(also called the "what"-pathway of visual processing)
(also called the "where"-pathway of visual processing)
(forming new memories)
One of the key tasks, namely planning and executing strategies , is performed by a brain area which also plays an important role for several other tasks correlated with problem solving – the prefrontal cortex (PFC) . This can be made clear if you take a look at several examples of damages to the PFC and their effects on the ability to solve problems. Patients with a lesion in this brain area have difficulty switching from one behaviouristic pattern to another. A well known example is the wisconsin card-sorting task . A patient with a PFC lesion who is told to separate all blue cards from a deck, would continue sorting out the blue ones, even if the experimenter told him to sort out all brown cards. Transferred to a more complex problem, this person would most likely fail, because he is not flexible enough to change his strategy after running into a dead end . Another example is the one of a young homemaker, who had a tumour in the frontal lobe. Even though she was able to cook individual dishes, preparing a whole family meal was an infeasible task for her.
As the examples above illustrate, the structure of our brain seems to be of great importance regarding problem solving, i.e. cognitive life. But how was our cognitive apparatus designed? How did perception-action integration as a central species specific property come about?
Charles Darwin developed the evolutionary theory which was primarily meant to explain why there are so many different kinds of species. This theory is also important for psychology because it explains how species were designed by evolutionary forces and what their goals are. By knowing the goals of species it is possible to explain and predict their behaviour.
The process of evolution involves several components, for instance natural selection – which is a feedback process that 'chooses' among 'alternative designs' on the basis of deciding how good the respective modulation is. As a result of this natural selection we find adaption . This is a process that constantly tests the variations among individuals in relation to the environment. If adaptions are useful they get passed on; if not they’ll just be an unimportant variation.
Another component of the evolutionary process is sexual selection, i.e. increasing of certain sex characteristics, which give individuals the ability to rival with other individuals of the same sex or an increased ability to attract individuals of the opposite sex.
Altruism is a further component of the evolutionary process, which will be explained in more detail in the following chapter Evolutionary Perspective on Social Cognitions .
After Knut read this WikiChapter he was relieved that he did not waste his time for the essay – quite the opposite! He now has a new view on problem solving – and recognises his problem as a well-defined one:
His initial state was the clear blank paper without any philosophical sentences on it. The goal state was just in front of his mind's eye: Him – grinning broadly – handing in the essay with some carefully developed arguments.
He decides to use the technique of Means-End Analysis and creates several subgoals:
Right after he hands in his essay Knut will go on reading this WikiBook. He now looks forward to turning the page over and to discovering the next chapter...
has related information at |
Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.
Learning objectives.
When faced with a problem to solve, should you go with intuition or with more measured, logical reasoning? Obviously, we use both of these approaches. Some of the decisions we make are rapid, emotional, and automatic. Daniel Kahneman (2011) calls this “fast” thinking. By definition, fast thinking saves time. For example, you may quickly decide to buy something because it is on sale; your fast brain has perceived a bargain, and you go for it quickly. On the other hand, “slow” thinking requires more effort; applying this in the same scenario might cause us not to buy the item because we have reasoned that we don’t really need it, that it is still too expensive, and so on. Using slow and fast thinking does not guarantee good decision-making if they are employed at the wrong time. Sometimes it is not clear which is called for, because many decisions have a level of uncertainty built into them. In this section, we will explore some of the applications of these tendencies to think fast or slow.
We will look further into our thought processes, more specifically, into some of the problem-solving strategies that we use. Heuristics are information-processing strategies that are useful in many cases but may lead to errors when misapplied. A heuristic is a principle with broad application, essentially an educated guess about something. We use heuristics all the time, for example, when deciding what groceries to buy from the supermarket, when looking for a library book, when choosing the best route to drive through town to avoid traffic congestion, and so on. Heuristics can be thought of as aids to decision making; they allow us to reach a solution without a lot of cognitive effort or time.
The benefit of heuristics in helping us reach decisions fairly easily is also the potential downfall: the solution provided by the use of heuristics is not necessarily the best one. Let’s consider some of the most frequently applied, and misapplied, heuristics in the table below.
Heuristic | Description | Examples of Threats to Accuracy |
---|---|---|
Representativeness | A judgment that something that is more representative of its category is more likely to occur | We may overestimate the likelihood that a person belongs to a particular category because they resemble our prototype of that category. |
Availability | A judgment that what comes easily to mind is common | We may overestimate the crime statistics in our own area because these crimes are so easy to recall. |
Anchoring and adjustment | A tendency to use a given starting point as the basis for a subsequent judgment | We may be swayed towards or away from decisions based on the starting point, which may be inaccurate. |
In many cases, we base our judgments on information that seems to represent, or match, what we expect will happen, while ignoring other potentially more relevant statistical information. When we do so, we are using the representativeness heuristic . Consider, for instance, the data presented in the table below. Let’s say that you went to a hospital, and you checked the records of the babies that were born on that given day. Which pattern of births do you think you are most likely to find?
6:31 a.m. | Girl | 6:31 a.m. | Boy |
8:15 a.m. | Girl | 8:15 a.m. | Girl |
9:42 a.m. | Girl | 9:42 a.m. | Boy |
1:13 p.m. | Girl | 1:13 p.m. | Girl |
3:39 p.m. | Boy | 3:39 p.m. | Girl |
5:12 p.m. | Boy | 5:12 p.m. | Boy |
7:42 p.m. | Boy | 7:42 p.m. | Girl |
11:44 p.m. | Boy | 11:44 p.m. | Boy |
Using the representativeness heuristic may lead us to incorrectly believe that some patterns of observed events are more likely to have occurred than others. In this case, list B seems more random, and thus is judged as more likely to have occurred, but statistically both lists are equally likely. |
Most people think that list B is more likely, probably because list B looks more random, and matches — or is “representative of” — our ideas about randomness, but statisticians know that any pattern of four girls and four boys is mathematically equally likely. Whether a boy or girl is born first has no bearing on what sex will be born second; these are independent events, each with a 50:50 chance of being a boy or a girl. The problem is that we have a schema of what randomness should be like, which does not always match what is mathematically the case. Similarly, people who see a flipped coin come up “heads” five times in a row will frequently predict, and perhaps even wager money, that “tails” will be next. This behaviour is known as the gambler’s fallacy . Mathematically, the gambler’s fallacy is an error: the likelihood of any single coin flip being “tails” is always 50%, regardless of how many times it has come up “heads” in the past.
The representativeness heuristic may explain why we judge people on the basis of appearance. Suppose you meet your new next-door neighbour, who drives a loud motorcycle, has many tattoos, wears leather, and has long hair. Later, you try to guess their occupation. What comes to mind most readily? Are they a teacher? Insurance salesman? IT specialist? Librarian? Drug dealer? The representativeness heuristic will lead you to compare your neighbour to the prototypes you have for these occupations and choose the one that they seem to represent the best. Thus, your judgment is affected by how much your neibour seems to resemble each of these groups. Sometimes these judgments are accurate, but they often fail because they do not account for base rates , which is the actual frequency with which these groups exist. In this case, the group with the lowest base rate is probably drug dealer.
Our judgments can also be influenced by how easy it is to retrieve a memory. The tendency to make judgments of the frequency or likelihood that an event occurs on the basis of the ease with which it can be retrieved from memory is known as the availability heuristic (MacLeod & Campbell, 1992; Tversky & Kahneman, 1973). Imagine, for instance, that I asked you to indicate whether there are more words in the English language that begin with the letter “R” or that have the letter “R” as the third letter. You would probably answer this question by trying to think of words that have each of the characteristics, thinking of all the words you know that begin with “R” and all that have “R” in the third position. Because it is much easier to retrieve words by their first letter than by their third, we may incorrectly guess that there are more words that begin with “R,” even though there are in fact more words that have “R” as the third letter.
The availability heuristic may explain why we tend to overestimate the likelihood of crimes or disasters; those that are reported widely in the news are more readily imaginable, and therefore, we tend to overestimate how often they occur. Things that we find easy to imagine, or to remember from watching the news, are estimated to occur frequently. Anything that gets a lot of news coverage is easy to imagine. Availability bias does not just affect our thinking. It can change behaviour. For example, homicides are usually widely reported in the news, leading people to make inaccurate assumptions about the frequency of murder. In Canada, the murder rate has dropped steadily since the 1970s (Statistics Canada, 2018), but this information tends not to be reported, leading people to overestimate the probability of being affected by violent crime. In another example, doctors who recently treated patients suffering from a particular condition were more likely to diagnose the condition in subsequent patients because they overestimated the prevalence of the condition (Poses & Anthony, 1991).
The anchoring and adjustment heuristic is another example of how fast thinking can lead to a decision that might not be optimal. Anchoring and adjustment is easily seen when we are faced with buying something that does not have a fixed price. For example, if you are interested in a used car, and the asking price is $10,000, what price do you think you might offer? Using $10,000 as an anchor, you are likely to adjust your offer from there, and perhaps offer $9000 or $9500. Never mind that $10,000 may not be a reasonable anchoring price. Anchoring and adjustment does not just happen when we’re buying something. It can also be used in any situation that calls for judgment under uncertainty, such as sentencing decisions in criminal cases (Bennett, 2014), and it applies to groups as well as individuals (Rutledge, 1993).
In contrast to heuristics, which can be thought of as problem-solving strategies based on educated guesses, algorithms are problem-solving strategies that use rules. Algorithms are generally a logical set of steps that, if applied correctly, should be accurate. For example, you could make a cake using heuristics — relying on your previous baking experience and guessing at the number and amount of ingredients, baking time, and so on — or using an algorithm. The latter would require a recipe which would provide step-by-step instructions; the recipe is the algorithm. Unless you are an extremely accomplished baker, the algorithm should provide you with a better cake than using heuristics would. While heuristics offer a solution that might be correct, a correctly applied algorithm is guaranteed to provide a correct solution. Of course, not all problems can be solved by algorithms.
As with heuristics, the use of algorithmic processing interacts with behaviour and emotion. Understanding what strategy might provide the best solution requires knowledge and experience. As we will see in the next section, we are prone to a number of cognitive biases that persist despite knowledge and experience.
Bennett, M. W. (2014). Confronting cognitive ‘anchoring effect’ and ‘blind spot’ biases in federal sentencing: A modest solution for reforming and fundamental flaw. Journal of Criminal Law and Criminology , 104 (3), 489-534.
Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.
MacLeod, C., & Campbell, L. (1992). Memory accessibility and probability judgments: An experimental evaluation of the availability heuristic. Journal of Personality and Social Psychology, 63 (6), 890–902.
Poses, R. M., & Anthony, M. (1991). Availability, wishful thinking, and physicians’ diagnostic judgments for patients with suspected bacteremia. Medical Decision Making, 11 , 159-68.
Rutledge, R. W. (1993). The effects of group decisions and group-shifts on use of the anchoring and adjustment heuristic. Social Behavior and Personality, 21 (3), 215-226.
Statistics Canada. (2018). Ho micide in Canada, 2017 . Retrieved from https://www150.statcan.gc.ca/n1/en/daily-quotidien/181121/dq181121a-eng.pdf
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5 , 207–232.
Psychology - 1st Canadian Edition Copyright © 2020 by Sally Walters is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
The Problem Space refers to the set of all possible states, actions, and outcomes in a given problem or task. It encompasses the various variables, constraints, and parameters that define the problem and shape its solution space.
The problem space typically comprises the following key components:
The problem space is characterized by the following attributes:
In summary, the problem space encompasses the range of possibilities, constraints, and variables associated with a given problem. Understanding and analyzing the problem space are crucial for defining effective problem-solving strategies and finding optimal solutions.
Advertisement
Supported by
Men in chat rooms have been victimizing women they know by putting their faces on pornographic clips. Some Korean women say the only thing new about it is the technology.
By Choe Sang-Hun
Reporting from Seoul
In 2020, as the South Korean authorities were pursuing a blackmail ring that forced young women to make sexually explicit videos for paying viewers, they found something else floating through the dark recesses of social media: pornographic images with other people’s faces crudely attached.
They didn’t know what to do with these early attempts at deepfake pornography. In the end, the National Assembly enacted a vaguely worded law against those making and distributing it. But that did not prevent a crime wave, using AI technology, that has now taken the country’s misogynistic online culture to new depths.
In the past two weeks, South Koreans have been shocked to find that a rising number of young men and teenage boys had taken hundreds of social media images of classmates, teachers and military colleagues — almost all young women and girls, including minors — and used them to create sexually exploitative images and video clips with deepfake apps.
They have spread the material through chat rooms on the encrypted messaging service Telegram, some with as many as 220,000 members. The deepfakes usually combine a victim’s face with a body in a sexually explicit pose, taken from pornography. The technology is so sophisticated that it is often hard for ordinary people to tell they are fake, investigators say. As the country scrambles to address the threat, experts have noted that in South Korea, enthusiasm for new technologies can sometimes outpace concerns about their ethical implications.
But to many women, these deepfakes are just the latest online expression of a deep-rooted misogyny in their country — a culture that has now produced young men who consider it fun to share sexually humiliating images of women online.
“Korean society doesn’t treat women as fellow human beings,” said Lee Yu-jin, a student whose university is among the hundreds of middle schools, high schools and colleges where students have been victimized. She asked why the government had not done more “before it became a digital culture to steal photos of friends and use them for sexual humiliation.”
We are having trouble retrieving the article content.
Please enable JavaScript in your browser settings.
Thank you for your patience while we verify access. If you are in Reader mode please exit and log into your Times account, or subscribe for all of The Times.
Thank you for your patience while we verify access.
Already a subscriber? Log in .
Want all of The Times? Subscribe .
COMMENTS
Terms in this set (21) A process in which one begins with a goal and seeks some steps that will lead toward that goal. The state one begins in, in working toward the solution of a problem. The state one is working toward in trying to solve a problem. A tool or action that one can use, in problem-solving, to move from the problem's initial state ...
Problem-solving stages. What problem-solving does is take you from an initial state (A) where a problem exists to a final or goal state (B), where the problem no longer exists. To move from A to B, you need to perform some actions called operators. Engaging in the right operators moves you from A to B. So, the stages of problem-solving are ...
It often is result of past experience. Fixation refers to the blocking of solution paths to a problem that is caused by past experiences related to the problem. NEGATIVE SET (set effects) - bias or tendency to solve a problem a particular way. NINE-DOT PROBLEM (Scheerer, 1931) fixation, negative set.
Additional Problem Solving Strategies:. Abstraction - refers to solving the problem within a model of the situation before applying it to reality.; Analogy - is using a solution that solves a similar problem.; Brainstorming - refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal ...
Problem solving refers to cognitive processing directed at achieving a goal when the problem solver does not initially know a solution method. A problem exists when someone has a goal but does not know how to achieve it. Problems can be classified as routine or nonroutine, and as well defined or ill defined.
After being given an additional hint — to use the story as help — 75 percent of them solved the problem. Following these results, Gick and Holyoak concluded that analogical problem solving consists of three steps: 1. Recognizing that an analogical connection exists between the source and the base problem.
Problem Solving.37. 11 Problem solving strategies (efficiency depends on problem representation) Analysis and hierarchical problem solving -Breaking the problem up into sub-problems -Solve series of sub-problems until done Heuristics -Means-ends analysis: Reduce distance between current state and goal state -Working forward, backward
Problem Solving. Virtually all cognitive activity resembles problem solving, the task of moving a system from its current state A to a goal state B. Any successful cognitive act (retrieving a memory, perceiving a scene, understanding a passage) can be seen as a goal-directed behavior. In visual perception, the goal is to come up with a ...
Newell and Simon. Problem-solving is a search from the problem to the solution. Much like how a computer (in the 60s) would solve a problem. We start in an initial state and have a goal state in mind. Solving the problem involves a sequence of choices of steps, with each action creating an intermediate state.
Several mental processes are at work during problem-solving. Among them are: Perceptually recognizing the problem. Representing the problem in memory. Considering relevant information that applies to the problem. Identifying different aspects of the problem. Labeling and describing the problem.
In this theory, people solve problems by searching in a problem space. The problem space consists of the initial (current) state, the goal state, and all possible states in between. The actions that people take in order to move from one state to another are known as operators. Consider the eight puzzle. The problem space for the eight puzzle ...
Solving a problem like this involves indirect and productive thinking and is mostly very helpful when somebody faces an ill-definedproblem, i.e. when either initial state or goal state cannot be stated clearly and operators or either insufficient or not given at all.
Algorithms. In contrast to heuristics, which can be thought of as problem-solving strategies based on educated guesses, algorithms are problem-solving strategies that use rules. Algorithms are generally a logical set of steps that, if applied correctly, should be accurate. For example, you could make a cake using heuristics — relying on your ...
Cognition Van Selst (Kellogg Chapter 9) Problem Solving. Directed and Undirected Thinking. •Directed: Goal-oriented and rational. •Requires a clear well-defined goal. •Undirected: Meanders (day dreams, dreaming, drifting thoughts, etc.) •Plays a role in creativity and poorly-defined problems. Well-Defined and Ill-Defined Problems.
Dynamic Nature: The problem space may evolve and change as new information is acquired or as the problem-solving process unfolds. In summary, the problem space encompasses the range of possibilities, constraints, and variables associated with a given problem. Understanding and analyzing the problem space are crucial for defining effective ...
In insight problem-solving, the cognitive processes that help you solve a problem happen outside your conscious awareness. 4. Working backward. Working backward is a problem-solving approach often ...
AP Psychology Unit 5: Cognition. 119 terms. Juliet_Piacsek. Preview. Conformity and compliance social psy. 21 terms. senichols12. Preview. Psychology Unit 3. 114 terms. ... The tendency to choose operators in problem solving that yield states more similar to the goal....(reduce the difference between the current state and the goal state) About ...
Online sexual violence is a growing problem globally, but South Korea is at the leading edge. ... C hat room operators attracted them with incentives, including Starbucks coupons , and asked them ...