli, which are subjectively perceived as “personally significant.” Three different approaches are used to monitor the changes in emotional states: (1) subjective approaches that assess subjective feelings and experiences; (2) behavioral investigations of facial expressions ( Jack and Schyns, 2015 ), vocal expressions ( Russell et al., 2003 ), and gestural changes ( Dael et al., 2012 ); and (3) objective approaches via physiological responses that include electrical and hemodynamic of the central nervous system (CNS) activities ( Vytal and Hamann, 2010 ) in addition to autonomic nervous system (ANS) responses such as heart rate, respiratory volume/rate, skin temperature, skin conductance and blood volume pulses ( Li and Chen, 2006 ). The CNS and ANS physiological responses (brain vs. body organs) can be objectively measured via neuroimaging and biosensors and are more difficult to consciously conceal or manipulate compared to subjective and behavioral responses. Although functional neuroimaging enables us to identify brain regions of interest for cognitive and emotional processing, it is difficult to comprehend emotional influences on learning and memory retrieval without a fundamental understanding of the brain’s inherent emotional operating systems.

The aim of this current article was to highlight an evolutionary approach to emotion, which may facilitate understanding of the effects of emotion on learning and memory. We initially present the terminology used in affective neuroscience studies, describe the roles of emotion and motivation in learning and memory, and outline the evolutionary framework and the seven primary emotional system. This is followed by the emotional-cognitive interactions in the various brain regions that are intimately involved in emotion and memory systems. This is performed to define the congruent interactions in these regions are associated with long-term memory (LTM) retention. We then discuss the emerging studies that further our understanding of emotional effects deriving from different modalities of emotional content. This is followed by a discussion of four major functional neuroimaging techniques, including functional magnetic resonance imaging (fMRI), positron emission tomography (PET), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS). We then present the important factors for consideration in experimental design, followed by a description of psychiatric disorders, such as depression and anxiety, which are emotionally charged dysfunctions that are strongly detrimental to cognitive performance. Our review ends with concluding remarks on the current issues and future research possibilities with respect to the efficient enhancement of educational practices and technologies.

Emotions, Moods, Feelings, Affects and Drives

Subjective terms used in affective neuroscience include emotions, moods, feelings, affects and drives. Although emotion has long been studied, it bears no single definition. A review of 92 putative definitions and nine skeptical statements ( Kleinginna and Kleinginna, 1981 ) suggests a definition with a rather broad consensus:

Emotions describe a complex set of interactions between subjective and objective variables that are mediated by neural and hormonal systems, which can (a) give rise to affective experiences of emotional valence (pleasure-displeasure) and emotional arousal (high-low activation/calming-arousing); (b) generate cognitive processes such as emotionally relevant perceptual affect, appraisals, labeling processes; (c) activate widespread psychological and physiological changes to the arousing conditions; and (d) motivate behavior that is often but not always expressive, goal-directed and adaptive.

Although this definition may be adequate for everyday purposes, it does not encompass some important aspects of emotional systems such as how emotions operate to create subjectively experienced feelings and how they control personality dimensions. Accordingly, Panksepp (1998) suggested the following:

Emotions are the psychoneural processes that are influential in controlling the vigor and patterning of actions in the dynamic flow of intense behavioral interchanges between animals as well as with certain objects that are important for survival. Hence, each emotion has a characteristic “feeling tone” that is especially important in encoding the intrinsic values of these interactions, depending on their likelihood of either promoting or hindering survival (both in the immediate “personal” and long-term “reproductive” sense). Subjective experiential-feelings arise from the interactions of various emotional systems with the fundamental brain substrates of “the self,” that is important in encoding new information as well as retrieving information on subsequent events and allowing individuals efficiently to generalize new events and make decisions.

He went further to propose seven primary emotional systems/prototype emotional states, namely SEEKING, RAGE, FEAR, LUST, CARE, PANIC/GRIEF, and PLAY that represent basic foundations for living and learning.

Moods last longer than emotions, which are also characterized by positive and negative moods. In contrast, feelings refer to mental experiences that are necessarily valence, either good or bad as well as accompanied by internal physiological changes in the body, specifically the viscera, including the heart, lungs, and gut, for maintaining or restoring homeostatic balances. Feelings are not commonly caused emotions. Because the generation of emotional feelings requires a neural re-mapping of different features of the body state in the CNS, resulting from cognitive “appraisal” where the anterior insular cortex plays a key integrative role ( Craig and Craig, 2009 ; Damasio and Carvalho, 2013 ). Nonetheless, Panksepp (2005) has defended the view that emotional operating systems (caudal and medial subcortical brain regions) appeared to generate emotional experiences via localized electrical stimulation of the brain stimulation (ESB) rather dependent on changes of the external environment or bodily states. Affects are subjective experienced emotional feelings that are difficult to describe, but have been linked to bodily states such as homeostatic drives (hunger and thirst) and external stimuli (visual, auditory, taste, touch, smell) ( Panksepp, 2005 ). The latter are sometimes called “core affect,” which refers to consciously accessible elemental processes involving pleasure and arousal that span bipolar dimensions ( Russell and Barrett, 1999 ). In addition, a “drive” is an inherent action program that is responsible for the satisfaction of basic and instinctual (biologically pre-set) physiological needs, e.g., hunger, thirst, libido, exploration, play, and attachment to mates ( Panksepp, 1998 ); this is sometimes called “homeostatic drive.” In brief, a crucial characteristic shared by emotion, mood, feeling, affect and drive is their intrinsic valence, which lies on the spectrum of positive and negative valence (pleasure-displeasure/goodness-badness). The term emotion exemplifies the “umbrella” concept that includes affective, cognitive, behavioral, expressive and physiological changes; emotion is triggered by external stimuli and associated with the combination of feeling and motivation.

Recent Evidence Regarding the Role of Emotion in Learning and Memory

The impact of emotion on learning processes is the focus of many current studies. Although it is well established that emotions influence memory retention and recall, in terms of learning, the question of emotional impacts remains questionable. Some studies report that positive emotions facilitate learning and contribute to academic achievement, being mediated by the levels of self-motivation and satisfaction with learning materials ( Um et al., 2012 ). Conversely, a recent study reported that negative learning-centered state (confusion) improve learning because of an increased focus of attention on learning material that leads to higher performances on post tests and transfer tests ( D’Mello et al., 2014 ). Confusion is not an emotion but a cognitive disequilibrium state induced by contradictory data. A confused student might be frustrated with their poor understanding of subject matter, and this is related to both the SEEKING and RAGE systems, with a low-level of activation of rage or irritation, and amplification of SEEKING. Hence, motivated students who respond to their confusion seek new understanding by doing additional cognitive work. Further clarification of this enhances learning. Moreover, stress, a negative emotional state, has also been reported to facilitate and/or impair both learning and memory, depending on intensity and duration ( Vogel and Schwabe, 2016 ). More specifically, mild and acute stress facilitates learning and cognitive performance, while excess and chronic stress impairs learning and is detrimental to memory performance. Many other negative consequences attend owing to overactivity of the hypothalamic-pituitary-adrenal (HPA) axis, which results in both impaired synaptic plasticity and learning ability ( Joëls et al., 2004 ). Nonetheless, confounding influences of emotions on learning and memory can be explained in terms of attentional and motivational components. Attentional components enhance perceptual processing, which then helps to select and organize salient information via a “bottom-up” approach to higher brain functions and awareness ( Vuilleumier, 2005 ). Motivational components induce curiosity, which is a state associated with psychological interest in novel and/or surprising activities (stimuli). A curiosity state encourages further exploration and apparently prepares the brain to learn and remember in both children and adults ( Oudeyer et al., 2016 ). The term “surprising” might be conceptualized as an incongruous situation (expectancy violation) refers to a discrepancy between prior expectations and the new information; it may drive a cognitive reset for “learned content” that draws one’s attention.

Similarly, emotionally enhanced memory functions have been reported in relation to selective attention elicited by emotionally salient stimuli ( Vuilleumier, 2005 ; Schupp et al., 2007 ). During the initial perceptual stage, attention is biased toward emotionally salient information that supports detection by the salient input. Thus, stimulating selective attention increases the likelihood for emotional information to become encoded in LTM storage associated with a top-down control in sensory pathways that are modulated by the frontal and parietal cortices. This is an example of an indirect influence on perception and attention that regulates selective sensory processing and behavioral determination ( Vuilleumier, 2005 ). Because the human sensory systems have no capacity to simultaneously process everything at once, which necessitates attentional mechanisms. Top-down attentional processing obtains adequate attentional resource allocation to process emotional valence information for encoding and retrieval via cooperation with the brain regions such as the ventromedial prefrontal cortex and superior temporal sulcus, along with the primary visual cortex (helps to realize both emotion and conceptualization). Similarly, experimental studies have examined the phenomenon by using various attentional tasks, including filtering (dichotic listening and Stroop task), search (visual search), cuing (attentional probe, spatial cuing) and attentional blink [rapid serial visual presentation (RSVP)] paradigms ( Yiend, 2010 ). These investigations demonstrated biased attentional processing toward emotionally stimulating material content attended by increased sensory responses. One study reported that emotional stimuli induce a “pop-out” effect that leads to the attentional capture and privileged processing ( Öhman et al., 2001 ). Moreover, a study using the RSVP paradigm compared healthy subjects with a group of patients with bilateral amygdala damage. The results revealed that healthy subjects exhibited increased perception and attention toward emotional words compared to patients, indicating that the amygdala plays a crucial role in emotional processing ( Anderson and Phelps, 2001 ). In addition, functional neuroimaging showed that the insular cortex, the secondary somatosensory cortex, the cingulate cortex and nuclei in the tegmentum and hypothalamus are the brain regions that regulate attentional focus by integrating external and internal inputs to create emotional feeling states, thus modulating a motivational state that obtains homeostasis ( Damasio et al., 2000 ). All emotional systems associated with strong motivational components such as psychological salient bodily need states operate through the SEEKING system that motivates appetitive/exploratory behavior to acquire resources needed for survival ( Montag and Panksepp, 2017 ).

The distinction between emotion and homeostasis, is the process of regulation for continuously changing internal states via appropriate corrective responses that respond to both internal and external environmental conditions to maintain an optimal physiological state in the body. Homeostatic affects , such as hunger and thirst, are not considered prototype emotional states. Because homeostatic affects have never been mapped using ESB that arouse basic emotional responses ( Panksepp, 2005 , 2007 ). However, emotional prototypes can be thought of as evolutionary extensions/predictions of impending homeostatic threats; for example, SEEKING might be an evolutionary extension of intense hunger and thirst (the major sources of suffering that signal energy depletion to search for food and water intake) ( Watt, 2012 ). Homeostatic imbalances engage the mesolimbic motivational system via hypothalamic interactions with the extended trajectory of the SEEKING system [centrally including the lateral hypothalamus, ventral basal ganglia, and ventral tegmental area (VTA)]. It is the distributed functional network that serves the general function of finding resources for survival that gets hungry animals to food, thirsty animals to water, cold animals to warmer environments, etc. ( Panksepp, 1998 ). To summarize, both emotion and motivation are crucial for the maintenance of psychological and physiological homeostasis, while emotional roles are particularly important in the process of encoding new information containing emotional components. The latter increases attention toward salient new information by selectively enhancing detection, evaluation, and extraction of data for memorization. In addition, motivational components promote learning and enhance subsequent memory retrieval while generalizing new events consequent to adaptive physiological changes.

The Evolutionary Framework of Emotion and the Seven Primary Emotional Systems

Evolution built our higher minds (the faculty of consciousness and thoughts) on a foundation of primary-process of emotional mechanism that preprogrammed executive action systems (the prototype emotions) rely on cognitive processing (interpretation) and appraisal in the organisms attempt to decipher the type of situation they might be in; in other words, how to deal with emotionally challenging situations, whether it is a play situation or a threat situation (where RAGE and FEAR might be the appropriate system to recruit). Emotion offers preprogrammed but partially modifiable (under the secondary process of learning and memory) behavioral routines in the service of the solution of prototypical adaptive challenges, particularly in dealing with friend vs. foe; these routines are evolutionary extensions of homeostasis and embed a prediction beyond the current situation to a potentially future homeostatic benefit or threat. Thus, evolution uses whatever sources for survival and procreative success. According to Panksepp and Solms (2012) , key CNS emotional-affective processes are (1) Primary-process emotions; (2) Secondary-process learning and memory; and (3) Tertiary-process higher cognitive functions. Fundamentally, primary emotional processes regulate unconditioned emotional actions that anticipate survival needs and consequently guide secondary process via associative learning mechanisms (classical/Pavlovian and instrumental/operant conditioning). Subsequently, learning process sends relevant information to higher brain regions such as the prefrontal cortex to perform tertiary cognition process that allows planning for future based on past experiences, stored in LTM. In other words, the brain’s neurodevelopment trajectory and “wiring up” activations show that there is a genetically coded aversion to situations that generate RAGE, FEAR and other negative states for minimizing painful things and maximizing pleasurable kinds of stimulation. These are not learned- all learning (secondary-process) is piggybacked on top of the “primary-process emotions” that are governed by “Law of Affect” (see Figure 1 ). What now follows is an explanation of these CNS emotional-affective processing sub-levels and their inter-relationships.

FIGURE 1. Shows the nested hierarchies of circular emotional control and cognitive regulation for “bottom-up” influences and “top-down” regulations. The schematic shows conceptual relationships between primary processes of emotional system (lower brain function), as well as secondary processes of cognitive system and tertiary processing (higher brain function). Primary emotional processing for homeostatic, sensory and emotional affects facilitate secondary learning and memory processing via the “SEEKING” system that promotes survival and reproductive success (bottom-up instinctual influences). As secondary processes are continually integrated with primary emotional processing, they mature to higher brain cognitive faculties to generate effective solutions for living and subsequently exert top-down regulatory control over behavior. The primary emotional processing is mediated by complex unconditioned emotional responses (evolutionary “memories”) through “Law of Affect”; sometimes called “reinforcement principle” that explains how the brain emotional networks control learning. This bi-circular causation for higher brain functionality is coordinated by lower brain functions [adapted from ( Panksepp and Solms, 2012 )].

Primary-Process Emotions (Prototype Emotional States)

The emotional operating system is an inherited and genetically encoded circuitry that anticipates key survival and homeostatic needs. Thus, animals and humans share primary emotional network at the subcortical level, which includes the midbrain’s periaqueductal grey (PAG) and VTA, basal ganglia (amygdala and nucleus accumbens), and insula, as well as diencephalon (the cingulate and medial frontal cortices through the lateral and medial hypothalamus and medial thalamus). Subcortical brain regions are involved in three sub-components of affects: (1) core emotional feelings (fear, anger, joy and various forms of distress); (2) homeostatic drives/motivational experiences (hunger and thirst); and (3) sensory affects (pain, taste, temperature and disgust). Primary-process emotions are not unconscious. Strong emotion is intrinsically conscious at least in the sense that it is experienced even if we might mislabel it, or animal clearly is not able to attach a semantic label-these are simply not realistic standards for determining whether something is conscious or not conscious. Nonetheless, the emotional experiences guide behavior to promote survival and procreative success as well as mediate learning (‘ rewarding ’ and ‘ punishing ’ learning effects) and thinking at secondary and tertiary levels.

Secondary-Process Emotions (Learning and Memory)

Primary emotional systems guide associative learning and memory (classical/operant conditioning and emotional habit) processes via the mediation of emotional networks. This includes the basal ganglia (basolateral and central amygdala, nucleus accumbens, thalamus and dorsal striatum), and the medial temporal lobe (MTL) including hippocampus as well as the entorhinal cortex, perirhinal cortex, and parahippocampal cortices that responsible for declarative memories. Thus, secondary processes of learning and memory scrutinize and regulate emotional feelings in relation to environmental events that subsequently refine effective solutions to living.

Tertiary-Process Emotions (Higher Cognitive Functions)

Higher cognitive functions operate within the cortical regions, including the frontal cortex for awareness and consciousness functions such as thinking, planning, emotional regulation and free-will (intention-to-act), which mediate emotional feelings. Hence, cognition is an extension of emotion (just as emotion is an extension of homeostasis aforementioned). Tertiary processes are continually integrated with the secondary processes and reach a mature level (higher brain functions) to better anticipating key survival issues, thus yielding cognitive control of emotion via “top-down” regulation. In other words, brain-mind evolution enables human to reason but also regulate our emotions.

Psychologist Neisser (1963) suggested that cognition serves emotion and homeostatic needs where environmental information is evaluated in terms of its ability to satisfy or frustrate needs. In other words, cognition is in the service of satisfying emotional and homeostatic needs. This infers that cognition modulates, activates and inhibits emotion. Hence, emotion is not a simple linear event but rather a feedback process that autonomously restores an individual’s state of equilibrium. More specifically stated, emotion regulates the allocation of processing resources and determines our behavior by tuning us to the world in certain biased ways, thus steering us toward things that “feel good” while avoiding things that “feel bad.” This indicates that emotion guides and motivates cognition that promotes survival by guiding behavior and desires according to unique goal orientation ( Northoff et al., 2006 ). Therefore, the CNS maintains complex processes by continually monitoring internal and external environments. For example, changes in internal environments (contraction of visceral muscles, heart rate, etc.) are sensed by an interoceptive system (afferent peripheral nerves) that signals the sensory cortex (primary, secondary and somatosensory) for integration and processing. Thus, from an evolutionary perspective, human mental activity is driven by the ancient emotional and motivational brain systems shared by cross-mammalians that encode life-sustaining and life-detracting features to promote adaptive instinctual responses. Moreover, emotional and homeostasis mechanisms are characterized by intrinsic valence processing that is either a positive/pleasure or negative/displeasure bias. Homeostasis imbalance is universally experienced as negative emotional feelings and only becomes positively valenced when rectified. Hence, individuals sustain bodily changes that underlie psychological (emotional) and biological (homeostatic) influences on two sides, i.e., one side is oriented toward the survival and reproductive success that is associated with positively valenced emotional and physiologic homeostasis (anticipatory response) and the other responds to survival and reproductive failure associated with negatively valenced emotional and physiologic homeostasis (reactive response). Consequently, cognition modulates both emotional and homeostatic states by enhancing survival and maximizing rewards while minimizing risk and punishments. Thus, this evolutionary consideration suggests the brain as a ‘predictive engine’ to make it adaptive in a particular environment. Figure 2 demonstrates this cyclic homeostatic regulation.

FIGURE 2. Conceptually maps the homeostatic regulation of internal and external inputs that affect cognition, emotion, feeling, and drive: Inputs → Homeostasis ↔ Emotion ∗ ↔ Cognition. This lead to the experience of one’s self via overt behavior that is biased by a specific emotion stimulated by bodily changes that underlie psychological/physiological states. ∗ Represents emotion associated with a combination of feeling and motivation/drive; ↔ indicates a bi-directional interaction; and → indicates a one-directional relationship. Adapted from Damasio and Carvalho (2013) .

Panksepp (1998) identified seven primary emotional systems that govern mammalian brains as follows: SEEKING, RAGE, FEAR, LUST, CARE, PANIC/GRIEF, and PLAY. Here, we use UPPERCASE letters to denote unconditional emotional responses (emotional primes). These primary emotional neural networks are situated in the subcortical regions; moreover, the evidence demonstrates that decortication leaves primary emotional systems intact ( Panksepp et al., 1994 ). Hence, cortical regions are non-essential for the generation of prototype emotional states but are responsible for their modulation and regulation. The present article emphasizes SEEKING because it is the most fundamental of the primary emotional systems and is crucial for learning and memory. The SEEKING system facilitates learning because when fully aroused, it fills the mind with interest that then motivates the individual to search out and learn things that they need, crave and desire. Accordingly, SEEKING generates and sustains curiosity’s engagement for a particular purpose while also promoting learning via its mediation of anticipatory eagerness ( Oudeyer et al., 2016 ). In other words, the SEEKING system has been designed to automatically learn by exploring anything that results in acquired behavioral manifestations for survival operations, all the way from the mesolimbic-mesocortical dopamine system through to the prefrontal cortex (PFC); thus, it is intimately linked with LTM formation ( Blumenfeld and Ranganath, 2007 ). Consequently, it is the foundation of secondary learning and higher cognitive processes when compared with the remaining six emotional systems. However, this system is less activated during chronic stress, sickness, and depression, all of which are likely to impair learning and various higher cognitions. On the other hand, overactivity of this system promotes excessively impulsive behaviors attended by manic thoughts and psychotic delusions. Moreover, massive lesion of SEEKING’s neural network (midline subcortical regions-the PAG, VTA, nucleus accumbens (NAc), medial forebrain and anterior cingulate) lead to consciousness disorder, specifically akinetic mutism (AKM) syndrome that the patient appears wakeful, attentive but motionless ( Schiff and Plum, 2000 ; Watt and Pincus, 2004 ). In brief, the SEEKING system holds a critical position that optimizes the performance of emotion, motivation, and cognition processes by generating positive subjective emotional states-positive expectancy, enthusiastic exploration, and hopefulness. Because the seven primary emotional systems and their associated key neuroanatomical and key neurochemical features have been reviewed elsewhere ( Panksepp, 2011a , b ), they are not covered in this review.

Emotion–Cognition Interactions and its Impacts on Learning and Memory

Studies in psychology ( Metcalfe and Mischel, 1999 ) and neuroscience ( Dolcos et al., 2011 ) proposed that cognition and emotion processes are operated at two separate but interacting systems: (i) the “cool cognitive system” is hippocampus-based that is associated with emotionally neutral cognitive functions as well as cognitive controls; and (ii) the “hot emotional system” is amygdala-based that responsible for emotional processing and responses toward unconditioned emotional stimuli such as appetitive and fear-evoking conditions. In addition, an early view of a dorsal/ventral stream distinction was commonly reported between both systems. The dorsal stream encompasses the dorsolateral prefrontal cortex (DLPFC) and lateral parietal cortex, which are involved in the cool system for active maintenance of controlled processes such as cognitive performance and the pursuit of goal-relevant information in working memory (WM) amidst interference. In contrast, the hot system involves the ventral neural system, including the amygdala, ventrolateral prefrontal cortex (VLPFC) and medial prefrontal cortex (mPFC) as well as orbitofrontal (OFC) and occipito-temporal cortex (OTC), all of which encompass emotional processing systems ( Dolcos et al., 2011 ). Nonetheless, recent investigations claim that distinct cognitive and emotional neural systems are not separated but are deeply integrated and contain evidence of mediation and modulation ( Dolcos et al., 2011 ; Okon-Singer et al., 2015 ). Consequently, emotions are now thought to influence the formation of a hippocampal-dependent memory system ( Pessoa, 2008 ), exerting a long-term impact on learning and memory. In other words, although cognitive and affective processes can be independently conceptualized, it is not surprising that emotions powerfully modify cognitive appraisals and memory processes and vice versa. The innate emotional systems interact with higher brain systems and probably no an emotional state that is free of cognitive ramifications. If cortical functions were evolutionarily built upon the pre-existing subcortical foundations, it provides behavioral flexibility ( Panksepp, 1998 ).

The hippocampus is located in the MTL and is thought to be responsible for the potentiation and consolidation of declarative memory before newly formed memories are distributed and stored in cortical regions ( Squire, 1992 ). Moreover, evidence indicates that the hippocampus functions as a hub for brain network communications-a type of continuous exchange of information center that establishes LTM dominated by theta wave oscillations ( Battaglia et al., 2011 ) that are correlated with learning and memory ( Rutishauser et al., 2010 ). In other words, hippocampus plays a crucial role in hippocampal-dependent learning and declarative memories. Numerous studies have reported that the amygdala and hippocampus are synergistically activated during memory encoding to form a LTM of emotional information, that is associated with better retention ( McGaugh et al., 1996 ; Richter-Levin and Akirav, 2000 ; Richardson et al., 2004 ). More importantly, these studies (fear-related learning) strongly suggest that the amygdala’s involvement in emotional processing strengthens the memory network by modulating memory consolidation; thus, emotional content is remembered better than neutral content.

In addition to amygdala-hippocampus interactions, one study reported that the PFC participates in emotional valence (pleasant vs. unpleasant) processing during WM ( Perlstein et al., 2002 ). Simons and Spiers (2003) also reviewed studies of interactions between the PFC and MTL during the memory encoding and retrieval processes underlying successful LTM. They demonstrated that the PFC is crucial for LTM because it engages with the active maintenance of information linked to the cognitive control of selection, engagement, monitoring, and inhibition. Hence, it detects relevant data that appears worthwhile, which is then referred for encoding, thus leading to successful LTM ( Simons and Spiers, 2003 ). Consistent findings were reported for recognition tasks investigated by fMRI where the left PFC-hippocampal network appeared to support successful memory encoding for neutral and negative non-arousing words. Simultaneously, amygdala-hippocampus activation was observed during the memory encoding of negative arousing words ( Kensinger and Corkin, 2004 ). Moreover, Mega et al. (1996) proposed two divisions for the limbic system: (i) the paleocortex division (the amygdala, orbitofrontal cortex, temporal polar and anterior insula), and (ii) the archicortical division (the hippocampus and anterior cingulate cortex). The first component is responsible for the implicit integration of affects, drives and object associations; the second deals with explicit sensory processing, encoding, and attentional control. Although divided into two sub-divisions, the paleocortex and archicortical cortex remain integrated during learning. Here, the paleocortex appears to manage the internal environment for implicit learning while integrating affects, drives, and emotions. Simultaneously, the archicortical division appears to manage external environment input for explicit learning by facilitating attention selection with attendant implicit encoding. To some extent, the paleocortex system might come to exercise a supervisory role and link the ancient affective systems to the newer cognitive systems.

Amygdala–Hippocampus Interactions

The findings of previous studies suggest that the amygdala is involved in emotional arousal processing and modulation of the memory processes (encoding and storage) that contribute to the emotional enhancement of memory ( McGaugh et al., 1996 ; Richter-Levin and Akirav, 2000 ). Activation of the amygdala during the encoding of emotionally arousing information (both pleasant/unpleasant) has been reported that correlates with subsequent recall. Because of the interaction between basolateral complex of the amygdala (BLA) with other brain regions that are involved in consolidating memories, including the hippocampus, caudate nucleus, NAc, and other cortical regions. Thus, BLA activation results from emotionally arousing events, which appear to modulate memory storage-related regions that influence long-term memories ( McGaugh, 2004 ). Memory consolidation is a part of the encoding and retention processes where labile memories of newly learned information become stabilized and are strengthened to form long-lasting memories ( McGaugh, 2000 ). Moreover, the amygdala transmits direct feedback/projection along the entire rostral-caudal cortices to the visual cortex of the ventral stream system, including primary visual (V1) and temporal cortices ( Amaral et al., 2003 ); furthermore, the amygdala activates the frontal and parietal regions during negative emotion processing that are involved in attention control. Consequently, during emotional processing, direct projections from the amygdala to sensory cortices enhance attentional mechanism might also allow the parallel processing of the attentional (fronto-parietal) system ( Vuilleumier, 2005 ). This suggests that amygdala activation is associated with enhanced attention and is a part of how salience enhances information retention.

In addition to attentional biases toward emotional content during memory encoding, emotionally arousing experiences have been found to induce the release of adrenal stress hormones, followed by the activation of β-noradrenergic receptors in the BLA, which then release epinephrine and glucocorticoids in the BLA, while enhancing memory consolidation of emotional experiences ( McGaugh and Roozendaal, 2002 ). Thus, there is evidence that the consolidation of new memory that is stimulated by emotionally arousing experiences can be enhanced through the modulating effects of the release of stress hormones and stress-activated neurotransmitters associated with amygdala activation. The BLA comprises the basal amygdala (BA) and lateral amygdala (LA), which project to numerous brain regions involved in learning and memory, including the hippocampus and PFC ( Cahill and McGaugh, 1998 ; Sharot and Phelps, 2004 ; McGaugh, 2006 ). However, stress and emotion do not always induce strong memories of new information. Indeed, they have also been reported to inhibit WM and LTM under certain conditions related to mood and chronic stress ( Schwabe and Wolf, 2010 ). Consequently, understanding, managing, and regulating emotion is critical to the development of enhanced learning programs informed by the significant impacts of learning and memory under different types of stress ( Vogel and Schwabe, 2016 ).

Prefrontal Cortex–Hippocampus Interaction

The PFC is located in the foremost anterior region of the frontal lobe and is associated with higher-order cognitive functions such as prediction and planning of/for the future ( Barbey et al., 2009 ). Moreover, it is thought to act as a control center for selective attention ( Squire et al., 2013 ), and also plays a critical role in WM as well as semantic processing, cognitive control, problem-solving, reasoning and emotional processing ( Miller and Cohen, 2001 ; Yamasaki et al., 2002 ). The PFC is connected to sub-cortical regions in the limbic system, including the amygdala and various parts of the MTL ( Simons and Spiers, 2003 ). Its involvement in WM and emotional processing are intimately connected with the MTL structures that decisively affect LTM encoding and retrieval ( Blumenfeld and Ranganath, 2007 ) in addition to self-referential processing ( Northoff et al., 2006 ). Structurally, the PFC is divided into five sub-regions: anterior (BA 10), dorsolateral (BA 9 and 46), ventrolateral (BA 44, 45, and 47), medial (BA 25 and 32) and orbitofrontal (BA 11, 12, and 14) ( Simons and Spiers, 2003 ).

The mPFC has been associated with anticipatory responses that reflect cognitive expectations for pleasant/unpleasant experiences (appraising rewarding/aversive stimuli to generate emotional responses) ( Ochsner et al., 2002 ; Ochsner and Gross, 2005 ). Specifically, increased mPFC activation has been noted during reappraisal and is associated with the suppressed subjective experience of negative emotions. Furthermore, an fMRI study revealed concurrent activation levels of the dorsomedial prefrontal cortex (dmPFC) with emotional valence when processing emotional stimuli: (i) activation was associated with positive valence, and (ii) deactivation was associated with negative valence ( Heinzel et al., 2005 ). Similarly, emotional and non-emotional judgment task using the International Affective Pictures System (IAPS) demonstrated increased activation of the mPFC, specifically both ventromedial prefrontal cortex (vmPFC) and dmPFC during emotional judgment when compared with non-emotional judgment. However, an inverse relationship was observed in the lateral prefrontal cortex (VLPFC and DLPFC) during non-emotional judgment ( Northoff et al., 2004 ). These findings suggested reciprocal interactions between cognitive and emotional processing between dorsal and lateral neural systems when processing emotional and cognitive tasking demands ( Bartolic et al., 1999 ).

Other studies reported strong cognition-emotion interactions in the lateral prefrontal cortex with increased activity in the DLPFC, which plays a key role in top-down modulation of emotional processing ( Northoff et al., 2004 ; Comte et al., 2014 ). This indicates increased attentional control of regulatory mechanisms that process emotional content. For instance, one study reported that cognitive task appeared to require active retention in WM, noting that the process was influenced by emotional stimuli when subjects were instructed to remember emotional valence information over a delay period ( Perlstein et al., 2002 ). Their findings revealed increased activation in the right DLPFC in response to pleasant IAPS pictures, but with an opposite effect in response to unpleasant pictures (decreased activity in the right DLPFC). This could be interpreted as increased WM-related activity when processing positive emotional stimuli, thus leading to positive emotion maintenance of stimulus representation in WM. Furthermore, they observed that the DLPFC contributed to increased LTM performance linked to stronger item associations and greater organization of information in WM during pleasant compared to unpleasant emotion ( Blumenfeld and Ranganath, 2006 ).

Another study investigated the PFC’s role in emotional mediation, reporting that the right VLPFC provided cognitive resources for both emotional reappraisal and learning processes via two separate subcortical pathways: (i) a path through NAc appeared to greater reappraisal success (suppress negative emotion) and (ii) another path through the ventral amygdala appeared to reduced reappraisal success (boost negative experience). This result indicates the VLPFC’s role in the regulation of emotional responses (reducing negative appraisal and generating positive appraisal) by retrieving appropriate information from memory ( Wager et al., 2008 ). Certain characteristics of emotional content were found to mediate the encoding and retrieval of selective information by leading high levels of attention, distinctiveness, and information organization that enhanced recall for emotional aspects of complex events ( Talmi, 2013 ). Hence, this direction of additional attention to emotional information appears to enhance LTM with the pronounced effects deriving from positive emotions compared with negative emotions. Effects of emotion on memory was also investigated using immediate (after 20 s) and delayed (after 50 min) testing paradigm, has shown that better recall for emotionally negative stimuli during immediate test compared to delayed test because of attentional allocation for encoding while the delayed test demonstrated that the role of amygdala in modulating memory consolidation of emotional stimuli. Because selective attention drives priority assignment for emotional material ( Talmi et al., 2007 ). Meanwhile, the distinctiveness and organization of information can improve memory because unique attributes and inter-item elaboration during encoding serve as retrieval cues, which then lead to high possibilities for correct recall ( Erk et al., 2003 ). Consistent findings were also reported by ( Dolcos et al., 2004 ), who suggested an emotional mediation effect deriving from PFC activity in relation to cognitive functions such as strategic memory, semantic memory, and WM, which subsequently enhanced memory formation. Table 1 summarizes cognitive-emotional functions associated with each sub-region of the PFC and corresponding Brodmann areas. Taken together, these findings indicate that the PFC is a key component in both cognitive and emotional processing for successful LTM formation and retrieval.

TABLE 1. The prefrontal cortex (PFC) sub-regions, corresponding Brodmann areas, and associated cognitive-emotional functions.

Effects Deriving From Different Modalities of Emotional Stimuli on Learning and Memory

As discussed above, evidence indicates the neural mechanisms underlying the emotional processing of valence and arousal involve the amygdala and PFC, where the amygdala responds to emotionally arousing stimuli and the PFC responds to the emotional valence of non-arousing stimuli. We have thus far primarily discussed studies examining neural mechanisms underlying the processing of emotional images. However, recent neuroimaging studies have investigated a wider range of visual emotional stimuli. These include words ( Sharot et al., 2004 ), pictures ( Dolcos et al., 2005 ; Weymar et al., 2011 ), film clips ( Cahill et al., 1996 ), and faces ( González-Roldan et al., 2011 ), to investigate neural correlates of emotional processing and the impact of emotion on subsequent memory. These studies provided useful supplemental information for future research on emotional effects of educational multimedia content (combination of words and pictures), an increasingly widespread channel for teaching and learning.

An event-related fMRI study examined the neural correlates of responses to emotional pictures and words in which both were manipulated in terms of positive and negative valence, and where neutral emotional content served as a baseline (“conditioned stimuli”/no activating emotion with valence rating of 5 that spans between 1/negative valence-9/positive valence), even though all stimuli were consistent in terms of arousal levels ( Kensinger and Schacter, 2006 ). Subjects were instructed to rate each stimulus as animate or inanimate and common or uncommon . The results revealed the activation of the amygdala in response to positive and negative valence (valence-independent) for pictures and words. A lateralization effect was observed in the amygdala when processing different emotional stimuli types. The left amygdala responded to words while either the right and/or bilateral amygdala activation regions responded to pictures. In addition, participants were more sensitive to emotional pictures than to emotional words. The mPFC responded more rigorously during the processing of positive than to that of negative stimuli, while the VLPFC responded more to negative stimuli. The researchers concluded that arousal-related responses occur in the amygdala, dmPFC, vmPFC, anterior temporal lobe and temporo-occipital junction, whereas valence-dependent responses were associated with the lateral PFC for negative stimuli and the mPFC for positive stimuli. The lateralization of the amygdala’s activation was consistent with that in other studies that also showed left-lateralized amygdala responses for words ( Hamann and Mao, 2002 ) vs. right-lateralized amygdala responses for images ( Pegna et al., 2005 ). However, a wide range of studies suggest that lateralization likely differs with sex ( Hamann, 2005 ), individual personality ( Hamann and Canli, 2004 ), mood ( Rusting, 1998 ), age ( Allard and Kensinger, 2014 ), sleep ( Walker, 2009 ), subject’s awareness of stimuli ( Morris et al., 1998 ), stress ( Payne et al., 2007 ) and other variables. Hence, these factors should be considered in future studies.

Event-related potentials (ERPs) were used to investigate the modality effects deriving from emotional words and facial expressions as stimuli in healthy, native German speakers ( Schacht and Sommer, 2009a ). German verbs or pseudo-words associated with positive, negative or neutral emotions were used, in addition to happy vs. angry faces, as well as neutral and slightly distorted faces. The results revealed that negative posterior ERPs were evoked in the temporo-parieto-occipital regions, while enhanced positive ERPs were evoked in the fronto-central regions (positive verbs and happy faces) when compared with neutral and negative stimuli. These findings were in agreement with the previous findings ( Schupp et al., 2003 ; Schacht and Sommer, 2009b ). While the same neuronal mechanisms appear to be involved in response to both emotional stimuli types, latency differences were also reported with faster responses to facial stimuli than to words, likely owing to more direct access to neural circuits-approximately 130 ms for happy faces compared to 380 ms for positive verbs ( Schacht and Sommer, 2009a ). Moreover, augmented responses observed in the later positive complex (LPP), i.e., larger late positive waves in response to emotional verbs (both positive and negative) and angry faces, all associated with the increased motivational significance of emotional stimuli ( Schupp et al., 2000 ) and increased selective attention to pictures ( Kok, 2000 ).

Khairudin et al. (2011) investigated effects of emotional content on explicit memory with two standardized stimuli: emotional words from the Affective Norms for English Words (ANEW) and emotional pictures from the IAPS. All stimuli were categorized as positive, negative or neutral, and displayed in two different trials. Results revealed that better memory for emotional images than for emotional words. Moreover, a recognition test demonstrated that positive emotional content was remembered better than negative emotional content. Researchers concluded that emotional valence significantly impacts memory and that negative valence suppressed the explicit memory. Another study by Khairudin et al. (2012) investigated the effects of emotional content on explicit verbal memory by assessing recall and recognition for emotionally positive, negative and neutral words. The results revealed that emotion substantially influences memory performance and that both positive and negative words were remembered more effectively than neutral words. Moreover, emotional words were remembered better in recognition vs. recall test.

Another group studied the impacts of emotion on memory using emotional film clips that varied in emotion with neutral, positive, negative and arousing contents ( Anderson and Shimamura, 2005 ). A subjective experiment for word recall and context recognition revealed that memory, for words associated with emotionally negative film clips, was lower than emotionally neutral, positive and arousing films. Moreover, emotionally arousing film clips were associated with enhanced context recognition memory but not during a free word recall test. Therefore, clarifying whether emotional stimuli enhance recognition memory or recall memory requires further investigation, as it appears that emotional information was better remembered for recognition compared to recall. In brief, greater attentional resource toward emotional pictures with large late positive waves of LPP in the posterior region, the amygdala responds to emotional stimuli (both words and pictures) independent on its valence, leading to enhanced memory. Table 2 summarizes studies on the brain regions that respond to standardized stimuli as cited above, and also for pictures of emotional facial expression or Pictures of Facial Affect (POFA), Affective Norms for English Words (ANEW) for emotional words, as well as for the International Affective Digitized Sound System (IDAS) for emotional sounds.

TABLE 2. Comparison of different emotional stimulus categories.

Neuroimaging Techniques for the Investigation of Emotional-Cognitive Interactions

The brain regions associated with cognitive-emotional interactions can be studied with different functional neuroimaging techniques (fMRI, PET, and fNIRS) to examine hemodynamic responses (indirect measurement). EEG is used to measure brain electrical dynamics (direct measurement) associated with responses to cognitive and emotional tasks. Each technique has particular strengths and weaknesses, as described below.

Functional Magnetic Resonance Imaging (fMRI)

Functional magnetic resonance imaging is a widely used functional neuroimaging tool for mapping of brain activation as it provides a high spatial resolution (a few millimeters). fMRI is an indirect measure of hemodynamic response by measuring changes in local ratios of oxy-hemoglobin vs. deoxy-hemoglobin, typically known as a blood oxygenation level dependent (BOLD) signal ( Cabeza and Nyberg, 2000 ). Dolcos et al. (2005) examined the effects of emotional content on memory enhancement during retrieval process using event-related fMRI to measure retrieval-related activity after a retention interval of 1 year. The researchers concluded that successful retrieval of emotional pictures involved greater activation of the amygdala as well as the entorhinal cortex and hippocampus than that of neutral pictures. Both the amygdala and hippocampus were rigorously activated during recollection compared to familiarity recognition, whereas no differences were found in the entorhinal cortex for either recollection or familiarity recognition. Moreover, a study investigates motivation effect (low vs. high monetary reward) on episodic retrieval by manipulating task difficulty, fMRI data reports that increased activation in the substantia nigra/VTA, MTL, dmPFC, and DLPFC when successful memory retrieval with high difficulty than with low difficulty. Moreover, reward-related of functional connectivities between the (i) SN/VTA–MTL and (ii) SN/VTA–dmPFC appear to increases significantly with increases retrieval accuracy and subjective motivation. Thus, Shigemune et al. (2017) suggest that reward/motivation-related memory enhancement modulated by networking between the SN/VTA (reward-related), dmPFC (motivation-related) and MTL (memory-related) network as well as DLPFC (cognitive controls) with high task difficulty.

Taken together, these findings indicate that the amygdala and MTL have important roles in the recollection of emotional and motivational memory. Another fMRI study reported that greater success for emotional retrieval (emotional hits > misses ) was associated with neural activation of the bilateral amygdala, hippocampus, and parahippocampus, whereas a higher success rate for neutral retrieval is associated with a greater activity in right posterior parahippocampus regions ( Shafer and Dolcos, 2014 ). Hence, fMRI has clearly revealed interactions between cognitive and emotional neural networks during information processing, particularly in response to emotion-related content. Such interactions appear to modulate memory consolidation while also mediating encoding and retrieval processes that underlie successful LTM formation and memory recall. More specifically, it appears that amygdala activation modulates both the hippocampus and visual cortex during visual perception and enhances the selection and organization of salient information via the “bottom-up” approach to higher cognitive functions directed at awareness. Although fMRI is widely used, it poses several limitations such as poor temporal resolution, expensive setup costs, plus the difficulty of having a subject hold still during the procedure in an electromagnetically shielded room (immobility). Furthermore, fMRI is slightly more metabolically sluggish, as BOLD signal exhibits an initial dip, where the increase of subsequent signal is delayed by 2–3 s and it takes approximately 6–12 s to reach to a peak value that reflects the neural responses elicited by a stimulus ( Logothetis et al., 2001 ). This means that fMRI having a coarse temporal resolution (several seconds) when compared with electrophysiological techniques (a few milliseconds) and also not a great technique for visualizing subcortical regions (mesencephalon and brainstem) due to metabolically sluggish compared to PET.

Positron Emission Tomography (PET)

Positron emission tomography is another functional neuroimaging tool that maps CNS physiology and neural activation by measuring glucose metabolism or regional cerebral blood flow (rCBF). PET uses positron-emitting radionuclides such as 18 F-fluorodeoxyglucose (FDG) and positron-emitting-oxygen isotope tagged with water ([ 15 O] H 2 O), etc. This technique identifies different neural networks involving pleasant, unpleasant and neutral emotions ( Lane et al., 1997 ). It thus far appears that increased rCBF in the mPFC, thalamus, hypothalamus, and midbrain associated with pleasant and unpleasant emotional processing, while unpleasant emotions are more specifically associated with the bilateral OTC, cerebellum, left parahippocampal gyrus, hippocampus, and amygdala; moreover, the caudate nucleus is associated with pleasant emotions.

Using PET scanning demonstrated that emotional information enhances visual memory recognition via interactions between perception and memory systems, specifically with greater activation of the lingual gyrus for visual stimuli ( Taylor et al., 1998 ). The results also showed that strong negative emotional valence appeared to enhance the processing of early sensory input. Moreover, differences in neural activation appeared in the left amygdaloid complex (AC) during encoding, while the right PFC and mPFC responded during recognition memory. Similarly, Tataranni et al. (1999) identified CNS regions associated with appetitive states (hunger and satiation) ( Tataranni et al., 1999 ). Hunger stimulated increased rCBF uptake in multiple regions including the hypothalamus, insular cortex, limbic and paralimbic regions (anterior cingulate cortex, parahippocampal and hippocampal formation, the anterior temporal and posterior orbitofrontal cortex), as well as the thalamus, caudate, precuneus, putamen, and cerebellum. Satiation was associated with increased rCBF uptake in the bilateral vmPFC, the DLPFC, and the inferior parietal lobule. These results imply that (i) subcortical regions associated with emotion/motivation involved in hunger that signals distressing feeling (discomfort, pain and anxiety) for the regulation of food intake; and (ii) the PFC associated with inhibition of inappropriate behavioral response involved in satiation that signals excessive food consumption for a termination of meal.

In a study of emotional self-generation using PET noted that the insular cortex, secondary somatosensory cortex, and hypothalamus, as well as the cingulate cortex and nuclei in the brainstem’s tegmentum, including PAG, parabrachial nucleus, and substantia nigra maintained current homeostasis by generating regulatory signals ( Damasio et al., 2000 ). PET scanning has also been used for neuroanatomical mapping of emotions ( Davidson and Irwin, 1999 ), emotional processing ( Choudhary et al., 2015 ), and cognitive functions ( Cabeza and Nyberg, 2000 ). Although PET scanning has a relatively good spatial resolution for both the brain and bodily functions, it is costly and yields lower temporal resolution than does EEG and is invasive as opposed to fMRI. Moreover, PET tends to show better activation of more ancient brain regions in the mesencephalon and brainstem when compared to fMRI. Hence, it is generally reserved for the clinical diagnoses of cancers, neurological diseases processes (e.g., epilepsy and Alzheimer’s disease), and heart diseases.

Electroencephalography (EEG)

Electroencephalography obtains high temporal resolution in milliseconds, portable, less expensive, and non-invasive techniques by attaching scalp electrodes to record brain electrical activity. Moreover, numerous studies reported that EEG is useful in mapping CNS cognitive and emotional processing. The technique offers a comprehensive range of feature extraction and analysis methods, including power spectral analysis, EEG coherence, phase delay, and cross-power analysis. One study examined changes in EEG oscillations in the amygdala during the consolidation of emotionally aroused memory processing that exhibited theta (4–8 Hz) activity ( Paré et al., 2002 ), indicating the facilitation of memory consolidation, improved retention of emotional content, and enhanced memory recall. This finding was later supported by the revelation of increased theta activity in the right frontal ( Friese et al., 2013 ) and right temporal cortices ( Sederberg et al., 2003 ) and consequently associated with the successful encoding of new information. Another study ( Buzsáki, 2002 ) revealed that theta oscillations were positively related to the activation of the hippocampus represent the active brain state during sensory, motor and memory-related processing. The theta waves are generated through an interaction between the entorhinal cortex, the Schaffer collateral (CA3 region) and the pyramidal cell dendrites (both CA3 and CA1 regions) that result in a synaptic modification underlie learning and memory. Thus, theta oscillation is thought to be associated with the encoding of new memories.

Increased gamma oscillation in the neocortex and right amygdala have been reported in response to emotionally arousing pictures during learning and memory tasks undertaken by 148 right-handed female participants ( Headley and Paré, 2013 ). A more detailed study by Müller et al. (1999) reported increased gamma potentials in the left frontal and temporal regions in response to images having a negative valence, whereas increased gamma-bands in the right frontal regions were observed in responses to images with positive valence for 11 right-handed male participants. During an emotionally positive experience, another study reported significantly increased EEG theta-alpha coherence between prefrontal and posterior parietal regions ( Aftanas and Golocheikine, 2001 ). They concluded the change was associated with heightened attention in association with improved performance in memory and emotional processing. Thus, we have a number of EEG investigations of left and right hemispheric activity while processing positive (pleasant) and negative (unpleasant) stimuli that revealed differences in regional electrophysiological activation. Nonetheless, EEG exhibits a relatively poor spatial resolution approximately 5 to 9 cm compared with fMRI and PET ( Babiloni et al., 2001 ). Thus, scalp EEG unable to measure activation much below cortex owing to the distortion of scalp potentials where different volume conduction effects of the cortex, dura mater, skull, and scalp resulting in imprecise localization of the electromagnetic field patterns associated with neural current flow. Subsequent studies have demonstrated that the EEG spatial resolution can be improved using high-resolution EEG (high-density electrode arrays to increase spatial sampling) with surface Laplacian estimation and cortical imaging (details discussion of this area is beyond the scope of this review, see ( Nunez et al., 1994 ) for theoretical and experimental study) or integrating multiple imaging modalities that provide complement information, for instance EEG-fMRI and EEG-fNIRS ( Dale and Halgren, 2001 ).

Functional Near-Infrared Spectroscopy (fNIRS)

Functional near-infrared spectroscopy is an emerging and relatively low-cost imaging technique that is also portable and non-invasive. It can be used to map the hemodynamic responses associated with brain activation. This technology measures cerebral changes in the concentration of oxygenated hemoglobin (oxy-Hb) vs. deoxygenated hemoglobin (deoxy-Hb) using optodes (light emitters and detectors) placed on the scalp ( Villringer et al., 1993 ). It is limited to visualizations of cortical activity compared to the subcortical regions, and findings only imply increased brain activity associated with increased glucose and oxygen consumption. Elevations in cerebral blood flow and oxygen delivery exceed quo oxygen consumption, thereby enabling changes in local cerebral blood oxygenation to be measured by optic penetration.

The number of studies that have implemented this investigative technique are associated with task performance ( Villringer et al., 1993 ), including exercise ( Perrey, 2008 ), cognitive workload ( Durantin et al., 2014 ), psychiatric disorders ( Ehlis et al., 2014 ), emotional processing ( Bendall et al., 2016 ), and aging ( Hock et al., 1995 ). One study used fNIRS to examine the relationship between subjective happiness and emotional changes ( Oonishi et al., 2014 ). The results revealed that the level of subjective happiness influenced the pattern of left-right PFC activation during the emotion-related task, showing increased oxy-Hb in the left PFC when viewing pleasant pictures, and increased oxy-Hb in the right PFC when viewing unpleasant pictures. Viewing unpleasant emotional stimuli accompanied increased in oxy-Hb levels in the bilateral VLPFC while also activating several regions in both the right VLPFC (BA45/47) and left VLPFC (BA10/45/46/47). However, another fNIRS study reported that viewing pleasant emotional stimuli was associated with decreased oxy-Hb in the left DLPFC (BA46/10) when affective images were presented for 6 s ( Hoshi et al., 2011 ). Thus, this study found an opposite pattern indicating left hemisphere involvement in positive/approach processing and right hemisphere involvement in negative/withdrawal processing ( Davidson, 1992 ; Davidson and Irwin, 1999 ). This inconsistent finding of frontal hemispheric asymmetric might result from the comparison of state-related changes rather than baseline levels of asymmetric. Thus, several issues should take into consideration: (i) methodological issues to assess hemispheric asymmetry, including requires repeat measures of anterior asymmetry for at least two sessions, stimulus content should comprise both positive valence and negative valence while maintaining at a similar level of arousal and with a baseline resting condition, appropriate selection of reference electrode and individual differences, etc; and (ii) conceptual issues is related to the fact that prefrontal cortex is an anatomically and functionally heterogeneous and complex region interacts with other cortical and subcortical structures during emotional processing ( Davidson, 2004 ). Another fNIRS study examined the relationship between PFC function and cognitive control of emotion ( Ozawa et al., 2014 ). This was done by presenting emotional IAPS pictures for 5.2 s, followed by the n -back task. The results revealed a significantly greater increase in oxy-HB in the mPFC and left superior frontal gyrus in response to negative pictures compared with neutral pictures. Meanwhile, no significant hemodynamic changes were observed during image presentation and the n -back task, indicating the need for further investigation.

Factors Affecting the Effect of Emotion on Learning and Memory

The preceding section described neuroimaging techniques used to examine brain responses to emotional stimuli during WM processing leading to LTM. This section presents six key factors that are recommended for consideration in the experimental design and appropriate protocol.

Individual Differences

A number of studies have reported numerous influences in addition to a range of individual differences in emotional processing. These include personality traits ( Montag and Panksepp, 2017 ), intellectual ability ( Brackett et al., 2004 ), and sex ( Cahill, 2003 ). Moreover, sex hormones and personality traits (e.g., extraversion and neuroticism) appear to influence individual responses to emotional stimuli as well as modulate emotional processing. Appropriate screening with psychological testing as well as balancing experimental cohorts in terms of sex can help reduce spurious results owing to individual differences.

Age-Related Differences

Studies have also shown that older adults are associated with the greater familiarity with psychological stress and emotional experiences, thus causing positivity biases in emotional processing and better emotional control than in younger adults ( Urry and Gross, 2010 ; Allard and Kensinger, 2014 ). Consequently, the age of participants in a sample population should be considered for both cognitive and emotional studies.

Emotional Stimulus Selection

The selection of emotional stimuli for experimental studies is generally divided into two streams: (1) discrete emotional, and (2) dimensional emotions of valence, arousal, dominance and familiarity ( Russell, 1980 ; Barrett, 1998 ). The latter include pictures from the IAPS database and words from the ANEW database, which are both available for non-commercial research. Appropriate selection of emotional stimuli is another important consideration that ensures experimental tasks are suitable for the investigation of emotional processing in learning and memory. Furthermore, the type of stimulus determines stimulus presentation duration, especially for experimental tasks involving the induction of emotions.

Self-assessment Techniques

There are numerous self-assessment techniques used to measure individual emotional states ( Bradley and Lang, 1994 ). The most widely used techniques are the Self-Assessment Manikin (SAM), the Semantic Differential (SD) scale, and the Likert scale. The SAM is a non-verbal pictorial assessment technique directly measures emotional responses to emotional stimuli for valence, arousal, and dominance. The SD scale consists of a set of bipolar adjective pairs for the subjective rating of image stimuli. The Likert’s “ x -point” scale allows participants to rate their own emotional responses. If a study does not seek to assess distinct emotional states but rather involves the assessment of two primary dimensions of emotion (positive and negative valence), then the Positive and Negative Affect Schedule (PANAS) is a recommended method ( Watson et al., 1988 ). Thus, selection of the most appropriate self-assessment technique is an important part of the experimental design but can also become an overwhelming task.

Selection of Brain Imaging Techniques

As mentioned above, the two major types of brain imaging techniques EEG (direct) and fMRI/PET/fNIRS (indirect) have respective advantages and disadvantages. To overcome these limitations, simultaneous or combined dual-modality imaging (EEG-fMRI or EEG-fNIRS) can now be implemented for complementary data collection. Although functional neuroimaging works to identify the neural correlates of emotional states, technologies such as deep brain stimulation (DBS) and connectivity maps might provide new opportunities to seek understanding of emotions and its corresponding psychological responses.

Neurocognitive Research Design

The neuroscience of cognition and emotion requires appropriate task designs to accomplish specific study objectives ( Amin and Malik, 2013 ). Environmental factors, ethical issues, memory paradigms, cognitive task difficulty, and emotional induction task intensity must be considered for this.

Numerous neuroimaging studies cited thus far have indicated that emotions influence memory processes, to include memory encoding, memory consolidation, and memory retrieval. Emotional attentional and motivational components might explain why emotional content exhibits privileged information processing. Emotion has a “pop-out” effect that increases attention and promotes bottom-up instinctual impact that enhances awareness. Significant emotional modulation affects memory consolidation in the amygdala, and emotional content also appears to mediate memory encoding and retrieval in the PFC, leading to slow rates of memory lapse accompanied by the accurate recall. Moreover, cognitive and emotional interactions also appear to modulate additional memory-related CNS regions, such as the frontal, posterior parietal and visual cortices. The latter are involved in attentional control, association information, and the processing of visual information, respectively. Therefore, higher-level cognitive functions such as learning and memory, appear to be generally guided by emotion, as outlined in the Panksepp’s framework of brain processing ( Panksepp, 1998 ).

Neuroimaging findings also indicate the involvement of the PFC in emotional processing by indirectly influencing WM and semantic memory ( Kensinger and Corkin, 2003 ). This is reflected by the involvement of the DLPFC in WM and the role played by VLPFC in semantic processing, both of which have been found to enhance or impair semantic encoding task performance when emotion is involved. Various parts of the lateral PFC (ventrolateral, dorsolateral and medial prefrontal cortical regions) are suspected of having key roles that support memory retrieval ( Simons and Spiers, 2003 ). All of these findings suggest that PFC-MTL interactions underlie effective semantic memory encoding and thus strategically mediate information processing with increased transfer to the hippocampus, consequently enhancing memory retrieval. Accordingly, learning strategies that emphasize emotional factors are more likely to result in long-term knowledge retention. This consideration is potentially useful in the design of educational materials for academic settings and informed intelligent tutoring systems.

Based on numerous previous findings, future research might take emotional factors more seriously and more explicitly in terms of their potential impact on learning. By monitoring the emotional state of students, the utilization of scientifically derived knowledge of stimulus selection can be particularly useful in the identification of emotional states that advance learning performance and outcomes in educational settings. Moreover, functional neuroimaging investigations now include single and/or combined modalities that obtain complementary datasets that inform a more comprehensive overview of neuronal activity in its entirety. For example, curiosity and motivation promote learning, as it appears cognitive network become energized by the mesolimbic-mesocortical dopamine system (generalized motivational arousal/SEEKING system). In addition, the identification of emotional impact on learning and memory potentially has direct implications for healthy individuals as well as patients with psychiatric disorders such as depression, anxiety, schizophrenia, autism, mania, obsessive-compulsive disorder and post-traumatic stress disorder (PTSD) ( Panksepp, 2011a ). To emphasize, depression and anxiety are the two most commonly diagnosed psychiatric disorders associated with learning/memory impairment and pose negative consequences that (i) limit the total amount of information that can otherwise be learned, and (ii) inhibit immediate recall as well as memory retention and retrieval of newly learned information. Depression and anxiety are also associated with negative emotions such as hopelessness, anxiety, apathy, attention deficit, lack of motivation, and motor and mental insufficiencies. Likewise, neuroscience studies report that decreased activation of the dorsal limbic (the anterior and posterior cingulate) as well as in the prefrontal, premotor and parietal cortices causes attentional disturbance, while increased neural activation in the ventral paralimbic region (the subgenual cingulate, anterior insula, hypothalamus and caudate) is associated with emotional and motivational disorders ( Mayberg, 1997 ).

Concluding Remarks, Open Questions, and Future Directions

Substantial evidence has established that emotional events are remembered more clearly, accurately and for longer periods of time than are neutral events. Emotional memory enhancement appears to involve the integration of cognitive and emotional neural networks, in which activation of the amygdala enhances the processing of emotionally arousing stimuli while also modulating enhanced memory consolidation along with other memory-related brain regions, particularly the amygdala, hippocampus, MTL, as well as the visual, frontal and parietal cortices. Similarly, activation of the PFC enhances cognitive functions, such as strategic and semantic processing that affect WM and also promote the establishment of LTM. Previous studies have primarily used standardized emotional visual, or auditory stimuli such as pictures, words, facial expression, and film clips, often based on the IAPS, ANEW, and POFA databases for emotional pictures, words and facial expressions, respectively. Further studies have typically focused on the way individuals memorize (intentional or incidental episodic memory paradigm) emotional stimuli in controlled laboratory settings. To our knowledge, there are few objective studies that employed brain-mapping techniques to examine semantic memory of learning materials (using subject matter) in the education context. Furthermore, influences derived from emotional factors in human learning and memory remains unclear as to whether positive emotions facilitate learning or negative emotions impair learning and vice versa. Thus, several remaining questions should be addressed in future studies, including (i) the impact of emotion on semantic knowledge encoding and retrieval, (ii) psychological and physiological changes associated with semantic learning and memory, and (iii) the development of methods that incorporate emotional and motivational aspects that improve educational praxes, outcomes, and instruments. The results of studies on emotion using educational learning materials can indeed provide beneficial information for informed designs of new educational courses that obtain more effective teaching and help establish better informed learning environments. Hence, to understand how emotion influence learning and memory requires understanding of an evolutionary consideration of the nested hierarchies of CNS emotional-affective processes as well as a large-scale network, including the midbrain’s PAG and VTA, basal ganglia (amygdala and NAc), and insula, as well as diencephalon (the cingulate and medial frontal cortices through the lateral and medial hypothalamus and medial thalamus) together with the MTL, including the hippocampus as well as the entorhinal cortex, perirhinal cortex, and parahippocampal cortices that responsible for declarative memories. Moreover, the SEEKING system generates positive subjective emotional states-positive expectancy, enthusiastic exploration, and hopefulness, apparently, initiates learning and memory in the brain. All cognitive activity is motivated from ‘underneath’ by basic emotional and homeostatic needs (motivational drives) that explore environmental events for survival while facilitating secondary processes of learning and memory.

Author Contributions

CMT drafted this manuscript. CMT, HUA, MNMS, and ASM revised this draft. All authors reviewed and approved this manuscript.

This research work was supported by the HiCoE grant for CISIR (Ref No. 0153CA-002), Ministry of Education (MOE), Malaysia.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Ministry of Education (MOE), Malaysia for the financial support. We gratefully thank Frontiers in Psychology, Specialty Section Emotion Sciences reviewers and the journal Associate Editor, for their helpful input and feedback on the content of this manuscript.

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Keywords : emotional valence, arousal, learning, memory, prefrontal cortex (PFC), medial temporal lobe (MTL), amygdala, neuroimaging

Citation: Tyng CM, Amin HU, Saad MNM and Malik AS (2017) The Influences of Emotion on Learning and Memory. Front. Psychol. 8:1454. doi: 10.3389/fpsyg.2017.01454

Received: 29 November 2016; Accepted: 10 August 2017; Published: 24 August 2017.

Reviewed by:

Copyright © 2017 Tyng, Amin, Saad and Malik. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Aamir S. Malik, [email protected]

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7 Thinking, Language, and Problem Solving

What is the best way to solve a problem? How does a person who has never seen or touched snow in real life develop an understanding of the concept of snow? How do young children acquire the ability to learn language with no formal instruction? Psychologists who study thinking explore questions like these and are called cognitive psychologists.

In other chapters, we discussed the cognitive processes of perception, learning, and memory. In this chapter, we will focus on high-level cognitive processes. As a part of this discussion, we will consider thinking and briefly explore the development and use of language. We will also discuss problem solving and creativity. After finishing this chapter, you will have a greater appreciation of the higher-level cognitive processes that contribute to our distinctiveness as a species.

Table of Contents

7.1 What is Cognition? 7.2 Language 7.3 Problem Solving

7.1 What is Cognition?

Learning Objectives

By the end of this section, you will be able to:

• Describe cognition
• Distinguish concepts and prototypes
• Explain the difference between natural and artificial concepts
• Describe how schemata are organized and constructed

Imagine all of your thoughts as if they were physical entities, swirling rapidly inside your mind. How is it possible that the brain is able to move from one thought to the next in an organized, orderly fashion? The brain is endlessly perceiving, processing, planning, organizing, and remembering—it is always active. Yet, you don’t notice most of your brain’s activity as you move throughout your daily routine. This is only one facet of the complex processes involved in cognition . Simply put,  cognition  is thinking, and it encompasses the processes associated with perception, knowledge, problem solving, judgment, language, and memory. Scientists who study cognition are searching for ways to understand how we integrate, organize, and utilize our conscious cognitive experiences without being aware of all of the unconscious work that our brains are doing (for example, Kahneman, 2011).

Upon waking each morning, you begin thinking—contemplating the tasks that you must complete that day. In what order should you run your errands? Should you go to the bank, the cleaners, or the grocery store first? Can you get these things done before you head to class or will they need to wait until school is done? These thoughts are one example of cognition at work. Exceptionally complex, cognition is an essential feature of human consciousness, yet not all aspects of cognition are consciously experienced.

Cognitive psychology  is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem solving, in addition to other cognitive processes. Cognitive psychologists strive to determine and measure different types of intelligence, why some people are better at problem solving than others, and how emotional intelligence affects success in the workplace, among countless other topics. They also sometimes focus on how we organize thoughts and information gathered from our environments into meaningful categories of thought, which will be discussed later.

Concepts and Prototypes

The human nervous system is capable of handling endless streams of information. The senses serve as the interface between the mind and the external environment, receiving stimuli and translating it into nervous impulses that are transmitted to the brain. The brain then processes this information and uses the relevant pieces to create thoughts, which can then be expressed through language or stored in memory for future use. To make this process more complex, the brain does not gather information from external environments only. When thoughts are formed, the mind synthesizes information from emotions and memories ( Figure 7.2 ). Emotion and memory are powerful influences on both our thoughts and behaviors.

Concepts are informed by our semantic memory (you will learn more about semantic memory in a later chapter) and are present in every aspect of our lives; however, one of the easiest places to notice concepts is inside a classroom, where they are discussed explicitly. When you study United States history, for example, you learn about more than just individual events that have happened in America’s past. You absorb a large quantity of information by listening to and participating in discussions, examining maps, and reading first-hand accounts of people’s lives. Your brain analyzes these details and develops an overall understanding of American history. In the process, your brain gathers details that inform and refine your understanding of related concepts like democracy, power, and freedom.

Concepts can be complex and abstract, like justice, or more concrete, like types of birds. Some concepts, like tolerance, are agreed upon by many people, because they have been used in various ways over many years. Other concepts, like the characteristics of your ideal friend or your family’s birthday traditions, are personal and individualized. In this way, concepts touch every aspect of our lives, from our many daily routines to the guiding principles behind the way governments function.

Another technique used by your brain to organize information is the identification of prototypes for the concepts you have developed. A  prototype  is the best example or representation of a concept. For example, what comes to your mind when you think of a dog? Most likely your early experiences with dogs will shape what you imagine. If your first pet was a Golden Retriever, there is a good chance that this would be your prototype for the category of dogs.

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories, natural and artificial. Natural concepts  are created “naturally” through your experiences and can be developed from either direct or indirect experiences. For example, if you live in Essex Junction, Vermont, you have probably had a lot of direct experience with snow. You’ve watched it fall from the sky, you’ve seen lightly falling snow that barely covers the windshield of your car, and you’ve shoveled out 18 inches of fluffy white snow as you’ve thought, “This is perfect for skiing.” You’ve thrown snowballs at your best friend and gone sledding down the steepest hill in town. In short, you know snow. You know what it looks like, smells like, tastes like, and feels like. If, however, you’ve lived your whole life on the island of Saint Vincent in the Caribbean, you may never have actually seen snow, much less tasted, smelled, or touched it. You know snow from the indirect experience of seeing pictures of falling snow—or from watching films that feature snow as part of the setting. Either way, snow is a natural concept because you can construct an understanding of it through direct observations, experiences with snow, or indirect knowledge (such as from films or books) ( Figure 7.3 ).

An  artificial concept , on the other hand, is a concept that is defined by a specific set of characteristics. Various properties of geometric shapes, like squares and triangles, serve as useful examples of artificial concepts. A triangle always has three angles and three sides. A square always has four equal sides and four right angles. Mathematical formulas, like the equation for area (length × width) are artificial concepts defined by specific sets of characteristics that are always the same. Artificial concepts can enhance the understanding of a topic by building on one another. For example, before learning the concept of “area of a square” (and the formula to find it), you must understand what a square is. Once the concept of “area of a square” is understood, an understanding of area for other geometric shapes can be built upon the original understanding of area. The use of artificial concepts to define an idea is crucial to communicating with others and engaging in complex thought. According to Goldstone and Kersten (2003), concepts act as building blocks and can be connected in countless combinations to create complex thoughts.

A  schema (plural: schemata)  is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.

There are several types of schemata. A  role schema  makes assumptions about how individuals in certain roles will behave (Callero, 1994). For example, imagine you meet someone who introduces himself as a firefighter. When this happens, your brain automatically activates the “firefighter schema” and begins making assumptions that this person is brave, selfless, and community-oriented. Despite not knowing this person, already you have unknowingly made judgments about him. Schemata also help you fill in gaps in the information you receive from the world around you. While schemata allow for more efficient information processing, there can be problems with schemata, regardless of whether they are accurate: Perhaps this particular firefighter is not brave, he just works as a firefighter to pay the bills while studying to become a children’s librarian.

An  event schema , also known as a  cognitive script , is a set of behaviors that can feel like a routine. Think about what you do when you walk into an elevator ( Figure 7.4 ). First, the doors open and you wait to let exiting passengers leave the elevator car. Then, you step into the elevator and turn around to face the doors, looking for the correct button to push. You never face the back of the elevator, do you? And when you’re riding in a crowded elevator and you can’t face the front, it feels uncomfortable, doesn’t it? Interestingly, event schemata can vary widely among different cultures and countries. For example, while it is quite common for people to greet one another with a handshake in the United States, in Tibet, you greet someone by sticking your tongue out at them, and in Belize, you bump fists (Cairns Regional Council, n.d.)

Because event schemata are automatic, they can be difficult to change. Imagine that you are driving home from work or school. This event schema involves getting in the car, shutting the door, and buckling your seatbelt before putting the key in the ignition. You might perform this script two or three times each day. As you drive home, you hear your phone’s ring tone. Typically, the event schema that occurs when you hear your phone ringing involves locating the phone and answering it or responding to your latest text message. So without thinking, you reach for your phone, which could be in your pocket, in your bag, or on the passenger seat of the car. This powerful event schema is informed by your pattern of behavior and the pleasurable stimulation that a phone call or text message gives your brain. Because it is a schema, it is extremely challenging for us to stop reaching for the phone, even though we know that we endanger our own lives and the lives of others while we do it (Neyfakh, 2013) ( Figure 7.5 ).

Remember the elevator? It feels almost impossible to walk in and  not  face the door. Our powerful event schema dictates our behavior in the elevator, and it is no different with our phones. Current research suggests that it is the habit, or event schema, of checking our phones in many different situations that makes refraining from checking them while driving especially difficult (Bayer & Campbell, 2012). Because texting and driving has become a dangerous epidemic in recent years, psychologists are looking at ways to help people interrupt the “phone schema” while driving. Event schemata like these are the reason why many habits are difficult to break once they have been acquired. As we continue to examine thinking, keep in mind how powerful the forces of concepts and schemata are to our understanding of the world.

7.2 LAnguage

• Define language and demonstrate familiarity with the components of language
• Understand the development of language
• Explain the relationship between language and thinking

Language  is a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another. While language is a form of communication, not all communication is language. Many species communicate with one another through their postures, movements, odors, or vocalizations. This communication is crucial for species that need to interact and develop social relationships with their conspecifics. However, many people have asserted that it is language that makes humans unique among all of the animal species (Corballis & Suddendorf, 2007; Tomasello & Rakoczy, 2003). This section will focus on what distinguishes language as a special form of communication, how the use of language develops, and how language affects the way we think.

Components of Language

Language, be it spoken, signed, or written, has specific components: a lexicon and lexicon grammar .  Lexicon  refers to the words of a given language. Thus, lexicon is a language’s vocabulary.  Grammar  refers to the set of rules that are used to convey meaning through the use of the lexicon (Fernández & Cairns, 2011). For instance, English grammar dictates that most verbs receive an “-ed” at the end to indicate past tense.

Words are formed by combining the various phonemes that make up the language. A  phoneme  (e.g., the sounds “ah” vs. “eh”) is a basic sound unit of a given language, and different languages have different sets of phonemes. For example, the phoneme English speakers associate with the letter ‘L’ is not used in the Japanese language. Similarly, many Southern African languages use phonemes, sometimes referred to as ‘click consonants’ that are not used in English.

Phonemes are combined to form  morphemes , which are the smallest units of language that convey some type of meaning. Some words are morphemes, but not all morphemes are words.  For example, “-ed” is a morpheme used to convey the past-tense in English, but it is not a word. The word “review” contains two morphemes: re- (meaning to do something again) and view (to see). Finally, some words like “I” and “a” are both a phonemes and morphemes.

We use semantics and syntax to construct language. Semantics and syntax are part of a language’s grammar.  Semantics  refers to the process by which we derive meaning from morphemes and words by connecting those morphemes and words to stored concepts.  Syntax  refers to the way words are organized into sentences (Chomsky, 1965; Fernández & Cairns, 2011). For example, you would never say “the dog walked I today” to let someone know you took your dog for a walk–that sentence does not obey English syntax and is therefore difficult to make sense of.

We apply the rules of grammar to organize the lexicon in novel and creative ways, which allow us to communicate information about both concrete and abstract concepts. We can talk about our immediate and observable surroundings as well as the surface of unseen planets. We can share our innermost thoughts, our plans for the future, and debate the value of a college education. We can provide detailed instructions for cooking a meal, fixing a car, or building a fire. Through our use of words and language, we are able to form, organize, and express ideas, schema, and artificial concepts.

Language Development

Given the remarkable complexity of a language, one might expect that mastering a language would be an especially arduous task; indeed, for those of us trying to learn a second language as adults, this might seem to be true. However, young children master language very quickly with relative ease. B. F.  Skinner  (1957) proposed that language is learned through reinforcement. Noam  Chomsky  (1965) criticized this behaviorist approach, asserting instead that the mechanisms underlying language acquisition are biologically determined. The use of language develops in the absence of formal instruction and appears to follow a very similar pattern in children from vastly different cultures and backgrounds. It would seem, therefore, that we are born with a biological predisposition to acquire a language (Chomsky, 1965; Fernández & Cairns, 2011). Moreover, it appears that there is a critical period for language acquisition, such that this proficiency at acquiring language is maximal early in life; generally, as people age, the ease with which they acquire and master new languages diminishes (Johnson & Newport, 1989; Lenneberg, 1967; Singleton, 1995).

Children begin to learn about language from a very early age ( Table 7.1 ). In fact, it appears that this is occurring even before we are born. Newborns show preference for their mother’s voice and appear to be able to discriminate between the language spoken by their mother and other languages. Babies are also attuned to the languages being used around them and show preferences for videos of faces that are moving in synchrony with the audio of spoken language versus videos that do not synchronize with the audio (Blossom & Morgan, 2006; Pickens, 1994; Spelke & Cortelyou, 1981).

DIG DEEPER: The Case of Genie

In the fall of 1970, a social worker in the Los Angeles area found a 13-year-old girl who was being raised in extremely neglectful and abusive conditions. The girl, who came to be known as Genie, had lived most of her life tied to a potty chair or confined to a crib in a small room that was kept closed with the curtains drawn. For a little over a decade, Genie had virtually no social interaction and no access to the outside world. As a result of these conditions, Genie was unable to stand up, chew solid food, or speak (Fromkin, Krashen, Curtiss, Rigler, & Rigler, 1974; Rymer, 1993). The police took Genie into protective custody.

Genie’s abilities improved dramatically following her removal from her abusive environment, and early on, it appeared she was acquiring language—much later than would be predicted by critical period hypotheses that had been posited at the time (Fromkin et al., 1974). Genie managed to amass an impressive vocabulary in a relatively short amount of time. However, she never developed a mastery of the grammatical aspects of language (Curtiss, 1981). Perhaps being deprived of the opportunity to learn language during a critical period impeded Genie’s ability to fully acquire and use language.

You may recall that each language has its own set of phonemes that are used to generate morphemes, words, and so on. Babies can discriminate among the sounds that make up a language (for example, they can tell the difference between the “s” in vision and the “ss” in fission); early on, they can differentiate between the sounds of all human languages, even those that do not occur in the languages that are used in their environments. However, by the time that they are about 1 year old, they can only discriminate among those phonemes that are used in the language or languages in their environments (Jensen, 2011; Werker & Lalonde, 1988; Werker & Tees, 1984).

After the first few months of life, babies enter what is known as the babbling stage, during which time they tend to produce single syllables that are repeated over and over. As time passes, more variations appear in the syllables that they produce. During this time, it is unlikely that the babies are trying to communicate; they are just as likely to babble when they are alone as when they are with their caregivers (Fernández & Cairns, 2011). Interestingly, babies who are raised in environments in which sign language is used will also begin to show babbling in the gestures of their hands during this stage (Petitto, Holowka, Sergio, Levy, & Ostry, 2004).

Generally, a child’s first word is uttered sometime between the ages of 1 year to 18 months, and for the next few months, the child will remain in the “one word” stage of language development. During this time, children know a number of words, but they only produce one-word utterances. The child’s early vocabulary is limited to familiar objects or events, often nouns. Although children in this stage only make one-word utterances, these words often carry larger meaning (Fernández & Cairns, 2011). So, for example, a child saying “cookie” could be identifying a cookie or asking for a cookie.

As a child’s lexicon grows, she begins to utter simple sentences and to acquire new vocabulary at a very rapid pace. In addition, children begin to demonstrate a clear understanding of the specific rules that apply to their language(s). Even the mistakes that children sometimes make provide evidence of just how much they understand about those rules. This is sometimes seen in the form of  overgeneralization . In this context, overgeneralization refers to an extension of a language rule to an exception to the rule. For example, in English, it is usually the case that an “s” is added to the end of a word to indicate plurality. For example, we speak of one dog versus two dogs. Young children will overgeneralize this rule to cases that are exceptions to the “add an s to the end of the word” rule and say things like “those two gooses” or “three mouses.” Clearly, the rules of the language are understood, even if the exceptions to the rules are still being learned (Moskowitz, 1978).

Language and Thought

When we speak one language, we agree that words are representations of ideas, people, places, and events. The given language that children learn is connected to their culture and surroundings. But can words themselves shape the way we think about things? Psychologists have long investigated the question of whether language shapes thoughts and actions, or whether our thoughts and beliefs shape our language. Two researchers, Edward Sapir and Benjamin Lee Whorf, began this investigation in the 1940s. They wanted to understand how the language habits of a community encourage members of that community to interpret language in a particular manner (Sapir, 1941/1964). Sapir and Whorf proposed that language determines thought. For example, in some languages there are many different words for love. However, in English we use the word love for all types of love. Does this affect how we think about love depending on the language that we speak (Whorf, 1956)? Researchers have since identified this view as too absolute, pointing out a lack of empiricism behind what Sapir and Whorf proposed (Abler, 2013; Boroditsky, 2011; van Troyer, 1994). Today, psychologists continue to study and debate the relationship between language and thought.

WHAT DO YOU THINK? The Meaning of Language

Think about what you know of other languages; perhaps you even speak multiple languages. Imagine for a moment that your closest friend fluently speaks more than one language. Do you think that friend thinks differently, depending on which language is being spoken? You may know a few words that are not translatable from their original language into English. For example, the Portuguese word  saudade  originated during the 15th century, when Portuguese sailors left home to explore the seas and travel to Africa or Asia. Those left behind described the emptiness and fondness they felt as  saudade  ( Figure 7.6 ) .  The word came to express many meanings, including loss, nostalgia, yearning, warm memories, and hope. There is no single word in English that includes all of those emotions in a single description. Do words such as  saudade  indicate that different languages produce different patterns of thought in people? What do you think??

One group of researchers who wanted to investigate how language influences thought compared how English speakers and the Dani people of Papua New Guinea think and speak about color. The Dani have two words for color: one word for  light  and one word for  dark . In contrast, the English language has 11 color words. Researchers hypothesized that the number of color terms could limit the ways that the Dani people conceptualized color. However, the Dani were able to distinguish colors with the same ability as English speakers, despite having fewer words at their disposal (Berlin & Kay, 1969). A recent review of research aimed at determining how language might affect something like color perception suggests that language can influence perceptual phenomena, especially in the left hemisphere of the brain. You may recall from earlier chapters that the left hemisphere is associated with language for most people. However, the right (less linguistic hemisphere) of the brain is less affected by linguistic influences on perception (Regier & Kay, 2009)

7.3 Problem Solving

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving and decision making

People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

Problem-Solving Strategies

When you are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

A  problem-solving strategy  is a plan of action used to find a solution. Different strategies have different action plans associated with them ( Table 7.2 ). For example, a well-known strategy is  trial and error . The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

Another type of strategy is an algorithm. An  algorithm  is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a  heuristic  is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards  is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

EVERYDAY CONNECTION: Solving Puzzles

Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below ( Figure 7.7 ) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

Here is another popular type of puzzle ( Figure 7.8 ) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A  mental set  is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness  is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. Duncker (1945) conducted foundational research on functional fixedness. He created an experiment in which participants were given a candle, a book of matches, and a box of thumbtacks. They were instructed to use those items to attach the candle to the wall so that it did not drip wax onto the table below. Participants had to use functional fixedness to solve the problem ( Figure 7.10 ). During the  Apollo 13  mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An  anchoring bias  occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The  confirmation bias  is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis.  Hindsight bias  leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did.  Representative bias  describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the  availability heuristic  is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision .  Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in  Table 7.3 .

Were you able to determine how many marbles are needed to balance the scales in  Figure 7.9 ? You need nine. Were you able to solve the problems in  Figure 7.7  and  Figure 7.8 ? Here are the answers ( Figure 7.11 ).

Chapter Summary

7.1 what is cognition.

In this section, you were introduced to cognitive psychology, which is the study of cognition, or the brain’s ability to think, perceive, plan, analyze, and remember. Concepts and their corresponding prototypes help us quickly organize our thinking by creating categories into which we can sort new information. We also develop schemata, which are clusters of related concepts. Some schemata involve routines of thought and behavior, and these help us function properly in various situations without having to “think twice” about them. Schemata show up in social situations and routines of daily behavior.

7.2 Language

Language is a communication system that has both a lexicon and a system of grammar. Language acquisition occurs naturally and effortlessly during the early stages of life, and this acquisition occurs in a predictable sequence for individuals around the world. Language has a strong influence on thought, and the concept of how language may influence cognition remains an area of study and debate in psychology.

Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

thinking; or, all of the processes associated with perception, knowledge, problem solving, judgement, language, and memory.

A modern school of psychological thought that empirically examines mental processes such as perception, memory, language, and judgement.

a category or grouping of linguistic information, images, ideas or memories, such as life experiences.

knowledge about words, concepts, and language-based knowledge and facts

the best example or representation of a concept, specific to an individual

concepts developed through direct or indirect experiences with the world

a concept defined by a specific set of characteristics.

a mental construct consisting of a cluster of related concepts

a set of ideas relating to how individuals in certain roles will behave.

also known as a cognitive script; a set of behaviors associated with a particular place or event

also known as an event schema; a set of behaviors associated with a particular place or event

a communication system that involves using words and systematic rules to organize those words to transmit information from one individual to another.

the words of a language

the rules of a language used to convey meaning through the use of the lexicon

the basic sounds that make up a language

the smallest unit of language that conveys meaning

the process by which we derive meaning from morphemes and words

the rules guiding the organization of morphemes into words and words into sentences.

Psychology 2e Copyright © 2020 by Openstax is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Ackerman S. Discovering the Brain. Washington (DC): National Academies Press (US); 1992.

Discovering the Brain.

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8 Learning, Recalling, and Thinking

The brain regulates an array of functions necessary to survival: the action of our five senses, the continuous monitoring of the spatial surround, contraction and relaxation of the digestive muscles, the rhythms of breathing and a regular heartbeat. As the vital functions maintain their steady course without our conscious exertion, we are accustomed to consider the brain as preeminently the organ of thought . The brain houses our mind and our memories, and we rely on its information-processing capacities when we set out to learn something new.

But where in the brain can we locate memory or thought itself? Chapter 7 offered some clues about the ways scientific investigation—from the molecular level to studies of the alert, behaving animal—has begun to define in physical terms an abstract quality such as "attention." Similar techniques and approaches are being applied to other mental functions, too, even those as seemingly intangible as learning, remembering, or thinking about the outside world.

Learning and memory, which for many years were considered central problems in psychology, the social sciences, and philosophy, have recently assumed greater importance in the area of neurobiology, itself a confluence of several lines of investigation.

FIGURE 8.1.

Most available evidence suggests that the functions of memory are carried out by the hippocampus and other related structures in the temporal lobe. (The hippocampus and the amygdala, nearby, also form part of the limbic system, a pathway in the brain (more...)

Neuroscientific interest in learning and memory has recently increased for two reasons, according to psychiatrist Eric Kandel, a senior scientist in the Howard Hughes Medical Institute at Columbia University. One reason is the proposal of cellular mechanisms that account for a basic kind of learning and long-term memory. The model was first identified in the relatively simple nervous systems of the marine snail and the crayfish, but it appears to hold good in the hippocampus of vertebrates as well, where it also may be associated with the formation of long-term memories.

The second reason for a new interest in learning and memory is the evidence accumulating to suggest that mechanisms involved in the structural change in the nervous system that accompanies learning may strongly resemble certain important steps in the nervous system's development . In other words, the sorts of adjustments among synapses that account for learning may be the same as the "fine-tuning" that occurs while the maturing system is assuming its unique elaborated form. Thus, the biological changes that accompany learning may be seen—in a very schematic way—as an old process put to a new use, or as a specialized way in which the brain continues to "grow" after maturation.

• A Molecular Account Of Long-Term Memory

Eric Kandel is best known for his work on the physical basis of learning and memory in the marine snail Aplysia . This animal, simple as its nervous system is (most of its 20,000 neurons have been identified by number), nevertheless provides an excellent model for the study of learning and memory, through its "gill withdrawal" reflex. When Aplysia perceives something touching its skin, it quickly withdraws both the siphon (a respiratory organ) and the gill, much as a person withdraws a hand from a hot stove without thinking about it. Although this withdrawal is a reflex, it is not completely hard-wired but can be modified by various forms of learning. One such form is sensitization, in which the animal becomes aware of a threatening factor in the environment and to protect itself learns to augment its reflex. The augmented version of the withdrawal reflex can also be maintained in short-term or long-term memory, depending on whether researchers administer the noxious stimulus (the negative reinforcement) only once or twice, or many times within a short period. The two forms of memory can be distinguished not only by their duration—the difference between minutes and days—but also at a molecular level, because it is possible to treat the snail with a chemical compound that interferes with long-term memory but leaves short-term memory unimpaired.

A major set of elements in this reflex are sensory neurons in the siphon skin, which perceive the stimulus; motor neurons in the gill, which contract the muscle and cause the gill to withdraw; and "facilitating neurons," or interneurons, which act on the sensory neurons to enhance their effect. The role of these facilitating neurons has recently become clearer, thanks to observations made from cell cultures, at the simplest level possible: the neurons themselves. A single sensory neuron and a single motor neuron, when implanted in a glass dish with a suitable nourishing culture, form functional interconnections. When a facilitating neuron is added or the cells are exposed to serotonin (the transmitter released by the facilitating neuron), the connection between the sensory and the motor neuron becomes stronger. The connection can last in this enhanced form for more than a day, even up to several weeks, and apparently includes some process of genetic transcription, or expression of part of the nerve cell's DNA.

This genetic transcription produces two results that set long-term memory apart from short-term memory. One is a sort of extension of a short-term effect, in which the potassium channels in the sensory neuron membrane remain closed for a longer time, while the calcium channels remain open. The net effect is that the sensory neuron is more easily excited and releases more neurotransmitter, which in turn activates the motor neuron more strongly. Actually, this effect can be produced on a short-term basis by increased levels of the second-messenger compound cyclic AMP; but after transcription, it is no longer dependent on such a factor and persists even without it. The effect can be disrupted, however, by inhibitors of protein synthesis and RNA synthesis. This constraint establishes that the recording of long-term memories involves not simply a momentary release of neurotransmitters but actual gene expression, with the synthesis of new proteins in the nerve cells themselves.

The new protein products that are synthesized—for example, under the stimulus of a repeated threatening signal—do more than merely reduce the dependence of the sensory neurons on serotonin or cyclic AMP for their activation. As a second transcription event, they induce new growth in certain parts of the sensory neurons themselves. These neurons develop many more presynaptic terminals, the structures through which they release neurotransmitter to the motor neurons; in addition, the number and the surface area of active zones in each presynaptic terminal increase, as does the total number of vesicles, the storage containers for the neurotransmitter. Thus, gene expression appears to build long-term memory out of several effective components, which come together in a formidable array: increased excitability of the sensory neurons, with the protein kinase continuing to work on its own to keep calcium channels open, allowing calcium ions in and more neurotransmitter out; more synapses for conveying signals between sensory and motor neurons; greater numbers of active zones in the synapses; and greater quantities of neurotransmitter contained in the active zones, ready for release. No wonder that memories built of such stuff tend to last awhile.

For closer study, the Kandel laboratory has replicated in cell culture the same conditions that in the living animal lead to protein synthesis and neuronal growth: a motor neuron, a sensory neuron (injected with a fluorescent dye to make imaging possible afterward), and exposure to serotonin repeated four or five times. The results are clear: within several hours, the main axon of the sensory neuron shows an increase in the number of synapses. Exposing the neurons to the second messenger cyclic AMP produces a similar result. But regardless of whether the facilitating compound is the neurotransmitter or the second messenger, neuronal growth occurs only if a target—a motor neuron—is also present.

The necessary presence of a target was the first similarity that Kandel and his collaborators noticed between the processes of structural change that accompany long-term memory, or learning, and those of development. The observation fit in well, too, with an earlier finding: the fine axonal branches of a sensory neuron in isolation adhere together in fat bundles, but on first contact with a motor neuron the branches tend to separate, each potentially to form its own synapse with the motor neuron. Here, at a mechanical level, is the explanation for a disassembly process that is required prior to the marked increase in synapses that takes place in the presence of serotonin. But in long-term memory, as in development, the presence of the target is necessary—a feature that makes for plasticity, or the all-important ability to change in response to the environment.

To study this learning-related plasticity at the molecular level, Kandel's research group is looking at the proteins that change in level when exposed to serotonin or cyclic AMP (or, in the living animal, to a noxious stimulus). Of the 15 proteins that change, 10 show an increase and 5 show a decrease. The reactions are transient: the levels go up, or down, and back again quite quickly.

Most interesting, in the investigators' view, are the proteins whose response is to decrease in level. Is there a way in which producing less of something can figure in a growth process? At a molecular level, the answer can be yes, if the something is an inhibitory factor of some kind. Such an answer may apply in this case, because four of these proteins that have been identified by genetic sequencing turn out to be none other than cell-adhesion molecules of the immunoglobulin type, first discovered by the research team of Gerald Edelman at Rockefeller University.

During development, the proteins apparently play a fundamental role; at least one of them is present at the very first stages, when the fertilized egg begins to divide. In the adult, however, these four proteins appear only in the nervous system, in both sensory and motor neurons. An interesting effect of these cell-adhesion proteins can be demonstrated on an isolated sensory neuron: if an antibody is added that blocks the cell-adhesion effect, the axonal filaments of the neuron start to come apart from their thick bundle and to separate out. The effect is similar to what happens when a sensory neuron is exposed to serotonin in the presence of a target, a motor neuron. This suggests that cell-adhesion molecules can indeed act as an inhibiting factor in particular circumstances. What they inhibit, apparently, is the growth and proliferation of signal-transmitting elements on the axons of sensory neurons.

By this reasoning, the effect of the cell-adhesion molecules would have to be held in abeyance at some point, to allow the sensory neurons to strengthen and increase their synaptic connections with the motor neurons. Perhaps there is even an innate tendency for some neurons, when they are near other target neurons, always to have their axons branching and proliferating, always to be seeking to form more synapses. (Indeed, during development, as discussed in Chapter 6 , the brain actually forms a great many more synapses than can ever be functional during the animal's lifetime.) The inhibitory action of the cell-adhesion molecules may thus be a crucial factor that keeps neuronal growth somewhat under control, and the temporary inhibition of cell-adhesion molecules in favor of long-term memory may be a single, notable exception to this form of containment. Of course, these results come from painstakingly close study of very simple nervous systems. The degree to which such findings can be extrapolated to the brains of primates, for example, which are many times more complex and which follow different patterns of development, is a matter of lively discussion among researchers in various specialized areas of neuroscience.

One striking aspect of such a system is the ingeniously high level of what, in a person, might be called thriftiness—the degree to which the same materials or biological processes are used and reused, but in novel contexts and to different ends. The protein kinase described earlier, which is dependent on cyclic AMP, appears in many other systems of the body and has various effects; but only in the nervous system, in relation to learning, does it play a role in long-term activation. Likewise, cell-adhesion molecules—better known to researchers for their general role in development—play a rather specialized part in the adult nervous system.

Just as intriguing, from a different perspective, is the evidence for significant common ground between biological mechanisms of learning and the early development of the organism: not only the common use of cell-adhesion proteins (although in different ways) but also the fact that growth in both contexts requires a target. Even the finding that a neurotransmitter such as serotonin is not restricted to moment-by-moment signaling but can actually be a factor that initiates neuronal growth in the case of long-term memory adds to an impression of the two contexts conjoining, with neurotransmitters sometimes acting as growth factors.

• The World In The Front Of The Brain

Short-term and long-term memory are not the only forms in which the brain stores information. All the time that the five senses are operating, the brain is assembling and sorting perceptions of the outside world, directing some to conscious attention and collecting others into a set of perpetually updated mental representations. Although we may seldom be aware of the full extent of these mental representations, or examine them directly, nevertheless, they hold great importance for our thought processes and our ability to carry out the simplest planned action or predictive step, even something as elementary as following a fast-moving target with our eyes. These mental representations are the data on which we base cognition—our thoughts, ideas, and abstract mental processes.

Animals, too, form complex mental representations of the world, which are shaped by their own brain structure and ecological requirements. For instance, information gathered through the sense of smell undoubtedly plays a much larger role in the mental representations of a dog than in those of a bird, which relies much more on its excellent vision (both in detail and in color) to help it recognize its kin, observe the territories of its rivals, and seek out food and mates. With such differences taken into account, the study of mental representation in animals can help scientists explain similar processes in humans, particularly if the neurobiology of the animal is also under study or is well known from earlier research.

Mental representation in the monkey, in the form of short-term or working memory, has actually been studied for more than 50 years. The earliest experiments were carried out as delayed-response tests: the monkey was shown a morsel of food being placed in one of two food wells and after a short delay had to open the correct one to claim the food as a reward. The reason for the delay was to force the monkey to rely on an internal mental representation rather than on immediate stimulation—that is, what it saw taking place at that moment. In the rhesus monkey, the area of the brain known to be important for this task is the prefrontal cortex; and in humans, too, homologous areas in the frontal region of the cortex, just behind the forehead, are sites of activity for tasks that test working memory.

Present-day research of this kind with monkeys uses a computer monitor. In such experiments, the animal directs its gaze to the center of the screen. While it keeps its attention fixed on the central spot, a visual target (a light) flashes briefly (for half a second) somewhere else on the screen. The monkey's task (which requires some months of training) is to keep its eyes fixed on the central spot as long as it is lit, and then, when the central spot has been switched off, to move its eyes to the place where the visual target had flashed some seconds before. Clearly, the test calls for working memory: the chances of turning one's eyes to the correct site by a lucky guess are slight, and since the visual target can appear anywhere at all on the screen, in any sequence—not simply location A alternating with location B—there is no possible way to ''prepare" the correct response beforehand. A monkey that is practiced in this task can perform with a high degree of accuracy; but when a portion of its principal sulcus is removed by surgery, an animal that was previously proficient performs with no more than 50 percent accuracy.

Given this sharp drop in performance, what is the nature of the deficit in the monkey's brain after surgery? Patricia Goldman-Rakic, who directs such investigations at Yale University Medical School, explains that it can be considered a "hole" in the memory—not in vision or in the ability to move the eyes. These faculties show up unimpaired in tests in which the visual target is left on (so that the monkey simply moves its eyes to the target at the appropriate time). Only the ability to guide the response by a mental image (memory) is missing.

Another complementary way of investigating the same topic is to record electrical activity from the brain during a working memory task. The ideal record in terms of clarity and precision is one obtained from a single neuron, by means of extremely fine microelectrodes. Recordings of this kind have become possible only in the past decade or so; those from Goodman-Rack's laboratory show several very interesting things. First, the neuron under study, in the prefrontal cortex, holds to a steady level of activity when the target light appears. But it increases its activity sharply once the target light is switched off and shows sustained activity during the delay, the interval over which a memory of the target must be maintained. Finally, the neuron's activity rather abruptly returns to a baseline level when the monkey begins its response—that is, when it moves its eyes to the site where the target had been. The neuron thus shows a high level of activity only during the time required to keep the correct spot "in mind" until the moment arrives to respond actively.

A second point of interest from these recordings is that the neurons of this region in the prefrontal cortex each tend to remember one precise location on the screen—and no others. For example, one neuron would respond accurately for targets at a 270-degree rotation from the center but would remain unresponsive to all other locations; another neuron would respond only to targets at a 90-degree rotation. In an analogy with the visual system, the neurons form a "memory field" in much the same way that nerve cells of the occipital lobe form a visual field. The memory field even shows the same cross-brain pattern that is traced by many signals: neurons oriented to the memory of stimuli that appeared in the right visual field predominate in the left hemisphere, and those oriented to the memory of stimuli presented in the left visual field predominate in the right hemisphere.

In Goodman-Rack's words, memory is an added-on feature of the representation system for visual space. Bearing out this interpretation are recordings from trials during which monkeys that were usually accurate made a mistake in their response, moving their eyes to the wrong place. The electrical data show that the particular neurons for that location were not highly active during the delay period, and so they failed to sustain the mental representation.

According to a current view, these neurons are organized in modules rather like the ocular dominance columns of the visual system. Several lines of research have established that the principal sulcus receives a great deal of its information about the outside world from the parietal cortex, which specializes in visual spatial information (as discussed in Chapter 7 ). The nerve tracts that project from the parietal lobe do in fact form a pattern of columns in the prefrontal cortex that alternates with columns for incoming signals from other regions. As in the visual system, each column is about half a millimeter wide.

These mental representations in the prefrontal cortex are too limited to be directly responsible for an animal's complex behavior. Goldman-Rakic and her colleagues believe that this representational knowledge does guide behavior in collaboration with other areas—particularly the parietal cortex—and that the larger network very probably represents the neural circuitry underlying spatial cognition in monkeys. Different parts of the network, and the connections among them, must be analyzed separately before the ensemble can be well understood as a network. A broad assortment of psychological studies have shown that when people are asked to perform any cognitive task, the prefrontal cortex invariably is activated; what remains to be discerned is which particular subdivisions of the area (visual or auditory or other) are involved. Increasingly specific testing, anatomical examination, and medical imaging of animals and human subjects are the tools that can provide this kind of information.

Meanwhile, noninvasive medical imaging of humans offers opportunities for the direct simultaneous study of physiology and mental functioning. In addition to NMR and PET scans, electroencephalographic studies can be quite useful, recording electrical activity at the scalp with great temporal precision. Recent EEG studies have shown that when a subject performs cognitive or judgment tasks that require keeping something in mind over a short period, a number of areas in the prefrontal cortex are active. When, on occasion, the subject makes an error, it appears that the network as a whole was not engaged.

• Neurotransmitters And The Information System

In addition to the information-processing circuits arranged in neuronal modules and in columns of incoming nerve tracts, the brain is replete with other systems of input. In the prefrontal cortex, for example, nerve fibers containing the neurotransmitter dopamine are found in especially high concentration, and researchers have wondered for some time what role dopamine might play in prefrontal circuits of information. The evidence gathered on this point over the past few years has begun to make clear the enormous extent to which dopamine shapes not only our physical functioning in the world but also our ability to process new information, to associate ideas effectively, and even to maintain a sense of well-being in balance with realistic perceptions.

In the human prefrontal cortex, the nerve fibers containing dopamine are not scattered evenly throughout the six cerebral cell layers but are concentrated in the outermost layers and the deep layers—that is, in layers 1, 5, and 6—and are less densely distributed in the middle layers. The cell bodies of these neurons are located relatively far away in the ventral tegmental area, a portion of the brainstem; they preferentially project their fibers to the frontal and prefrontal cortex. In addition, researchers have identified at least two distinct kinds of receptor sites for dopamine, and each has its own pattern in the layers of the cortex. The preponderance of the D-1 receptor fairly matches that of the dopamine-containing fibers: very high in the outermost layers and also considerable in the deep layers. The D-2 receptor, by contrast, shows a lower concentration throughout, with just a mild peak in layer 5.

In a test to see whether interference with the D-1 receptors would have any effect on cognitive function, Goldman-Rakic's research team injected a compound that blocks the D-1 receptor sites in the prefrontal cortex of monkeys trained in the delayed-response test described earlier. About 20 minutes after the injection, the animals showed an impairment of working memory, moving their eyes to the wrong location when the trial included a delay; but they responded correctly in a "sensory-guided" version of the task, in which the target light was left on as a guide. The D-1 receptors thus appear to be implicated in the efficiency of working memory.

A chemical compound developed for use in research that selectively stains neurons in the cerebral cortex bearing D-1 receptor sites has provided the Yale research team with an interesting lead. These neurons have been identified as pyramidal cells, the large principal cells that are the main element of cerebral cortex layer 6. The axons of these cells carry signals to another region—in this case, the thalamus (which plays an important role in the control of movement and forms part of the limbic system).

It appears from electron-microscopy studies that the dopamine receptors on these cells may modulate excitatory synapses, possibly from other pyramidal cells in the same or another region. Therefore, since dopamine acts directly on the output neurons of the prefrontal cortex—which are involved in processing, sorting, and assembling information about the outside world—the dopamine circuits can be considered a physical pathway by which this neurotransmitter can influence cognitive function. With each neuron bearing millions of spines on which dopamine synapses may act, a mechanism of this kind can have a pervasive effect, and even a slight deficiency or excess of dopamine could powerfully alter the ability of many neurons to integrate information from other regions of the brain. Goldman-Rakic and her colleagues are looking closely at the identified dopamine synapses to understand more precisely the mechanism by which dopamine may affect cognition.

The prefrontal cortex, with its importance for cognition, shows a form of dysfunction when tested in patients suffering from schizophrenia. (An often disabling mental illness, schizophrenia interferes with the capacity for logical thought and greatly disturbs the emotions and social behavior; see Chapter 4 for a discussion of current theories about the importance of dopamine levels in schizophrenia.) In experiments calling for cognitive tasks, which normally require the participation of the prefrontal cortex, schizophrenic patients show significantly lower rates of activity in this region of the brain. This does not mean that a disorder as complex and varied in form as schizophrenia can be explained as a simple failure of one part of the brain—particularly since the prefrontal cortex is known to be so richly interconnected with many other regions. But the findings that indicate a less active prefrontal cortex, which have been replicated in numerous studies, fit in well with other evidence suggesting that some dysfunction in a network of areas, including the prefrontal cortex, is implicated in schizophrenia.

Studies are under way to probe the state of working memory in schizophrenic patients as a way of learning more about the normal and impaired functioning of the prefrontal cortex. Meanwhile, rhesus monkeys treated in such a way as to mimic some of the deficits characteristic of schizophrenia are also being tested for working memory, thereby allowing more direct study of the neurobiology involved. One of the behavioral deficits that has been experimentally produced in monkeys is the inability to track a fast-moving target with the eyes. The deficit is not based in the visual or motor system; this much is clear, because the monkeys remain able to track targets moving more slowly. Instead, the problem seems to be cognitive, an inability to predict where the target will be in the next fraction of a second. This predictive aspect of eye movements, which falters in schizophrenic patients and in the experimentally treated animals, may well draw on the type of mental representations that the prefrontal cortex is largely occupied in assembling. The research being conducted in animals and humans is mutually helpful, offering the prospect over the next decade of significant advances in a neuroscientific account of the workings of the prefrontal cortex—including a cellular explanation of this area's memory functions. A view shared currently by Goldman-Rakic and many colleagues is that the main function of this greatly enlarged part of the brain, so recently evolved in the primate line, is to guide behavior by means of mental representations of stimuli, rather than by the stimuli themselves. Over the course of primate evolution, the advantages of this mode of mental functioning would have been considerable, greatly expanding the animal's options for varied and complex behavior.

• What Kind Of Computer Is This?

The types of mental representation discussed above, such as the continuous monitoring of the spatial surround by the parietal lobes, illustrate a vital point that is often overlooked when comparisons are made between the human brain and the computer. The fact is that the human brain—or the brain of many other animals—is solving quite difficult computational problems at every moment, just in seeing, recognizing a voice, or moving in a coordinated fashion on four limbs, or two limbs, or two wings. Most of these problems are so complex that they have yet to be formulated in explicit terms by computer scientists, which is why machines that can perceive and move and communicate as animals do—and perform all these functions at once—are still largely the stuff of science fiction.

If computers are not really brains, what does it mean to call the brain a kind of computer? Terrence J. Sejnowski, whose work at the Salk Institute for Biological Studies in San Diego focuses on computer models of cognition and brain structure, answers this question by pointing to a simple device designed to do one thing optimally, and one thing only: play tic-tac-toe. This "computer," built from electronic Tinkertoys at MIT's Artificial Intelligence Laboratory, is programmed with every possible position in the game. (These have been reduced, through mathematical operations that apply the principle of symmetry, to a subset of about 48.) The positions, each with its one optimal response, are encoded as the computer's memory. When presented with a particular position, the computer matches it to one in its subset and produces the correct response. By contrast, a digital computer would meet the challenge with a set of programmed instructions that it would run through recursively at each move to arrive at the optimal response.

The MIT device does not carry out a string of calculations or algorithms, the kind of task we generally think of a computer performing; instead, what it offers is essentially a "look-up table," with the correct answer precomputed and readily available. To obtain swift access to that answer, however, one must present a problem that exactly matches one of the problems originally encoded in the computer's memory. Beyond that pre-encoded set, the computer cannot provide any correct answer—or even a partial answer—unlike a digital computer, which can be reprogrammed for new problems because of its more general mode of operation. Still, within the realm of its pre-encoded problems and responses, the "look-up table" is extremely fast and effective.

This kind of device, however, requires a great deal of memory, since every significant aspect of each pre-encoded problem must be specified if the match is to be accurate. For the game of tic-tac-toe this is manageable; for chess, with its 10 40 possible game positions, or for real-life contexts in which the rules are less clear, it is impossible, at least at present. As a practical device, the look-up table is strictly limited. However, the principle of precomputing certain responses and being able to retrieve them with minimal additional effort appears to Sejnowski and others in his field as a likely clue to some of the workings of the brain. True, the abundant memory required by a look-up table was extremely expensive in the first computers and still poses a practical challenge today. But if the amount of memory were tremendously expanded, it would be possible to store many more solutions—in other words, to address many more and different kinds of problems.

It is hardly a revelation at this point that the human brain exhibits just such a tremendous capacity to store information. With somewhere between a hundred billion and a trillion neurons, the human brain already looks fairly impressive—but what really expands its storage capacity far beyond anything we can yet envision on an engineer's drawing board is the brain's proliferation of synapses. Each neuron contains several thousand points at which signals can be transmitted. Even if the brain were to store information at the low average rate of one bit per synapse (in terms comparable to a digital code, the synapse would be either active or inactive), the structure as a whole could still build up vast stores of memory, on the order of 10 14 bits. Meanwhile, today's most advanced supercomputers command a memory of about 10 9 bits. The human brain, to use Sejnowski's phrase, is memory-rich by comparison.

Of course, organization is crucial to managing such a vast resource, and the brain exhibits this feature at several levels, as discussed throughout this book. Research conducted on the simpler nervous system of invertebrates, as well as on nonhuman primates, other vertebrates, and humans, has indicated how learning brings about structural changes in nerve cells and how the neurons in turn form regions, which take part in networks. The networks are organized into distributed systems, which collaborate with other systems, both sensory and associative, to produce the total working effect.

Memory itself is organized so as to take advantage of these many levels of information: it appears to be arranged along associative paths, by the principle of contiguity. That is, the brain associates bits of information in such a way that we can recall items either on their own or by being "reminded" of them by a cue. The name of an acquaintance may come to mind when needed, or we may search for it under one heading or another: the name sounded like that of another friend, or the person looked like a former co-worker, or the meeting took place at the lunch following a difficult business negotiation. Considering the brain in purely physical terms, researchers have suggested that another form of contiguity may apply as well, that is, the simple proximity that builds up into maps. It may be that neurons close enough to one another to be activated together keep some trace of that contiguity as part of their bit of information.

Just what the memory-forming mechanisms might be, at a physiological level, has long puzzled psychologists as well as neurobiologists. Evidence of several kinds is gathering, however, in support of a model first suggested in 1949 by Donald Hebb, that a memory forms as a result of at least two kinds of activity taking place at a synapse simultaneously. The activities would have to include both the preand postsynaptic elements, the neuron transmitting the signal and the one receiving it. Hebb reasoned that the strength of the signal received in the postsynaptic cell would depend on the interaction of many details—the amount of transmitter released, the presence or absence of neuromodulators that affect the postsynaptic cell's excitability, the number of receptor sites on the receiving cell, and other such variables. Whatever the specifics, the underlying principle would be that information is stored as a result of two or more biochemical factors coming together in time, at the same instant, and in space, at the same synapse.

Physical evidence that indirectly supports this model has come recently from Eric Kandel's work with Aplysia . Hebb postulated two active elements (the pre- and postsynaptic terminals), but the nervous system in the marine snail appears to include a third element, the facilitating neuron that enhances the excitability of the sensory neuron. The Hebbian principle still applies, however, to the extent that the variables have to meet in time and space at a synapse.

In mammals, an example that conforms even better to the Hebbian model is found in part of the hippocampus of rats. The particular area, designated CA-3, contains about half a million neurons with recurrent connections—in other words, many of their axons lead back into the same population of neurons. Some axons also lead into the adjacent area CA-1. At the synapses in this area, both among CA-3 cells and between CA-3 and CA-1 cells, the neurotransmitter glutamate is released. It binds to two types of receptors: at one type of receptor site the glutamate slightly lowers the excitability threshold of the neuron, but at the other the binding of glutamate does not in itself affect the cell. Another simultaneous event is required: depolarization of the receiving cell, perhaps by other synapses. When this occurs together with the binding of glutamate, the cell membrane becomes momentarily permeable to ions—particularly calcium ions, which are important for bringing about persistent changes in the structure of the cell.

This receptor system illustrates the principle of contiguity outlined by Hebb: the binding of glutamate to a particular kind of receptor site and the depolarization of the postsynaptic cell must occur simultaneously, or at least within the same 20 to 50 thousandths of a second, for calcium ions to enter the cell and induce structural changes.

• Assembling A Brain In The Laboratory

Hebbian synapses have also been demonstrated in another kind of laboratory, where computer scientists and engineers have built them into a computer chip. The device is a simple one, with only 16 synapses, but it performs Hebbian learning quite efficiently, at the rate of a million times per second. Newer chips have already been developed to represent more realistic neurons, with many thousands of synapses; and technology to represent the connections between such neurons will make the assembly of something more nearly resembling a working brain a little easier to envision. Such a device will have to combine analog signals, like those propagated within neurons, and digital signals, the off or on impulses transmitted from one neuron to another. It will not be simply a larger, or even an unbelievably faster, version of today's familiar computer.

An artificial brain of this kind could be invaluable for further research along two main lines. For one, it could be set to work on some of the more difficult problems in an emerging field that might be called "artificial perception": problems of computer vision and of speech recognition that can be delineated by current devices but that cannot be resolved by them in a practical way. For a second main line of research, this kind of artificial brain can offer an advanced testing ground for neuroscientists' ideas about how the brain functions. Theoretical models of memory, in particular, cannot be tested adequately on a digital computer simulation of a few hundred model neurons, because the living brain works on such an enormously larger scale. But a computerized circuit of several million model neurons, with information circulating in real time, could yield a whole new order of information about such circuits in the living animal.

The field of artificial perception already boasts chips developed at the California Institute of Technology that are capable of much of the sensory processing performed just outside the brain by the retina, for example, and by the cochlea, the spiral passage of the inner ear whose hair cells respond to vibrations by sending impulses to the auditory nerve. Now in development as well are chips to simulate some of the functions of the visual cortex; others, with some of the memory-storing capacity of the hippocampus, are being scaled up, closer to the dimensions of a living system.

But more time and knowledge are needed to produce a device that can successfully mimic the information processing of the five senses and of short-term and long-term memory, and that can, moreover, integrate these systems into a unit that functions as a whole with respect to the outside world. This is not to say that progress has not occurred: early computers of the 1950s carried out only a few thousand instructions per second (a speed matched by today's pocket calculators), whereas the fastest of the supercomputers in use today can perform billions of operations per second. Still, this rate of processing, at 10 9 or so operations per second, is far from that of the human brain, in which an estimated 10 14 synapses are each active about 10 times per second—giving a total of 10 15 operations per second.

An interesting constraint that confronts computer designers who work with the current top speeds is the simple, unchanging limitation posed by the speed of light. Signals simply cannot be transmitted faster than about 1 foot per billionth of a second (10 -9 , the speed of light); to achieve the effect of speeds higher than this, the computer must be reduced in size to less than a cubic foot. This reduction is made possible by duplicating the central processor many times, even thousands of times, within the same computer, so that signals have less distance to travel. Even so, extrapolating from the recent rate of increase and from today's highest known speeds of computer processing, Terrence Sejnowski estimates that an artificial device approximating the human brain cannot be expected before at least the year 2015.

This prediction should not be considered discouraging—far from it. For such a project to be within sight at all is the clearest possible sign of the progress of neuroscience, gaining impetus as it does from an increasing number of fields that are related in some way to its investigations. Now not only the biological sciences, medicine, biochemistry, pharmacology, and psychology have an interest in improving our understanding of the brain's functioning; the computer sciences, physics, and mathematics also contribute to such models and stand to gain much from their continued exploration and testing. And along the way toward the assembly of a fully functioning artificial brain, it should become increasingly possible to construct devices that satisfactorily replicate some of the principles at work in the human brain. Although the devices probably would not resemble a brain in their material form any more than an airplane resembles a bird, they will be successful if they can show some of the brain's operating principles adapted to their own form, just as an airplane carries out, in mechanical translation, some of the aerodynamic principles of natural flight.

• The Benefits Of An Artificial Brain

Of course, the brain cannot ever be completely characterized in terms of a computer because in addition to all its computing faculties it possesses the properties of a biological organ in a living system. But, points out Gerald Edelman of the Neurosciences Institute at Rockefeller University, computers can indeed do something that, until recently, only a brain could do: they can carry out logical functions. Today, a computer can address any challenge or problem that can be described in a logical formula. This still leaves unexplored vast areas of human experience, such as perception; but as described earlier in this chapter, computer and mathematical modeling on one side, and more detailed neurobiological examination on the other side, are making inroads in this area too.

Edelman and his colleagues have used an approach they call synthetic neural modeling to build an automaton that is able to explore its environment by simulated vision and touch; moreover, it can categorize objects on the basis of its perceptions, and its responses draw on previous experiences with similar objects. Darwin III (the third generation of its kind) is a robot whose nervous system is built of about 50,000 cells of different types. The signals transmitted at its approximately 640,000 synaptic junctions enable Darwin III to control the functioning of its one eye and its multijointed arm. In analogy with the way living brains enter the world, Darwin III has no specific information built into its systems about the objects it may encounter in its environment. The nervous system is pre-encoded only to the extent that the devices for perception are made to detect certain features, such as light or movement or rough texture.

An important principle of Darwin III's nervous system is that the strength of the synaptic connections can increase selectively with greater activity when that activity leads to an adaptive end. What is ''adaptive" for Darwin III is defined by arbitrary values built into its programming. For example, the built-in principle that light is "better" than no light serves to direct and refine the system's eye movements toward a target. Just as in living neurons, the enhanced connection provides a stronger response the next time that particular neural pathway is active.

This selective strengthening of connections is reminiscent of the competition among synapses in the developing brain (as discussed in Chapter 6 ). Together with the ability to categorize, it means that the system can produce behaviors that we commonly call "recognition," for instance, or "association." At present, Darwin III can turn its head to track a moving object with its eye; it can extend its arm to trace the contours of an object; and, alternatively, if the stimulus is noxious, it can swat the object away. In all these responses the system shows increasing accuracy with practice, as the relevant synapses are strengthened. Eventually, such a system should be able to teach itself to apply both visual and motor abilities to a complex task—for instance, distinguishing a particular object or kind of object, and picking it out with the arm from among many others.

Although Darwin III cannot represent the nervous system of living animals in a highly detailed way, its synapses and circuits provide a much-needed testing ground for ideas about what takes place inside the real thing that makes those 3 pounds of semisoft tissue the most complex information-processing system ever known. Perhaps computers can never be brains in the full sense of serving as the nervous center of a biological system, but they can be designed with increasing success to carry out some of the functions that are routinely managed by a living brain. Gerald Edelman, like Terrence Sejnowski, believes that the prospects for building more complex "perception machines" are good—and the benefits in both intellectual and economic terms will be enormous. Most important of all would be the expanded opportunities for an understanding of higher brain functions—those that make us human—to be gained by using the computer not so much as a model of the brain, but as a tool for exploring it.

• Acknowledgments

Chapter 8 is based on presentations by Gerald Edelman, Patricia Goldman-Rakic, Eric Kandel, and Terrence Sejnowski.

• Cite this Page Ackerman S. Discovering the Brain. Washington (DC): National Academies Press (US); 1992. 8, Learning, Recalling, and Thinking.
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THE LEARNINGCOG BLOG

Developing Emotional Intelligence – Part 10 – Problem Solving

This blog is part of a series of blogs exploring Emotional Intelligence. Looking at ways to be able to develop and enhance our own perceived levels of Emotional Intelligence.

What is Emotional Intelligence?

To gain a greater understanding you can read a previous blog What is Emotional Intelligence and How Can I develop it , for more detail. However, Emotional intelligence is all about how well you understand your own emotions and the emotions of others, and the ability to identify and manage them. Emotional Intelligence, also known as “Ei” or “EQ”, is now well established set of “Competencies” that contribute to performance, engagement and success.

Their are five key areas of Emotional Intelligence, Self Perception, Self Expression, Interpersonal, Decision Making and Stress Management. Each of these areas has three traits. We are going to discuss each of these traits in more detail with their own blog. We have previous looked at the area of Self Perception and now looking at Self Expression. This week we will be exploring the trait, Problem Solving.

What is Problem Solving and the relations to Emotional Intelligence?

Problem solving is the ability to identify and define problems as well as to generate an implement potentially effective solutions. In short, it involves effectively solving problems of the personal and interpersonal nature. Problem solving includes the ability to understand how emotions affect decision making. This trait is much more about your ability to solve a problem and not let it affect you and the people around you. To work through the problem in a calm and undressed manner. This may even be while the world around you is going crazy.

“We can not solve our problems with the same level of thinking that created them” – Albert Einstein 

How much do you use the trait of Problem Solving?

Does it take a lot of emotional effort to solve a problem?

When Problem Solving is operating well:

• Takes in enough information to make informed conclusions, but not so many details that you are overwhelmed
• Keeps a clear head on the pertinent issues, without becoming frustrated
• Generates motivation for others to act in a way that will achieve goals
• Likely to take action

When Problem Solving is low:

• May prefer others to make decisions for you
• May struggle to keep a clear focus on the situation at hand
• Much of their time and energy is spent worrying about decisions rather than trying to solve them
• Prefers to deal with impractical problems rather than people

Developing skills around Problem Solving

During Learning Cog’s Emotional Intelligence (EQ) Masterclass, starting with ‘Self-Perception’, we explain how to assess you own emotional intelligence and how to develop your EQ awareness. Here in this blog we have added some areas to think about when developing Problem Solving.

Observation

• Who do you know who is good in their problem solving?
• What do they actually do to solve problems (list the steps)?
• How do they go about finding out information about the problem, in order to get a complete picture?
• When trying to solve the problem, how much time are they talking and how much time do they allow others to talk?

Self Coaching

• What is your preferred approach to problem-solving? e.g. Avoid the problem, we looked at the last minute and grab the first answer that comes to mind, take a systematic approach
• How do you respond when I have a problem?
• When you deal with problems, either well or badly, what do people say about what you did?
• Last time you handled a problem well, how did you feel, and what did you do that was key to successful outcome?

Thinking and Reflection

Here is an exercise for you to complete to help build your understanding of your own Problem Solving.

Exercise – Structured Problem Solving 

Try this step-by-step guide to problem solving

• Define the real problem: find out the real problem rather than the symptoms. e.g. Complaints from employees are symptoms, the underlying cause(s) is the real problem.
• Set objectives: what objective do you want to achieve and how will you measure their successful achievement? Identify any constraints: Are any other parts of the organisation affected? Are there any time are financial are all the constraints?
• Generates and prioritise options: how many different ideas have been generated? Prioritise them and select the ones you wish to develop further.
• Choose and evaluate option: think about the possible effects of a particular option our solution. Choose the option which matches your objectives.
• Implement: put your solution into action.
• Monitor and evaluate: monitor progress, make adjustments if necessary. Have your objectives being met? What worked well that you can use next time?

It is important to actual do something when taking part in any self development. The practical is more important than the theory.

• Distinguish between important and not so important problems, so you know how much time to spend on them.
• Try to define and clarify what exactly the problem is.
• Make an effort to understand how the problem developed, see the way it is affecting you and others and why.
• Identify all the stakeholders in a problem and what their interest/needs are.
• Practice differentiating important from not so important problems to develop the appropriate amount of energy to solving down.
• Generate solutions to problems before making a decision.
• Attempt to achieve a win/win solution, which meets the needs of all parties.
• Use problem-solving and analysis tools and processes e.g. Force field analysis, pros and cons, fish bone technique, etc
• When you have decided on the best way of dealing with the problem, go ahead and do it. It doesn’t work, try another possible solution.
• Bank and the good times.
• When you have done something really well take time to reflect on how well you did it and bank. To be able to use next time your are faced with a similar problem.

The more time you spend observing yourself and the people around you, the more you develop your Problem Solving. Give yourself time, it may feel mechanical, clumsy and awkward at first, but with practice it will become quick and easy and automatic. Why not get in touch and talk to us more about developing Emotional Intelligence in yourself, your Leadership Team or your whole business. [email protected]

Look out for the next blog on Developing Emotional Intelligence – Part 11 – Reality Testing

Or read previous blogs:

What is emotional intelligence? and how can I develop it…

1 Developing Emotional Intelligence – Part 1 – Self Regard

2 Developing Emotional Intelligence – Part 2 – Self Actualisation

3 Developing Emotional Intelligence – Part 3 – Emotional Self Awareness

4 Developing Emotional Intelligence – Part 4 – Emotional Expression

5 Developing Emotional Intelligence – Part 5 – Assertiveness

6 Developing Emotional Intelligence – Part 6 – Independence

7 Developing Emotional Intelligence – Part 7 – Social Responsibility

8 Developing Emotional Intelligence – Part 8 – Empathy

9 Developing Emotional Intelligence – Part 9 – Interpersonal Relationships 

Did you know we currently offer [Virtual] Emotional Intelligence testing and training?

Before the workshop you will be invited to take part in a pre-course activity which includes an online self-assessment producing a 20 page personal Emotional Intelligence Report.

An employee’s skills and qualifications are important for success within their role. An employee’s Emotional Intelligence is just as important, if not more so, for fulfilment within, or potentially beyond, their current role. The Emotional Intelligence in the Workplace workshop is designed to as part of an individual’s development in work settings. It helps individuals focus on the impact of emotional intelligence at work and offers suggestions for working more effectively in one’s role, with colleagues, managers and clients.

• Understand the impact of Emotional Intelligence on themselves and the people around them
• Quickly identify patterns in own and others Emotional Intelligence
• Create a clear, organised understanding of their strengths and weaknesses in a constructive way.
• Effectively measure where they currently are and wants to be by comparing results against sample groups of general population
• Make instant connections between different subscales of Emotional Intelligence and help leverage EI strengths and improve EI weaknesses.
• Create an action plan to develop key areas of Emotional Intelligence
• Become a more effect member of the team and organisation – This virtual session is 4 hours with a 1 hour break. – All of our Virtual Learning workshops are conducted via  Zoom . – Virtual learning begins at 10.30am through to 12.30pm and then again from 1.30pm to 3.30pm.  – You will receive an electronic version of your Emotional Intelligence report, and workshop materials will be sent via post.

To discover more about Emotional Intelligence and how LearningCog can help you, head over to our dedicated Emotional Intelligence page.

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Module 5: Thinking and Analysis

Problem-solving with critical thinking, learning outcomes.

• Describe how critical thinking skills can be used in problem-solving

Most of us face problems that we must solve every day. While some problems are more complex than others, we can apply critical thinking skills to every problem by asking questions like, what information am I missing? Why and how is it important? What are the contributing factors that lead to the problem? What resources are available to solve the problem? These questions are just the start of being able to think of innovative and effective solutions. Read through the following critical thinking, problem-solving process to identify steps you are already familiar with as well as opportunities to build a more critical approach to solving problems.

Problem-Solving Process

Step 1: define the problem.

Albert Einstein once said, “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions.”

Often, when we first hear of or learn about a problem, we do not have all the information. If we immediately try to find a solution without having a thorough understanding of the problem, then we may only be solving a part of the problem.  This is called a “band-aid fix,” or when a symptom is addressed, but not the actual problem. While these band-aid fixes may provide temporary relief, if the actual problem is not addressed soon, then the problem will continue and likely get worse. Therefore, the first step when using critical thinking to solve problems is to identify the problem. The goal during this step is to gather enough research to determine how widespread the problem is, its nature, and its importance.

Step 2: Analyze the Causes

This step is used to uncover assumptions and underlying problems that are at the root of the problem. This step is important since you will need to ensure that whatever solution is chosen addresses the actual cause, or causes, of the problem.

Asking “why” questions to uncover root causes

A common way to uncover root causes is by asking why questions. When we are given an answer to a why question, we will often need to question that answer itself. Thus the process of asking “why” is an  iterative process —meaning that it is a process that we can repeatedly apply. When we stop asking why questions depends on what information we need and that can differ depending on what the goals are. For a better understanding, see the example below:

Problem: The lamp does not turn on.

• Why doesn’t the lamp turn on? The fuse is blown.
• Why is the fuse blown? There was overloaded circuit.
• Why was the circuit overloaded? The hair dryer was on.

If one is simply a homeowner or tenant, then it might be enough to simply know that if the hair dryer is on, the circuit will overload and turn off.  However, one can always ask further why questions, depending on what the goal is. For example, suppose someone wants to know if all hair dryers overload circuits or just this one. We might continue thus:

• Why did this hair dryer overload the circuit? Because hair dryers in general require a lot of electricity.

But now suppose we are an electrical engineer and are interested in designing a more environmentally friendly hair dryer. In that case, we might ask further:

• Why do hair dryers require so much energy?

As you can see from this example, what counts as a root cause depends on context and interests. The homeowner will not necessarily be interested in asking the further why questions whereas others might be.

Step 3: Generate Solutions

The goal of this step is to generate as many solutions as possible. In order to do so, brainstorm as many ideas as possible, no matter how outrageous or ineffective the idea might seem at the time. During your brainstorming session, it is important to generate solutions freely without editing or evaluating any of the ideas. The more solutions that you can generate, the more innovative and effective your ultimate solution might become upon later review.

You might find that setting a timer for fifteen to thirty minutes will help you to creatively push past the point when you think you are done. Another method might be to set a target for how many ideas you will generate. You might also consider using categories to trigger ideas. If you are brainstorming with a group, consider brainstorming individually for a while and then also brainstorming together as ideas can build from one idea to the next.

Step 4: Select a Solution

Once the brainstorming session is complete, then it is time to evaluate the solutions and select the more effective one.  Here you will consider how each solution will address the causes determined in step 2. It is also helpful to develop the criteria you will use when evaluating each solution, for instance, cost, time, difficulty level, resources needed, etc. Once your criteria for evaluation is established, then consider ranking each criterion by importance since some solutions might meet all criteria, but not to equally effective degrees.

In addition to evaluating by criteria, ensure that you consider possibilities and consequences of all serious contenders to address any drawbacks to a solution. Lastly, ensure that the solutions are actually feasible.

Step 6: Put Solution into Action

While many problem-solving models stop at simply selecting a solution, in order to actually solve a problem, the solution must be put into action. Here, you take responsibility to create, communicate, and execute the plan with detailed organizational logistics by addressing who will be responsible for what, when, and how.

Step 7: Evaluate progress

The final step when employing critical thinking to problem-solving is to evaluate the progress of the solution. Since critical thinking demands open-mindedness, analysis, and a willingness to change one’s mind, it is important to monitor how well the solution has actually solved the problem in order to determine if any course correction is needed.

While we solve problems every day, following the process to apply more critical thinking approaches in each step by considering what information might be missing; analyzing the problem and causes; remaining open-minded while brainstorming solutions; and providing criteria for, evaluating, and monitoring solutions can help you to become a better problem-solver and strengthen your critical thinking skills.

iterative process: one that can be repeatedly applied

• Problem solving. Authored by : Anne Fleischer. Provided by : Lumen Learning. License : CC BY: Attribution
• College Success. Authored by : Matthew Van Cleave. Provided by : Lumen Learning. License : CC BY: Attribution
• wocintech stock - 178. Authored by : WOCinTech Chat. Located at : https://flic.kr/p/FiGVWt . License : CC BY-SA: Attribution-ShareAlike
• Five whys. Provided by : Wikipedia. Located at : https://en.wikipedia.org/wiki/Five_whys . License : CC BY-SA: Attribution-ShareAlike

Tapping into Your Emotions for Effective Problem Solving

Introduction.

In both our personal and professional lives, problem-solving is an essential skill that allows us to overcome challenges and achieve our goals. Whether it’s finding a solution to a complex issue at work or navigating through obstacles in our personal relationships, effective problem-solving is necessary for success.

While most of us approach problem-solving from a rational and logical standpoint, there is another important tool that we can tap into to enhance our problem-solving abilities: our emotions. Emotions play a crucial role in our decision-making process, and by learning how to harness and utilize them effectively, we can unlock a whole new level of problem-solving prowess.

Understanding Emotions

To effectively tap into our emotions for problem-solving, it’s important to first have a clear understanding of what emotions are and how they influence our decision-making. Emotions are complex psychological experiences that involve a range of physiological and psychological responses. They can be triggered by internal factors such as thoughts and beliefs, or by external factors such as events and interactions.

Different types of emotions, such as joy, anger, fear, and sadness, can heavily impact our problem-solving abilities. For example, fear may make us more conservative and risk-averse, while anger can fuel our motivation to take action. On the other hand, joy can enhance our creativity and ability to connect ideas.

Emotional intelligence, which refers to the ability to recognize, understand, and manage our own emotions as well as the emotions of others, has been found to be closely linked to problem-solving abilities. People with high emotional intelligence are better able to regulate their emotions, adapt to changing circumstances, and make more effective decisions.

Benefits of Emotional Problem Solving

Incorporating emotions into our problem-solving process offers several notable benefits. Firstly, emotions can provide valuable insights and perspectives that may not be immediately apparent through a purely rational lens. By tapping into our emotions, we can uncover underlying desires, motivations, and concerns that can guide us towards more holistic and impactful solutions.

Additionally, emotional problem-solving can lead to more creative and innovative solutions. Emotions can serve as a catalyst for new ideas and unconventional approaches to problem-solving. As we tap into our emotions, we may discover unique connections and possibilities that we may have otherwise overlooked.

Furthermore, incorporating emotions into our problem-solving process can foster stronger relationships. By understanding and considering the emotions of others, we can build empathy and create a more collaborative and compassionate problem-solving environment. This can lead to stronger connections and more sustainable solutions that address the needs and concerns of all parties involved.

Steps to Tap into Your Emotions for Effective Problem Solving

To harness the power of emotions for effective problem-solving, there are several key steps that can be taken:

1. Self-awareness

Self-awareness is the foundation for emotional problem-solving. It involves recognizing and understanding our own emotions, as well as the impact they have on our thoughts and behaviors. Techniques such as mindfulness and reflection can help improve self-awareness, allowing us to more effectively navigate and utilize our emotions in the problem-solving process.

2. Emotional regulation

Emotional regulation is the ability to manage and regulate our emotions in order to stay focused and rational during problem-solving. Strategies such as deep breathing, positive self-talk, and taking breaks can help regulate emotions and prevent them from overwhelming us during the problem-solving process. By keeping our emotions in check, we can approach problems with a clear and rational mindset.

Empathy plays a crucial role in problem-solving, as it allows us to understand and consider the emotions and perspectives of others. Developing empathy involves actively listening to others, seeking to understand their experiences, and practicing perspective-taking. By cultivating empathy, we can build stronger relationships and collaborate effectively towards finding solutions.

4. Emotional problem analysis

Emotional problem analysis involves looking beyond the rational aspects of a problem and considering the emotions involved. This involves asking questions such as “What emotions are present in this situation?” and “How are these emotions influencing the problem?” By analyzing problems from an emotional perspective, we can gain deeper insights and uncover hidden solutions.

5. Creative brainstorming

Emotions have the power to inspire creativity and generate new ideas. By incorporating emotional stimulation into our brainstorming sessions, we can unlock innovative solutions. Strategies such as using visualization exercises, engaging in arts-based activities, and exploring different perspectives can help stimulate emotional creativity and generate fresh insights.

6. Evaluating emotional solutions

While emotions can provide valuable insights and ideas, it’s important to evaluate the practicality and effectiveness of emotional solutions through a logical lens. By combining emotions with logical reasoning, we can identify the most viable and impactful solutions to our problems.

Putting Emotional Problem-Solving into Practice

To truly benefit from emotional problem-solving, it’s important to put these principles into practice in our daily lives. Real-world examples of individuals or organizations that have successfully utilized emotional problem-solving can serve as inspiration and guidance. Additionally, incorporating practical tips and exercises into our routines can help us develop the necessary skills and mindset for effective emotional problem-solving.

By tapping into our emotions, we can elevate our problem-solving abilities to new heights. Understanding the role of emotions in decision-making, harnessing the benefits of emotional problem-solving, and taking specific steps to cultivate emotional intelligence can lead to personal growth and improved problem-solving skills. So, let’s embrace our emotions as a valuable tool and tap into their power to achieve more effective problem-solving in all areas of our lives.

Emotions play a crucial role in our everyday lives, and they significantly impact our decision-making process. When it comes to problem-solving, emotions help us navigate through challenges and find effective solutions.

Defining Emotions and Their Role in Decision-Making

Emotions can be defined as powerful feelings that arise in response to our perceptions and thoughts. They can range from happiness and excitement to anger and sadness. Emotions are not just fleeting experiences; they have a purpose and function in our lives. One of the primary functions of emotions is to guide our decision-making process.

Emotions provide us with valuable information and insights that help us understand how we feel about certain situations or problems. They act as signals, letting us know what is important to us and what needs attention. By recognizing and understanding our emotions, we can make more informed decisions and take appropriate actions.

The Impact of Different Emotions on Problem-Solving

Different types of emotions have different effects on our problem-solving abilities. While positive emotions like joy and enthusiasm can enhance creativity and cognitive flexibility, negative emotions like anger and anxiety can narrow our focus and limit our ability to think broadly.

Positive emotions broaden our attention and cognition. They increase our willingness to explore new ideas and perspectives, which is essential in problem-solving. For example, when we are feeling joyful or excited, we are more likely to consider unconventional solutions and think outside the box.

On the other hand, negative emotions can have a narrowing effect on our attention and thinking. When we are feeling angry or anxious, our focus becomes more rigid, and we tend to rely on familiar strategies and solutions. This can hinder our problem-solving process, especially when faced with complex or unfamiliar challenges.

Emotional Intelligence and Problem-Solving

Emotional intelligence refers to our ability to recognize, understand, and regulate our own emotions, as well as the emotions of others. It plays a crucial role in effective problem-solving. When we are emotionally intelligent, we can tap into our own emotions and use them as a tool to navigate through problems.

People with high emotional intelligence are more self-aware, which allows them to understand their own thoughts, feelings, and reactions. This self-awareness enables them to better manage their emotions during problem-solving, preventing emotional biases or impulsivity from hindering their decision-making process.

Additionally, emotional intelligence helps us develop empathy, which is vital in problem-solving. Empathy allows us to understand and consider the perspectives and emotions of others involved in the problem. This broader understanding leads to more comprehensive and effective solutions.

Overall, understanding emotions and their role in decision-making is essential in effective problem-solving. By recognizing and harnessing our emotions, we can gain valuable insights, think creatively, and build stronger relationships. In the following sections, we will explore the benefits of emotional problem-solving and provide practical steps to tap into our emotions for more effective problem-solving.

Gaining Valuable Insights and Perspectives : When emotions are brought into problem-solving, individuals gain a deeper understanding of the situation at hand. Emotions provide valuable insights into their own desires, fears, and motivations, as well as those of others involved. By considering emotions alongside rational thoughts, individuals can develop a more comprehensive understanding of the problem and its potential solutions.

Enhancing Creativity and Innovation : Emotions play a crucial role in fueling creativity and inspiring innovative solutions. When individuals tap into their emotions, they can think outside the box and consider unconventional approaches to problem-solving. Emotions can spark new ideas, provoke curiosity, and encourage individuals to explore alternative perspectives and possibilities.

Strengthening Relationship-Building : Emotional problem-solving goes beyond finding solutions for the immediate problem. It also focuses on building strong relationships and effective collaborations. By incorporating emotions into problem-solving, individuals can develop a deeper sense of empathy and understanding towards others. This empathy allows for better communication, mutual respect, and the ability to work through conflicts in a more constructive manner. Emotional problem-solving fosters stronger relationships, which can lead to increased cooperation, support, and a sense of unity among team members.

Improved Decision-Making : Making decisions based solely on rationality may overlook important emotional aspects that can greatly influence the outcome. Emotional problem-solving takes into account both logical reasoning and emotional factors, leading to more informed and balanced decision-making. By considering both the rational and emotional implications of a decision, individuals can make choices that align with their values, personal preferences, and the needs of others involved.

Reduced Stress and Improved Well-being : Engaging in emotional problem-solving can reduce stress and improve overall well-being. Acknowledging and processing emotions during the problem-solving process can help individuals release tension and anxiety. This, in turn, allows for clearer thinking, better focus, and increased resilience when facing challenges. Emotional problem-solving encourages individuals to take care of their emotional needs, leading to enhanced mental and emotional well-being.

Promoting Personal Growth : Embracing emotional problem-solving is an opportunity for personal growth and self-awareness. By exploring and understanding their emotions, individuals can gain valuable insights into their own thought patterns, beliefs, and behaviors. This self-awareness allows for personal reflection and growth, leading to improved problem-solving skills and a greater sense of self-confidence. Emotional problem-solving empowers individuals to navigate challenges more effectively and develop a stronger sense of self.

Fostering Adaptability : Emotions play a central role in adaptation and resilience. Emotional problem-solving equips individuals with the skills to adapt to changing circumstances and overcome obstacles. By incorporating emotions into the problem-solving process, individuals become more flexible in their thinking, better able to manage unexpected challenges, and more open to embracing new perspectives and solutions.

Within these benefits lies the potential for individuals to unleash their full problem-solving capabilities and tap into the rich resource of emotions. Emotional problem-solving not only leads to effective solutions but also offers personal growth, strengthened relationships, and improved overall well-being. By harnessing the power of emotions, individuals can navigate through life’s challenges with greater clarity, creativity, and fulfillment.

Self-awareness is an essential first step in tapping into your emotions for effective problem-solving. It involves recognizing and understanding your own emotions, as well as how they influence your thoughts, actions, and decision-making processes.

Techniques for improving self-awareness:

Mindfulness: Practice mindfulness techniques, such as meditation or deep breathing exercises, to bring your attention to the present moment and observe your emotions without judgment. This can help you become more aware of the specific emotions you are experiencing and the triggers that elicit them.

Reflection: Take time to reflect on your experiences and emotions. Keep a journal and write down your thoughts and feelings about different situations. Regular reflection will allow you to gain a deeper understanding of your emotional patterns and how they impact your problem-solving abilities.

Emotional regulation involves managing and regulating your emotions during the problem-solving process. It is crucial to keep your emotions in check, so they don’t hinder your ability to think clearly and make rational decisions.

Strategies for emotional regulation:

Deep breathing: When you feel overwhelmed by emotions, take a moment to practice deep breathing. Breathe in deeply through your nose, hold for a few seconds, and then exhale slowly through your mouth. Deep breathing can help calm your nervous system and bring your emotions under control.

Positive self-talk: Replace negative or self-defeating thoughts with positive and encouraging ones. Remind yourself that you are capable of finding solutions and that emotions are a natural part of the problem-solving process. Use affirmations or positive statements to keep yourself motivated and focused.

Empathy plays a significant role in problem-solving by allowing us to understand and connect with others’ emotions and perspectives. It helps to foster effective communication, build relationships, and find collaborative solutions.

Tips for developing empathy:

Active listening: Practice active listening skills by paying full attention to others when they are speaking. Show genuine interest in their thoughts and emotions, and try to understand their point of view without interrupting or judging. Reflect back their feelings to show that you understand and empathize with their experience.

Perspective-taking: Put yourself in someone else’s shoes and try to imagine how they might be feeling or thinking about the problem at hand. Consider their background, experiences, and values to gain a deeper understanding of their emotions and perspectives.

Emotional problem analysis involves examining problems from an emotional perspective in addition to the rational aspects. It requires acknowledging and understanding the emotions involved in a situation and how they may impact the problem and its potential solutions.

Techniques for emotional problem analysis:

Recognize emotional biases: Be aware of how emotions can bias your perception of a problem, as well as your judgment and decision-making. Recognize any emotional biases that may be clouding your understanding of the situation and consider alternative viewpoints.

Consider the emotional impact: Take into account the emotional impact that different solutions may have on yourself and others involved. Consider whether a particular solution addresses the underlying emotional needs and concerns and whether it promotes positive emotions and well-being.

Emotions can be a powerful source of inspiration for generating innovative solutions to problems. By tapping into your emotions, you can unlock your creativity and come up with outside-the-box ideas.

Strategies for encouraging creative brainstorming through emotional stimulation:

Create a positive environment: Surround yourself with things that evoke positive emotions, such as music, art, or natural scenery. A positive environment can enhance your mood and stimulate creative thinking.

Engage in activities that inspire emotions: Engage in activities that evoke different emotions, such as watching movies, reading books, or talking to people with diverse backgrounds. Exposing yourself to a variety of emotional experiences can provide fresh perspectives and inspire creative problem-solving.

While emotional solutions can offer valuable insights and perspectives, it’s crucial to assess their practicality and effectiveness. Combining emotions with logical reasoning ensures that the solutions you choose are both emotionally meaningful and logically feasible.

Considerations when evaluating emotional solutions:

Practicality: Assess the practicality of emotional solutions by considering their feasibility and potential obstacles. Analyze whether the solution aligns with your resources, capabilities, and constraints.

Effectiveness: Evaluate the effectiveness of emotional solutions by examining how well they address the underlying emotional needs and concerns. Consider whether the solution promotes positive emotions, strengthens relationships, and leads to long-term positive outcomes.

By following these steps and incorporating emotional intelligence into your problem-solving process, you can unlock new perspectives, enhance creativity, and build stronger relationships. Remember that emotions are valuable tools, and harnessing them effectively can lead to more innovative and impactful solutions.

In order to truly benefit from the power of emotional problem-solving, it is important to put these concepts into practice in our daily lives. Here are some practical tips and exercises to incorporate emotional problem-solving into your daily routine:

Journaling: Take a few minutes each day to jot down your thoughts and emotions about a particular problem or challenge you are facing. This simple act of writing can help you gain clarity and insight into the emotional aspects of the problem.

Mindfulness Meditation: Practice mindfulness meditation to cultivate self-awareness and emotional regulation. Set aside a few minutes each day to focus on your breath and observe your emotions without judgment. This can help you become more attuned to your own emotional state and develop the ability to regulate your emotions in challenging situations.

Role-Playing: Engage in role-playing exercises to develop empathy and perspective-taking skills. Imagine yourself in someone else’s shoes and try to understand their emotions and motivations. This can help you broaden your perspective and consider different viewpoints when solving problems.

Group Discussions: Discuss problems with others and encourage open, honest conversations about emotions. Create a safe and supportive environment where everyone feels comfortable sharing their emotions and experiences. This can lead to deeper understanding and more innovative solutions.

Artistic Expression: Engage in creative activities such as painting, writing, or music to explore and express your emotions. These creative outlets can help you tap into your emotional side and inspire new ideas and solutions.

Emotionally Intelligent Leadership: If you are in a leadership role, practice emotionally intelligent leadership by creating a work environment that values open communication and emotional expression. Encourage your team members to share their emotions and perspectives when facing challenges, and foster a culture of empathy and creativity.

Reflect and Learn: After solving a problem using emotional problem-solving techniques, take the time to reflect on the process. What emotions did you experience? How did they influence your decision-making? What could you have done differently? By reflecting on your experiences, you can learn from them and continue to improve your emotional problem-solving skills.

Seek Feedback: Ask for feedback from trusted friends, family members, or colleagues on your problem-solving approach. Get their perspective on how effectively you incorporated emotions into your decision-making process. Their insights can help you gain new perspectives and identify areas for improvement.

By consistently practicing emotional problem-solving techniques, you can develop your emotional intelligence and enhance your problem-solving skills. Remember, emotional problem-solving is not about letting your emotions control your decisions, but rather using them as a tool to gain valuable insights and find more creative and innovative solutions. So, start tapping into your emotions and unlock the power of emotional problem-solving in your life.

In conclusion, tapping into your emotions can be a powerful tool for effective problem-solving. Throughout this article, we have explored the importance of emotions in decision-making and problem-solving, as well as the benefits of emotional problem-solving. By understanding and harnessing our emotions, we can gain valuable insights and perspectives that can lead to more creative and innovative solutions.

The first step to tapping into your emotions for effective problem-solving is self-awareness. By recognizing and understanding your own emotions, you can better navigate and utilize them in the problem-solving process. Techniques such as mindfulness and reflection can help improve your self-awareness and emotional intelligence.

Emotional regulation is another crucial aspect of emotional problem-solving. Managing and regulating your emotions during problem-solving is essential to ensure that they do not hinder your ability to think clearly and make rational decisions. Strategies such as deep breathing and positive self-talk can help you control and regulate your emotions effectively.

Empathy is also a key component of emotional problem-solving. By developing empathy, you can better understand and connect with others, which is especially important when working collaboratively on solving problems. Tips for developing empathy include active listening and perspective-taking, which enable you to see problems from different viewpoints and consider the emotions of others involved.

Analyzing problems from an emotional perspective is another step in tapping into your emotions for effective problem-solving. Looking beyond the rational aspects of a problem and considering the emotions involved can provide valuable insights and lead to more comprehensive and well-rounded solutions.

Emotions can also inspire creativity and stimulate innovative ideas. By encouraging creative brainstorming through emotional stimulation, you can think outside the box and come up with unique solutions. Strategies such as engaging in activities that evoke specific emotions or using emotions as a source of inspiration can help stimulate creative thinking.

However, it is important to evaluate emotional solutions using both emotions and logical reasoning. Assessing the practicality and effectiveness of emotional solutions ensures that they are viable and can be implemented successfully. Combining emotions with logical reasoning can lead to well-rounded and balanced problem-solving outcomes.

Putting emotional problem-solving into practice requires practice and commitment. Organizations and individuals who have successfully used emotional problem-solving to overcome challenges serve as real-world examples to learn from. By incorporating practical tips and exercises into your daily life, you can develop your emotional intelligence and improve your problem-solving skills.

In summary, by tapping into your emotions for effective problem-solving, you can enhance your problem-solving abilities, build better relationships, and achieve personal growth. Emphasizing the importance of emotional intelligence and its impact on problem-solving, this article encourages readers to embrace their emotions and utilize them as a valuable tool for navigating life’s challenges. So, the next time you encounter a problem, remember to tap into your emotions and let them guide you towards innovative and effective solutions.

Powerful Techniques for Designers

The funny side of external thinking, how to solve adaptive challenges with agile problem solving, unleashing the power of adaptive problem solving to boost your business.

Addressing Current Challenges with Innovative Problem-Solving Techniques

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7 Module 7: Thinking, Reasoning, and Problem-Solving

This module is about how a solid working knowledge of psychological principles can help you to think more effectively, so you can succeed in school and life. You might be inclined to believe that—because you have been thinking for as long as you can remember, because you are able to figure out the solution to many problems, because you feel capable of using logic to argue a point, because you can evaluate whether the things you read and hear make sense—you do not need any special training in thinking. But this, of course, is one of the key barriers to helping people think better. If you do not believe that there is anything wrong, why try to fix it?

The human brain is indeed a remarkable thinking machine, capable of amazing, complex, creative, logical thoughts. Why, then, are we telling you that you need to learn how to think? Mainly because one major lesson from cognitive psychology is that these capabilities of the human brain are relatively infrequently realized. Many psychologists believe that people are essentially “cognitive misers.” It is not that we are lazy, but that we have a tendency to expend the least amount of mental effort necessary. Although you may not realize it, it actually takes a great deal of energy to think. Careful, deliberative reasoning and critical thinking are very difficult. Because we seem to be successful without going to the trouble of using these skills well, it feels unnecessary to develop them. As you shall see, however, there are many pitfalls in the cognitive processes described in this module. When people do not devote extra effort to learning and improving reasoning, problem solving, and critical thinking skills, they make many errors.

As is true for memory, if you develop the cognitive skills presented in this module, you will be more successful in school. It is important that you realize, however, that these skills will help you far beyond school, even more so than a good memory will. Although it is somewhat useful to have a good memory, ten years from now no potential employer will care how many questions you got right on multiple choice exams during college. All of them will, however, recognize whether you are a logical, analytical, critical thinker. With these thinking skills, you will be an effective, persuasive communicator and an excellent problem solver.

The module begins by describing different kinds of thought and knowledge, especially conceptual knowledge and critical thinking. An understanding of these differences will be valuable as you progress through school and encounter different assignments that require you to tap into different kinds of knowledge. The second section covers deductive and inductive reasoning, which are processes we use to construct and evaluate strong arguments. They are essential skills to have whenever you are trying to persuade someone (including yourself) of some point, or to respond to someone’s efforts to persuade you. The module ends with a section about problem solving. A solid understanding of the key processes involved in problem solving will help you to handle many daily challenges.

7.1. Different kinds of thought

7.2. Reasoning and Judgment

7.3. Problem Solving

READING WITH PURPOSE

Remember and understand.

By reading and studying Module 7, you should be able to remember and describe:

• Concepts and inferences (7.1)
• Procedural knowledge (7.1)
• Metacognition (7.1)
• Characteristics of critical thinking:  skepticism; identify biases, distortions, omissions, and assumptions; reasoning and problem solving skills  (7.1)
• Reasoning:  deductive reasoning, deductively valid argument, inductive reasoning, inductively strong argument, availability heuristic, representativeness heuristic  (7.2)
• Fixation:  functional fixedness, mental set  (7.3)
• Algorithms, heuristics, and the role of confirmation bias (7.3)
• Effective problem solving sequence (7.3)

By reading and thinking about how the concepts in Module 6 apply to real life, you should be able to:

• Identify which type of knowledge a piece of information is (7.1)
• Recognize examples of deductive and inductive reasoning (7.2)
• Recognize judgments that have probably been influenced by the availability heuristic (7.2)
• Recognize examples of problem solving heuristics and algorithms (7.3)

Analyze, Evaluate, and Create

By reading and thinking about Module 6, participating in classroom activities, and completing out-of-class assignments, you should be able to:

• Use the principles of critical thinking to evaluate information (7.1)
• Explain whether examples of reasoning arguments are deductively valid or inductively strong (7.2)
• Outline how you could try to solve a problem from your life using the effective problem solving sequence (7.3)

7.1. Different kinds of thought and knowledge

• Take a few minutes to write down everything that you know about dogs.
• Do you believe that:
• Psychic ability exists?
• Hypnosis is an altered state of consciousness?
• Magnet therapy is effective for relieving pain?
• Aerobic exercise is an effective treatment for depression?
• UFO’s from outer space have visited earth?

On what do you base your belief or disbelief for the questions above?

Of course, we all know what is meant by the words  think  and  knowledge . You probably also realize that they are not unitary concepts; there are different kinds of thought and knowledge. In this section, let us look at some of these differences. If you are familiar with these different kinds of thought and pay attention to them in your classes, it will help you to focus on the right goals, learn more effectively, and succeed in school. Different assignments and requirements in school call on you to use different kinds of knowledge or thought, so it will be very helpful for you to learn to recognize them (Anderson, et al. 2001).

Factual and conceptual knowledge

Module 5 introduced the idea of declarative memory, which is composed of facts and episodes. If you have ever played a trivia game or watched Jeopardy on TV, you realize that the human brain is able to hold an extraordinary number of facts. Likewise, you realize that each of us has an enormous store of episodes, essentially facts about events that happened in our own lives. It may be difficult to keep that in mind when we are struggling to retrieve one of those facts while taking an exam, however. Part of the problem is that, in contradiction to the advice from Module 5, many students continue to try to memorize course material as a series of unrelated facts (picture a history student simply trying to memorize history as a set of unrelated dates without any coherent story tying them together). Facts in the real world are not random and unorganized, however. It is the way that they are organized that constitutes a second key kind of knowledge, conceptual.

Concepts are nothing more than our mental representations of categories of things in the world. For example, think about dogs. When you do this, you might remember specific facts about dogs, such as they have fur and they bark. You may also recall dogs that you have encountered and picture them in your mind. All of this information (and more) makes up your concept of dog. You can have concepts of simple categories (e.g., triangle), complex categories (e.g., small dogs that sleep all day, eat out of the garbage, and bark at leaves), kinds of people (e.g., psychology professors), events (e.g., birthday parties), and abstract ideas (e.g., justice). Gregory Murphy (2002) refers to concepts as the “glue that holds our mental life together” (p. 1). Very simply, summarizing the world by using concepts is one of the most important cognitive tasks that we do. Our conceptual knowledge  is  our knowledge about the world. Individual concepts are related to each other to form a rich interconnected network of knowledge. For example, think about how the following concepts might be related to each other: dog, pet, play, Frisbee, chew toy, shoe. Or, of more obvious use to you now, how these concepts are related: working memory, long-term memory, declarative memory, procedural memory, and rehearsal? Because our minds have a natural tendency to organize information conceptually, when students try to remember course material as isolated facts, they are working against their strengths.

One last important point about concepts is that they allow you to instantly know a great deal of information about something. For example, if someone hands you a small red object and says, “here is an apple,” they do not have to tell you, “it is something you can eat.” You already know that you can eat it because it is true by virtue of the fact that the object is an apple; this is called drawing an  inference , assuming that something is true on the basis of your previous knowledge (for example, of category membership or of how the world works) or logical reasoning.

Procedural knowledge

Physical skills, such as tying your shoes, doing a cartwheel, and driving a car (or doing all three at the same time, but don’t try this at home) are certainly a kind of knowledge. They are procedural knowledge, the same idea as procedural memory that you saw in Module 5. Mental skills, such as reading, debating, and planning a psychology experiment, are procedural knowledge, as well. In short, procedural knowledge is the knowledge how to do something (Cohen & Eichenbaum, 1993).

Metacognitive knowledge

Floyd used to think that he had a great memory. Now, he has a better memory. Why? Because he finally realized that his memory was not as great as he once thought it was. Because Floyd eventually learned that he often forgets where he put things, he finally developed the habit of putting things in the same place. (Unfortunately, he did not learn this lesson before losing at least 5 watches and a wedding ring.) Because he finally realized that he often forgets to do things, he finally started using the To Do list app on his phone. And so on. Floyd’s insights about the real limitations of his memory have allowed him to remember things that he used to forget.

All of us have knowledge about the way our own minds work. You may know that you have a good memory for people’s names and a poor memory for math formulas. Someone else might realize that they have difficulty remembering to do things, like stopping at the store on the way home. Others still know that they tend to overlook details. This knowledge about our own thinking is actually quite important; it is called metacognitive knowledge, or  metacognition . Like other kinds of thinking skills, it is subject to error. For example, in unpublished research, one of the authors surveyed about 120 General Psychology students on the first day of the term. Among other questions, the students were asked them to predict their grade in the class and report their current Grade Point Average. Two-thirds of the students predicted that their grade in the course would be higher than their GPA. (The reality is that at our college, students tend to earn lower grades in psychology than their overall GPA.) Another example: Students routinely report that they thought they had done well on an exam, only to discover, to their dismay, that they were wrong (more on that important problem in a moment). Both errors reveal a breakdown in metacognition.

The Dunning-Kruger Effect

In general, most college students probably do not study enough. For example, using data from the National Survey of Student Engagement, Fosnacht, McCormack, and Lerma (2018) reported that first-year students at 4-year colleges in the U.S. averaged less than 14 hours per week preparing for classes. The typical suggestion is that you should spend two hours outside of class for every hour in class, or 24 – 30 hours per week for a full-time student. Clearly, students in general are nowhere near that recommended mark. Many observers, including some faculty, believe that this shortfall is a result of students being too busy or lazy. Now, it may be true that many students are too busy, with work and family obligations, for example. Others, are not particularly motivated in school, and therefore might correctly be labeled lazy. A third possible explanation, however, is that some students might not think they need to spend this much time. And this is a matter of metacognition. Consider the scenario that we mentioned above, students thinking they had done well on an exam only to discover that they did not. Justin Kruger and David Dunning examined scenarios very much like this in 1999. Kruger and Dunning gave research participants tests measuring humor, logic, and grammar. Then, they asked the participants to assess their own abilities and test performance in these areas. They found that participants in general tended to overestimate their abilities, already a problem with metacognition. Importantly, the participants who scored the lowest overestimated their abilities the most. Specifically, students who scored in the bottom quarter (averaging in the 12th percentile) thought they had scored in the 62nd percentile. This has become known as the  Dunning-Kruger effect . Many individual faculty members have replicated these results with their own student on their course exams, including the authors of this book. Think about it. Some students who just took an exam and performed poorly believe that they did well before seeing their score. It seems very likely that these are the very same students who stopped studying the night before because they thought they were “done.” Quite simply, it is not just that they did not know the material. They did not know that they did not know the material. That is poor metacognition.

In order to develop good metacognitive skills, you should continually monitor your thinking and seek frequent feedback on the accuracy of your thinking (Medina, Castleberry, & Persky 2017). For example, in classes get in the habit of predicting your exam grades. As soon as possible after taking an exam, try to find out which questions you missed and try to figure out why. If you do this soon enough, you may be able to recall the way it felt when you originally answered the question. Did you feel confident that you had answered the question correctly? Then you have just discovered an opportunity to improve your metacognition. Be on the lookout for that feeling and respond with caution.

concept :  a mental representation of a category of things in the world

Dunning-Kruger effect : individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

inference : an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

metacognition :  knowledge about one’s own cognitive processes; thinking about your thinking

Critical thinking

One particular kind of knowledge or thinking skill that is related to metacognition is  critical thinking (Chew, 2020). You may have noticed that critical thinking is an objective in many college courses, and thus it could be a legitimate topic to cover in nearly any college course. It is particularly appropriate in psychology, however. As the science of (behavior and) mental processes, psychology is obviously well suited to be the discipline through which you should be introduced to this important way of thinking.

More importantly, there is a particular need to use critical thinking in psychology. We are all, in a way, experts in human behavior and mental processes, having engaged in them literally since birth. Thus, perhaps more than in any other class, students typically approach psychology with very clear ideas and opinions about its subject matter. That is, students already “know” a lot about psychology. The problem is, “it ain’t so much the things we don’t know that get us into trouble. It’s the things we know that just ain’t so” (Ward, quoted in Gilovich 1991). Indeed, many of students’ preconceptions about psychology are just plain wrong. Randolph Smith (2002) wrote a book about critical thinking in psychology called  Challenging Your Preconceptions,  highlighting this fact. On the other hand, many of students’ preconceptions about psychology are just plain right! But wait, how do you know which of your preconceptions are right and which are wrong? And when you come across a research finding or theory in this class that contradicts your preconceptions, what will you do? Will you stick to your original idea, discounting the information from the class? Will you immediately change your mind? Critical thinking can help us sort through this confusing mess.

But what is critical thinking? The goal of critical thinking is simple to state (but extraordinarily difficult to achieve): it is to be right, to draw the correct conclusions, to believe in things that are true and to disbelieve things that are false. We will provide two definitions of critical thinking (or, if you like, one large definition with two distinct parts). First, a more conceptual one: Critical thinking is thinking like a scientist in your everyday life (Schmaltz, Jansen, & Wenckowski, 2017).  Our second definition is more operational; it is simply a list of skills that are essential to be a critical thinker. Critical thinking entails solid reasoning and problem solving skills; skepticism; and an ability to identify biases, distortions, omissions, and assumptions. Excellent deductive and inductive reasoning, and problem solving skills contribute to critical thinking. So, you can consider the subject matter of sections 7.2 and 7.3 to be part of critical thinking. Because we will be devoting considerable time to these concepts in the rest of the module, let us begin with a discussion about the other aspects of critical thinking.

Let’s address that first part of the definition. Scientists form hypotheses, or predictions about some possible future observations. Then, they collect data, or information (think of this as making those future observations). They do their best to make unbiased observations using reliable techniques that have been verified by others. Then, and only then, they draw a conclusion about what those observations mean. Oh, and do not forget the most important part. “Conclusion” is probably not the most appropriate word because this conclusion is only tentative. A scientist is always prepared that someone else might come along and produce new observations that would require a new conclusion be drawn. Wow! If you like to be right, you could do a lot worse than using a process like this.

A Critical Thinker’s Toolkit 

Now for the second part of the definition. Good critical thinkers (and scientists) rely on a variety of tools to evaluate information. Perhaps the most recognizable tool for critical thinking is  skepticism (and this term provides the clearest link to the thinking like a scientist definition, as you are about to see). Some people intend it as an insult when they call someone a skeptic. But if someone calls you a skeptic, if they are using the term correctly, you should consider it a great compliment. Simply put, skepticism is a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided. People from Missouri should recognize this principle, as Missouri is known as the Show-Me State. As a skeptic, you are not inclined to believe something just because someone said so, because someone else believes it, or because it sounds reasonable. You must be persuaded by high quality evidence.

Of course, if that evidence is produced, you have a responsibility as a skeptic to change your belief. Failure to change a belief in the face of good evidence is not skepticism; skepticism has open mindedness at its core. M. Neil Browne and Stuart Keeley (2018) use the term weak sense critical thinking to describe critical thinking behaviors that are used only to strengthen a prior belief. Strong sense critical thinking, on the other hand, has as its goal reaching the best conclusion. Sometimes that means strengthening your prior belief, but sometimes it means changing your belief to accommodate the better evidence.

Many times, a failure to think critically or weak sense critical thinking is related to a  bias , an inclination, tendency, leaning, or prejudice. Everybody has biases, but many people are unaware of them. Awareness of your own biases gives you the opportunity to control or counteract them. Unfortunately, however, many people are happy to let their biases creep into their attempts to persuade others; indeed, it is a key part of their persuasive strategy. To see how these biases influence messages, just look at the different descriptions and explanations of the same events given by people of different ages or income brackets, or conservative versus liberal commentators, or by commentators from different parts of the world. Of course, to be successful, these people who are consciously using their biases must disguise them. Even undisguised biases can be difficult to identify, so disguised ones can be nearly impossible.

Here are some common sources of biases:

• Personal values and beliefs.  Some people believe that human beings are basically driven to seek power and that they are typically in competition with one another over scarce resources. These beliefs are similar to the world-view that political scientists call “realism.” Other people believe that human beings prefer to cooperate and that, given the chance, they will do so. These beliefs are similar to the world-view known as “idealism.” For many people, these deeply held beliefs can influence, or bias, their interpretations of such wide ranging situations as the behavior of nations and their leaders or the behavior of the driver in the car ahead of you. For example, if your worldview is that people are typically in competition and someone cuts you off on the highway, you may assume that the driver did it purposely to get ahead of you. Other types of beliefs about the way the world is or the way the world should be, for example, political beliefs, can similarly become a significant source of bias.
• Racism, sexism, ageism and other forms of prejudice and bigotry.  These are, sadly, a common source of bias in many people. They are essentially a special kind of “belief about the way the world is.” These beliefs—for example, that women do not make effective leaders—lead people to ignore contradictory evidence (examples of effective women leaders, or research that disputes the belief) and to interpret ambiguous evidence in a way consistent with the belief.
• Self-interest.  When particular people benefit from things turning out a certain way, they can sometimes be very susceptible to letting that interest bias them. For example, a company that will earn a profit if they sell their product may have a bias in the way that they give information about their product. A union that will benefit if its members get a generous contract might have a bias in the way it presents information about salaries at competing organizations. (Note that our inclusion of examples describing both companies and unions is an explicit attempt to control for our own personal biases). Home buyers are often dismayed to discover that they purchased their dream house from someone whose self-interest led them to lie about flooding problems in the basement or back yard. This principle, the biasing power of self-interest, is likely what led to the famous phrase  Caveat Emptor  (let the buyer beware) .  

Knowing that these types of biases exist will help you evaluate evidence more critically. Do not forget, though, that people are not always keen to let you discover the sources of biases in their arguments. For example, companies or political organizations can sometimes disguise their support of a research study by contracting with a university professor, who comes complete with a seemingly unbiased institutional affiliation, to conduct the study.

People’s biases, conscious or unconscious, can lead them to make omissions, distortions, and assumptions that undermine our ability to correctly evaluate evidence. It is essential that you look for these elements. Always ask, what is missing, what is not as it appears, and what is being assumed here? For example, consider this (fictional) chart from an ad reporting customer satisfaction at 4 local health clubs.

Clearly, from the results of the chart, one would be tempted to give Club C a try, as customer satisfaction is much higher than for the other 3 clubs.

There are so many distortions and omissions in this chart, however, that it is actually quite meaningless. First, how was satisfaction measured? Do the bars represent responses to a survey? If so, how were the questions asked? Most importantly, where is the missing scale for the chart? Although the differences look quite large, are they really?

Well, here is the same chart, with a different scale, this time labeled:

Club C is not so impressive any more, is it? In fact, all of the health clubs have customer satisfaction ratings (whatever that means) between 85% and 88%. In the first chart, the entire scale of the graph included only the percentages between 83 and 89. This “judicious” choice of scale—some would call it a distortion—and omission of that scale from the chart make the tiny differences among the clubs seem important, however.

Also, in order to be a critical thinker, you need to learn to pay attention to the assumptions that underlie a message. Let us briefly illustrate the role of assumptions by touching on some people’s beliefs about the criminal justice system in the US. Some believe that a major problem with our judicial system is that many criminals go free because of legal technicalities. Others believe that a major problem is that many innocent people are convicted of crimes. The simple fact is, both types of errors occur. A person’s conclusion about which flaw in our judicial system is the greater tragedy is based on an assumption about which of these is the more serious error (letting the guilty go free or convicting the innocent). This type of assumption is called a value assumption (Browne and Keeley, 2018). It reflects the differences in values that people develop, differences that may lead us to disregard valid evidence that does not fit in with our particular values.

Oh, by the way, some students probably noticed this, but the seven tips for evaluating information that we shared in Module 1 are related to this. Actually, they are part of this section. The tips are, to a very large degree, set of ideas you can use to help you identify biases, distortions, omissions, and assumptions. If you do not remember this section, we strongly recommend you take a few minutes to review it.

skepticism :  a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

bias : an inclination, tendency, leaning, or prejudice

• Which of your beliefs (or disbeliefs) from the Activate exercise for this section were derived from a process of critical thinking? If some of your beliefs were not based on critical thinking, are you willing to reassess these beliefs? If the answer is no, why do you think that is? If the answer is yes, what concrete steps will you take?

7.2 Reasoning and Judgment

• What percentage of kidnappings are committed by strangers?
• Which area of the house is riskiest: kitchen, bathroom, or stairs?
• What is the most common cancer in the US?
• What percentage of workplace homicides are committed by co-workers?

An essential set of procedural thinking skills is  reasoning , the ability to generate and evaluate solid conclusions from a set of statements or evidence. You should note that these conclusions (when they are generated instead of being evaluated) are one key type of inference that we described in Section 7.1. There are two main types of reasoning, deductive and inductive.

Deductive reasoning

Suppose your teacher tells you that if you get an A on the final exam in a course, you will get an A for the whole course. Then, you get an A on the final exam. What will your final course grade be? Most people can see instantly that you can conclude with certainty that you will get an A for the course. This is a type of reasoning called  deductive reasoning , which is defined as reasoning in which a conclusion is guaranteed to be true as long as the statements leading to it are true. The three statements can be listed as an  argument , with two beginning statements and a conclusion:

Statement 1: If you get an A on the final exam, you will get an A for the course

Statement 2: You get an A on the final exam

Conclusion: You will get an A for the course

This particular arrangement, in which true beginning statements lead to a guaranteed true conclusion, is known as a  deductively valid argument . Although deductive reasoning is often the subject of abstract, brain-teasing, puzzle-like word problems, it is actually an extremely important type of everyday reasoning. It is just hard to recognize sometimes. For example, imagine that you are looking for your car keys and you realize that they are either in the kitchen drawer or in your book bag. After looking in the kitchen drawer, you instantly know that they must be in your book bag. That conclusion results from a simple deductive reasoning argument. In addition, solid deductive reasoning skills are necessary for you to succeed in the sciences, philosophy, math, computer programming, and any endeavor involving the use of logic to persuade others to your point of view or to evaluate others’ arguments.

Cognitive psychologists, and before them philosophers, have been quite interested in deductive reasoning, not so much for its practical applications, but for the insights it can offer them about the ways that human beings think. One of the early ideas to emerge from the examination of deductive reasoning is that people learn (or develop) mental versions of rules that allow them to solve these types of reasoning problems (Braine, 1978; Braine, Reiser, & Rumain, 1984). The best way to see this point of view is to realize that there are different possible rules, and some of them are very simple. For example, consider this rule of logic:

therefore q

Logical rules are often presented abstractly, as letters, in order to imply that they can be used in very many specific situations. Here is a concrete version of the of the same rule:

I’ll either have pizza or a hamburger for dinner tonight (p or q)

I won’t have pizza (not p)

Therefore, I’ll have a hamburger (therefore q)

This kind of reasoning seems so natural, so easy, that it is quite plausible that we would use a version of this rule in our daily lives. At least, it seems more plausible than some of the alternative possibilities—for example, that we need to have experience with the specific situation (pizza or hamburger, in this case) in order to solve this type of problem easily. So perhaps there is a form of natural logic (Rips, 1990) that contains very simple versions of logical rules. When we are faced with a reasoning problem that maps onto one of these rules, we use the rule.

But be very careful; things are not always as easy as they seem. Even these simple rules are not so simple. For example, consider the following rule. Many people fail to realize that this rule is just as valid as the pizza or hamburger rule above.

if p, then q

therefore, not p

Concrete version:

If I eat dinner, then I will have dessert

I did not have dessert

Therefore, I did not eat dinner

The simple fact is, it can be very difficult for people to apply rules of deductive logic correctly; as a result, they make many errors when trying to do so. Is this a deductively valid argument or not?

Students who like school study a lot

Students who study a lot get good grades

Jane does not like school

Therefore, Jane does not get good grades

Many people are surprised to discover that this is not a logically valid argument; the conclusion is not guaranteed to be true from the beginning statements. Although the first statement says that students who like school study a lot, it does NOT say that students who do not like school do not study a lot. In other words, it may very well be possible to study a lot without liking school. Even people who sometimes get problems like this right might not be using the rules of deductive reasoning. Instead, they might just be making judgments for examples they know, in this case, remembering instances of people who get good grades despite not liking school.

Making deductive reasoning even more difficult is the fact that there are two important properties that an argument may have. One, it can be valid or invalid (meaning that the conclusion does or does not follow logically from the statements leading up to it). Two, an argument (or more correctly, its conclusion) can be true or false. Here is an example of an argument that is logically valid, but has a false conclusion (at least we think it is false).

Either you are eleven feet tall or the Grand Canyon was created by a spaceship crashing into the earth.

You are not eleven feet tall

Therefore the Grand Canyon was created by a spaceship crashing into the earth

This argument has the exact same form as the pizza or hamburger argument above, making it is deductively valid. The conclusion is so false, however, that it is absurd (of course, the reason the conclusion is false is that the first statement is false). When people are judging arguments, they tend to not observe the difference between deductive validity and the empirical truth of statements or conclusions. If the elements of an argument happen to be true, people are likely to judge the argument logically valid; if the elements are false, they will very likely judge it invalid (Markovits & Bouffard-Bouchard, 1992; Moshman & Franks, 1986). Thus, it seems a stretch to say that people are using these logical rules to judge the validity of arguments. Many psychologists believe that most people actually have very limited deductive reasoning skills (Johnson-Laird, 1999). They argue that when faced with a problem for which deductive logic is required, people resort to some simpler technique, such as matching terms that appear in the statements and the conclusion (Evans, 1982). This might not seem like a problem, but what if reasoners believe that the elements are true and they happen to be wrong; they will would believe that they are using a form of reasoning that guarantees they are correct and yet be wrong.

deductive reasoning :  a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

argument :  a set of statements in which the beginning statements lead to a conclusion

deductively valid argument :  an argument for which true beginning statements guarantee that the conclusion is true

Inductive reasoning and judgment

Every day, you make many judgments about the likelihood of one thing or another. Whether you realize it or not, you are practicing  inductive reasoning   on a daily basis. In inductive reasoning arguments, a conclusion is likely whenever the statements preceding it are true. The first thing to notice about inductive reasoning is that, by definition, you can never be sure about your conclusion; you can only estimate how likely the conclusion is. Inductive reasoning may lead you to focus on Memory Encoding and Recoding when you study for the exam, but it is possible the instructor will ask more questions about Memory Retrieval instead. Unlike deductive reasoning, the conclusions you reach through inductive reasoning are only probable, not certain. That is why scientists consider inductive reasoning weaker than deductive reasoning. But imagine how hard it would be for us to function if we could not act unless we were certain about the outcome.

Inductive reasoning can be represented as logical arguments consisting of statements and a conclusion, just as deductive reasoning can be. In an inductive argument, you are given some statements and a conclusion (or you are given some statements and must draw a conclusion). An argument is  inductively strong   if the conclusion would be very probable whenever the statements are true. So, for example, here is an inductively strong argument:

• Statement #1: The forecaster on Channel 2 said it is going to rain today.
• Statement #2: The forecaster on Channel 5 said it is going to rain today.
• Statement #3: It is very cloudy and humid.
• Statement #4: You just heard thunder.
• Conclusion (or judgment): It is going to rain today.

Think of the statements as evidence, on the basis of which you will draw a conclusion. So, based on the evidence presented in the four statements, it is very likely that it will rain today. Will it definitely rain today? Certainly not. We can all think of times that the weather forecaster was wrong.

A true story: Some years ago psychology student was watching a baseball playoff game between the St. Louis Cardinals and the Los Angeles Dodgers. A graphic on the screen had just informed the audience that the Cardinal at bat, (Hall of Fame shortstop) Ozzie Smith, a switch hitter batting left-handed for this plate appearance, had never, in nearly 3000 career at-bats, hit a home run left-handed. The student, who had just learned about inductive reasoning in his psychology class, turned to his companion (a Cardinals fan) and smugly said, “It is an inductively strong argument that Ozzie Smith will not hit a home run.” He turned back to face the television just in time to watch the ball sail over the right field fence for a home run. Although the student felt foolish at the time, he was not wrong. It was an inductively strong argument; 3000 at-bats is an awful lot of evidence suggesting that the Wizard of Ozz (as he was known) would not be hitting one out of the park (think of each at-bat without a home run as a statement in an inductive argument). Sadly (for the die-hard Cubs fan and Cardinals-hating student), despite the strength of the argument, the conclusion was wrong.

Given the possibility that we might draw an incorrect conclusion even with an inductively strong argument, we really want to be sure that we do, in fact, make inductively strong arguments. If we judge something probable, it had better be probable. If we judge something nearly impossible, it had better not happen. Think of inductive reasoning, then, as making reasonably accurate judgments of the probability of some conclusion given a set of evidence.

We base many decisions in our lives on inductive reasoning. For example:

Statement #1: Psychology is not my best subject

Statement #2: My psychology instructor has a reputation for giving difficult exams

Statement #3: My first psychology exam was much harder than I expected

Judgment: The next exam will probably be very difficult.

Decision: I will study tonight instead of watching Netflix.

Some other examples of judgments that people commonly make in a school context include judgments of the likelihood that:

• A particular class will be interesting/useful/difficult
• You will be able to finish writing a paper by next week if you go out tonight
• Your laptop’s battery will last through the next trip to the library
• You will not miss anything important if you skip class tomorrow
• Your instructor will not notice if you skip class tomorrow
• You will be able to find a book that you will need for a paper
• There will be an essay question about Memory Encoding on the next exam

Tversky and Kahneman (1983) recognized that there are two general ways that we might make these judgments; they termed them extensional (i.e., following the laws of probability) and intuitive (i.e., using shortcuts or heuristics, see below). We will use a similar distinction between Type 1 and Type 2 thinking, as described by Keith Stanovich and his colleagues (Evans and Stanovich, 2013; Stanovich and West, 2000). Type 1 thinking is fast, automatic, effortful, and emotional. In fact, it is hardly fair to call it reasoning at all, as judgments just seem to pop into one’s head. Type 2 thinking , on the other hand, is slow, effortful, and logical. So obviously, it is more likely to lead to a correct judgment, or an optimal decision. The problem is, we tend to over-rely on Type 1. Now, we are not saying that Type 2 is the right way to go for every decision or judgment we make. It seems a bit much, for example, to engage in a step-by-step logical reasoning procedure to decide whether we will have chicken or fish for dinner tonight.

Many bad decisions in some very important contexts, however, can be traced back to poor judgments of the likelihood of certain risks or outcomes that result from the use of Type 1 when a more logical reasoning process would have been more appropriate. For example:

Statement #1: It is late at night.

Statement #2: Albert has been drinking beer for the past five hours at a party.

Statement #3: Albert is not exactly sure where he is or how far away home is.

Judgment: Albert will have no difficulty walking home.

Decision: He walks home alone.

As you can see in this example, the three statements backing up the judgment do not really support it. In other words, this argument is not inductively strong because it is based on judgments that ignore the laws of probability. What are the chances that someone facing these conditions will be able to walk home alone easily? And one need not be drunk to make poor decisions based on judgments that just pop into our heads.

The truth is that many of our probability judgments do not come very close to what the laws of probability say they should be. Think about it. In order for us to reason in accordance with these laws, we would need to know the laws of probability, which would allow us to calculate the relationship between particular pieces of evidence and the probability of some outcome (i.e., how much likelihood should change given a piece of evidence), and we would have to do these heavy math calculations in our heads. After all, that is what Type 2 requires. Needless to say, even if we were motivated, we often do not even know how to apply Type 2 reasoning in many cases.

So what do we do when we don’t have the knowledge, skills, or time required to make the correct mathematical judgment? Do we hold off and wait until we can get better evidence? Do we read up on probability and fire up our calculator app so we can compute the correct probability? Of course not. We rely on Type 1 thinking. We “wing it.” That is, we come up with a likelihood estimate using some means at our disposal. Psychologists use the term heuristic to describe the type of “winging it” we are talking about. A  heuristic   is a shortcut strategy that we use to make some judgment or solve some problem (see Section 7.3). Heuristics are easy and quick, think of them as the basic procedures that are characteristic of Type 1.  They can absolutely lead to reasonably good judgments and decisions in some situations (like choosing between chicken and fish for dinner). They are, however, far from foolproof. There are, in fact, quite a lot of situations in which heuristics can lead us to make incorrect judgments, and in many cases the decisions based on those judgments can have serious consequences.

Let us return to the activity that begins this section. You were asked to judge the likelihood (or frequency) of certain events and risks. You were free to come up with your own evidence (or statements) to make these judgments. This is where a heuristic crops up. As a judgment shortcut, we tend to generate specific examples of those very events to help us decide their likelihood or frequency. For example, if we are asked to judge how common, frequent, or likely a particular type of cancer is, many of our statements would be examples of specific cancer cases:

Statement #1: Andy Kaufman (comedian) had lung cancer.

Statement #2: Colin Powell (US Secretary of State) had prostate cancer.

Statement #3: Bob Marley (musician) had skin and brain cancer

Statement #4: Sandra Day O’Connor (Supreme Court Justice) had breast cancer.

Statement #5: Fred Rogers (children’s entertainer) had stomach cancer.

Statement #6: Robin Roberts (news anchor) had breast cancer.

Statement #7: Bette Davis (actress) had breast cancer.

Judgment: Breast cancer is the most common type.

Your own experience or memory may also tell you that breast cancer is the most common type. But it is not (although it is common). Actually, skin cancer is the most common type in the US. We make the same types of misjudgments all the time because we do not generate the examples or evidence according to their actual frequencies or probabilities. Instead, we have a tendency (or bias) to search for the examples in memory; if they are easy to retrieve, we assume that they are common. To rephrase this in the language of the heuristic, events seem more likely to the extent that they are available to memory. This bias has been termed the  availability heuristic   (Kahneman and Tversky, 1974).

The fact that we use the availability heuristic does not automatically mean that our judgment is wrong. The reason we use heuristics in the first place is that they work fairly well in many cases (and, of course that they are easy to use). So, the easiest examples to think of sometimes are the most common ones. Is it more likely that a member of the U.S. Senate is a man or a woman? Most people have a much easier time generating examples of male senators. And as it turns out, the U.S. Senate has many more men than women (74 to 26 in 2020). In this case, then, the availability heuristic would lead you to make the correct judgment; it is far more likely that a senator would be a man.

In many other cases, however, the availability heuristic will lead us astray. This is because events can be memorable for many reasons other than their frequency. Section 5.2, Encoding Meaning, suggested that one good way to encode the meaning of some information is to form a mental image of it. Thus, information that has been pictured mentally will be more available to memory. Indeed, an event that is vivid and easily pictured will trick many people into supposing that type of event is more common than it actually is. Repetition of information will also make it more memorable. So, if the same event is described to you in a magazine, on the evening news, on a podcast that you listen to, and in your Facebook feed; it will be very available to memory. Again, the availability heuristic will cause you to misperceive the frequency of these types of events.

Most interestingly, information that is unusual is more memorable. Suppose we give you the following list of words to remember: box, flower, letter, platypus, oven, boat, newspaper, purse, drum, car. Very likely, the easiest word to remember would be platypus, the unusual one. The same thing occurs with memories of events. An event may be available to memory because it is unusual, yet the availability heuristic leads us to judge that the event is common. Did you catch that? In these cases, the availability heuristic makes us think the exact opposite of the true frequency. We end up thinking something is common because it is unusual (and therefore memorable). Yikes.

The misapplication of the availability heuristic sometimes has unfortunate results. For example, if you went to K-12 school in the US over the past 10 years, it is extremely likely that you have participated in lockdown and active shooter drills. Of course, everyone is trying to prevent the tragedy of another school shooting. And believe us, we are not trying to minimize how terrible the tragedy is. But the truth of the matter is, school shootings are extremely rare. Because the federal government does not keep a database of school shootings, the Washington Post has maintained their own running tally. Between 1999 and January 2020 (the date of the most recent school shooting with a death in the US at of the time this paragraph was written), the Post reported a total of 254 people died in school shootings in the US. Not 254 per year, 254 total. That is an average of 12 per year. Of course, that is 254 people who should not have died (particularly because many were children), but in a country with approximately 60,000,000 students and teachers, this is a very small risk.

But many students and teachers are terrified that they will be victims of school shootings because of the availability heuristic. It is so easy to think of examples (they are very available to memory) that people believe the event is very common. It is not. And there is a downside to this. We happen to believe that there is an enormous gun violence problem in the United States. According the the Centers for Disease Control and Prevention, there were 39,773 firearm deaths in the US in 2017. Fifteen of those deaths were in school shootings, according to the Post. 60% of those deaths were suicides. When people pay attention to the school shooting risk (low), they often fail to notice the much larger risk.

And examples like this are by no means unique. The authors of this book have been teaching psychology since the 1990’s. We have been able to make the exact same arguments about the misapplication of the availability heuristics and keep them current by simply swapping out for the “fear of the day.” In the 1990’s it was children being kidnapped by strangers (it was known as “stranger danger”) despite the facts that kidnappings accounted for only 2% of the violent crimes committed against children, and only 24% of kidnappings are committed by strangers (US Department of Justice, 2007). This fear overlapped with the fear of terrorism that gripped the country after the 2001 terrorist attacks on the World Trade Center and US Pentagon and still plagues the population of the US somewhat in 2020. After a well-publicized, sensational act of violence, people are extremely likely to increase their estimates of the chances that they, too, will be victims of terror. Think about the reality, however. In October of 2001, a terrorist mailed anthrax spores to members of the US government and a number of media companies. A total of five people died as a result of this attack. The nation was nearly paralyzed by the fear of dying from the attack; in reality the probability of an individual person dying was 0.00000002.

The availability heuristic can lead you to make incorrect judgments in a school setting as well. For example, suppose you are trying to decide if you should take a class from a particular math professor. You might try to make a judgment of how good a teacher she is by recalling instances of friends and acquaintances making comments about her teaching skill. You may have some examples that suggest that she is a poor teacher very available to memory, so on the basis of the availability heuristic you judge her a poor teacher and decide to take the class from someone else. What if, however, the instances you recalled were all from the same person, and this person happens to be a very colorful storyteller? The subsequent ease of remembering the instances might not indicate that the professor is a poor teacher after all.

Although the availability heuristic is obviously important, it is not the only judgment heuristic we use. Amos Tversky and Daniel Kahneman examined the role of heuristics in inductive reasoning in a long series of studies. Kahneman received a Nobel Prize in Economics for this research in 2002, and Tversky would have certainly received one as well if he had not died of melanoma at age 59 in 1996 (Nobel Prizes are not awarded posthumously). Kahneman and Tversky demonstrated repeatedly that people do not reason in ways that are consistent with the laws of probability. They identified several heuristic strategies that people use instead to make judgments about likelihood. The importance of this work for economics (and the reason that Kahneman was awarded the Nobel Prize) is that earlier economic theories had assumed that people do make judgments rationally, that is, in agreement with the laws of probability.

Another common heuristic that people use for making judgments is the  representativeness heuristic (Kahneman & Tversky 1973). Suppose we describe a person to you. He is quiet and shy, has an unassuming personality, and likes to work with numbers. Is this person more likely to be an accountant or an attorney? If you said accountant, you were probably using the representativeness heuristic. Our imaginary person is judged likely to be an accountant because he resembles, or is representative of the concept of, an accountant. When research participants are asked to make judgments such as these, the only thing that seems to matter is the representativeness of the description. For example, if told that the person described is in a room that contains 70 attorneys and 30 accountants, participants will still assume that he is an accountant.

inductive reasoning :  a type of reasoning in which we make judgments about likelihood from sets of evidence

inductively strong argument :  an inductive argument in which the beginning statements lead to a conclusion that is probably true

heuristic :  a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

availability heuristic :  judging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

representativeness heuristic:   judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

Type 1 thinking : fast, automatic, and emotional thinking.

Type 2 thinking : slow, effortful, and logical thinking.

• What percentage of workplace homicides are co-worker violence?

Many people get these questions wrong. The answers are 10%; stairs; skin; 6%. How close were your answers? Explain how the availability heuristic might have led you to make the incorrect judgments.

• Can you think of some other judgments that you have made (or beliefs that you have) that might have been influenced by the availability heuristic?

7.3 Problem Solving

• Please take a few minutes to list a number of problems that you are facing right now.
• Now write about a problem that you recently solved.
• What is your definition of a problem?

Mary has a problem. Her daughter, ordinarily quite eager to please, appears to delight in being the last person to do anything. Whether getting ready for school, going to piano lessons or karate class, or even going out with her friends, she seems unwilling or unable to get ready on time. Other people have different kinds of problems. For example, many students work at jobs, have numerous family commitments, and are facing a course schedule full of difficult exams, assignments, papers, and speeches. How can they find enough time to devote to their studies and still fulfill their other obligations? Speaking of students and their problems: Show that a ball thrown vertically upward with initial velocity v0 takes twice as much time to return as to reach the highest point (from Spiegel, 1981).

These are three very different situations, but we have called them all problems. What makes them all the same, despite the differences? A psychologist might define a  problem   as a situation with an initial state, a goal state, and a set of possible intermediate states. Somewhat more meaningfully, we might consider a problem a situation in which you are in here one state (e.g., daughter is always late), you want to be there in another state (e.g., daughter is not always late), and with no obvious way to get from here to there. Defined this way, each of the three situations we outlined can now be seen as an example of the same general concept, a problem. At this point, you might begin to wonder what is not a problem, given such a general definition. It seems that nearly every non-routine task we engage in could qualify as a problem. As long as you realize that problems are not necessarily bad (it can be quite fun and satisfying to rise to the challenge and solve a problem), this may be a useful way to think about it.

Can we identify a set of problem-solving skills that would apply to these very different kinds of situations? That task, in a nutshell, is a major goal of this section. Let us try to begin to make sense of the wide variety of ways that problems can be solved with an important observation: the process of solving problems can be divided into two key parts. First, people have to notice, comprehend, and represent the problem properly in their minds (called  problem representation ). Second, they have to apply some kind of solution strategy to the problem. Psychologists have studied both of these key parts of the process in detail.

When you first think about the problem-solving process, you might guess that most of our difficulties would occur because we are failing in the second step, the application of strategies. Although this can be a significant difficulty much of the time, the more important source of difficulty is probably problem representation. In short, we often fail to solve a problem because we are looking at it, or thinking about it, the wrong way.

problem :  a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

problem representation :  noticing, comprehending and forming a mental conception of a problem

Defining and Mentally Representing Problems in Order to Solve Them

So, the main obstacle to solving a problem is that we do not clearly understand exactly what the problem is. Recall the problem with Mary’s daughter always being late. One way to represent, or to think about, this problem is that she is being defiant. She refuses to get ready in time. This type of representation or definition suggests a particular type of solution. Another way to think about the problem, however, is to consider the possibility that she is simply being sidetracked by interesting diversions. This different conception of what the problem is (i.e., different representation) suggests a very different solution strategy. For example, if Mary defines the problem as defiance, she may be tempted to solve the problem using some kind of coercive tactics, that is, to assert her authority as her mother and force her to listen. On the other hand, if Mary defines the problem as distraction, she may try to solve it by simply removing the distracting objects.

As you might guess, when a problem is represented one way, the solution may seem very difficult, or even impossible. Seen another way, the solution might be very easy. For example, consider the following problem (from Nasar, 1998):

Two bicyclists start 20 miles apart and head toward each other, each going at a steady rate of 10 miles per hour. At the same time, a fly that travels at a steady 15 miles per hour starts from the front wheel of the southbound bicycle and flies to the front wheel of the northbound one, then turns around and flies to the front wheel of the southbound one again, and continues in this manner until he is crushed between the two front wheels. Question: what total distance did the fly cover?

Please take a few minutes to try to solve this problem.

Most people represent this problem as a question about a fly because, well, that is how the question is asked. The solution, using this representation, is to figure out how far the fly travels on the first leg of its journey, then add this total to how far it travels on the second leg of its journey (when it turns around and returns to the first bicycle), then continue to add the smaller distance from each leg of the journey until you converge on the correct answer. You would have to be quite skilled at math to solve this problem, and you would probably need some time and pencil and paper to do it.

If you consider a different representation, however, you can solve this problem in your head. Instead of thinking about it as a question about a fly, think about it as a question about the bicycles. They are 20 miles apart, and each is traveling 10 miles per hour. How long will it take for the bicycles to reach each other? Right, one hour. The fly is traveling 15 miles per hour; therefore, it will travel a total of 15 miles back and forth in the hour before the bicycles meet. Represented one way (as a problem about a fly), the problem is quite difficult. Represented another way (as a problem about two bicycles), it is easy. Changing your representation of a problem is sometimes the best—sometimes the only—way to solve it.

Unfortunately, however, changing a problem’s representation is not the easiest thing in the world to do. Often, problem solvers get stuck looking at a problem one way. This is called  fixation . Most people who represent the preceding problem as a problem about a fly probably do not pause to reconsider, and consequently change, their representation. A parent who thinks her daughter is being defiant is unlikely to consider the possibility that her behavior is far less purposeful.

Problem-solving fixation was examined by a group of German psychologists called Gestalt psychologists during the 1930’s and 1940’s. Karl Dunker, for example, discovered an important type of failure to take a different perspective called  functional fixedness . Imagine being a participant in one of his experiments. You are asked to figure out how to mount two candles on a door and are given an assortment of odds and ends, including a small empty cardboard box and some thumbtacks. Perhaps you have already figured out a solution: tack the box to the door so it forms a platform, then put the candles on top of the box. Most people are able to arrive at this solution. Imagine a slight variation of the procedure, however. What if, instead of being empty, the box had matches in it? Most people given this version of the problem do not arrive at the solution given above. Why? Because it seems to people that when the box contains matches, it already has a function; it is a matchbox. People are unlikely to consider a new function for an object that already has a function. This is functional fixedness.

Mental set is a type of fixation in which the problem solver gets stuck using the same solution strategy that has been successful in the past, even though the solution may no longer be useful. It is commonly seen when students do math problems for homework. Often, several problems in a row require the reapplication of the same solution strategy. Then, without warning, the next problem in the set requires a new strategy. Many students attempt to apply the formerly successful strategy on the new problem and therefore cannot come up with a correct answer.

The thing to remember is that you cannot solve a problem unless you correctly identify what it is to begin with (initial state) and what you want the end result to be (goal state). That may mean looking at the problem from a different angle and representing it in a new way. The correct representation does not guarantee a successful solution, but it certainly puts you on the right track.

A bit more optimistically, the Gestalt psychologists discovered what may be considered the opposite of fixation, namely  insight . Sometimes the solution to a problem just seems to pop into your head. Wolfgang Kohler examined insight by posing many different problems to chimpanzees, principally problems pertaining to their acquisition of out-of-reach food. In one version, a banana was placed outside of a chimpanzee’s cage and a short stick inside the cage. The stick was too short to retrieve the banana, but was long enough to retrieve a longer stick also located outside of the cage. This second stick was long enough to retrieve the banana. After trying, and failing, to reach the banana with the shorter stick, the chimpanzee would try a couple of random-seeming attempts, react with some apparent frustration or anger, then suddenly rush to the longer stick, the correct solution fully realized at this point. This sudden appearance of the solution, observed many times with many different problems, was termed insight by Kohler.

Lest you think it pertains to chimpanzees only, Karl Dunker demonstrated that children also solve problems through insight in the 1930s. More importantly, you have probably experienced insight yourself. Think back to a time when you were trying to solve a difficult problem. After struggling for a while, you gave up. Hours later, the solution just popped into your head, perhaps when you were taking a walk, eating dinner, or lying in bed.

fixation :  when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

functional fixedness :  a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

mental set :  a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

insight :  a sudden realization of a solution to a problem

Solving Problems by Trial and Error

Correctly identifying the problem and your goal for a solution is a good start, but recall the psychologist’s definition of a problem: it includes a set of possible intermediate states. Viewed this way, a problem can be solved satisfactorily only if one can find a path through some of these intermediate states to the goal. Imagine a fairly routine problem, finding a new route to school when your ordinary route is blocked (by road construction, for example). At each intersection, you may turn left, turn right, or go straight. A satisfactory solution to the problem (of getting to school) is a sequence of selections at each intersection that allows you to wind up at school.

If you had all the time in the world to get to school, you might try choosing intermediate states randomly. At one corner you turn left, the next you go straight, then you go left again, then right, then right, then straight. Unfortunately, trial and error will not necessarily get you where you want to go, and even if it does, it is not the fastest way to get there. For example, when a friend of ours was in college, he got lost on the way to a concert and attempted to find the venue by choosing streets to turn onto randomly (this was long before the use of GPS). Amazingly enough, the strategy worked, although he did end up missing two out of the three bands who played that night.

Trial and error is not all bad, however. B.F. Skinner, a prominent behaviorist psychologist, suggested that people often behave randomly in order to see what effect the behavior has on the environment and what subsequent effect this environmental change has on them. This seems particularly true for the very young person. Picture a child filling a household’s fish tank with toilet paper, for example. To a child trying to develop a repertoire of creative problem-solving strategies, an odd and random behavior might be just the ticket. Eventually, the exasperated parent hopes, the child will discover that many of these random behaviors do not successfully solve problems; in fact, in many cases they create problems. Thus, one would expect a decrease in this random behavior as a child matures. You should realize, however, that the opposite extreme is equally counterproductive. If the children become too rigid, never trying something unexpected and new, their problem solving skills can become too limited.

Effective problem solving seems to call for a happy medium that strikes a balance between using well-founded old strategies and trying new ground and territory. The individual who recognizes a situation in which an old problem-solving strategy would work best, and who can also recognize a situation in which a new untested strategy is necessary is halfway to success.

Solving Problems with Algorithms and Heuristics

For many problems there is a possible strategy available that will guarantee a correct solution. For example, think about math problems. Math lessons often consist of step-by-step procedures that can be used to solve the problems. If you apply the strategy without error, you are guaranteed to arrive at the correct solution to the problem. This approach is called using an  algorithm , a term that denotes the step-by-step procedure that guarantees a correct solution. Because algorithms are sometimes available and come with a guarantee, you might think that most people use them frequently. Unfortunately, however, they do not. As the experience of many students who have struggled through math classes can attest, algorithms can be extremely difficult to use, even when the problem solver knows which algorithm is supposed to work in solving the problem. In problems outside of math class, we often do not even know if an algorithm is available. It is probably fair to say, then, that algorithms are rarely used when people try to solve problems.

Because algorithms are so difficult to use, people often pass up the opportunity to guarantee a correct solution in favor of a strategy that is much easier to use and yields a reasonable chance of coming up with a correct solution. These strategies are called  problem solving heuristics . Similar to what you saw in section 6.2 with reasoning heuristics, a problem solving heuristic is a shortcut strategy that people use when trying to solve problems. It usually works pretty well, but does not guarantee a correct solution to the problem. For example, one problem solving heuristic might be “always move toward the goal” (so when trying to get to school when your regular route is blocked, you would always turn in the direction you think the school is). A heuristic that people might use when doing math homework is “use the same solution strategy that you just used for the previous problem.”

By the way, we hope these last two paragraphs feel familiar to you. They seem to parallel a distinction that you recently learned. Indeed, algorithms and problem-solving heuristics are another example of the distinction between Type 1 thinking and Type 2 thinking.

Although it is probably not worth describing a large number of specific heuristics, two observations about heuristics are worth mentioning. First, heuristics can be very general or they can be very specific, pertaining to a particular type of problem only. For example, “always move toward the goal” is a general strategy that you can apply to countless problem situations. On the other hand, “when you are lost without a functioning gps, pick the most expensive car you can see and follow it” is specific to the problem of being lost. Second, all heuristics are not equally useful. One heuristic that many students know is “when in doubt, choose c for a question on a multiple-choice exam.” This is a dreadful strategy because many instructors intentionally randomize the order of answer choices. Another test-taking heuristic, somewhat more useful, is “look for the answer to one question somewhere else on the exam.”

You really should pay attention to the application of heuristics to test taking. Imagine that while reviewing your answers for a multiple-choice exam before turning it in, you come across a question for which you originally thought the answer was c. Upon reflection, you now think that the answer might be b. Should you change the answer to b, or should you stick with your first impression? Most people will apply the heuristic strategy to “stick with your first impression.” What they do not realize, of course, is that this is a very poor strategy (Lilienfeld et al, 2009). Most of the errors on exams come on questions that were answered wrong originally and were not changed (so they remain wrong). There are many fewer errors where we change a correct answer to an incorrect answer. And, of course, sometimes we change an incorrect answer to a correct answer. In fact, research has shown that it is more common to change a wrong answer to a right answer than vice versa (Bruno, 2001).

The belief in this poor test-taking strategy (stick with your first impression) is based on the  confirmation bias   (Nickerson, 1998; Wason, 1960). You first saw the confirmation bias in Module 1, but because it is so important, we will repeat the information here. People have a bias, or tendency, to notice information that confirms what they already believe. Somebody at one time told you to stick with your first impression, so when you look at the results of an exam you have taken, you will tend to notice the cases that are consistent with that belief. That is, you will notice the cases in which you originally had an answer correct and changed it to the wrong answer. You tend not to notice the other two important (and more common) cases, changing an answer from wrong to right, and leaving a wrong answer unchanged.

Because heuristics by definition do not guarantee a correct solution to a problem, mistakes are bound to occur when we employ them. A poor choice of a specific heuristic will lead to an even higher likelihood of making an error.

algorithm :  a step-by-step procedure that guarantees a correct solution to a problem

problem solving heuristic :  a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

confirmation bias :  people’s tendency to notice information that confirms what they already believe

An Effective Problem-Solving Sequence

You may be left with a big question: If algorithms are hard to use and heuristics often don’t work, how am I supposed to solve problems? Robert Sternberg (1996), as part of his theory of what makes people successfully intelligent (Module 8) described a problem-solving sequence that has been shown to work rather well:

• Identify the existence of a problem.  In school, problem identification is often easy; problems that you encounter in math classes, for example, are conveniently labeled as problems for you. Outside of school, however, realizing that you have a problem is a key difficulty that you must get past in order to begin solving it. You must be very sensitive to the symptoms that indicate a problem.
• Define the problem.  Suppose you realize that you have been having many headaches recently. Very likely, you would identify this as a problem. If you define the problem as “headaches,” the solution would probably be to take aspirin or ibuprofen or some other anti-inflammatory medication. If the headaches keep returning, however, you have not really solved the problem—likely because you have mistaken a symptom for the problem itself. Instead, you must find the root cause of the headaches. Stress might be the real problem. For you to successfully solve many problems it may be necessary for you to overcome your fixations and represent the problems differently. One specific strategy that you might find useful is to try to define the problem from someone else’s perspective. How would your parents, spouse, significant other, doctor, etc. define the problem? Somewhere in these different perspectives may lurk the key definition that will allow you to find an easier and permanent solution.
• Formulate strategy.  Now it is time to begin planning exactly how the problem will be solved. Is there an algorithm or heuristic available for you to use? Remember, heuristics by their very nature guarantee that occasionally you will not be able to solve the problem. One point to keep in mind is that you should look for long-range solutions, which are more likely to address the root cause of a problem than short-range solutions.
• Represent and organize information.  Similar to the way that the problem itself can be defined, or represented in multiple ways, information within the problem is open to different interpretations. Suppose you are studying for a big exam. You have chapters from a textbook and from a supplemental reader, along with lecture notes that all need to be studied. How should you (represent and) organize these materials? Should you separate them by type of material (text versus reader versus lecture notes), or should you separate them by topic? To solve problems effectively, you must learn to find the most useful representation and organization of information.
• Allocate resources.  This is perhaps the simplest principle of the problem solving sequence, but it is extremely difficult for many people. First, you must decide whether time, money, skills, effort, goodwill, or some other resource would help to solve the problem Then, you must make the hard choice of deciding which resources to use, realizing that you cannot devote maximum resources to every problem. Very often, the solution to problem is simply to change how resources are allocated (for example, spending more time studying in order to improve grades).
• Monitor and evaluate solutions.  Pay attention to the solution strategy while you are applying it. If it is not working, you may be able to select another strategy. Another fact you should realize about problem solving is that it never does end. Solving one problem frequently brings up new ones. Good monitoring and evaluation of your problem solutions can help you to anticipate and get a jump on solving the inevitable new problems that will arise.

Please note that this as  an  effective problem-solving sequence, not  the  effective problem solving sequence. Just as you can become fixated and end up representing the problem incorrectly or trying an inefficient solution, you can become stuck applying the problem-solving sequence in an inflexible way. Clearly there are problem situations that can be solved without using these skills in this order.

Additionally, many real-world problems may require that you go back and redefine a problem several times as the situation changes (Sternberg et al. 2000). For example, consider the problem with Mary’s daughter one last time. At first, Mary did represent the problem as one of defiance. When her early strategy of pleading and threatening punishment was unsuccessful, Mary began to observe her daughter more carefully. She noticed that, indeed, her daughter’s attention would be drawn by an irresistible distraction or book. Fresh with a re-representation of the problem, she began a new solution strategy. She began to remind her daughter every few minutes to stay on task and remind her that if she is ready before it is time to leave, she may return to the book or other distracting object at that time. Fortunately, this strategy was successful, so Mary did not have to go back and redefine the problem again.

Pick one or two of the problems that you listed when you first started studying this section and try to work out the steps of Sternberg’s problem solving sequence for each one.

a mental representation of a category of things in the world

an assumption about the truth of something that is not stated. Inferences come from our prior knowledge and experience, and from logical reasoning

knowledge about one’s own cognitive processes; thinking about your thinking

individuals who are less competent tend to overestimate their abilities more than individuals who are more competent do

Thinking like a scientist in your everyday life for the purpose of drawing correct conclusions. It entails skepticism; an ability to identify biases, distortions, omissions, and assumptions; and excellent deductive and inductive reasoning, and problem solving skills.

a way of thinking in which you refrain from drawing a conclusion or changing your mind until good evidence has been provided

an inclination, tendency, leaning, or prejudice

a type of reasoning in which the conclusion is guaranteed to be true any time the statements leading up to it are true

a set of statements in which the beginning statements lead to a conclusion

an argument for which true beginning statements guarantee that the conclusion is true

a type of reasoning in which we make judgments about likelihood from sets of evidence

an inductive argument in which the beginning statements lead to a conclusion that is probably true

fast, automatic, and emotional thinking

slow, effortful, and logical thinking

a shortcut strategy that we use to make judgments and solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

udging the frequency or likelihood of some event type according to how easily examples of the event can be called to mind (i.e., how available they are to memory)

judging the likelihood that something is a member of a category on the basis of how much it resembles a typical category member (i.e., how representative it is of the category)

a situation in which we are in an initial state, have a desired goal state, and there is a number of possible intermediate states (i.e., there is no obvious way to get from the initial to the goal state)

noticing, comprehending and forming a mental conception of a problem

when a problem solver gets stuck looking at a problem a particular way and cannot change his or her representation of it (or his or her intended solution strategy)

a specific type of fixation in which a problem solver cannot think of a new use for an object that already has a function

a specific type of fixation in which a problem solver gets stuck using the same solution strategy that has been successful in the past

a sudden realization of a solution to a problem

a step-by-step procedure that guarantees a correct solution to a problem

The tendency to notice and pay attention to information that confirms your prior beliefs and to ignore information that disconfirms them.

a shortcut strategy that we use to solve problems. Although they are easy to use, they do not guarantee correct judgments and solutions

Introduction to Psychology Copyright © 2020 by Ken Gray; Elizabeth Arnott-Hill; and Or'Shaundra Benson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• The Brain Facts Book

Mapping the Brain

• Published 1 Apr 2012
• Reviewed 1 Apr 2012
• Source BrainFacts/SfN

The cerebrum, the largest part of the human brain, is associated with higher order functioning, including the control of voluntary behavior. Thinking, perceiving, planning, and understanding language all lie within the cerebrum’s control.

The top image shows the four main sections of the cerebral cortex: the frontal lobe, the parietal lobe, the occipital lobe, and the temporal lobe. Functions such as movement are controlled by the motor cortex, and the sensory cortex receives information on vision, hearing, speech, and other senses. The bottom image shows the location of the brain's major internal structures.

The cerebrum is divided into two hemispheres — the right hemisphere and the left hemisphere. Bridging the two hemispheres is a bundle of fibers called the corpus callosum. The two hemispheres communicate with one another across the corpus callosum.

Covering the outermost layer of the cerebrum is a sheet of tissue called the cerebral cortex. Because of its gray color, the cerebral cortex is often referred to as gray matter. The wrinkled appearance of the human brain also can be attributed to characteristics of the cerebral cortex. More than two-thirds of this layer is folded into grooves. The grooves increase the brain’s surface area, allowing for inclusion of many more neurons.

The function of the cerebral cortex can be understood by dividing it somewhat arbitrarily into zones, much like the geographical arrangement of continents.

The frontal lobe is responsible for initiating and coordinating motor movements; higher cognitive skills, such as problem solving, thinking, planning, and organizing; and for many aspects of personality and emotional makeup.

The parietal lobe is involved with sensory processes, attention, and language. Damage to the right side of the parietal lobe can result in difficulty navigating spaces, even familiar ones. If the left side is injured, the ability to understand spoken and/or written language may be impaired.

The occipital lobe helps process visual information, including recognition of shapes and colors.

The temporal lobe helps process auditory information and integrate information from the other senses. Neuroscientists also believe that the temporal lobe has a role to play in short-term memory through its hippocampal formation, and in learned emotional responses through its amygdala.

All of these structures make up the forebrain. Other key parts of the forebrain include the basal ganglia, which are cerebral nuclei deep in the cerebral cortex; the thalamus; and the hypothalamus. The cerebral nuclei help coordinate muscle movements and reward useful behaviors; the thalamus passes most sensory information on to the cerebral cortex after helping to prioritize it; and the hypothalamus is the control center for appetites, defensive and reproductive behaviors, and sleep-wakefulness.

The midbrain consists of two pairs of small hills called colliculi. These collections of neurons play a critical role in visual and auditory reflexes and in relaying this type of information to the thalamus. The midbrain also has clusters of neurons that regulate activity in widespread parts of the central nervous system and are thought to be important for reward mechanisms and mood.

The hindbrain includes the pons and the medulla oblongata, which control respiration, heart rhythms, and blood glucose levels.

Another part of the hindbrain is the cerebellum which, like the cerebrum, also has two hemispheres. The cerebellum’s two hemispheres help control movement and cognitive processes that require precise timing, and also play an important role in Pavlovian learning.

The spinal cord is the extension of the brain through the vertebral column. It receives sensory information from all parts of the body below the head. It uses this information for reflex responses to pain, for example, and it also relays the sensory information to the brain and its cerebral cortex. In addition, the spinal cord generates nerve impulses in nerves that control the muscles and the viscera, both through reflex activities and through voluntary commands from the cerebrum.

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You’re Only as Smart as Your Emotions

By David Brooks

Opinion Columnist

If I were asked to list the major intellectual breakthroughs of the last half-century, I would certainly include the revolution in our understanding of emotion.

For thousands of years, it was common in Western thought to imagine that there was an eternal war between reason and our emotions. In this way of thinking, reason is cool, rational and sophisticated. Emotions are primitive, impulsive and likely to lead you astray. A wise person uses reason to override and control the primitive passions. A scientist, business executive or any good thinker should try to be objective and emotionally detached, kind of like a walking computer that cautiously weighs evidence and calculates the smartest way forward.

Modern neuroscience has delivered a body blow to this way of thinking. If people thought before that passions were primitive and destructive, now we understand that they are often wise. Most of the time emotions guide reason and make us more rational. It’s an exaggeration, but maybe a forgivable one, to say that this is a turnabout to rival the Copernican Revolution in astronomy.

The problem is that our culture and our institutions haven’t caught up with our knowledge. Today we still live in a society overly besotted with raw brainpower. Our schools sort children according to their ability to do well on standardized tests, slighting the kind of wisdom held in the body that is just as important for navigating life. Our economic models are based on the idea that humans are rational creatures coolly calculating their self-interest, and then we are surprised when investors whip themselves into the frenzy of a stock market bubble.

A lot of people are estranged from their own inner lives because they don’t know how their emotions function. I look at all the sadness and meanness in the world and conclude that we’re just not good at building healthy emotional connections.

So what are some of the things modern neuroscience has taught us? Well, things really got rolling in 1994 when Antonio Damasio published his classic book “Descartes’ Error.” Damasio had studied patients who had trouble processing emotions. They weren’t supersmart Mr. Spocks. They were unable to make decisions and their lives spiraled. He demonstrated that emotions deftly assign value to things, and without knowing what’s important, or what’s good or bad, the brain just spins its wheels. Emotions and reason are one system integral to good decision-making.

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• Published on May 29, 2023
• May 29, 2023

What Part of the Brain Controls Thinking? Here’s How It Affects You

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With more than 86 billion functional neurons, the brain is the most complex organ in the human body that deals with thinking. It controls everything it does and thinks.

In other words, it’s the “boss of your body.” So, many people wonder, “What part of the brain controls thinking?”

The thing is, this particular organ plays such a crucial role in our whole system. It develops functions for the five senses: sight, sound, touch, taste, and hearing. And it helps primary functions such as breathing, talking, storing memories, and thinking.

It’s our most precious gift, according to Jim Kwik , international brain coach and trainer of Mindvalley’s Superbrain Quest. Why? Simply because it’s what “ allows us to learn, love, think, create, and even to experience joy. ”

Not only that, it’s “ the gateway to our emotions, to our capacity for deeply experiencing life, to our ability to have lasting intimacy ,” as well as helping us innovate, grow, and accomplish. 

Which Part of the Brain Controls Thinking?

The brain consists of three main parts:

• The cerebrum. It’s the outer part of the brain, which consists of the frontal, parietal, temporal, and occipital lobes.
• The cerebellum. It’s located at the bottom of the brain, near the back of your head.  
• The brain stem. This third part is located beneath the cerebrum and in front of the cerebellum in the brain stem.

These three parts control processes in the body, including movement, memory, and thinking.

The role of the cerebrum

The cerebrum makes up more than 85% of the brain’s weight. It’s the part of the brain that controls daily activities such as reading, learning, and speech. It also assists planned muscle movements such as walking, running, and body movement.

The cerebrum is the thinking part of the brain. It helps you play chess, solve a crossword puzzle, or figure out your next move in a complex video game.

The cerebrum has two hemispheres—the left hemisphere and the right hemisphere. Each hemisphere controls the opposite side of the body. 

The two hemispheres have four sections called lobes—frontal, parietal, occipital, and temporal. Each of these lobes controls specific aspects of the thinking process.

The role of the cerebellum

If the brain is a super-efficient, bustling office complex, the cerebellum is the office’s facilities manager, maintaining the heartbeat of operations.

It works behind the scenes, taking care of fine motor movements, balance, and coordination. You know, the day-to-day tasks, like making sure you can navigate through your house without bumping into furniture or helping you catch a football mid-air on a Sunday afternoon.

Although it isn’t directly involved in thinking, the cerebellum plays an important role in this process. This part of the brain takes up to 10% of its total volume yet contains more than half of all the neurons in the brain.

Scientists have discovered that the “unconscious” cerebellum interacts with the “conscious” cerebrum to perform functions. The cerebellum carries out planned muscle movements such as running and jumping. That’s why sometimes scientists call it the “thinking cerebellum.”

Research nowadays suggests that the cerebellum is a key player in predicting our emotional reactions. The study found that “the cerebellum, which was initially considered to be mainly involved in motor coordination and execution, is now recognized as an associative center for higher cognitive and emotional functions even in the developing brain.”

The brainstem

Tucked away at the base of our brain is the brainstem. It connects the brain to the spinal cord and holds the reins of many automatic, essential bodily functions.

Here’s one way to look at it: Imagine your body as a busy city. The brainstem, then, would be akin to its efficient and diligent city hall, managing essential services like heart rate, breathing, and blood pressure. 

These are our body’s critical life-support systems, running constantly in the background, much like a city’s water and power supply.

The brainstem also acts as a vital communication highway, ferrying messages between the brain and the rest of the body. It’s a bit like the city’s central post office, handling the constant flow of information to and from various parts of the body.

Within its compact structure, the brainstem houses numerous nuclei involved in different functions and relaying sensory information. It’s also where cranial nerves originate, controlling functions from eye movement to facial sensations and movements.

Other Brain Regions You Should Know About

Different brain activities are linked to different parts of the brain. Here are a few of the main ones explained:

Which part of the brain controls critical thinking?

When it comes to which part of the brain controls critical thinking and intelligence, we have to consider that the prefrontal cortex, anterior cingulate cortex, and parietal lobe work together like a highly trained Olympic relay team. 

Each player has its own role: the prefrontal cortex in decision-making, the anterior cingulate cortex in conflict detection, and the parietal lobe in information processing. Understanding this synchrony can unlock our true critical thinking potential. 

Which part of the brain controls memory?

When it comes to the art of memory, we turn our attention to the hippocampus. 

Nestled deep within the brain, this small region takes on the monumental task of memory formation and recall. It stores information of past experiences and opens up the space to create new ones.

Learn more: What Part of the Brain Controls Memory?

Which part of the brain controls breathing?

Breathe in, breathe out—a simple act that sustains life and is meticulously regulated by the medulla oblongata in our brainstem.

This unsung hero of our nervous system ensures the continuity of this essential function, similar to the ceaseless rhythm of the ocean tides. Acknowledging its role can help us appreciate the fascinating intricacies of our bodies.

Learn more: What Part of the Brain Controls Breathing?

Which part of the brain deals with emotions?

Now, you know which parts of the brain control thinking and memory. But what about our emotions?

All positive and negative emotions and spontaneous feelings, from excitement to sadness, are processed in the limbic system. This system controls your emotions and interacts with other parts of the brain. 

At the same time, another part of the brain called the amygdala handles emotional reactions such as love, hate, and sexual desire.

Learn more: The Anatomy of Feelings: What Part of the Brain Controls Emotions?

Unleash Your Superbrain

With centuries of research, the human brain remains the biggest mystery in the world. It is the most complex part of the body and controls movement, sight, and thinking. And of course, it’s the part of our system that’s most closely related to our minds. 

And as Jim says, “We need to understand how our minds work so we can work our minds better.” 

If you need some guidance to start unlocking your superbrain and mind, Mindvalley is the place to be. With transformational quests such as Superbrain , guided by Jim Kwik, you’ll master your mind’s functions in no time. And you can embark on a journey of:

• Techniques for supercharging your memory, focus, and learning capacity
• The most beneficial brain diet
• How to clear your mind of unwanted thoughts
• How to transform the way your brain is wired
• Achieving top performance when learning

By unlocking your free access , you can sample classes from this program and many others. All you have to do is open your mind to your greatest potential. And don’t be afraid to take the first step.

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Known as the “boy with the broken brain” due to a childhood injury, Jim discovered strategies to dramatically enhance his mental performance.

He is now committed, through programs like Mindvalley’s Superbrain and Speed Reading Quest , to helping people improve their memory, learn to speed-read, increase their decision-making skills, and turn on their superbrain.

He has also shared his techniques with Hollywood actors, Fortune 500 companies, and trailblazing entrepreneurs like Elon Musk and Richard Branson to reach their highest level of mental performance. He is also one of the most sought-after trainers for top organizations like Harvard University, Nike, Virgin, and GE.

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2020 study: the cerebellum’s role in movement and cognition, consensus paper: the cerebellum’s role in movement and cognition, you might also like.

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