research on language families

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• Brief Communication
• Published: 18 July 2022

An investigation across 45 languages and 12 language families reveals a universal language network

• Saima Malik-Moraleda   ORCID: orcid.org/0000-0003-1224-5323 1 , 2 , 3   na1 ,
• Dima Ayyash 1 , 2   na1 ,
• Jeanne Gallée   ORCID: orcid.org/0000-0002-9338-2727 3 ,
• Josef Affourtit 1 , 2 ,
• Malte Hoffmann   ORCID: orcid.org/0000-0002-5511-0739 4 , 5 ,
• Zachary Mineroff 1 , 2 , 6 ,
• Olessia Jouravlev 1 , 2 , 7 &
• Evelina Fedorenko   ORCID: orcid.org/0000-0003-3823-514X 1 , 2 , 3  

Nature Neuroscience volume  25 ,  pages 1014–1019 ( 2022 ) Cite this article

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• Auditory system
• Cognitive neuroscience
• Functional magnetic resonance imaging

To understand the architecture of human language, it is critical to examine diverse languages; however, most cognitive neuroscience research has focused on only a handful of primarily Indo-European languages. Here we report an investigation of the fronto-temporo-parietal language network across 45 languages and establish the robustness to cross-linguistic variation of its topography and key functional properties, including left-lateralization, strong functional integration among its brain regions and functional selectivity for language processing.

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The language network as a natural kind within the broader landscape of the human brain

Language network lateralization is reflected throughout the macroscale functional organization of cortex

Language is primarily a tool for communication rather than thought

Data availability.

The data that support the findings of this study are available at https://osf.io/cw89s .

Code availability

The code used to analyze the data in this study is available at https://osf.io/cw89s .

Lewis, M. P. Ethnologue: Languages of the World (SIL International, 2009).

Chomsky, N. Knowledge of Language: Its Nature, Origin, and Use (Greenwood Publishing Group, 1986).

Gibson, E. et al. How efficiency shapes human language. Trends Cogn. Sci . 23, 389–407 (2019).

Evans, N. & Levinson, S. C. The myth of language universals: language diversity and its importance for cognitive science. Behav. Brain Sci. 32 , 429–448 (2009).

Article   PubMed   Google Scholar  

Bates, E., McNew, S., MacWhinney, B., Devescovi, A. & Smith, S. Functional constraints on sentence processing: a cross-linguistic study. Cognition 11 , 245–299 (1982).

Article   CAS   PubMed   Google Scholar  

Bornkessel-Schlesewsky, I. & Schlesewsky, M. The importance of linguistic typology for the neurobiology of language. Linguistic Typology 20 , 615–621 (2016).

Hudley, A. H. C., Mallinson, C. & Bucholtz, M. Toward racial justice in linguistics: interdisciplinary insights into theorizing race in the discipline and diversifying the profession. Language 96 , e200–e235 (2020).

Rueckl, J. G. et al. Universal brain signature of proficient reading: evidence from four contrasting languages. Proc. Natl Acad. Sci. USA 112 , 15510–15515 (2015).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Fedorenko, E., Hsieh, P.-J., Nieto-Castañón, A., Whitfield-Gabrieli, S. & Kanwisher, N. New method for fMRI investigations of language: defining ROIs functionally in individual subjects. J. Neurophysiol. 104 , 1177–1194 (2010).

Article   PubMed   PubMed Central   Google Scholar  

Mahowald, K. & Fedorenko, E. Reliable individual-level neural markers of high-level language processing: a necessary precursor for relating neural variability to behavioral and genetic variability. Neuroimage 139 , 74–93 (2016).

Fedorenko, E., Behr, M. K. & Kanwisher, N. Functional specificity for high-level linguistic processing in the human brain. Proc. Natl Acad. Sci. USA 108 , 16428–16433 (2011).

Scott, T. L., Gallée, J. & Fedorenko, E. A new fun and robust version of an fMRI localizer for the frontotemporal language system. Cogn. Neurosci. 8 , 167–176 (2017).

Blank, I. A., Kanwisher, N. & Fedorenko, E. A functional dissociation between language and multiple-demand systems revealed in patterns of BOLD signal fluctuations. J. Neurophysiol. 112 , 1105–1118 (2014).

Fedorenko, E. & Blank, I. A. Broca’s area is not a natural kind. Trends Cogn. Sci. 24 , 270–284 (2020).

Duncan, J. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends Cogn. Sci. 14 , 172–179 (2010).

Gurunandan, K., Arnaez-Telleria, J., Carreiras, M. & Paz-Alonso, P. M. Converging evidence for differential specialization and plasticity of language systems. J. Neurosci. 40 , 9715–9724 (2020).

Nieto-Castañón, A. & Fedorenko, E. Subject-specific functional localizers increase sensitivity and functional resolution of multi-subject analyses. Neuroimage 63 , 1646–1669 (2012).

Bornkessel-Schlesewsky, I. et al. Think globally: cross-linguistic variation in electrophysiological activity during sentence comprehension. Brain Lang. 117 , 133–152 (2011).

Bickel, B., Witzlack-Makarevich, A., Choudhary, K. K., Schlesewsky, M. & Bornkessel-Schlesewsky, I. The neurophysiology of language processing shapes the evolution of grammar: evidence from case marking. PLoS ONE 10 , e0132819 (2015).

Kemmerer, D. Do language-specific word meanings shape sensory and motor brain systems? the relevance of semantic typology to cognitive neuroscience. Linguistic Typology 20 , 623–634 (2016).

Article   Google Scholar  

Albert, M. L. Auditory sequencing and left cerebral dominance for language. Neuropsychologia 10 , 245–248 (1972).

Grafton, S. T., Hazeltine, E. & Ivry, R. B. Motor sequence learning with the nondominant left hand. A PET functional imaging study. Exp. Brain Res. 146 , 369–378 (2002).

Bornkessel-Schlesewsky, I., Schlesewsky, M., Small, S. L. & Rauschecker, J. P. Neurobiological roots of language in primate audition: common computational properties. Trends Cogn. Sci. 19 , 142–150 (2015).

Norman-Haignere, S., Kanwisher, N. & McDermott, J. H. Cortical pitch regions in humans respond primarily to resolved harmonics and are located in specific tonotopic regions of anterior auditory cortex. J. Neurosci. 33 , 19451–19469 (2013).

Li, Y., Tang, C., Lu, J., Wu, J. & Chang, E. F. Human cortical encoding of pitch in tonal and non-tonal languages. Nat. Commun. 12 , 1161 (2021).

Gil, D. Riau Indonesian: a language without nouns and verbs. In: Flexible Word Classes: Typological Studies of Underspecified Parts of Speech 89–130 (Oxford Scholarship Online, 2013).

Beeman, M. Semantic processing in the right hemisphere may contribute to drawing inferences from discourse. Brain Lang. 44 , 80–120 (1993).

Saxe, R. & Kanwisher, N. People thinking about thinking people: the role of the temporo-parietal junction in ‘theory of mind’. Neuroimage 19 , 1835–1842 (2003).

Radford, A. et al. Language models are unsupervised multitask learners. https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf (2019).

Schrimpf, M. et al. The neural architecture of language: Integrative modeling converges on predictive processing. Proc. Natl Acad. Sci. USA 118 , e2105646118 (2021).

Bender, E. M. Linguistically naïve ! = language independent: why NLP needs linguistic typology. In: Proceedings of the EACL 2009 Workshop on the Interaction between Linguistics and Computational Linguistics: Virtuous, Vicious or Vacuous? 26–32 (Association for Computational Linguistics, 2009).

Chi, E. A., Hewitt, J. & Manning, C. D. Finding universal grammatical relations in multilingual BERT. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 5564–5577 (Association for Computational Linguistics, 2020).

Kovelman, I., Baker, S. A. & Petitto, L. A. Bilingual and monolingual brains compared: a functional magnetic resonance imaging investigation of syntactic processing and a possible ‘neural signature’ of bilingualism. J. Cogn. Neurosci. 20 , 153–169 (2008).

Costa, A. & Sebastián-Gallés, N. How does the bilingual experience sculpt the brain? Nat. Rev. Neurosci. 15 , 336–345 (2014).

Oldfield, R. C. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9 , 97–113 (1971).

Braga, R. M., DiNicola, L. M., Becker, H. C. & Buckner, R. L. Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks. J. Neurophysiol. 124 , 1415–1448 (2020).

Fedorenko, E., Nieto-Castanon, A. & Kanwisher, N. Lexical and syntactic representations in the brain: an fMRI investigation with multi-voxel pattern analyses. Neuropsychologia 50 , 499–513 (2012).

Blank, I., Balewski, Z., Mahowald, K. & Fedorenko, E. Syntactic processing is distributed across the language system. Neuroimage 127 , 307–323 (2016).

Fedorenko, E., Blank, I. A., Siegelman, M. & Mineroff, Z. Lack of selectivity for syntax relative to word meanings throughout the language network. Cognition 203 , 104348 (2020).

Chen, X. et al. The human language system does not support music processing. Preprint at https://www.biorxiv.org/content/10.1101/2021.06.01.446439v1.full.pdf (2021).

Carroll, L. Alice’s Adventures in Wonderland (Broadview Press, 2011).

Lindseth, J. & Tannenbaum, A. Alice in a World of Wonderlands: The Translations of Lewis Carroll’s Masterpiece (Oak Knoll Press, 2015).

Wolff, P. Observations on the early development of smiling. Determ. Infant Behav. 2 , 113–138 (1963).

Google Scholar  

Thesen, S., Heid, O., Mueller, E. & Schad, L. R. Prospective acquisition correction for head motion with image-based tracking for real-time fMRI. Magn. Reson. Med. 44 , 457–465 (2000).

Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2 , 125–141 (2012).

Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37 , 90–101 (2007).

Cordes, D. et al. Frequencies contributing to functional connectivity in the cerebral cortex in ‘resting-state’ data. AJNR Am. J. Neuroradiol. 22 , 1326–1333 (2001).

CAS   PubMed   PubMed Central   Google Scholar  

Dale, A., Fischl, B. & Sereno, M. Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9 , 179–194 (1999).

Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F. & Baker, C. I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12 , 535–540 (2009).

Silver, N. C. & Dunlap, W. P. Averaging correlation coefficients: should Fisher’s z transformation be used? J. Appl. Psychol. 72 , 146–148 (1987).

Seghier, M. L. Laterality index in functional MRI: methodological issues. Magn. Reson. Imaging 26 , 594–601 (2008).

Duncan, J. The structure of cognition: attentional episodes in mind and brain. Neuron 80 , 35–50 (2013).

Paunov, A. M., Blank, I. A. & Fedorenko, E. Functionally distinct language and Theory of Mind networks are synchronized at rest and during language comprehension. J. Neurophysiol. 121 , 1244–1265 (2019).

Fedorenko, E., Duncan, J. & Kanwisher, N. Broad domain generality in focal regions of frontal and parietal cortex. Proc. Natl Acad. Sci. USA 110 , 16616–16621 (2013).

Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15 , 273–289 (2002).

Lipkin, B. et al. LanA (Language Atlas): a probabilistic atlas for the language network based on fMRI data from >800 individuals. Preprint at https://www.biorxiv.org/content/10.1101/2022.03.06.483177v1 (2022).

Rombouts, S. A. R. B. et al. Test–retest analysis with functional MR of the activated area in the human visual cortex. Am. Soc. Neuroradiol. 18 , 195–6108 (1997).

Davis, M. & Johnsrude, I. Hierarchical processing in spoken language comprehension. J. Neurosci. 23 , 3423–3431 (2003).

Hervais-Adelman, A. G., Carlyon, R. P., Johnsrude, I. S. & Davis, M. H. Brain regions recruited for the effortful comprehension of noise-vocoded words. Language and Cognitive Processes 27 , 1145–1166 (2012).

Erb, J., Henry, M. J., Eisner, F., & Obelser, J. The brain dynamics of rapid perceptual adaptation to adverse listening conditions. J. Neurosci. 33 , 10688–10697 (2013).

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Acknowledgements

We thank Z. Fan, F. Frank and J. Vera-Rebollar for help with finding and recording the speakers; Z. Fan, J. Vera-Rebollar, F. Frank, A. Verkerk, the Max Planck Institute in Nijmegen, C. Kidd and M. Xiang for help with locating the texts of Alice in Wonderland in different languages; I. Blank, A. Paunov, B. Lipkin, D. Greve and B. Fischl for help with some of the analyses; J. McDermott for letting us use the sound booths in his laboratory for the recordings; J. Wu, N. Jhingan and B. Lipkin for creating a website for disseminating the localizer materials and script; M. Lewis for allowing us to use the linguistic family maps from the GeoCurrents website; B. A. Cabrera for help with figures; EvLab and TedLab members and collaborators; the audiences at the Neuroscience of Language Conference at NYU-AD (2019) and at the virtual Cognitive Neuroscience Society conference (2020) for helpful feedback; T. Gibson, D. Blasi, M. Seghier and two anonymous reviewers for comments on earlier drafts of the manuscript; Y. Diachek for collecting the data for the Russian speakers (used in Supplementary Fig. 4 ); J. Pryor and S. Lall for promoting this work when it was still at the early stages; and our participants. The authors would also like to acknowledge the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT and the support team (S. Shannon and A. Takahashi). S.M.-M. was supported by la Caixa Fellowship LCF/BQ/AA17/11610043, a Friends of McGovern Fellowship and the Dingwall Foundation Fellowship. E.F. was supported by NIH awards R00-HD057522, R01-DC016607 and R01-DC-NIDCD and research funds from the Brain and Cognitive Sciences Department, the McGovern Institute for Brain Research and the Simons Center for the Social Brain.

Author information

These authors contributed equally: Saima Malik-Moraleda, Dima Ayyash.

Authors and Affiliations

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

Saima Malik-Moraleda, Dima Ayyash, Josef Affourtit, Zachary Mineroff, Olessia Jouravlev & Evelina Fedorenko

McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA

Program in Speech and Hearing Bioscience and Technology, Harvard University, Boston, MA, USA

Saima Malik-Moraleda, Jeanne Gallée & Evelina Fedorenko

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA

Malte Hoffmann

Department of Radiology, Harvard Medical School, Boston, MA, USA

Eberly Center, Carnegie Mellon University, Pittsburgh, PA, USA

Zachary Mineroff

Department of Cognitive Science, Carleton University, Ottawa, ON, Canada

Olessia Jouravlev

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Contributions

Conceptualization, project administration and supervision: E.F. Methodology: S.M.-M., D.A., J.G. and E.F. Investigation (data collection): S.M.-M., D.A., J.G., J.A., Z.M. and O.J. Data curation: S.M.-M., D.A. and J.A. Formal analysis: S.M.-M. Validation: S.M.-M. and J.A. Visualization: S.M.-M. and M.H. Software: S.M.-M., D.A., J.A. and Z.M. Writing—original draft: S.M.-M., D.A. and E.F. Writing—review and editing: J.G., J.A., M.H., Z.M. and O.J.

Corresponding authors

Correspondence to Saima Malik-Moraleda or Evelina Fedorenko .

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The authors declare no competing interests.

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Nature Neuroscience thanks M. Florencia Assaneo, Narly Golestani and Mohamed Seghier for their contribution to the peer review of this work.

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Extended data

Extended data fig. 1 comparison of the individual activation maps for the sentences  >  nonwords contrast and the native-language  >  degraded-language contrast in the two native-english-speaking participants..

The two maps are voxel-wise (within the union of the language parcels) spatially correlated at r = 0.77 and r = 0.99 for participants 492 and 502, respectively (the correlations are Fisher-transformed). Across the full set of participants, the average Fisher-transformed spatial correlation between the maps for the Sentences  >  Nonwords contrast in English and the Native-language  >  Degraded-language contrast in the participant’s native language (again, constrained to the language parcels) is r = 0.88 (SD = 0.43) for the left hemisphere and 0.73 (SD = 0.38) for the right hemisphere. (Note that using the union of the language parcels rather than the whole brain is conservative for computing these correlations; including all the voxels would inflate the correlations due to the large difference in activation levels between voxels that fall within the language parcels vs. outside their boundaries. Instead, we are zooming in on the activation landscape within the frontal, temporal, and parietal areas that house the language network and showing that these landscapes are spatially similar between the two contrasts in their fine-grained activation patterns).

Extended Data Fig. 2 Activation maps for the Alice language localizer contrast ( Native-language  >  Degraded-languag e) in the right hemisphere of a sample participant for each language (see Fig. 1 for the maps from the left hemisphere).

A significance map was generated for each participant by FreeSurfer 44 ; each map was smoothed using a Gaussian kernel of 4 mm full-width half-max and thresholded at the 70 th percentile of the positive contrast for each participant (this was done separately for each hemisphere; note that the same participants are used here as those used in Fig. 1 ). The surface overlays were rendered on the 80% inflated white-gray matter boundary of the fsaverage template using FreeView/FreeSurfer. Opaque red and yellow correspond to the 80 th and 99 th percentile of positive-contrast activation for each subject, respectively. Further, here and in Fig. 1 , small and/or idiosyncratic bits of activation (relatively common in individual-level language maps for example, 9, 10 ) were removed. In particular, clusters were excluded if a) their surface area was below 100 mm^2, or b) they did not overlap (by > 10%) with a mask created for a large number (n = 804 56 ) participants by overlaying the individual maps and excluding vertices that did not show language responses in at least 5% of the cohort. (We ensured that the idiosyncrasies were individual- and not language-specific: for each cluster removed, we checked that a similar cluster was not present for the second native speaker of that language.) These maps were used solely for visualization; all the statistical analyses were performed on the data analyzed in the volume.

Extended Data Fig. 3 Volume-based activation maps for the Native-language  >  Degraded-language contrast in the left hemisphere of a sample participant for each language (the same participants are used as those used in Fig. 1 and Extended Fig. 2 ).

a) Binarized maps that were generated for each participant by selecting the top 10% most responsive (to this contrast) voxels within each language parcel. These sets of voxels correspond to the fROIs used in the analyses reported in Extended Data Fig. 4 (except for the estimation of the responses to the conditions of the Alice localizer, where a subset of the runs was used to ensure independence; the fROIs in those cases will be similar but not identical to those displayed). b) Whole-brain maps that are thresholded at the p < 0.001 uncorrected level.

Extended Data Fig. 4 Percent BOLD signal change across (panel a) and within each of (panel b) the LH language functional ROIs (defined by the Native-language  >  Degraded-language contrast from the Alice localizer, cf. the Sentences  >  Nonwords contrast from the English localizer as in the main text and analyses; Fig. 3a and Supplementary Fig. 3 ) for the three language conditions of the Alice localizer task (Native language, Acoustically degraded native language, and Unfamiliar language), the spatial working memory (WM) task and the math task.

The dots correspond to languages (n = 45), and the labels (panel a only) mark the averages for each language family. In all panels, box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. Across the six fROIs, the Native-language condition elicits a reliably greater response than both the Degraded-language condition (2.32 vs. 0.91 % BOLD signal change relative to the fixation baseline; t(44)=18.57, p < 0.001) and the Unfamiliar-language condition (2.32 vs. 0.99; t(44)=18.02, p < 0.001). Responses to the Native-language condition are also significantly higher than those to the spatial working memory task (2.32 vs. 0.06; t(44)=11.16, p < 0.001) and the math task (2.32 vs. −0.02; t(40)=20.8, p < 0.001). These results also hold for each fROI separately, correcting for the number of fROIs ( Native-language  >  Degraded-language : ps<0.05; Native-language  >  Unfamiliar-language : ps<0.05; Native-language  >  Spatial WM : ps<0.05; and Native-language  >  Math : ps<0.05). All t-tests were two-tailed and corrected for the number of fROIs in the per-fROI analyses.

Extended Data Fig. 5 Percent BOLD signal change across the LH language functional ROIs (defined by the Sentences  >  Nonwords contrast) for the three language conditions of the Alice localizer task (Native language, Acoustically degraded native language, and Unfamiliar language), the spatial working memory (WM) task, and the math task shown for each language separately.

The dots correspond to participants for each language (n = 2 in all languages except Slovene, Swahili, Tagalog, Telugu, where n = 1). Box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. (Note that the scale of the y-axis differs across languages in order to allow for easier between-condition comparisons in each language).

Extended Data Fig. 6 A comparison of individual LH topographies between speakers of the same language vs. between speakers of different languages.

The goal of this analysis was to test whether inter-language / inter-language-family similarities might be reflected in the similarity structure of the activation patterns. To perform this analysis, we computed a Dice coefficient 57 for each pair of individual activation maps for the Intact-language  >  Degraded-language contrast (a total of n = 3,655 pairs across the 86 participants). To do so, we used the binarized maps like those shown in Extended Data Fig. 3a , where in each LH language parcel the top 10% of most responsive voxels were selected. Then, for each pair of images, we divided the number of overlapping voxels multiplied by 2 by the sum of the voxels across the two images (this value was always the same and equaling 1,358 given that each map had the same number of selected voxels). The resulting values can vary from 0 (no overlapping voxels) to 1 (all voxels overlap). a) A comparison of Dice coefficients for pairs of maps between languages (left, n = 3,655 pairs) vs. within languages (right; this could be done for 41/45 languages for which two speakers were tested). If the activation landscapes are more similar within than between languages, then the Dice coefficients for the within-language comparisons should be higher. Instead, no reliable difference was observed by an independent-samples t-test (average within-language: 0.17 (SD = 0.07), average between-language: 0.16 (SD = 0.06); t(40.7)=−0.52, p = 0.61; see also Extended Data Fig. 8 for evidence that the range of overlap values in probabilistic atlases created from speakers of diverse languages vs. speakers of the same language are comparable). Box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. b) Dice coefficient values for all pairs of within- and between-language comparisons (the squares in black on the diagonal correspond to languages with only one speaker tested). As can be seen in the figure and in line with the results in panel a, no structure is discernible that would suggest greater within-language / within-language-family topographic similarity. Similar to the results from the within- vs. between-language comparison in a, the within-language-family vs. between-language-family comparison did not reveal a difference (t(19.8)=0.71, p = 0.49). In summary, in the current dataset (collected with the shallow sampling approach, that is, a small number of speakers from a larger number of languages), no clear similarity structure is apparent that would suggest more similar topographies among speakers of the same language, or among speakers of languages that belong to the same language family.

Extended Data Fig. 7 Inter-region functional correlations in the language and the Multiple Demand networks during story comprehension for each of the 45 languages.

Inter-region functional correlations for the LH and RH of the language and the Multiple Demand (MD) networks during a naturalistic cognition paradigm (story comprehension in the participant’s native language) shown for each language separately.

Extended Data Fig. 8 Comparison of three probabilistic overlap maps (atlases).

Comparison of three probabilistic overlap maps (atlases): a) the Alice atlas (n = 86 native speakers of 45 languages) created from the Native-language  >  Degraded-language maps; b) the English atlas (n = 629 native English speakers; this is a subset of the Fedorenko lab’s Language Atlas (LanA 56 ) created from the Sentences  >  Nonwords maps; and) the Russian Atlas (n = 19 native Russian speakers) created from the Native-language  >  Degraded-language maps for the Russian version of the Alice localizer. All three atlases were created by selecting for each participant the top 10% of voxels (across the brain) based on the t-values for the relevant contrast in each participant, binarizing these maps, and then overlaying them in the common space. In each atlas, the value in each voxel corresponds to the proportion of participants (between 0 and 1) for whom that voxel belongs to the 10% of most language-responsive voxels. The probabilistic landscapes are similar across the atlases: within the union of the language parcels (see Extended Data Fig. 1 caption for an explanation of why this approach is more conservative than performing the comparison across the brain), the Alice atlas is voxel-wise spatially correlated with both the English atlas (r = 0.83) and the Russian atlas (r = 0.85). Furthermore, the range of non-zero overlap values is comparable between the Alice atlas (0.1–0.87; average within the language parcels=0.08, median=0.05) and each of the other atlases (the English atlas: 0.002–0.79; average within the language parcels=0.07, median=0.03; the Russian atlas: 0.05–0.84; average within the language parcels=0.13, median=0.11). The latter result suggests that the inter-individual variability in the topographies of activation landscapes elicited in 86 participants of 45 diverse languages is comparable to the inter-individual variability observed among native speakers of the same language.

Extended Data Fig. 9 Responses in the domain-general Multiple Demand network to the conditions of the Alice localizer task, the spatial working memory task, and the math task.

Percent BOLD signal change across the domain-general Multiple Demand (MD) network 15 , 52 functional ROIs for the three language conditions of the Alice localizer task (Native language, Acoustically degraded native language, and Unfamiliar language), the hard and easy conditions of the spatial working memory (WM) task, and the hard and easy conditions of the math task. The dots correspond to languages (n = 45 except for the Math Task, where n = 41). Box plots include the first quartile (lower hinge), third quartile (upper hinge), and median (central line); upper and lower whiskers extend from the hinges to the largest value no further than 1.5 times the inter-quartile range; darker-colored dots correspond to outlier data points. As in the main analyses (Fig. 3c ), the individual MD fROIs were defined by the Hard  >  Easy contrast in the spatial WM task (see 54 for evidence that other Hard  >  Easy contrasts activate similar areas). As expected given past work e.g., 54 , the MD fROIs show strong responses to both the spatial WM task and the math task, with stronger responses to the harder condition in each (3.05 vs. 1.93 for the spatial WM task, t(44)=23.1, p < 0.001; and 1.68 vs. 0.62 for the math task, t(40)=8.87, p < 0.001). These robust responses in the MD network suggest that the lack of responses to the spatial WM and math tasks in the language areas can be meaningfully interpreted. Furthermore, in line with past work e.g. 58 , 59 , 60 , MD fROIs show a stronger response to the acoustically degraded condition than the native language condition (0.26 vs. -0.10, t(44)=4.92, p < 0.01), and to the unfamiliar language condition than the native language condition (0.15 vs. -0.10, t(44)=4.96, p < 0.01). All t-tests were two-tailed with no adjustment for multiple comparisons.

Extended Data Fig. 10 Comparison of the individual activation maps for the Native-language  >  Degraded-language contrast and the Native-language  >  Unfamiliar-language contrast in four sample participants.

The activation landscapes are broadly similar: across the full set of 86 participants, the average Fisher-transformed voxel-wise spatial correlation within the union of the language parcels between the maps for the two contrasts is r = 0.66 (SD = 0.40). (Note that this correlation is lower than the correlation between the Native-language  >  Degraded-language contrast and the Sentences  >  Nonwords contrast in English (see Extended Data Fig. 1 ). This difference may be due to the greater variability in the participants’ responses to an unfamiliar language.) Furthermore, across the language fROIs, the magnitudes of the Native-language  >  Degraded-language and the Native-language  >  Unfamiliar-language effects are similar (mean = 1.02, SD(across languages)=0.41 vs. mean=1.07, SD = 0.37, respectively; t(44)=1.15, p = 0.26).

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Malik-Moraleda, S., Ayyash, D., Gallée, J. et al. An investigation across 45 languages and 12 language families reveals a universal language network. Nat Neurosci 25 , 1014–1019 (2022). https://doi.org/10.1038/s41593-022-01114-5

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Conceptualisation of family and language practice in family language policy research on migrants: a systematic review

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• Published: 28 June 2023
• Volume 22 , pages 343–365, ( 2023 )

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• Priyanka Bose 1 ,
• Xuesong Gao   ORCID: orcid.org/0000-0002-3426-8721 1 ,
• Sue Starfield 1 ,
• Shuting Sun 1 &
• Junjun Muhamad Ramdani 1  

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Family language policy (FLP) is increasingly recognised as a distinct domain of language policy concerned with the family as an arena of language policy formulation and implementation. While FLP is a relatively new research area, its conceptualisation of family and language practice requires re-examination due to social changes and technological developments, including the expansion of digital communication within families  and  the rise of globally dispersed families a product of global migration and transnationalism. In this systematic review of migrant FLP research, we investigate how the notions of family and language practice are conceptualised in research. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we identified a total of 163 articles for analysis. Our analysis reveals that the majority of studies were conducted in nuclear families, i.e., those consisting of a father, a mother, and one or more children. Studies also tend to conceptualise the family as fixed and physically located in one place. Paradoxically, around half of the studies acknowledge the presence of geographically dispersed family relations, but this does not necessarily affect their conceptualisation of what comprises a family. Language practice was conceptualised as physical and face-to-face communication in 51% of instances, with only 11% incorporating an analysis of digital communications. Based on our review, we recommend that FLP researchers researching migrant families reconceptualise the family as geographically dispersed and language practice as digital and multimodal when necessary. Such a reconceptualisation will help researchers understand the hitherto underexamined contributions of dispersed family members and multimodal digital communications in migrant FLP.

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Introduction

Over the last few decades, the family has emerged as one of the most important domains for language management and practice (Clyne & Kipp, 2011 ; Pauwels, 2016 ), making it a critical site of language policy-making and implementation. Many studies have contributed to the vibrancy of family language policy (FLP) research as a distinctive subfield of language policy (Curdt-Christiansen & Gao, 2021 ; Smith‐Christmas, 2022 ). FLP can be defined as a process in which individual family members, especially parents, try to regulate the use of specific language(s) that may help or hinder the maintenance of their cultural ties or cultural heritage (Caldas, 2012 ).

Most studies in early language policy research focused on public domains such as state, school, and workplace (King et al., 2008 ), paying limited attention to private domains such as the family (Robinson et al., 2006 ; Wan & Gao, 2021 ; Wiley & Wright, 2004 ). The relevance of private domains has, however, been recognised in major conceptualisations of language policy, such as the conceptualisation of FLP as a process by Spolsky ( 2004 ), where he stated, “language policy in the family may be analyzed as language practice, ideology and management” (p. 43). Spolsky’s ( 2004 ) conceptualisation of FLP comprises language ideology (i.e., beliefs/views about HL maintenance), language management (efforts to maintain the language) and language practice (the actual use of the language). Early FLP research focused on the “policy” aspect of FLP, for instance, how FLP emerges from the interactions between research on child language acquisition and language policy (King et al., 2008 ). While relevant research draws on a variety of related fields of inquiry, such as heritage language maintenance (Lee, 2006 ) and home language maintenance (Shen et al., 2021 ; Tseng, 2020 ), the term “family language policy” has been increasingly used to define this body of research in publications (Lanza & Gomes, 2020 ). Lanza and Gomes ( 2020 ) note that “there has been an increase over time of publications with various degrees of intensity over the past decade” (p. 159) and they have recorded an almost nine fold increase in the number of publications using the term “family language policy” from 2008 to 2018.

The salience of FLP in language policy research has been driven by an overall increase in global mobility, which has required many migrant families to make challenging decisions regarding their language practice, especially concerning the maintenance of the heritage language (HL) among their children in their host contexts. In the backdrop of the rising awareness of FLP concepts such as family may need to be reexamined and perhaps reconceptualised because global migration and transnationalism have significantly changed the nature of what one may call a “family”. For instance, the number of migrants worldwide has increased, both as an absolute figure and as a fraction of the global population, rising from 192 million (2.93%) in 2005 to 281 million (3.6%) in 2020, with Europe, North America and Oceania seeing some of the most significant increases in immigration (Institute of Migration, 2021 ). This means more families are becoming globally dispersed and are not physically confined to one location.

In addition, language practice is one of the key components of language policy (Spolsky, 2004 ). The growth of this globally dispersed population has coincided with a parallel growth in internet use, with the number of users growing from just 413 million in 2000 to 3.4 billion, or almost 60% of the world’s population, in 2016 (Roser et al., 2022 ). Geographically dispersed families increasingly rely on digital communication technologies such as Skype and WhatsApp to interact with their relatives and friends in other countries (Palviainen & Kędra, 2020 ). Thus, for instance, in 2014, Skype accounted for approximately 40% of the entire telephony market (Worstall, 2014 ), and in 2011, according to the New York Times, “170 million users each month connected for more than 100 min on average. In the last year or two, video use has surged, now accounting for 40 per cent of Skype’s traffic, with 170 million users each month connected for more than 100 min on average” (Lohr, 2011 ).

The COVID pandemic and associated travel restrictions have only helped to accelerate this trend (Hatoss, 2023 ). In addition, many senior family members, such as grandparents, have recently begun using digital communication technologies, making it easier for younger migrant family members to maintain contact with each other (Taipale, 2019 ). Taipale ( 2019 ) refers to families that make extensive use of technology to remain in contact across significant distances as “digital families” (p. 14), defined as “one form of distributed extended family, consisting of related individuals living in one or more households who use at least basic information and communication technologies and social media applications to stay connected and maintain a sense of unity despite no more than occasional in-person encounters between them” (Taipale, 2019 , p. 14). Families may communicate using different media, exchanging voice, text, video, and images via various services like video calling, text messaging, and email (Taipale, 2019 , p. 90). These multimodal communications may help sustain family bonds and maintain the HL (Lexander, 2021 ). Language practice is thus neither confined to face-to-face settings nor situated in any one physical location but is instead embedded within the broader context of communication involving multimodal digital tools. The use of digital media for communication has also happened within a broader context of increasing electronic literacy (Lee, 2006 ), the use of various internet based, digital HL learning tools such as weblogs (Lee, 2006 ) and the use of HL related apps (Little, 2019 ) among immigrant families.

The question thus arises as to whether the conceptualisation of family and language practice in the existing migrant FLP research appropriately reflects social changes and technological developments. One recent systematic review attempted to examine the concept of the family in the FLP literature on dispersed families, concluding that the concepts of ideology, management, and practice within FLP should be expanded to accommodate the realities of transnational families (Hirsch & Lee, 2018 ). Nevertheless, several unknowns remain. For instance, it is not known how common it is for families represented in the current migrant FLP literature to have dispersed members contributing to language maintenance in some form. Not much is known about who these family members are (in terms of relationships), how contact is maintained with such dispersed family members, or to what extent digital communication modes are used in the maintenance of that contact, i.e., to what extent they may be considered "digital families" as discussed above. Indeed, little is known about the types of families (e.g., nuclear families) that the broader migrant FLP literature has surveyed to date. If digital communication modes are an increasingly important component of language maintenance in FLP, then it is necessary to understand the extent to which the migrant FLP literature has already engaged with this idea relative to traditional face-to-face modes of language transmission in FLP. Such unknowns point to a literature-wide issue which can be addressed through a systematic review exploring how the notions of family and language practice are conceptualised and operationalised in existing studies and how the migrant FLP literature should respond to shifting understandings of family and language practice. Our review addressed the following research questions:

How is the concept of family constructed in migrant FLP studies?

How is the concept of language practice constructed in migrant FLP studies?

By ‘FLP studies’, we refer to the FLP-related publications included in this systematic review.

Methodology

We employed a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach to conduct this study. PRISMA is a standardised protocol for conducting systematic reviews and delivering transparent reporting (Liberati et al., 2009 ). A systematic search strategy with pre-selected search terms and selection criteria was used based on the PRISMA guidelines, checklist, and flow diagram. Our review identified the studies via five databases: Education Resources and Information Center (ERIC), Linguistics & Language Behaviour Abstracts (LLBA), Psych Info, Scopus, and Web of Science. While the term "family language policy" has been in use at least since the early 2000s (Hollebeke et al., 2020 ), the tripartite FLP framework of language ideology-management-practice (discussed in the Introduction section) was proposed by Spolsky in 2004 (Spolsky, 2004 ). Thus, our search was confined to the period from January 2004 through to December 2022.

Search terms

The search terms were determined based on Spolsky’s ( 2004 ) framework (Figure  1 ), in addition to our exploration of existing FLP systematic reviews (Hirsch & Lee, 2018 ; Hollebeke et al., 2020 ) and discussion between the first and second authors.

Search terms and queries

We designed these terms to capture the FLP literature concerning transnational immigrant or migrant families. Since this research aims to examine conceptualisations of family and language practice within the FLP literature, our search terms were also designed to extract literature from within the FLP domain. While other scholars have used a broader set of terms (Hirsch & Lee, 2018 ; Hollebeke et al., 2020 ), our approach to FLP in this research is limited to studies that explicitly use the term, “family language policy” as FLP has become an established, well defined subfield of inquiry in language policy research (Lanza & Gomes, 2020 ).

Wildcard (*) characters were used to maximise article capture as multiple variations of some words may exist. Note that for the rest of this article, we use the term "migrant" to refer to both migrants and immigrants for notational ease. The distinguishing factor between the two is the intention of the latter to stay permanently. We consider a family to be a “migrant” if at least one parent of the children being studied is a migrant. Thus, if the father lives abroad and the mother and child do not, or if the entire family has migrated and has extended family members elsewhere, in both cases, the family would be considered a migrant family. This definition allowed us to examine the likelihood of the use of digital modes of communication between the child and other family members. Our search terms were applied to the five databases listed above. The search was designed to return articles which contained the search term anywhere within them. As can be seen in Figure  2 , our search yielded 592 results, including duplicates. After automatic and manual deduplication, 535 manuscripts remained. These manuscripts were screened based on several criteria. First, manuscripts in languages other than English were excluded. Next, manuscripts that were not peer-reviewed (e.g., conference papers) were excluded, following protocols followed in other FLP systematic reviews (Hollebeke et al., 2020 ). After this screening process, 482 peer-reviewed manuscripts remained. All 482 retained articles were further screened in a second stage to determine inclusion. Manuscripts not involving bi/multilingual immigrant families (123) were removed since we aimed to investigate conceptualisations of family and language practice among bi/multilingual immigrant families. Since this selection criterion requires the selected research to be conducted among families who are immigrants and who are also bi/multilingual, the vast majority of studies that were excluded in this stage were studies that were not conducted among immigrants. This stage of screening reduced the number of manuscripts to 359.

Selection criteria

Of these 359 manuscripts, 71 studies were excluded as secondary research and review papers. Thus, only articles presenting the results of primary research were retained. The remaining articles were then scanned to see if they discussed the topics significant to our review, that is, FLP, family, or language practice; the 29 manuscripts that did not address these topics were excluded. Finally, an in-depth screening was implemented for relevance to the research questions, and 96 manuscripts were rejected as irrelevant. This resulted in a final set of 163 articles.

In the initial screening, three authors (Authors 1, 4, and 5) screened all the articles based on the selection criteria, as presented below.

To summarise, the following selection criteria (Figure  2 ), based on the PRISMA guidelines, were applied: (i) peer-reviewed articles and peer-reviewed book chapters published in English only; (ii) research population confined to bi/multilingual migrant families; (iii) research papers limited to primary research; (iv) studies mentioning the terms FLP or family or language practice; and (v) studies relevant to the research questions.

Various data points of interest were extracted from the final set of reviewed studies. This included the number of participants, study design, and various demographic features of the study participants, such as education and country of origin. The family types in each study were coded independently by the first and second  authors. If differences in coding choices were found between the two authors, these were resolved through discussion using an open coding process and the coding was finalized together with the second and third authors. Notes were made on whether each family represented in the literature had dispersed relatives and, if so, who these dispersed relatives were. Visits to home countries and vice versa were noted. To understand how language practice is conceptualised with regard to the different modes of communication, a set of predetermined codes (closed coding) was used, with the remainder of the coding procedure being similar to that for families. Language practice is coded as follows: (a) traditional HL practice:-that does not involve communication, such as reading HL books; (b) traditional HL communication practice:- involving traditional communication using postcards etc. or face-to-face (physical) communications; (c) digital HL practice:- that involves the use of digital modes of HL learning (but not communication), such as watching television or online videos in HL that do not involve communication with another person; and (d) digital HL communication practice:- that includes digital communication, mediated through various digital modes of communication, such as Skype. Unique instances of most items, such as the number of times digital HL communication practice was mentioned, were coded. Thus, one research paper could have multiple instances of a specific item, such as HL communication practice. Since this is a systematic review that seeks to establish how families and language practice are conceptualised in the literature, the results were tabulated as numbers and percentages, and modal statistics (i.e., the most frequently reported statistics across all reviewed papers) were reported when necessary. Such an approach is common when a literature review seeks to establish a baseline, first-time estimate of the phenomenon in question, for example, see Hirsch and Lee ( 2018 ).

Before proceeding to our findings, it is necessary to provide a summary of the analytical approaches and research designs used by the reviewed studies since this has a bearing on their generalisability (for quantitative studies) and trustworthiness (for qualitative studies). In addition, it is essential to understand the demographics of the participants represented in the reviewed studies so that any findings can be interpreted within their appropriate contexts.

The most common analytical approach was thematic analysis (27% of included studies), followed by quantitative methods (16%), discourse analysis (13%), and grounded theory (9%). However, around 23% of the research papers did not clearly mention a specific design or analytical approach. Various data collection methods were used, with interviews (45%), questionnaires/forms/tests/surveys/assessments (22%), and direct observation of participants (13%) being the most common. Note that by the term 'questionnaires', we mean a set of questions that may be posed during the interview process or manually handed to a set of participants, with generally full participation or close to full participation. In contrast, by 'surveys', we imply lists of questions that are sent out by various means, with a certain fraction being returned to the researcher. Other methods were also used, such as focus-group interviews, stimulated recall, field notes, and linguistic assessment.

The number of families represented in each study varied from one to 258, with the median being four. The number of study participants ranged from a minimum of one to a maximum of 500, with a median of 11. The families included a minimum of one child to a maximum of eight children. The parents were from 60 different countries of origin, with China (11%), Russia (6%), and Korea (5%) being the most common. The studies also involved around 30 different countries of residence, with the United States of America (USA) (17%), Australia (10%), Israel (8%), and Canada (6%) being the most common. The language most frequently spoken by parents was reported as English (21%), followed by Spanish (8%), Chinese (7%), and Russian (7%). The most commonly reported (statistical mode) minimum length of residence of migrants in the current host country was 10 years, while the statistical mode of maximum length of residence was 20 years. The range varied from 6 months to 50 years.

Around 29% of the studies involved both parents. Data were collected from children in about 22% of studies, and only mothers were sampled in 22%. A smaller number of studies targeted specific groups. For instance, one study specifically targeted adolescents and another targeted young adults. Also, around 5% of studies targeted single-parent families, and 7% targeted just fathers.

Below, we discuss our results in the context of our research questions.

The concept of 'family’

Four types of families were represented in the reviewed FLP literature,—nuclear, extended, extended digital and nuclear digital (Figure  3 ), with some studies including more than one type. We start by discussing the most commonly reported family type: nuclear families.

Types of families

Nuclear families

Nuclear families form the vast majority of families examined (73%). Our investigation identified variations within these nuclear families with regard to HL and family composition. For the remainder of this section, we use italics to indicate a specific family type.

In the studies examined in this review, parents in nuclear families generally spoke at least two languages, one being their HL and the other being the language of the community to which they had immigrated. As such, two types of nuclear families can be distinguished. The first, of which 32 instances were found, are nuclear bilingual inter-married families or mixed families , in which parents from different nations spoke different languages. These included Russian-Estonian and Russian-Spanish families maintaining Russian as an HL in Estonia and Spain (Ivanova & Zabrodskaja, 2021 ), Russian-German families in Germany (Brehmer, 2021 ), and Portuguese-German families in Germany (Costa Waetzold & Melo-Pfeifer, 2020 ). Families with parents from different cultures and countries tended to interact with dispersed relatives (Lexander, 2021 ).

The vast majority of families are, however, of the second type, where both parents speak the same HL in addition to the language of the dominant community. We call these nuclear bilingual families . For example, one study examined families living in Germany in which parents spoke both Portuguese and German (Costa Waetzold & Melo-Pfeifer, 2020 ). Kopeliovich ( 2010 ) described a bilingual Russian-Hebrew nuclear family in Israel with eight children. The family had immigrated to Israel from Russia with four children and had another four children in Israel. This family settled in Jerusalem along with other Russian-speaking migrant families. Kopeliovich ( 2010 ) explained that these families possessed a multicultural ideology focusing on maintaining Russian as their HL in Israel. Other examples of nuclear bilingual families include Albanian families in Greece (Chatzidaki & Maligkoudi, 2013 ) and Pakistani families in the UK, including those with one British-born parent and one Pakistani-born migrant parent sharing the same HL (Curdt-Christiansen & La Morgia, 2018 ).

While social scientists sometimes distinguish nuclear families from single-parent families , we mention them here since they share similarities with nuclear families. As the name implies, in single-parent families , the child or children lived with one parent who was solely responsible for their child(ren)’s HL maintenance. For instance, Brehmer’s ( 2021 ) study involved several Russian single mothers in Germany who were divorced or separated. Another study investigated Russian single-mother families in Israel, focusing on child-rearing and language (Zbenovich & Lerner, 2013 ). Three instances of such families were found.

Certain other interesting variations of nuclear families were also observed. One study described a nuclear family in the context of international adoption (Fogle & King, 2013 ). In such an international adoptive family, the parent(s) needed to learn the children’s HL (through a language course or by employing other resources) to prepare for adoption. This was seen in a study of three international adoptive families in the USA: the English-speaking parents attempted to use the children’s HL (Russian) in daily conversations after adopting Russian children (Fogle & King, 2013 ). In another instance, sojourning families lived temporarily for a few years in a foreign country and thus had to also plan for their return to their home country. In a study by Bahhari ( 2020 ), 10 Saudi-Arabian families in Australia maintained their HL (Arabic) in their home during their sojourn in Australia. The connotation of the term “sojourn” varies in the literature, with one author defining home visits to Taiwan during school holidays in Australia as sojourns (Eisenchlas et al., 2021 ). A final yet intriguing instance of nuclear families is vulnerable families, such as those represented in a study of HL maintenance among Mandarin and Cantonese-speaking parents in the USA with children with autism (Yu & Hsia, 2019 ). The study found a lack of HL-based language support for children with autism in the community and a shift in FLP priorities from HL learning to be able to communicate in English after an autism diagnosis.

Extended families

While nuclear families form the bread and butter of FLP research, some studies examined extended families (16%). In these families, children and parents lived together with other adult relatives, such as grandparents, uncles, or aunts, in the same physical place. Extended families offer an interesting basis for FLP research, with quite a few studies highlighting the significance of extended family members for HL maintenance. Examples included an Italian family in Canada consisting of a child and their parents and grandparents (Corsi, 2020 ) and a Chinese family in Singapore, in which the grandmother lived with the family, took care of the grandchildren, and helped them maintain their HL (Ren & Hu, 2013 ). Studies involving extended families most frequently reported grandparents as the extended family member living in the home (26 studies), with 31 reporting a variety of other extended family members. One study each reported the presence of uncles and aunts. Extended families may use multiple languages, which may provide a further layer of language support for children Curdt-Christiansen ( 2016 ) provided examples from Singapore of grandparents acting as linguistic gatekeepers in multilingual extended families, including a three-generation trilingual Chinese family, a bilingual Indian family, and a bilingual Malay family. The trilingual Chinese family had two English-speaking children living with their English-speaking parents, aunt and other family members. All family members spoke Mandarin, and all except the children spoke Hokkien. The aunt was the homemaker and the caregiver for the grandmother, uncle, and children when the parents worked. The bilingual Indian family was a family of teachers, in which the Tamil-speaking grandparents took care of the children while the parents taught at a school. Other members of the household included a Tamil-speaking uncle and a Filipina helper. This family is representative of an extended Indian migrant family living in Singapore, with family members mainly speaking Tamil (their HL) and English. The bilingual Malay family included Malay-speaking grandparents, two aunts who were university students, and an Indonesian helper living with the two children and their parents. Two important features to note from these families are the presence of multiple languages within the families and the promotion of a positive attitude towards the HL and other languages in the children through exposure to these languages. Some researchers have noted that families often tend to address the absence of extended relatives in their midst through the use of digital communication tools (Robertson et al., 2016 ). Conversely, the presence of relatives within the physical realm of the home reduces the likelihood of digital communication with dispersed relatives (Robertson et al., 2016 ).

Dispersed family

While extended families have many family members living in close proximity, nuclear families also retain connections with their dispersed extended family members. Indeed, one characteristic evident in many nuclear families was the practice of home visits to dispersed family members. Home visits to family abroad were found in around 47 studies, with five of these studies reporting (reciprocal) visits from family abroad to the focal family.

For example, Cape Verdean families in the USA consider it essential for children to visit Cape Verde and speak to grandparents in Cape Verdean Creole (Kaveh & Sandoval, 2020 ). Kim et al. ( 2015 ) observed three Korean parents in the USA who similarly considered home visits an essential aspect of language maintenance as home visits allowed children to practice their HL (Korean) with their dispersed family members. This trend points to the fact that nuclear families have dispersed relatives with whom they seek connection, which is also relevant to the use of digital communications, as discussed later.

The migrant FLP literature engages well with the concept of a dispersed family, or as introduced briefly earlier, geographically dispersed families who maintain contact with each other physically or digitally (Yeoh, 2009 ). As mentioned, most studies examined in this systematic review appear to be aware of the existence of dispersed family members, with 65 of 163 papers (40%) mentioning some form of a dispersed relative and 46 of 163 (28%) mentioning home visits. However, this engagement does not necessarily extend further into how these dispersed family members maintain contact with each other beyond physical visits and what effect this has on HL maintenance. It is very likely, however, that dispersed family members interact with each other in ways other than through physical visits (such as through digital means).

Digital families

A small number of studies explored what can be conceptualised as digital families , with two studies examining FLP within a digital nuclear family , with the father living away from the family (Lee & Pang, 2021 ), and 17 studies examining digital extended families , with various extended family members maintaining contact with the focal family (which was almost always a nuclear family) through digital means, such as Skype and WhatsApp (Gharibi & Seals, 2020 ; Hua & Wei, 2016 ; Lexander, 2021 ). Digital families are discussed further in the ‘Digital HL Communication Practice’ section below.

Other noticeable family features

Most studies investigated first- or second-generation migrants. Very few studies reported on the educational backgrounds or professions of family members. Among those that did, the modal value (most frequently reported statistic) of the minimum level of education reported among parents was a few years of schooling. In contrast, the mode for the maximum level of education was postgraduate education. While both professional/white-collar and trades/blue-collar professions were reported, the former were twice as numerous in the data. It is possible that parents with higher education are more concerned about their children’s HL proficiency and use. These results may also represent a selection bias, with better-educated parents being more likely to participate in such studies (Li, 2015 ).

The concept of 'language practice’

The concept of language practice in the reviewed studies is closely related to the conceptualisation of the family. Since most studies conceptualise a family as a group living in the same physical location, language practice is also conceptualised as occurring within the home or the same physical location. Indeed, language practice does not have to involve communication, and may be done without the help of digital tools. We therefore begin our discussion by exploring studies that examine some of these means of language practice that do not involve direct communication. We use the term 'digital HL practice' for practice that involves digital media, and 'traditional HL practice' for that which does not.

Traditional and digital HL practice

Around 20% (57 of 281) of all instances of language practice mentioned in the examined papers involved traditional HL practice and did not include any communication or use of digital media (Figure  4 ). These include reading in HL and language study, such as children reading picture books in their HL (Portuguese) in Germany (Costa Waetzold & Melo-Pfeifer, 2020 ; Kirsch, 2012 ; Kirsch & Gogonas, 2018 ) and children practising their HL by reading textbooks on their own (Curdt-Christiansen & La Morgia, 2018 ). Parents also attempted to maintain their HL by reading newspapers (Russian) (Kang, 2015 ; Mori & Calder, 2017 ; Schwartz, 2008 ).

Modes of heritage language (HL) practice

Digital tools provide unique and interactive opportunities to practice HL; thus, the FLP literature has extensively engaged with this topic. Around 51 instances of digital HL practice were found, accounting for 18% of all observed practices. Television, video, and radio remained common forms of practising and learning HL. Some studies showed how children’s HL maintenance is influenced by watching films and cartoons in the HL (Eriksson, 2015 ; Hua & Wei, 2016 ). For example, children in some Greek families in Luxembourg watched Greek television (TV) channels to support HL maintenance (Kirsch & Gogonas, 2018 ), and children in Chinese families in Canada watched videos in their HL (Li, 2015 ). Russian children in Israel listened to the radio in their HL (Schwartz, 2008 ), and some Hakka Chinese children in Malaysia listened to Hakka radio programs at the suggestion of their fathers (Xiaomei, 2017 ). Browsing the Internet for HL material was also reported. One study, for instance, reported Russian children in Canada browsing websites in their HL (Makarova et al., 2019 ).

Fuentes ( 2020 ) provides an example of HL practice using digital media among two Sinhalese families in the USA to illustrate their HL maintenance. However, these two transnational Sinhalese families had different motivations for maintaining their HL. The Kola (pseudonym) family maintained their HL (Sinhalese) because they had the long-term goal of returning to their home country of Sri Lanka. In contrast, the Nil (pseudonym) family emphasised HL maintenance out of fear of HL loss in the USA. Alongside their traditional HL practices, these families developed strategies to maintain some digital HL practices. The children in the Kola family practised their Sinhalese by reading Sinhalese newspapers online, while the children in the Nil family practised their Sinhalese through active engagement on Sinhalese websites. The children of both families regularly watched Sinhalese TV programs. Both families thus sought to maintain their HL (Sinhalese) through digital HL practice, thereby maintaining their Sri Lankan identity in the USA (Fuentes, 2020 ).

However, HL practice cannot remain confined to activities that do not involve communication, and thus, the FLP literature has also examined HL practices that involve communication. As such, we discuss traditional and digital means of language communication practice next.

Traditional and digital HL communication practice

The majority of studies in this review conceptualised language practice as face-to-face communication located within one physical location, such as the home. Thus, 51% of instances (143 of 281) of language practice instances identified in the reviewed studies consisted of traditional HL communication practice. As an example of traditional HL communication practice, we can turn to Eriksson ( 2015 ), who highlights how 15 Russian migrant families and 15 Latvian migrant families settled in Ireland maintained their HL using traditional means. These families described using traditional HL practices at home with their children, for instance, having a "Russian-only" or "Latvian-only" policy at home. They also taught their children Russian or Latvian and read HL texts with them. Some parents also gave dictations in HL (Russian) to their children at home, played memory games with them to develop Russian vocabulary, and invited Russian teachers into their homes to develop their children’s HL skills. The previously discussed home visit strategy was also used, with some parents and children going to their home country, i.e., Russia or Latvia, to visit their friends and relatives during vacations to maintain their HL. Also, grandparents living in the families’ homes in Ireland played a significant role in HL maintenance. All 15 children in this study communicated with their grandparents only in their HL (Russian). The HL practice strategies used by the Russian families in this study for their children were more formal (for example, involving dictation practice) and frequent compared to those of the Latvian families.

Other examples of traditional HL communication practice observed include children communicating orally in their HL at home with their parents (Gomes, 2019 ; Gu & Tong, 2020 ; Wilson, 2020 ), speaking in their HL (Mandarin) with their mother and nanny at home (Yu & Hsia, 2019 ), practising their HL (Russian) at home with their grandmother (Zbenovich & Lerner, 2013 ), or practising HL (e.g., Persian) with siblings (Kheirkhah & Cekaite, 2018 ). Still other examples include mothers reading storybooks in the HL to their children (Kirsch & Gogonas, 2018 ) and reading to their children for pleasure, as seen in the reading of Russian, Korean, and Japanese books by parents (Kang, 2015 ; Mori & Calder, 2017 ; Schwartz, 2008 ).

As discussed earlier, the family is not confined to one physical location, and neither is language practice. Dispersed families have existed since long before digital means of communication were available. These families have often retained contact through various traditional communication modes. One study reported traditional HL communication practice wherein the child maintained communication with their grandparents by sending postcards in the HL (Hakyoon & Myoungeun, 2020 ). Al-Sahafi ( 2015 ) reported communication between children, local family members, and dispersed family members, including grandparents, via the telephone, a communication mode that perhaps lies within both the traditional and digital realms. However, since most reviewed studies that mention the telephone (Brehmer, 2021 ; Gharibi & Seals, 2020 ; Lexander, 2021 ) do so in the context of mobile phones, and because at the beginning of the first decade of the 21 st Century, many traditional telephone calls are now routed through digital networks, we consider phone calls (voice calls) to be a form of digital communication.

Another option for retaining contact is physically visiting dispersed family members. Thus, some children communicated with their dispersed family members in their HL during their visits to their home countries (Bahhari, 2020 ; Chowdhury & Rojas-Lizana, 2020 ; Makarova et al., 2019 ; Tran et al., 2021 ). However, physical visits are expensive and time-consuming, albeit a more personal means of retaining contact and the development of digital modes of communication have allowed the retaining of contact among dispersed families and the practising of HL when opportunities arise (Taipale, 2019 ). Thirty instances of digital HL communication practice were found, representing around 11% of all instances of communication practice. Studies often treated digital HL communication practice as an afterthought in comparison to other traditional HL practices, and very few engaged directly with questions related to digital FLP (Lexander, 2021 ; Revis, 2021 ). Still, studies documented digital communication in HL between children and relatives (such as grandparents) overseas through video calls, voice calls, and other digital tools such as WhatsApp (Bahhari, 2020 ; Chowdhury & Rojas-Lizana, 2020 ; Hua & Wei, 2016 ).

A unique study by Lexander ( 2021 ) explored four Senegalese families' digital language practices in Norway. This study also reported digital communication between family members living under the same roof, unlike other studies in this space. In this study, digital extended families used a range of social media tools (including WhatsApp, Messenger, Short Message Service (SMS), Viber, Snapchat, Facetime, and Skype) to interact with their family members – both located physically together and dispersed – in their HL (Wolof) and other known languages. One family in this study was the Norwegian-Senegalese Coly (pseudonym) family, which consisted of a Senegalese mother and four children in their teens and twenties. Norwegian was the primary language of communication in this family. The children had learnt some English at their Norwegian schools, on TV, and on the Internet. Three of the family's four children had learnt Arabic from Koranic schools. In addition, Awa (the eldest child) had learnt French at her school. Lexander ( 2021 ) highlighted the modes of digital communication and the languages Awa used with her immediate family members for everyday interactions: she communicated with her mother and her siblings digitally through regular phone calls in their HL (Wolof) and through SMS messages in Arabic and sometimes in English using Facebook Messenger. With her dispersed family members, Awa used other modes of communication that are popular in Senegal. Her communication with her uncle in Senegal was mainly in English and written through Messenger. With her two aunts living in Senegal, Awa would speak and write in English and Wolof, using Messenger and Facebook with one aunt and Viber and WhatsApp with the other. Awa also communicated with her friends in Senegal through Messenger (in English), Skype and WhatsApp. Overall, her linguistic repertoire with families and friends, whether physically proximate or dispersed, was diverse, consisting of Wolof (HL), English, French, and Arabic, with some minor conversations in German and Norwegian. These languages were transmitted over multiple media; thus, for instance, she used WhatsApp, Messenger, Skype, Viber, Snapchat, and FaceTime when communicating with a friend in Germany. Conversations and language practices among the multilingual family members were thus negotiated or managed, and their relationships and HL were maintained in a polymedia environment (Lexander, 2021 ) that included digital communication modes.

Digital voice calls were reported even with the proliferation of more interactive video and multimodal communication services. There were reports of children interacting with their dispersed family members through mobile phone calls (Brehmer, 2021 ; Gharibi & Seals, 2020 ); one child from a Hispanic family in New Zealand spoke in their HL (Spanish) over the phone with dispersed members (Navarro & Macalister, 2016 ). An essential aspect of digital FLP communication is that while many migrants are situated within developed countries in the Global North, their dispersed families may be located in relatively underserviced rural areas of the Global South, where despite the ongoing proliferation of mobile and data services, connections can be poor and communication intermittent. One example appears in Revis ( 2021 ) study, which describes digital communications between a migrant family in New Zealand and their dispersed family in rural Columbia. Connectivity issues caused the family to be able to video chat only twice a year despite their desire to communicate more frequently.

Discussion and conclusion

This systematic review has identified the diversity of family types and language practices represented in contemporary migrant FLP studies. Nuclear families accounted for more than half of all families identified in this review. This is unsurprising since the father-mother–child(ren) conceptualisation of a family physically located in one place is the norm in FLP and elsewhere (Oyewumi, 2002 ). While 40% of all reviewed papers mention dispersed relatives in some form, they are typically considered separate and distinct from the focal family, and while their contribution to the child’s language practice may be examined as an afterthought, their role within the conceptualisation of family is generally not considered in the FLP literature. The FLP literature, thus, broadly fails to account for the dramatic changes in the concept of what constitutes a family that have occurred in recent years due to geopolitical, sociocultural, and technological changes.

With regard to the families examined in the reviewed studies, our research highlights issues with access, equity and diversity in FLP research. For instance, none of the 163 reviewed studies, focused on lesbian, gay, bisexual, or transgender migrant families. No study involved families that have members with disability (i.e. vision or hearing impairment). There was also a significant selection bias towards well-educated migrants and those employed in white-collar professions. Issues with access to digital communication services were also identified among those with dispersed family members who were either not tech-savvy or lived in poor rural areas of the Global South (Brehmer, 2021 ; Revis, 2021 ). Finally, a third of all FLP studies focused on Chinese, Korean, and Russian migrants, and there seems to be a lack of studies concerning members of other large migrant groups, such as South Asians (Indians, Bangladeshis, etc.). These issues deserve attention in future research exploring language practice in the domain of the family.

The phenomenon of dispersed relatives raises the issue of language practice through digital communication in FLP. However, our analysis reveals an asymmetry between traditional and digital modes of communication, with traditional modes being both observed and investigated more often (51% vs 11%). Indeed, similar to traditional HL practice not involving communication (20%), digital HL practice not involving communication has been investigated more frequently (18%) than digital HL communication practice. While one possible explanation is that traditional HL communication practice may be far more prevalent than digital modes of language communication, it may also be that digital language communication practice, when present, is less likely to be reported in FLP studies. Another factor that may explain the relatively low prevalence of digital language communication practice in the reported studies is that although the technologies for such practices have existed for longer, their popularity has only taken off in the last decade, especially during the COVID-19 pandemic. Research conducted during the COVID-19 pandemic may not have been published at the time of data collection for this systematic review. Nevertheless, we believe that given the increasing importance of digital communication tools, there is a need for future FLP studies to investigate the role played by these tools in HL maintenance.

We identified a range of studies that mention the use of digital communication. Video conversations between children and grandparents were a common theme in many of these (Chowdhury & Rojas-Lizana, 2020 ; Nakamura, 2019 ). While some of these studies mentioned issues with digital literacy and access (especially, in the case of access, in rural areas) as barriers to utilising these technologies (Brehmer, 2021 ; Revis, 2021 ), most studies agreed on their usefulness. According to Lexander ( 2021 ), mobile phones provide the opportunity for migrants to enjoy personal HL conversations in the privacy and comfort of their rooms. In contrast, media such as Skype may serve as tools for parent-monitored conversations between children and dispersed family members (Chowdhury & Rojas-Lizana, 2020 ). Different digital communication media serve different purposes and may complement each other in providing various semiotic resources through which meaningful communications may be implemented (Lexander, 2021 ). Thus, text, video, images, and voice may be transmitted over different digital platforms, together providing semiotic resources for communication; this is very different from the face-to-face language practice that the majority of the FLP literature investigates.

The salience of grandparents in HL maintenance (Curdt-Christiansen, 2016 ) was clear across numerous papers included in this review, and is associated with both traditional families physically located in one place and families with dispersed family members, and with both traditional and digital modes of communication. While our research identified a variety of dispersed relatives with whom the HL was maintained, grandparents were the extended family member most likely to be physically present with the parents and children, and thus to play a role in the child’s HL maintenance; they were also the dispersed family member most commonly discussed in the literature. Visits to grandparents in their home countries were commonplace (Eriksson, 2015 ). Not surprisingly, as discussed previously, grandparents were also the dispersed family members with whom children communicated most regularly in HL using digital communication media such as Skype. Given the salience of grandparents in both traditional and digital FLP, there is a surprising lack of attention to this topic in research. Future FLP research thus needs to focus on research questions related to this unique aspect of FLP.

While the findings above deserve further attention, we must also treat them with caution. Although our review uses the PRISMA approach to systematic reviewing and a comprehensive set of search terms, there is a possibility that we may have missed some studies on the topic due to any of several potential reasons, including the non-comprehensiveness of the searched databases and the specificity of the search terms; this is to be expected of any systematic review (Greenhalgh & Peacock, 2005 ). For example, our review used search terms limited to “family language policy”, since our motivation was to engage with key concepts, including family and language practice, within studies that use the term “family language policy”. Future reviews may include additional terms to broaden their coverage of FLP research. Given that we have reviewed a set of 163 studies identified from five databases, it is unlikely that the argument for the reconceptualization of family and language practice in FLP research would be undermined by the omission of papers due to our use of search terms. In addition, our review investigated the prevalence of certain FLP-related practices and characteristics within a comprehensive set of studies, which does not necessarily reflect the prevalence of these data points in the population. In other words, for example, the fact that 11% of the FLP cases described in the reviewed literature involved digital communication does not mean that 11% of all FLP communications among migrants are, in fact, digital; our statistics simply reflect what was found in the surveyed studies. It may also be possible for researchers to examine the distribution of dispersed families and the overall prevalence of digital communication media use among dispersed families. However, this would likely be an exercise outside of the sociolinguistics or language policy domain.

Finally, we would also like to use this opportunity to call for more research on FLP-related language learning outcomes from the use of digital communication media. The importance of these media can only be appropriately understood if their effects on children’s HL learning can be understood, both quantitatively and qualitatively. Nevertheless, despite these limitations, our review is one of three existing systematic reviews on FLP and complements the other two studies on this topic, which focus on FLP outcomes and transnational families in migrant FLP studies (Hirsch & Lee, 2018 ; Hollebeke et al., 2020 ).

Recommendations

This review raises a few critical questions to be addressed in FLP. First of all, the results show that the concept of family is not homogenous and that there are wide variations within this concept. Seventy-three percent of the families represented in the surveyed studies were typical father-mother–child(ren) nuclear families. Studies on the effect of FLP on outcomes (Hollebeke et al., 2020 ) need to be cognisant of this variability, and FLP researchers should demonstrate greater sensitivity to the diversity of families in which FLP unfolds. It is essential to recognise that the family is a socio-cultural construction and that family members negotiate and inhabit roles constructed on cultural, social, and political bases, which profoundly mediate their involvement in managing language practices within the domain of the family (Gharibi & Seals, 2020 ).

Second, FLP research needs to demonstrate a critical awareness of different modes of language practice. While the vast majority of FLP language practice is still traditional, there is evidence that digital HL communication practice is occurring and probably increasing in frequency and significance. Future research should focus on exploring the various aspects of digital HL communication practice and be cognisant of such communication occurring even when the research is focused on traditional FLP. Researchers can incorporate these shifting conceptualisations of language practice by utilising research tools such as multimodal analysis and online ethnography, which acknowledge the multimodal, semiotic nature of digital communication among dispersed families (Kress, 2001 ).

To summarise, the shifting nature of the family due to global migration needs to be acknowledged, and we believe that future research on FLP should engage with the reality of dispersed family members and their role in FLP. This is best realised through a conceptualisation of language practice as multimodal and digital and as providing semiotic resources for communication in addition to being face-to-face and physical.

Al-Sahafi, M. (2015). The role of Arab fathers in heritage language maintenance in New Zealand. International Journal of English Linguistics, 5 (1), 73–83. https://doi.org/10.5539/ijel.v5n1p73

Article   Google Scholar  

Bahhari, A. (2020). Arabic language maintenance amongst sojourning families in Australia. Journal of Multilingual and Multicultural Development . https://doi.org/10.1080/01434632.2020.1829631

Brehmer, B. (2021). Maintenance of Russian as a heritage language in Germany: A longitudinal approach. Russian Journal of Linguistics, 25 (4), 855–885. https://doi.org/10.22363/2687-0088-2021-25-4-855-885

Caldas, S. J. (2012). Language policy in the family. The Cambridge handbook of language policy (pp. 351–373). Cambridge: Cambridge University Press.

Chapter   Google Scholar  

Chatzidaki, A., & Maligkoudi, C. (2013). Family language policies among Albanian immigrants in Greece. International Journal of Bilingual Education and Bilingualism, 16 (6), 675–689.

Chowdhury, F. Y., & Rojas-Lizana, S. (2020). Family language policies among Bangladeshi migrants in Southeast Queensland, Australia. International Multilingual Research Journal, 15 , 1–16.

Google Scholar  

Clyne, M., & Kipp, S. (2011). Pluricentric languages in an immigrant context . New York: De Gruyter Mouton.

Corsi, E. (2020). Insight into plurilingual and pluricultural identity in multigenerational homes: A case study of three young adults. Journal of Multilingual and Multicultural Development . https://doi.org/10.1080/01434632.2020.1817045

Costa Waetzold, J., & Melo-Pfeifer, S. (2020). How is the bilingual development of Portuguese heritage children perceived by their parents? Results from an ethnographic case study of a non-formal learning setting in Germany. International Journal of Bilingual Education and Bilingualism . https://doi.org/10.1080/13670050.2020.1731415

Curdt-Christiansen, X. L. (2016). Conflicting language ideologies and contradictory language practices in Singaporean multilingual families. Journal of Multilingual and Multicultural Development, 37 (7), 694–709.

Curdt-Christiansen, X. L., & Gao, X. (2021). Family language policy and planning in China: The changing langscape. Current Issues in Language Planning, 22 (4), 353–361. https://doi.org/10.1080/14664208.2020.1819049

Curdt-Christiansen, X. L., & La Morgia, F. (2018). Managing heritage language development: Opportunities and challenges for Chinese, Italian and Pakistani Urdu-speaking families in the UK. Multilingua, 37 (2), 177–200.

Eisenchlas, S. A., Schalley, A. C., Qi, G. Y., & Tsai, P. S. (2021). Home and away – Implications of short-term sojourning of young Australian bilinguals. Lingua, 263 , 102673. https://doi.org/10.1016/j.lingua.2019.02.007

Eriksson, S. (2015). Societal, community, family, and individual factors affecting Russian language maintenance in migrant families in Ireland. Russian Journal of Communication, 7 (2), 150–163. https://doi.org/10.1080/19409419.2015.1044868

Fogle, L. W., & King, K. A. (2013). Child agency and language policy in transnational families. Issues in Applied Linguistics, 19 , 1–25.

Fuentes, R. (2020). Transnational Sri Lankan Sinhalese family language policy: Challenges and contradictions at play in two families in the US. Multilingua, 39 (4), 475–498.

Gharibi, K., & Seals, C. (2020). Heritage language policies of the Iranian diaspora in New Zealand. International Multilingual Research Journal, 14 (4), 287–303. https://doi.org/10.1080/19313152.2019.1653746

Gomes, J. A. (2019). Portuguese as a heritage language in Spain: Towards a specific didactics for non-formal contexts. Lengua y Migracion, 11 (2), 135–161.

Greenhalgh, T., & Peacock, R. (2005). Effectiveness and efficiency of search methods in systematic reviews of complex evidence: Audit of primary sources. BMJ, 331 (7524), 1064–1065.

Gu, M. Y., & Tong, H. K. (2020). Constructing classed linguistic practices across borders: Family language policy in South(east) Asian families in Hong Kong. Journal of Multilingual and Multicultural Development, 41 (7), 581–599. https://doi.org/10.1080/01434632.2019.1622708

Hakyoon, L., & Myoungeun, P. (2020). Korean transnational mothers’ bilingual support for their children - language ideologies and practices at home. Journal of the International Network for Korean Language and Culture, 17 (1), 309–348. https://doi.org/10.15652/ink.2020.17.1.309

Hatoss, A. (2023). Shifting ecologies of family language planning: Hungarian Australian families during COVID-19. Current Issues in Language Planning . https://doi.org/10.1080/14664208.2023.2205793

Hirsch, T., & Lee, J. S. (2018). Understanding the complexities of transnational family language policy. Journal of Multilingual and Multicultural Development, 39 (10), 882–894.

Hollebeke, I., Struys, E., & Agirdag, O. (2020). Can family language policy predict linguistic, socio-emotional and cognitive child and family outcomes? A systematic review. Journal of Multilingual and Multicultural Development, 45 , 1–32. https://doi.org/10.1080/01434632.2020.1858302

Hua, Z., & Wei, L. (2016). Transnational experience, aspiration and family language policy. Journal of Multilingual and Multicultural Development, 37 (7), 655–666. https://doi.org/10.1080/01434632.2015.1127928

Institute of Migration. (2021). World migration report 2020 . Retrieved from https://worldmigrationreport.iom.int/wmr-2020-interactive/

Ivanova, O., & Zabrodskaja, A. (2021). Family language policy in Russian-Estonian and Russian-Spanish multilingual settings. Russian Journal of Linguistics, 25 (4), 1047–1070. https://doi.org/10.22363/2687-0088-2021-25-4-1047-1070

Kang, H.-S. (2015). Korean families in America: Their family language policies and home-language maintenance. Bilingual Research Journal, 38 (3), 275–291.

Kaveh, Y. M., & Sandoval, J. (2020). “No! I’m going to school, I need to speak English!”: Who makes family language policies? Bilingual Research Journal, 43 (4), 362–383. https://doi.org/10.1080/15235882.2020.1825541

Kheirkhah, M., & Cekaite, A. (2018). Siblings as language socialization agents in bilingual families. International Multilingual Research Journal, 12 (4), 255–272. https://doi.org/10.1080/19313152.2016.1273738

Kim, A. Y., Lee, J. S., & Lee, W. (2015). Examining Korean American parent-child relationships through Bilingual language use. Journal of Family Communication, 15 (3), 269–287. https://doi.org/10.1080/15267431.2015.1044089

King, K. A., Fogle, L., & Logan-Terry, A. (2008). Family language policy. Language and Linguistics Compass, 2 (5), 907–922.

Kirsch, C. (2012). Ideologies, struggles and contradictions: An account of mothers raising their children bilingually in Luxembourgish and English in Great Britain. International Journal of Bilingual Education and Bilingualism, 15 (1), 95–112. https://doi.org/10.1080/13670050.2011.607229

Kirsch, C., & Gogonas, N. (2018). Transnational experiences, language competences and worldviews: Contrasting language policies in two recently migrated Greek families in Luxembourg. Multilingua: Journal of Cross-Cultural and Interlanguage Communication, 37 (2), 153–175. https://doi.org/10.1515/multi-2017-0017

Kopeliovich, S. (2010). Family language policy: A case study of a Russian-Hebrew bilingual family–toward a theoretical framework. Diaspora, Indigenous, and Minority Education, 4 (3), 162–178.

Kress, G. R. (2001). Multimodal discourse: The modes and media of contemporary communication . Oxford: Oxford University Press.

Lanza, E., & Gomes, R. L. (2020). Family language policy: Foundations, theoretical perspectives and critical approaches. Handbook of home language maintenance and development (pp. 153–173). New York: De Gruyter Mouton.

Lee, H., & Pang, M. E. (2021). Educational discourse and knowledge construction in family language policy: The case of a Korean transnational family. Journal of Multilingual and Multicultural Development . https://doi.org/10.1080/01434632.2021.1996581

Lee, J. S. (2006). Exploring the relationship between electronic literacy and heritage language maintenance . Honolulu: University of Hawaii National Foreign Language Resource Center.

Lexander, K. V. (2021). Polymedia and family multilingualism Linguistic repertoires and relationships in digitally mediated interaction. Pragmatics and Society, 12 (5), 782–804. https://doi.org/10.1075/ps.20052.lex

Li, G. (2015). Social class, culture, and asian children’s home and school literacy connection: The case for cultural reciprocity in early literacy education. Language, learning, and culture in early childhood: Home, school, and community contexts (pp. 103–122). New York: Routledge.

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P., Clarke, M., Devereaux, P., Kleijnen, J., & Moher, D. (2009). The PRISMA Statement for reporting reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ, 339 , b2700.

Little, S. (2019). ‘Is there an app for that?’ Exploring games and apps among heritage language families. Journal of Multilingual and Multicultural Development, 40 (3), 218–229.

Lohr, S. (2011). For Microsoft, Skype Opens Vast New Market in Telecom . Retrieved https://www.nytimes.com/2011/05/11/technology/11skype.html

Makarova, V., Terekhova, N., & Mousavi, A. (2019). Children’s language exposure and parental language attitudes in Russian-as-a-heritage-language acquisition by bilingual and multilingual children in Canada. International Journal of Bilingualism, 23 (2), 457–485. https://doi.org/10.1177/1367006917740058

Mori, Y., & Calder, T. M. (2017). The role of parental support and family variables in L1 and L2 vocabulary development of Japanese heritage language students in the United States. Foreign Language Annals, 50 (4), 754–775. https://doi.org/10.1111/flan.12304

Nakamura, J. (2019). Parents’ impact belief in raising bilingual and biliterate Children in Japan. Psychology of Language and Communication, 23 (1), 137–161. https://doi.org/10.2478/plc-2019-0007

Navarro, D., & Macalister, J. (2016). Adrift in an anglophone world: Refugee families’ language policy challenges. Family language policies in a multilingual world: Opportunities, challenges, and consequences (pp. 115–132). New York: Routledge.

Oyewumi, O. (2002). Conceptualising gender: Eurocentric foundations of feminist concepts and the challenge of African epistemologies. Jenda, 2 , 1530–5686.

Palviainen, Å., & Kędra, J. (2020). What’s in the family app?: Making sense of digitally mediated communication within multilingual families. Journal of Multilingual Theories and Practices, 1 (1), 89–111.

Pauwels, A. (2016). Identifying and understanding trends and patterns in the dynamics of language maintenance and shift. language maintenance and shift (pp. 81–82). Cambridge: Cambridge University Press.

Ren, L., & Hu, G. (2013). Prolepsis, syncretism, and synergy in early language and literacy practices: A case study of family language policy in Singapore. Language Policy, 12 (1), 63–82. https://doi.org/10.1007/s10993-012-9260-9

Revis, M. (2021). Exploring the ‘languaging habitus’ of a diasporic community: Colombians in New Zealand [Article]. Lingua, 263 , 102941. https://doi.org/10.1016/j.lingua.2020.102941

Robertson, Z., Wilding, R., & Gifford, S. (2016). Mediating the family imaginary: Young people negotiating absence in transnational refugee families. Global Networks, 16 (2), 219–236.

Robinson, J. P., Rivers, W. P., & Brecht, R. D. (2006). Demographic and sociopolitical predictors of␣ American attitudes towards foreign language policy. Language Policy, 5 (4), 421–442.

Roser, M., Ritchie, H., & Ortiz-Ospina, E. (2022). Internet . Retrieved from https://ourworldindata.org/internet

Schwartz, M. (2008). Exploring the relationship between family language policy and heritage language knowledge among second generation Russian-Jewish immigrants in Israel. Journal of Multilingual and Multicultural Development, 29 (5), 400–418.

Shen, Q., Wang, L., & Gao, X. (2021). An ecological approach to family language policy research: the case of Miao families in China. Current Issues in Language Planning, 22 (4) 427–445. https://doi.org/10.1080/14664208.2020.1764730

Smith-Christmas, C. (2022). Using a ‘family language policy’lens to explore the dynamic and relational nature of child agency. Children & Society, 36 (3), 354–368.

Spolsky, B. (2004). Language policy . Cambridge University Press.

Taipale, S. (2019). What is a ‘digital family’? Intergenerational connections in digital families (pp. 11–24). Cham: Springer.

Tran, V. H., McLeod, S., Verdon, S., & Wang, C. (2021). Vietnamese-Australian parents: Factors associated with language use and attitudes towards home language maintenance. Journal of Multilingual and Multicultural Development . https://doi.org/10.1080/01434632.2021.1904963

Tseng, A. (2020). Identity in home-language maintenance. Handbook of home language maintenance and development (pp. 109–129). New York: De Gruyter Mouton Berlin.

Wan, Z., & Gao, X. (2021). ‘Home away from home’: understanding Chinese parents’ ideological beliefs and involvement in international school students’ language learning. Current Issues in Language Planning, 22 (5), 495–515. https://doi.org/10.1080/14664208.2021.1882767

Wiley, T. G., & Wright, W. E. (2004). Against the undertow: Language-minority education policy and politics in the “age of accountability.” Educational Policy, 18 (1), 142–168.

Wilson, S. (2020). Family language policy through the eyes of bilingual children: The case of French heritage speakers in the UK. Journal of Multilingual and Multicultural Development, 41 (2), 121–139.

Worstall, T. (2014). Skype is now 40% of the entire international telephone market . Retrieved from http://www.anorak.co.uk/383464/money/skype-is-now-40-of-the-entire-international-telephone-market.html

Xiaomei, W. (2017). Family language policy by Hakkas in Balik Pulau, Penang. International Journal of the Sociology of Language, 2017 (244), 87–118. https://doi.org/10.1515/ijsl-2016-0058

Yeoh, B. S. (2009). Making sense of ‘Asian’families in the age of migration. Asian Population Studies, 5 (1), 1–3.

Yu, B., & Hsia, S. (2019). Inclusion of heritage language learners on the autism spectrum: Lessons from second-generation parents. International Journal of Applied Linguistics (united Kingdom), 29 (3), 356–369. https://doi.org/10.1111/ijal.12233

Zbenovich, C., & Lerner, J. (2013). Vospitanie–eto rabotaintercultural encounters in educational communication within Russian-speaking families in Israel. Russian Journal of Communication, 5 (2), 119–140. https://doi.org/10.1080/19409419.2013.805668

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Bose, P., Gao, X., Starfield, S. et al. Conceptualisation of family and language practice in family language policy research on migrants: a systematic review. Lang Policy 22 , 343–365 (2023). https://doi.org/10.1007/s10993-023-09661-8

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The Impact of Family Environment on Language Development of Children With Cochlear Implants: A Systematic Review and Meta-Analysis

Holzinger, Daniel 1–3 ; Dall, Magdalena 1,2 ; Sanduvete-Chaves, Susana 4 ; Saldaña, David 5 ; Chacón-Moscoso, Salvador 4,6 ; Fellinger, Johannes 1,2,7

1 Konventhospital Barmherzige Brüder, Institut für Sinnes- und Sprachneurologie, Linz, Austria

2 Research Institute for Developmental Medicine, Johannes Kepler University Linz, Linz, Austria

3 University of Graz, Institute of Linguistics, Graz, Austria

4 Departamento de Psicología Experimental, Universidad de Sevilla, Seville, Spain

5 Laboratorio de Diversidad, Cognición y Lenguaje Departamento de Psicología Evolutiva y de la Educación, Universidad de Sevilla, Seville, Spain

6 Departamento de Psicología, Universidad Autónoma de Chile, Santiago, Chile

7 Division of Social Psychiatry, Medical University of Vienna, Vienna, Austria.

Received February 27, 2019; accepted December 28, 2019.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and text of this article on the journal’s Web site ( www.ear-hearing.com ).

This research was partly funded by MED-EL Elektromedizinische Geräte GmbH Innsbruck Austria and, in addition, by the Research Projects 1150096 and 1190945. FONDECYT. CONYCIT. Chile, COST IS1406, US-1263096, Junta de Andalucía, and PSI-2015-65656-P of the Spanish Ministry of Economy and Competitiveness. The research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Product brands were not included in this review.

D.H., M.D., D.S., and J.F. decided together on all variables important for the data extraction. D.H., M.D., and J.F. performed the literature search and data extraction. D.H. and M.D. performed the quality assessment of all studies included. S.S.-C. and S.C.-M. conducted statistical analysis and critical revision. All authors discussed the results and implications and were involved in writing the manuscript at all stages.

Systematic review registration number PROSPERO 2017 CRD42017060568.

The authors have no conflicts of interest to disclose.

Address for correspondence: Daniel Holzinger, Konventhospital Barmherzige Brüder, Seilerstätte 2, 4020 Linz, Austria. E-mail: [email protected]

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Objectives: 

The authors conducted a systematic review of the literature and meta-analyses to assess the influence of family environment on language development in children with cochlear implants.

Design: 

The Pubmed, excerpta medica dataBASE (EMBASE), Education Research Information Center, cumulative index to nursing and allied health literature (CINAHL), Healthcare Literature Information Network, PubPsych, and Social SciSearch databases were searched. The search strategy included terms describing family environment, child characteristics, and language development. Studies were included that (a) assessed distal family variables (such as parental income level, parental education, family size, and parental stress) with child language outcomes, and/or more proximal correlates that directly affect the child (such as family engagement and participation in intervention, parenting style, and more specifically, the quantity and quality of parental linguistic input) on child language; (b) included children implanted before the age of 5 years; (c) measured child language before the age of 21 years with standardized instruments; (d) were published between 1995 and February 2018; and (e) were published as peer-reviewed articles. The methodological quality was assessed with an adaptation of a previously validated checklist. Meta-analyses were conducted assuming a random-effects model.

Results: 

A total of 22 study populations reported in 27 publications were included. Methodological quality was highly variable. Ten studies had a longitudinal design. Three meta-analyses on the correlations between family variables and child language development could be performed. A strong effect of the quality and quantity of parental linguistic input in the first 4½ years postimplantation on the child’s language was found, r = 0.564, p ≤ 0.001, 95% confidence interval (CI) = 0.449 to 0.660, accounting for 31.7% of the variance in child language outcomes. Results demonstrated high homogeneity, Q (3) = 1.823, p = 0.61, I 2 = 0. Higher-level facilitative language techniques, such as parental expansions of the child’s utterances or the use of open-ended questions, predicted child language skills. Risk of publication bias was not detected. The results on the impact of family involvement/participation in intervention on child language development were more heterogeneous. The meta-analysis included mainly cross-sectional studies and identified low to moderate benefits, r = 0.380, p ≤ 0.052, 95% CI = −0.004 to 0.667, that almost attained significance level. Socioeconomic status, mainly operationalized by parental level of education, showed a positive correlation with child language development in most studies. The meta-analysis confirmed an overall low and nonsignificant average correlation coefficient, r = 0.117, p = 0.262, 95% CI = −0.087 to 0.312. A limitation of the study was the lack of some potentially relevant variables, such as multilingualism or family screen time.

Conclusions: 

These data support the hypothesis that parental linguistic input during the first years after cochlear implantation strongly predicts later child language outcomes. Effects of parental involvement in intervention and parental education are comparatively weaker and more heterogeneous. These findings underscore the need for early-intervention programs for children with cochlear implants focusing on providing support to parents for them to increase their children’s exposure to high-quality conversation.

INTRODUCTION

Cochlear implants (CIs) have significantly improved speech and language development of children with profound hearing loss. However, on average, children with CIs are delayed in spoken language development compared with children with normal hearing ( Geers et al. 2009 ; Niparko et al. 2010 ; Lund 2016 ; Yoshinaga-Itano et al. 2018 ). Research on predictors of language development in children with CIs has identified many child characteristics (such as nonverbal intelligence, residual hearing, sex, additional disabilities, or residual hearing preimplantation), implant-related variables (such as age at implantation, duration of implant use, bilateral versus unilateral implantation, implant technology, or surgical factors), and intervention characteristics (such as communication mode or school setting), but leaves a high proportion of unexplained variance ( Geers et al. 2007 ; Wu et al. 2011 ; Geers & Sedey 2011 ; Pisoni et al. 2017 ).

In research on typical child development, effects of the family on child language have been demonstrated extensively and consistently ( Hoff 2006 ; Rowe 2012 ). Besides more distal variables such as socioeconomic status (SES) ( Whitehurst 1978 ; Rowe et al. 2005 ), measures of variables representing the proximal environment have been shown to add significantly to the prediction of child language development. These include the style of parenting, for example, parental sensitivity and positive regard, and parental language, such as language input quantity and quality ( Hart & Risley 1995 ), and home literacy environment. More recent findings demonstrate the role of early developing neural mechanisms that underlie the relationship between children’s language exposure and their language development. As an example, Garcia-Sierra et al. (2016) found a specific impact of the amount of child-directed language on young children’s brain functioning by showing significant correlations between higher quantity of language input and speech perception at 11 to 14 months of age as measured by event-related potentials, thus demonstrating that early neural reorganization is dependent on input (also referred to as neural commitment to language). Beyond that, Romeo et al. (2018) found significant correlations between the amount of early adult–child conversation and children’s brain structure, specifically the strength of connectivity in the left hemisphere dorsal white matter language tracts. In both studies, children’s real-world language exposure had been assessed with the Language Environment Analysis System ( Gilkerson et al. 2017 ).

Research on the role of specific family characteristics and behaviors in child language development is further justified by converging evidence on the effectiveness of parent-implemented language interventions for different populations, such as children with primary and secondary language impairments (meta-analysis by Roberts & Kaiser 2011 ) or children with low SES ( Hoff 2006 ; Hirsh-Pasek et al. 2015 ). As a consequence of the high and partly unexplained variance of language outcomes in children with CIs on the one hand, and the well-documented effects of the family variables in populations with normal hearing on the other, the role of family environment has gained interest in more recent studies ( Niparko et al. 2010 ; Holt et al. 2013 ; Geers et al. 2017 ). But, so far, no systematic review and meta-analysis of the effects of family environment on language development in children with CIs have been carried out.

The aims of the current review were to assess the evidence showing an impact of family environment on child language development in children with early cochlear implantation by conducting a systematic narrative review. In addition, meta-analyses on the correlations between family environment and language outcomes were contemplated, dependent on a sufficient number of studies with sufficient data.

To reduce the number of confounding auditory variables, the review was restricted to children with CIs (versus children with hearing aids or other technologies). Only studies referring specifically to spoken language outcomes (versus written or signed language development) were included.

Based on research results for typical language development, we included distal variables (such as parental SES, parental education, and family size), as well as more proximal factors, such as parenting style (e.g., parental sensitivity, emotional availability, and provision of control and structure), parental engagement in intervention, and the amount and kind of parental language input. Distal family variables may be less modifiable clinically than proximal variables, the latter of which may be more susceptible to the influences of interventions. Proximal family variables were expected to have a stronger influence on child language than distal family variables.

MATERIALS AND METHODS

A systematic review of the literature was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) ( Moher et al. 2009 ; see Supplemental Digital Content 1, https://links.lww.com/EANDH/A623 ) and the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) ( Stroup et al. 2000 ) checklists, and registered on the international PROSPEctive Register Of systematic reviews (PROSPERO) ( Holzinger et al. 2017 ). Before commencing the review, the authors specified, with internal protocols, the search strategy, selection criteria, procedures for data extraction, and a catalog of criteria for quality assessment. At the time of registration, the use of meta-analyses was considered subject to the availability of comparable data on family environment and child language outcomes. Evidence for policy and practice information reviewer 4 software was used for data handling.

Search Strategy

The following electronic databases were searched: Pubmed, EMBASE, Education Research Information Center, CINAHL, Healthcare Literature Information Network, PubPsych, and Social SciSearch. The search strategy included terms describing the child, language development, and family environment. The search string was adapted to each database in combination with database-specific filters and search terms (for the search string adapted to Pubmed, see Supplemental Digital Content 2, https://links.lww.com/EANDH/A643 ). The last update of the search results was on February 6, 2018. Articles published in non–peer-reviewed journals or unpublished literature were not included in this review. The reference lists of the papers that met the inclusion criteria after title-and-abstract screening (see later) were also searched for potentially eligible articles. This procedure was replicated until no more studies of interest were found.

Selection Criteria

The same criteria were used for the title-and-abstract and full-text screening phase. Research in English, German, or Spanish language and published after the year 1995 was included (to ensure that single-channel technology was excluded). The selection criteria were defined in line with Population, Intervention, Comparator, Outcome, Study Design ( Schardt et al. 2007 ). The participants were children with hearing loss who received their cochlear implantation before the age of 5 years. Studies that included children implanted at an older age or children supplied with hearing aids were included only if they reported separate data on the population of interest. Family characteristics had to be mentioned in the abstract. The outcome had to be measured before the age of 21 years. Both prospective and retrospective studies were included in the review. Systematic reviews were excluded. Unspecified information in the abstract on any of the criteria was not a reason for exclusion at the title-and-abstract phase. In those cases, the full text was screened before deciding if it was included or not.

Study Selection

The first 202 abstracts (20% of the total obtained) were reviewed independently by 2 of the authors with high level of expertise in the field of pediatric hearing loss (D.H. and M.D.). Any disagreements were resolved by consensus. Afterward, the same 2 authors independently coded another 100 abstracts and reached an inter-rater reliability of over 90%. Finally, M.D. reviewed the rest of the abstracts.

For the publications that met the inclusion criteria in title-and-abstract, the full texts were retrieved and valued by D.H. In this case, it was required that all information included in the criteria was present in the text.

Most of the studies included after the full-text screening did not contain sufficient data for a meta-analysis. The authors of these primary studies were contacted for additional information via e-mail.

Description of the Variables

The variables used for data extraction were agreed on by D.H., M.D., and D.S. Information was extracted on the following: study characteristics, language outcomes (dependent variables), family characteristics (considered independent variables [IV], and potential moderator variables that can influence the relationship between other family characteristics and language outcomes), and confounders (other potential moderator variables). A full list of variables included within each group can be found in the Supplemental Digital Content 3, https://links.lww.com/EANDH/A644 .

Language variables extracted from the literature exclusively referred to spoken language (rather than signed or written), expressive or receptive communication, and the dimensions of speech sounds (phonology and speech perception), vocabulary (lexicon), or grammar (morpho-syntax). In addition, social (pragmatic) communication, including narrative skills, was considered.

Family variables included general sociodemographic characteristics, such as parental SES (parental income level, neighborhood index, parental education level), family size, and family-system characteristics (e.g., relationships within the family, parenting style, and parental stress). Other family variables referred to the families’ involvement in intervention and their self-efficacy. Finally, family behaviors assumed to be more directly related to child language outcomes were considered, such as parenting style (e.g., sensitivity, emotional availability, warmth, and regard), and parental language input quality and quantity. Although quantity and quality measures are strongly associated with one another ( Hoff & Naigles 2002 ), research suggests that both the amount and type of caregiver–child interaction play a significant role in language development and were considered distinct concepts in the systematic review. In a number of studies ( Hirsh-Pasek et al. 2015 ; Gilkerson et al. 2018 ; Romeo et al. 2018 ), quality variables such as vocabulary sophistication and diversity, grammatical complexity, the use of facilitative language strategies, and interactional features (contiguous and contingent back-and-forth conversation) were shown to be stronger predictors of later language ability than the caregivers’ total number of words or utterances during interaction. In a study by Rowe (2012) , quality emerged as a distinct source of variability in language performances even after controlling for quantity of language input. Adult reading time, frequency of dialogic book reading, and parental teaching or tutoring literacy skills (e.g., the alphabet, phoneme awareness, reading of words) were categorized as home literacy environment ( Sénéchal & LeFevre 2014 ). Parental communication with the child who is deaf or hard-of-hearing was usually assessed with time-consuming transcriptions and ratings of videotaped interactions, whereas other family environment variables were collected by the use of standardized or nonstandardized questionnaires from parents or practitioners.

Children’s sex, intelligence quotient (I.Q.), chronological age, age at implantation, age at hearing loss, residual hearing, and unilateral/bilateral hearing loss were included as potential confounders.

Methodological Quality

The methodological quality of each study was assessed with a selection and adaptation of items relevant to our research question from a tool proposed by Chacón-Moscoso et al. (2016) . Methodological quality assessment was based on (1) adequate inclusion and exclusion criteria for the participants, (2) the study design, (3) proportion and inclusion or not of attrition rates, (4) occasion of measurement, more than one measurement occasion (concurrent and post intervention) or only one measurement occasion (concurrent or postintervention only), (5) for longitudinal studies, whether the outcome measurements at time 1 also appeared at time 2, (6) the use of standardized instruments for the measurement of language outcomes (dependent variables), (7) family input and environment (IV), (8) use of control techniques such as double blinding (language and family variables assessed by different evaluators), (9) replicability of the construct definition of outcome, (10) family variables, (11) proportion of participants contacted who actually responded, (12) participant representativeness, (13) imputation of missing data, and (14) perspective (prospective or retrospective) (see Supplemental Digital Content 4, https://links.lww.com/EANDH/A645 ). The degree of methodological quality was considered an indicator of the level of credibility of the final results.

Data Extraction

Two coders, D.H. and M.D., piloted the data extraction on 5 full texts and discussed the disagreements with the arbitration of DS, a third researcher with high level of expertise in the area of child development. Afterward, data extraction was performed by D.H. and M.D independently, double-coding all papers.

A meta-analysis was performed on the data from the papers which, after following up with the authors, provided enough data. For the meta-analysis, only one language outcome variable was produced. Because there are high correlations between lexical, grammatical, and global language, as well as between receptive and expressive language development in children with hearing loss ( Geers et al. 2009 ; Holzinger et al. 2011 ; Lund 2016 ), all those variables were regarded as representations of a common language category. When a study included several language measures, only one was selected, according to the following sequential criteria: first, global over specific language measures were preferred; second, expressive over receptive language measures; and third, language measures available for the largest sample were chosen.

Intercoder reliability was calculated using Cohen’s k coefficient. Disagreements were solved through consensus.

Statistical Methods

Calculations were made with the Comprehensive Meta-Analysis v. 3 software ( Borenstein et al. 2013 ). The individual and average effect sizes were calculated as Pearson’s correlation coefficients. Three average effect sizes were obtained separately, depending on the IV measured: (a) parental linguistic input, (b) family involvement, and (c) parental SES. Two coauthors (S.S.-C. and S.C.-M.) calculated the individual and average effect sizes independently, obtaining an intercoder reliability of 0.992, using the intraclass correlation coefficient.

For each average effect size, a random-effects model was assumed. This type of model is recommended in this case, given the representativeness of the studies found as a result of the exhaustive systematic review carried out, and the diversity of such studies in characteristics of the samples, the scenarios where they were conducted, and the results obtained ( Borenstein et al. 2009 ). Individual effect sizes were converted to the Fisher’s z scale, with its statistical significance and confidence interval. Then, the summary of Fisher’s z was calculated. Finally, this result was transformed into an r scale ( Borenstein et al. 2009 ), with values around 0.1 considered low effect sizes, around 0.25, medium, and around 0.4, high ( Cohen 1988 ). Confidence intervals and statistical significance ( p ) are also reported. A p < 0.05 was interpreted as a statistically significant average effect size.

Heterogeneity was calculated with the Q statistic, where p < 0.05 would imply a possible statistically significant heterogeneity between effect sizes. In addition, given that Q is sensitive to the number of studies included, I 2 was also calculated. Values around 25% were interpreted as a low heterogeneity; around 50%, medium heterogeneity; and around 75%, high.

When heterogeneity was found, all the potential moderator variables that presented enough information to carry out the analyses were studied assuming a mixed-effects model. Meta-regression was used, given that all the moderated variables with available information to be included were quantitative. Z values with associated p values were obtained. A statistically significant influence of the moderator variable over the effect size was detected when p < 0.05.

Finally, publication bias was analyzed using Duval and Tweedie’s Trim and Fill ( Borenstein et al. 2009 ). When the observed point estimate (represented by an open diamond) is close to the imputed point estimate (shown as a filled diamond), we interpreted that there was no risk of publication bias. When we found differences between the observed and the imputed point estimate, we calculated Egger regression test for bias ( Borenstein et al. 2009 ). A risk of bias was considered significant when p values were below 0.05.

In total, 1012 individual publications were identified and included into the first of 3 selection steps. After screening on title-and-abstract, 172 publications remained and the full texts were retrieved. The screening of full texts excluded 145 publications that either did not meet the inclusion criteria or referred to the same study sample without reporting on an additional family variable, which led to 27 studies remaining to be included in the review. These 27 studies refer to 22 different study samples ( Fig. 1 and Supplemental Digital Content 5, https://links.lww.com/EANDH/A646 ).

Specifically, the same study samples in full or partly were Cruz et al. (2013) and Quittner et al. (2013) . The studies Geers et al. (2003 , 2011 ) were looking at the longitudinal effects of the same study population. The studies Holt and Svirsky (2008) and Holt et al. (2013) investigated subpopulations from a larger longitudinal study. The study from Szagun and Schramm (2016) included the study sample from Szagun and Stumper (2012) . The studies Sarant and Garrard (2014) and Sarant et al. (2014) were most likely sharing at least some of the study participants.

The results for intercoder reliability for each item of the quality assessment are presented in Supplemental Digital Content 6, https://links.lww.com/EANDH/A647 . Seven of 14 items had a very good kappa (above 0.8). The other seven items had a substantial kappa (values between 0.6 and 0.8).

The quality assessment for the 27 publications can be found in the Supplemental Digital Content 7, https://links.lww.com/EANDH/A648 . In total, 16 (59.3%) studies gave clear inclusion/exclusion criteria. Only 10 (37%) studies had a longitudinal study design. In 23 (85.2%) studies, attrition was mentioned. Eight (29.6%) studies measured the outcome variable and the family variable concurrently and had post measurement of the family variable. Eight (29.6%) studies collected all outcome and family measures at every time point. All studies (100%) used standardized measurements for language outcome measures. Twenty-three (85.2%) studies used at least one standardized measurement for the family variable. In none of the studies (0%), control techniques were reported. The construct definitions for the language outcome variables as well as family variables were described in all studies (100%), although in 2 of them, the definition of the family variables was vague. Within this review, only 11 (40.7%) studies mentioned a responder rate. Eight (29.6%) studies mentioned representativeness of the samples. In those that did, the sample was highly representative in 6 and low in 2. From the 23 studies that presented attrition, only 2 (8.7%) imputed missing data statistically. Twenty-one publications had a prospective design (77.8%). Inclusion of a study in a meta-analysis was not determined by its methodological quality but exclusively by the availability of correlational data between the family and child language variables of interest.

Study Characteristics

Table 1 provides an overview of the most relevant study characteristics. In addition, the number of studies with sufficient information available to be included in one of the meta-analyses is reported. The availability of data is reported separately for the three meta-analyses. Finally, the number of the most recent studies, published since 2015, is shown.

Thirty-five percent of all studies were conducted in North America; there is a complete lack of literature from African and Asian countries in the final selection of studies, except for China. Therefore, the available data mostly pertain to high-income countries. Of the studies reporting family SES, there is a tendency to disproportionally include families with high SES. As expected, more distal family characteristics such as family education level are reported in many publications (22), although there is also a significant number including family involvement (8) or parental linguistic input (9). Other family variables such as parenting style, family values, relationships, or parental stress are investigated rather rarely. For the language measures, global language variables measured by comprehensive language tests as well as measures of expressive vocabulary are used most often. There is a dearth of studies with a focus on social language use or narrative language skills. Age at implantation is the most commonly reported confounding variable. Surprisingly, other variables known to be significantly associated with language development, such as child sex or I.Q., are only reported in about one-third of the studies.

Narrative Syntheses and Meta-Analyses

The studies investigating child speech and social communication skills and family variables could not be included in any meta-analyses due to insufficient correlational data. Based on the availability of correlations with same/similar dependent and IV, three meta-analyses for the influence of different family characteristics on child language were conducted. The presentation of results begins with the more proximal variables. For each of the three family variables, a narrative description of the studies not included in the meta-analysis is presented first and followed by the results of the meta-analysis (of only studies with sufficient data).

Parental Linguistic Input

A direct and significant correlation between parental language input and child language development was anticipated. Seven studies reported relationships between child-directed parental language and child language outcomes; for 4 of those studies, data were available to perform a meta-analysis. All of the studies that could be included in a narrative review or meta-analysis referred to quality rather than quantity of parental language.

With the exception of a single-case study ( Szagun 1997 ), the studies only included in the narrative review were quite recent (2012 to 2013) and related to children implanted at a young age (range = 1.25 to 1.7 years, total n = 66) ( Table 2 ). The German study with the smallest sample size ( Szagun & Stumper 2012 ) and the case study ( Szagun 1997 ) were the only longitudinal ones. Szagun & Stumper (2012) found significant correlations between the structural complexity of maternal language, that is, mean length of utterance (MLU) and the frequency of maternal expansions (reacting to a child by adding linguistic information to his/her utterance) 12 months postimplantation, and the child’s MLU at 24 and 30 months postimplantation. The correlations demonstrated a significant moderate effect of quality aspects of maternal language, even after partialling out age at implantation and the child’s MLU at 12 months, which indicates a specific causal influence. Ceh’s et al. (2013) findings indicated positive effects of the use of open-ended questions during book reading encouraging the child’s more active participation in linguistic interaction. In Szagun’s (1997) case study, longitudinal data of mother–child interactions demonstrate substantial differences in language development between the 2 cases as well as in the language of their mothers (speech and pragmatic functions). However, an influence of maternal language on the children’s linguistic development cannot be inferred, because the aspects of parental language expected to be related to child language variables did not chronologically precede these.

All 4 studies included in the meta-analysis had a longitudinal design ( Table 3 ). Two of them were conducted in the United States, the other 2 in Germany. Even though the size of the total number of participants was limited (n = 176), the data were considered as valuable being based on time-consuming transcriptions of videos of parent–child interactions. All the family measures pertained to facilitative language techniques (FLT), that is, qualitative language input. In 2 of the studies ( DesJardin et al. 2009 ; Cruz et al. 2013 ), parental higher-level FLTs significantly predicted growth in children’s expressive language. Higher-level FLTs included parental reactions to a child’s linguistic utterance with expansions or recasts (restating the child’s utterance in a question format), reactions to their child’s current interests by describing and commenting on them, and the use of open questions encouraging the child’s use of more complex language. Szagun & Schramm’s (2016) study related to the use of parental expansions. Rüter (2011) , in addition, referred to their grammatical complexity. Three of the studies measured parental language use with their children at 6 to 24 months postimplantation; DesJardin et al. (2009) , at 53 months postimplantation. Child language outcomes were assessed 1 to 3 years later.

The meta-analysis demonstrated a strong effect ( r = 0.564; p ≤ 0.001; 95% CI = 0.449 to 0.660), explaining a variance of 31.7% of the children’s global expressive language development, expressive grammar, or vocabulary size ( Fig. 2 ). The results demonstrated high homogeneity among effect sizes, Q (3) = 1.823, p = 0.61, I 2 = 0. A study of moderator variables was thus not considered necessary.

The funnel plot showed no apparent risk of publication bias ( Fig. 3 ).

Family Involvement

The relationship between family involvement and child language development was investigated in a total of 6 studies.

Due to insufficient data, 2 of the studies could not be included in the meta-analysis ( Table 4 ). One of them was cross-sectional and was conducted in Belgium ( Boons et al. 2012 ). Another was a longitudinal Australian study ( Yanbay et al. 2014 ). Both studies demonstrated statistically significant positive correlations between family involvement and at least some of the language measures. However, for both studies, there were serious methodological constraints. Boons et al. (2012) used a nonvalidated binary scale to classify parental motivation and ability to fulfill their commitments in rehabilitation from information in the child’s file. In Yanbay et al.’s study (2014), Moeller’s Family Participation Rating Scale was filled in by educators/therapists working directly with the families and thus not blinded for the children’s language development. Moeller’s concept of family involvement includes parental adjustment to the child’s hearing impairment, regular attendance and active participation in sessions, and advocating for the child. In addition, qualitative aspects of parental linguistic input (like those reported earlier) were included in the concept of family involvement; that is, the ability of families to become effective conversation partners with their children, the way they function as language models, the use of FLT, and their facility in the child’s communication mode.

The meta-analysis included 4 studies referring to a total sample of 335 children in 4 different countries ( Table 5 ). The study with the smallest sample size ( Moreno-Torres et al. 2016 ) was the only longitudinal one. Most studies (except Geers et al. 2003 ) had been published recently and included children who, on average, had been implanted in their second year of life. Again, there were severe methodological limitations related to the construct validity of family involvement and the measures used in all the studies, which limited the validity of the results. Boons et al. (2013) asked audiologists or speech-language therapists to complete a self-constructed nonvalidated questionnaire with seven mostly indirect indicators of parental involvement (such as parental knowledge of their child’s abilities, understanding how the CI works, attending appointments, or contacting professionals for help). The nonvalidated questionnaire used by Geers et al. (2003) asked parents to report the frequency with which they participated in activities to stimulate auditory and speech development in the home. The 2 remaining studies used Moeller’s scale.

Three of the studies found positive weak to strong correlations between parental involvement and mainly expressive language development, whereas in Geers et al. (2003) , a nonsignificant negative relationship was reported. Overall, a high-moderate mean correlation ( r = 0.380, p = 0.052, 95% CI = −0.004 to 0.667), which almost reached significance was found ( Fig. 4 ).

We found there was heterogeneity between the different effect sizes, Q (3) = 30.639, p < 0.001, I 2 = 90.208. However, the available moderator variables, that is, sex ( z = −1.01, p = 0.313, 95% CI = −0.0984 to 0.0315) and age at implantation ( z = 1.39, p = 0.164, 95% CI = −0.4306 to 2.5372) were found to be nonsignificant confounders.

Based on Figure 5 , a certain degree of publication bias could be interpreted. However, the nonsignificant results in Egger regression test for bias, t (2) = 2.7588, p = 0.11, 95% CI = −3.4639 to 15.8440, indicated the risk of bias was not significant.

Parental SES

SES was conceptualized as the social standing or class of an individual or group, often measured as a combination of education, income, and occupation ( American Psychological Association, Task Force on Socioeconomic Status 2007 ). Parental SES was commonly reported in the publications (22 publications, 16 study samples). From an overall of 16 studies, SES was measured using the maternal/main carer’s or family’s highest education (n = 12) and/or an income/neighborhood index (n = 6).

Eight studies could not be included in the meta-analysis. Five of these studies reported statistically significant but rather weak positive correlations between SES and child language, specifically with expressive/receptive vocabulary or global language (see Supplemental Digital Content 8, https://links.lww.com/EANDH/A649 ).

Eight studies pertaining to a total of 512 children and with good geographical variation could be included in a meta-analysis on the relationship between parental education level and child language (see Supplemental Digital Content 9, https://links.lww.com/EANDH/A650 ). All except for one study (looking at parental income) looked at parental/maternal education level. The meta-analysis resulted in an overall low- and nonsignificant average correlation coefficient, r = 0.117, p = 0.262, 95% CI = −0.087 to 0.312, so there was no statistical evidence of the relationship between parental SES and the language development of the child ( Fig. 6 ).

There was a high level of heterogeneity between studies, Q (7) = 28,598, p < 0.001. I 2 = 75.523. The I.Q. of the child was a positive significant moderator variable, z = 3.2080, p = 0.001, 95% CI = 1.0069 to 4.1693. Sex and age at implantation were nonsignificant moderators, z = 0.0569, p = 0.955, 95% CI = −1.7194 to 1.8222 and z = −0.1861, p = 0.852, 95% CI = −0.5170 to 0.4273, respectively.

The funnel plot did not present indications of publication bias ( Fig. 7 ).

Other Family Variables

In addition to the three family variables reported earlier, some additional aspects of family environments deserve mentioning, even though meta-analyses could not be performed due to a lack of data ( Table 1 ).

Two studies ( Ceh et al. 2013 ; Sarant et al. 2014 ) reported significant benefits of the frequency of the child’s exposure to books in the home (home literacy environment) for his/her global language development. In Sarant’s et al. (2014) study, time spent reading books even predicted oral language development more strongly than an extra 10 I.Q. points.

Another 2 studies ( Geers et al. 2003 , 2011 ) provided information on the relationship between family size and child speech and language development. Both of them showed statistically significant negative correlations, demonstrating possible advantages of smaller families for speech-language acquisition of a child with hearing loss.

Parenting style was found to be significantly related to child language development in a number of studies. Quittner et al. (2013) reported positive effects of maternal sensitivity and cognitive stimulation on the growth of oral language. In the study of Ketelaar et al. (2017) , a negative and uninvolved parenting style was found to be negatively correlated with child language development. Holt et al. (2013) demonstrated statistically significant correlations indicating that lower family self-reported levels of control, implying less rule emphasis and less obvious hierarchy of power, and higher levels of organization (more planning, clearer expectations, and neatness without the control power) related to larger receptive vocabularies in their children.

Lower levels of parental stress were significantly correlated with children’s speech-language development in one study ( Sarant & Garrard 2014 ).

To our knowledge, this study is the first systematic review of the literature on the effects of family environment on language outcomes in children with CIs. The review was performed to decrease the unexplained variability in language outcomes in children with CIs by investigating the role of family variables. Twenty-seven studies that contained information on family variables as related to child language development were identified. Available data permitted the conduction of 3 meta-analyses on the influence of parental linguistic input (4 studies; DesJardin et al. 2009 ; Rüter 2011 ; Cruz et al. 2013 ; Szagun & Schramm 2016 ), family involvement (4 studies; Geers et al. 2003 ; Boons et al. 2013 ; Sarant & Garrard 2014 ; Moreno-Torres et al. 2016 ), and parental SES (8 studies; Geers et al. 2009 , 2011 ; Huber & Kipman 2012 ; Sarant & Garrard 2014 ; Cupples et al. 2016 ; Guerzoni et al. 2016 ; Moreno-Torres et al. 2016 ; Ketelaar et al. 2017 ) on child linguistic skills. Our findings demonstrated strong and homogenous effects ( r = 0.536) of the amount of high-quality early parental language input explaining variance of almost 32% of child language development after cochlear implantation. The use of parental expansions, such as reacting to the child’s utterance by “playing it back” to the child in a linguistically correct form and with some new information, proved to be a highly effective facilitative language strategy. Another one was the use of open-ended questions. A common feature of both strategies is that they stimulate the child’s active participation in linguistic interaction. Other characteristics of high-quality parental linguistic input were grammatical complexity (maternal MLU) and lexical diversity (type-token ratio). Frequency of a child’s exposure to books in the home (home literacy) was found to predict child language development in 2 studies included in the narrative systematic review.

However, despite strong correlations between parental linguistic input and child language development documented in the longitudinal studies, it could be argued that these correlations could also reflect the influence of child characteristics on parental behavior. For example, there is evidence from twin studies ( Dale et al. 2015 ) that, in addition to causal influences of parental language on child development, there are also child-to-parent effects. Children who were more talkative or advanced in language development elicit parental speech with more advanced language facilitating features, thereby creating their own language environment.

But the data from the primary studies in our review support the view that even considering the influence of child variables, parental input has an impact on language development. Two of the studies included in the meta-analysis ( Rüter 2011 ; Szagun & Schramm 2016 ) controlled for the influence of the child’s language level at baseline in their analysis of the correlation between early parental language input and later child language outcomes. Rüter (2011) demonstrated strong effects of parental expansions on different aspects of child expressive grammar outcomes ( r of 0.42 to 0.70) even after partialling out child language at baseline (and age at implantation). In Szagun and Schramm’s (2016) study, variance of child MLU at 24 months postimplantation explained by parental expansions at 11 to 12 months postimplantation ( R 2 = 0.048) was still 12% (and 15% for early child MLU) when child and parental variable were inserted simultaneously into a multiple-regression model. In another study ( Cruz et al. 2013 ), the authors conducted analyses to separate these unidirectional and bidirectional effects, examining whether parents’ use of higher-level FLT led to increases in child language, and simultaneously, whether children’s expressive or receptive language skills led to increases in parents’ use of higher-level facilitative language strategies. They found a bidirectional association between higher-level parental FLT and expressive language only within the first year of cochlear implantation. Parents of recently implanted children with more spoken language before implantation may have been reinforced in interactions with their child to use higher-level strategies. In contrast, for receptive language, there was only a unidirectional effect between the number of different word types used by parents and receptive language development. In summary, despite possible bidirectional influences of parental language and child language, the role of parents to promote linguistic interactions is supported by the data. Children learn through interactions with their parents. They also use their language skills to elicit and expand their interactions.

The results for children with CIs and their families are in line with what is known about predictors of language development in children with normal hearing. Many studies have substantiated empirical evidence for a strong connection between early rich language exposure and developmental outcomes ( Huttenlocher et al. 1991 ; Hart & Risley 1992 , 1995 ; Hoff 2003 ; Landry et al. 2006 ) in typical development that remained strong even after controlling for parental SES ( Rowe 2012 ; Weisleder & Fernald 2013 ). An outstanding recent study ( Gilkerson et al. 2018 ) demonstrated that the amount of turn-taking interactions with children with normal hearing 18 to 24 months old measured by use of Language Environment Analysis software accounted for 32% of the variance of verbal comprehension even about 10 years later. The prediction remained strong after adjustment for parental SES ( R 2 = 0.027) confirming the specific impact of parental talk and interaction on child language. Noteworthy, the prediction of child language by parent–child conversational turns was much stronger than by the number of adult child-centered words (quantity of parental language input). Our findings demonstrating the role of the frequency of exposure to books for child oral language development are in line with research on typical development, which shows an enhancement of language development by the informal literacy environment at home (National Early Literacy Panel (US) 2008; Sénéchal & LeFevre 2014 ).

Due to incomplete data, the specific influence of parental language in addition to family SES could not be analyzed as part of our meta-analysis. However, individual studies demonstrate the specific character of parental language as shown for typical development. In a model to predict child language including SES, initial child language and higher parental facilitative language strategies ( Cruz et al. 2013 ), SES did not significantly affect changes in child language over time.

Family involvement was shown to correlate moderately high with child language development, explaining 14% of variance and almost attaining significance. However, heterogeneity between the studies’ effect sizes was high. Heterogeneity was assumed to be a consequence of a missing unified construct and the use of nonvalidated instruments for measuring family involvement. Furthermore, the lack of data did not permit investigation of suspected moderator variables such as parenting style, which was included in some of the measures of family involvement. Therefore, the results on the role of family involvement must be interpreted with caution.

As hypothesized, the more distal variable of family SES, mainly operationalized by parental (mostly maternal) education, was shown to have weaker effects on child language development ( R 2 = 0.054) compared with specific parental behaviors related to children’s language exposure, such as parental linguistic input or involvement in intervention. The seminal study of Hart and Risley (1992 , 1995 ) demonstrated higher correlations between parental SES and child language in typical development. Lower correlations in populations of children with deafness or profound hearing loss might be due to limited variability of SES in the study sample and/or a consequence of a leveling effect of early-intervention programs supporting parents in their use of facilitative language strategies. As described earlier (compare Cruz et al. 2013 ), in studies including both SES and parental language input, SES played a minor or even statistically insignificant role for their child’s language trajectories, in accordance with the results obtained from the meta-analysis.

Implications for Intervention

The results speak strongly to the importance of high-quality parental child-centered language following cochlear implantation. Early-intervention programs should carefully respect the context of language learning that takes place in parent–child interactions, with responsive parents stimulating the child’s active participation in conversational exchanges. Children profit from opportunities for language-rich interactions. Early-intervention programs need to be truly family-centered, supporting parents in the frequent use of FLT in everyday situations. Irrespective of the economic status or the educational level of families, all of them could be a positive influence in their children's language development.

Implications for Further Research

Studies on outcomes in children with CIs including family environment as a critical factor for language development are still scarce. Based on the quality assessment of the 27 studies, implications for further research were identified. The use of standardized instruments to measure language outcome variables can be regarded as a strength in the field. Furthermore, almost all studies give clear descriptions of the measures used for dependent and IV, which permit replication. But there is still a great need for longitudinal multicenter studies that follow large study populations over a longer period of time. The merging of databases as well as open access databases will be of great importance to increase reliability of results with substantial sample sizes.

Many studies failed to mention the representativeness of their study population. In most cases, samples were not representative: they included a selection of families with higher SES, monolingual and majority culture background and children without additional disabilities. To draw conclusions for the total population of children with CIs, it would be important to include children from the whole variety of SES backgrounds, as well as multilingual families and children with special needs.

Regarding the family variables, there is a demand for valid standardized measures for constructs such as family involvement. Also, future studies need to implement control techniques such as measuring language outcomes and family variables independently.

Regarding the presentation of the results of the individual studies, almost all of them publish exclusively multiple-regression analyses, which could not be used for the calculation of meta-analyses. In line with current open science recommendations, there is still the need to provide access to the raw data or at least to full correlational data of the main variables and confounders.

Limitations

Precisely because the availability of correlational data was limited, the number of possible meta-analyses and the respective number of included studies was reduced. Some additional variables that could be of interest were beyond the scope of the review, but could be of future interest, such as the level of oral and signed family multilingualism, family screen-viewing time, and written and signed language outcomes.

ACKNOWLEDGMENTS

We thank all authors who provided additional data for their studies necessary for inclusion in the meta-analyses.

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Cochlear implantation; Child; Family; Family involvement; Hearing loss; Language; Pediatric; Meta-analysis; Parental education; Parental linguistic input; Socioeconomic status; Systematic review

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To Speak or Not To Speak My Language: Supporting Families’ Home Language Practices

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As a researcher who examines families’ language practices and their impact on children’s emerging bilingualism and multilingualism, I work with Latino/a families who often have questions about home language maintenance. Many of these families are recent immigrants. Recognizing the power of English in the United States, they wonder if they should continue to speak their home languages or focus on English. They also voice concerns about their children’s abilities to develop two (or more) languages at a young age. They worry children might get confused or delayed in their English language development.

Contrary to these concerns, research has shown that young children are adept at learning multiple languages. Moreover, maintaining children’s cultural and linguistic heritages leads to stronger identity development and long-term academic success. This, in turn, contributes to rich early learning communities that value families’ funds of knowledge. It is why NAEYC’s position statement on advancing equity recommends that early childhood educators communicate the value of multilingualism so that “families of emergent bilinguals understand the academic benefits and the significance of supporting their child’s home language as English is introduced through the early childhood program.”

Equity-minded educators understand the power of families’ home languages. By encouraging families to speak to their children in the languages in which they feel most competent and comfortable, they help children make sense of their multicultural heritage. In this article, I offer strategies teachers can use to create learning communities that welcome and support families’ home languages as children develop their English-speaking skills.

Multilingualism as Part of Developmentally Appropriate Practice

Families’ language choices depend on multiple factors, including historical and current inequities that have shaped the US educational system. Traditionally, schools advised families to change their language of choice because the focus was on learning English as quickly as possible. For example, I grew up in a town on the border of Texas and Mexico. My home language was Spanish. When I entered school, my parents were told I was “behind” due to my lack of English skills. They were encouraged to speak only English with me. As a result, I lost most of my Spanish-speaking abilities. Essentially, I was denied the opportunity to become bilingual because educators did not understand how my home language skills could help me learn my second language, English. Although many educators see the value in home language maintenance, these scenarios still occur across this country.

Families also must contend with young children who increase their English skills at the expense of their home language. Research has shown that emergent bilingual and multilingual children often move rapidly toward favoring the language spoken at school before they develop a strong foundation in their home language. This can lead to difficulties in communicating with their families, and it may impact future learning. To offset this tendency, emergent bilingual and multilingual children need to be exposed to all of their languages consistently to support both the maintenance of the home language and the development of English.

Equitable teaching and learning compel early childhood educators to embrace children’s home languages as they are exposed to English. Recognizing that home languages enhance children’s self-expression and learning, they work to support and sustain children’s connections to their cultures, languages, and families. This is part of the developmentally appropriate practice (DAP) framework.

The following are strategies that educators can use to recognize and support children’s home languages in the classroom. These are gleaned from my research, the broader research literature on multilingualism, and expectations and recommendations for early childhood educators outlined in NAEYC’s recent position statements. (See "Further Resources" below.)

Be Curious and Make Time to Learn About Families

Ms. Cantú, a Spanish/English bilingual teacher, invites families into the classroom at the start of each school year. She does this to get to know everyone and, more importantly, to let families know they will become partners with her in their children’s learning and development. Some families feel more ready and able to participate than others. Mrs. Linares, a recent immigrant from El Salvador, worries that she has nothing to offer her child’s teacher. To allay her fears, Ms. Cantú asks questions that highlight the mother’s expertise, such as “What does your child like to do at home?” and “What does your child like to talk about, and in what language?” She also shows Mrs. Linares around the classroom and shares examples of what her child is learning. During this visit, Mrs. Linares mentions using an herb garden for various teas. Ms. Cantú invites her to return during the class’s plant unit to share about herbs and their uses with the children.

Before teachers can address instruction and learning environments for emerging bilingual and multilingual children, they must get to know the children and families in their programs. This will help develop positive, reciprocal relationships and provide teachers with the knowledge of families’ contexts, strengths, and skills to integrate into their settings.

Inviting individual families to visit at the beginning of the year is helpful. Many immigrant families do not yet know the expectations in the US school context. They may not realize they can and should visit their children’s classroom. Ms. Cantú’s invitation signals “you are welcome, and I want to learn from you.” Educators can also offer alternative locations and times to meet together. During this time, educators can ask questions to learn about families’ contexts and develop meaningful connections with them. (See “Questions to Get to Know Multilingual Families” below.)

Connecting a child’s program or school experiences to their home and community settings will help build on families’ cultural and linguistic assets. Throughout the year, teachers can continue to connect with families through different avenues (in person, by phone or video call, in emails or text messages, or visiting outside the program) to share their stories of children’s development. This creates further opportunities for them to learn about the cultural and linguistic resources families and their children draw from daily.

Questions to Get to Know Multilingual Families 

Early childhood educators can gain valuable knowledge about families and their language practices by inviting them to share their backgrounds, routines, and experiences. Their answers can invoke powerful tales of a family’s resiliency and resourcefulness and establish who their networks are within the community. One caveat: It is not appropriate to ask about a family’s immigration status.  

Questions could include the following: 

• Can you tell me about yourself? 
• What language(s) do you speak in your home? 
• What are some things you’d like me to know about the language(s) your family speaks? 
• Who are some of the important people in your child’s life?  
• What are some important things you’d like me to know about your child? 
• What are some activities your child likes to do on their own and with other people? 
• What are your favorite things to do as a family? 
• Are there certain times of the year that are special to your family?  
• Are there certain places, near or far, that your child likes to visit? What do they like to do there? 
• How does your family like to communicate with each other and with people outside of the family? 

Communicate the Value of Multilingualism

Mariela is a 41-year-old migrant worker who immigrated to the United States from Oaxaca, Mexico, looking for work and a better life for her family. She now works on a strawberry farm in Florida. Her 4-year-old daughter, Alicia, is enrolled in a local Spanish/English Head Start program.

Mariela’s first language is Mixtec, an Indigenous Oaxacan language. Spanish is her second language. Mariela struggles with the idea of teaching Mixtec to her daughter, which would give Alicia the opportunity to be multilingual. Mariela says she wants to spare her daughter the discrimination she felt when she arrived in this country. At home, she only speaks Mixtec with her husband and adult family members. After listening to her concerns, Alicia’s teacher suggests that Mariela speak with Alicia in Mixtec. She encourages Mariela to sing songs and rhymes, ask open-ended questions, and engage in child-directed conversations.

Studies have shown that one of the key predictors of an emergent bilingual or multilingual learner’s future academic success is the quality of their experiences with their home language. By encouraging families to use their home languages, early childhood educators show that they value children’s diverse languages and multilingual identities. In an ideal world, teachers would speak with children in their home languages; however, strategies are available to support children’s language maintenance regardless of a teacher’s language abilities. They can learn some words or phrases from the children’s home languages to communicate the value of multilingualism. They can ask family members to share some basic phrases, such as “good morning” and “thank you,” which can be incorporated into daily routines.

Teachers can also create playful learning spaces where children can use their entire linguistic repertoire when engaging with others. These might include

• creating culturally relevant play spaces. Families can send in pictures and labels from items in their homes to add to these areas. They can also help label classroom areas and materials in their home languages.
• encouraging peer interactions where children can practice their oral language skills in risk-free spaces without fear of correction.

Engage with Families in Designing and Implementing Learning Activities

Many Mexican families celebrate Día de las Madres, or Mother’s Day, twice a year. In Mexico, it is celebrated on May 10; in the US, it is celebrated on the second Sunday of May. Mrs. Vela, a Head Start teacher, asks children’s families to integrate their cultural knowledge and practices around Día de las Madres into learning activities. This includes storytelling, performances using songs, poems, and creating homemade cards.

Creating a culturally responsive and emotionally supportive climate for multilingual children and their families is vital for them to feel comfortable, accepted, safe, and connected to the learning setting. Teachers can partner with families to share their funds of knowledge in a variety of ways. For example,

• families can share music, songs, or rhymes in their home languages
• children can create artwork or crafts using techniques or designs from the cultures represented in the classroom
• children can reenact scenes from a favorite story or dicho (saying) that they have learned at home
• families can record themselves reading books in their home languages

These asset-focused instructional activities place families’ language practices at the forefront of family-educator collaboration. As seen in the vignette, they also engage families in meaningful learning activities that reflect home language practices, cultures, and beliefs.

Early childhood educators are challenged to recognize and respond to the diversity in today’s classrooms and communities. Families should not feel that they need to forego teaching their children their home languages. Educators can and should enact equitable practices by leveraging the cultural and linguistic wealth children have developed. Fostering a space where children see themselves reflected leads to the creation of affirming environments, activities, and assessments for all children.

Further Resources 

Tap these resources to learn more about encouraging families to speak their home languages with children. 

“ In Our Voices: Creating Community Responsive Listening Centers ,” by Emily Brown Hoffman and Kristin Cipollone ( Teaching Young Children , 2022)  

Promoting the Educational Success of Children and Youth Learning English: Promising Futures, by the National Academies of Science, Engineering, and Medicine (2017) 

“ Supporting Language: Culturally Rich Dramatic Play ,” by Irasema Salinas-González, María G. Arreguín, and Iliana Alanís ( Teaching Young Children , 2018) 

Want to Learn More?

NAEYC offers an array of resources about bilingual and multilingual learners, including  The Essentials: Supporting Dual Language Learners in Diverse Environments in Preschool and Kindergarten , by Iliana Alanís, María G. Arreguín, and Irasema Salinas-González. In this book, you will read about key concepts from a developmental and asset-based perspective as well as examples of children’s and teachers’ experiences in early educational settings. Connections are made to NAEYC Early Learning Program Accreditation Standards, and answers are provided for frequently asked questions. Visit  NAEYC.org/essentials-supporting-DLLs  for more information.

Photographs: © Getty Images Copyright © 2023 by the National Association for the Education of Young Children. See permissions and reprints online at  NAEYC.org/resources/permissions .

PhD, es profesora adjunta de educación elemental y de la primera infancia en el Departamento Interdisciplinario de Aprendizaje y Enseñanza de la Universidad de Texas en San Antonio.

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Capable: Common Academic Practices and Abilities in Learning for Research

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The aim of the project is to provide knowledge, skills, and network opportunities to doctoral students in Ukraine, helping them to reach international research standards and creating a network for future cooperation.   

With Ukraine coming closer to the EU, there is a distinct need to align, integrate and prepare the education frameworks and systems with European education standards, particularly the European Research Area. Limiting the brain drain from the country, especially amongst young researchers is also key.   

Through the project, Ukrainian PhD students will be given opportunities not only to receive knowledge about research methodology, thesis writing, and other relevant skills but will be consulted by external experts on the topic of their research. Moreover, the training will be organised together with international PhD students to broaden their cooperation opportunities.   

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SES differences in language processing skill and vocabulary are evident at 18 months

This research revealed both similarities and striking differences in early language proficiency among infants from a broad range of advantaged and disadvantaged families. English-learning infants ( n = 48) were followed longitudinally from 18 to 24 months, using real-time measures of spoken language processing. The first goal was to track developmental changes in processing efficiency in relation to vocabulary learning in this diverse sample. The second goal was to examine differences in these crucial aspects of early language development in relation to family socioeconomic status (SES). The most important findings were that significant disparities in vocabulary and language processing efficiency were already evident at 18 months between infants from higher- and lower-SES families, and by 24 months there was a six-month gap between SES groups in processing skills critical to language development.

There are striking differences among children in patterns of early language growth. Some infants start speaking before their first birthday, while others don't produce words until the end of the second year ( Fenson et al., 2006 ). Although some late talkers catch up in vocabulary a few months later, others continue to show slower trajectories of language growth and achieve lower levels of language proficiency ( Bates, Dale & Thal, 1994 ; Fernald & Marchman, 2012 ). Differences in socioeconomic status (SES) are strongly associated with variation in language outcomes. By the time they enter kindergarten, children from disadvantaged backgrounds differ substantially from their more advantaged peers in verbal and other cognitive abilities ( Ramey & Ramey, 2004 ), disparities that are predictive of later academic success or failure ( Lee & Burkum, 2002 ). In adults as well, SES differences in language proficiency are robust ( Pakulak & Neville, 2010 ), reflecting the cumulative influence of a wide range of endogenous and environmental factors over a lifetime.

Despite such evidence for significant differences among children in early language learning, research on acquisition has tended to focus much more on elucidating common patterns of language growth than on understanding the causes and consequences of variability. This emphasis has been driven by several factors: First, the search for similarities rather than differences among children is grounded in a philosophy of science that underlies psychological research more broadly – one that gives priority to processes assumed to be universal rather than to endogenous and experiential factors that can lead to variability ( Arnett, 2008 ). Second, the use of controlled experimental methods in research on early language development favors between-group comparisons of infants at different ages, with limited attention to variability within age groups ( Fernald, 2010 ). Third, the vast majority of developmental studies in the U.S. rely on ‘convenience samples’ of children from higher-SES families that are unrepresentative of the larger population and thus are inherently restricted in variability ( Henrich, Heine & Norenzayan, 2010 ). Fourth, although educational researchers have documented robust differences in verbal abilities among school-age children varying in SES (e.g., Dickinson & Tabors, 2001 ; Lee & Burkam, 2002 ), this literature is often viewed as ‘applied’ research with limited relevance to ‘basic’ research on language development. We argue here that understanding the extent and origins of variability among children in the emergence of early language proficiency should be central to any developmental theory that acknowledges, at whatever level, the influence of children's early experience on language growth.

This perspective motivates the current study of differences as well as similarities in early language proficiency among children from higher- and lower-SES families. In experimental studies using looking-time measures, we have shown that infants develop speed and efficiency in interpreting spoken language in real time ( Fernald, Pinto, Swingley, Weinberg, & McRoberts, 1998 ) and that individual differences in early processing efficiency are strongly linked to variation in children's later language outcomes (e.g., Fernald, Perfors & Marchman, 2006 ; Marchman & Fernald, 2008 ). However, in these previous studies, as in many other university-based studies with English-learning children, most participants came from highly-educated and affluent families. The goal of the present study was to examine the development of language processing efficiency in relation to vocabulary learning in English-learning infants from families varying in SES. Using real-time processing measures, we followed children longitudinally from 18 to 24 months, focusing on two sets of questions: First, to what extent do infants across this broader SES range show parallel gains in processing efficiency and vocabulary between 18 and 24 months? And second, is there evidence that SES-related differences in processing skills critical to language development are already present in infancy?

SES Differences in Verbal Abilities and their Long-Term Consequences

The finding that children from disadvantaged families start kindergarten with lower language and cognitive skills than those from more advantaged families is old news, emerging repeatedly in studies since the 1950's (e.g., Bereiter & Englemann, 1966 ; Deutsch, Katz, & Jensen, 1968 ). The robustness of such differences is confirmed in more recent research such as the Early Childhood Longitudinal Study, Kindergarten Cohort (ECLS-K), a comprehensive analysis of young children's achievement scores in literacy and mathematics based on a large and nationally representative sample ( Lee & Burkam, 2002 ). Even before they entered kindergarten, children in the highest SES-quintile group had scores that were 60% above those in the lowest group. In terms of effect size, children in the highest SES-quintile scored .7 standard-deviation (SD) units above middle-SES children in reading achievement, while children in the lowest SES-quintile scored almost .5 SD units below the middle-SES mean. Moreover, the disparities in children's cognitive performance at kindergarten entry that were attributable to SES differences were significantly greater than those associated with race/ethnicity. Another recent study found that 65% of low-SES preschoolers in Head Start programs had clinically significant language delays ( Nelson, Welsh, Vance Trup, & Greenberg, 2011 ). This research revealed a systematic relation between degree of language delay and other weaknesses in academic and socio-emotional skills that were well established by 4 years of age. Socioeconomic gradients in language proficiency are also found within populations living in extreme poverty ( Fernald, L., Weber, Galasso, & Ratsifandrihamanana, 2011 ).

A Challenging and Controversial Question: When Do SES Differences Begin to Emerge?

Results showing that SES differences in verbal abilities are already evident in the preschool years suggest that these disparities must start to develop in the first years of life, setting children on particular trajectories with far-reaching consequences for later academic success. How early do such differences begin to emerge? Research on this important developmental question has been limited for a variety of reasons - ranging from methodological challenges in evaluating language proficiency in young children, to the complexities of engaging in debate about politically sensitive issues related to social stratification. The methodological problem is easy to characterize: Until recently, measures available for assessing language and cognitive proficiency in children younger than 3 years have not been high in predictive validity, limiting their effectiveness in linking characteristics in infancy to long-term outcomes. But with the refinement of more sensitive methods for evaluating early language, recent studies have revealed considerable variability in verbal skills among very young children - to be reviewed in the following section. Another set of issues that has discouraged research on early origins of cognitive differences among children from different backgrounds is more difficult to characterize. The legacy of a prolonged and bitter debate about the nature of racial and SES differences in the U.S. has reinforced the reluctance of researchers to pursue the question of early origins of SES-related disparities in cognitive skills that are relevant to school success.

A brief history of this complex debate is relevant to the issues raised in the current study. The scientific consensus in the early 20th century was that cognitive abilities were entirely genetically determined, a view that changed gradually with mounting evidence that experiential factors were also influential (see Fernald & Weisleder, 2011). By the 1960's, when the Civil Rights movement focused national attention on inequities in educational opportunities for Black children, there was intense interest in eliminating achievement gaps that could no longer be ignored. Riessman (1962) argued that SES disparities in school success resulted from cultural differences in minority children's early experience with parents in the home, rather than from immutable genetic differences. This ‘cultural deprivation’ argument appeared to offer hope for solutions through appropriate intervention, although characterizing the home environment of minority children as deficient in cognitive stimulation clearly had negative implications. While this idea rallied political support for new programs such as Operation Head Start, what came to be known as the ‘deficit model’ also generated intense controversy among educators who objected that parents should not be blamed for their children's difficulties in school. By the 1970's, politically motivated backlash to the deficit model converged with the rise of nativist theories of language development, which focused on modal patterns of development presumed to be universal rather than on differences among children. Fernald and Weisleder (2011) argue that this convergence was influential in curtailing debate on questions that had generated extensive research over the previous two decades – namely, whether SES differences in children's verbal abilities are rooted to some extent in differences in their early language experience at home, and if so, whether these experiential differences contribute to the substantial disparities observed among children in their later academic success.

Although interest in variability in language learning had declined substantially by the 1980's, a few researchers began to explore in greater depth the potential contributions of early parent-child interaction to differences in language development (e.g., Hart & Risley, 1995 ; Hoff-Ginsberg, 1998 ; Huttenlocher, Haight, Bryk, & Seltzer, 1991 ). Based on detailed analyses of mothers’ speech to infants at home, these studies used longitudinal designs to identify features of maternal speech that predict language outcome measures. Hart and Risley found that by 36 months, the higher-SES children in their sample spoke twice as many words as the lower-SES children. But their most remarkable finding was the extreme variation in amounts of child-directed speech among families at different SES levels, differences that were correlated with children's early vocabulary and also predictive of later school performance ( Walker, Greenwood, Hart & Carta, 1994 ). According to Hoff (2003) , it was the quality of infants’ early language environment that actually mediated the link between SES and children's vocabulary knowledge.

Assessing Differences in Language Proficiency in Very Young Children

These studies of variability in early language environments with small samples of families laid the foundation for research exploring the early emergence of cognitive disparities in much larger and more diverse samples of advantaged and disadvantaged children. Farkas and Beron (2004) examined the monthly growth trajectory of oral vocabulary knowledge in Black and White children from 36 months to 13 years of age, using a large, representative national data set. Their most striking finding was that most of the inequality in vocabulary growth attributable to race and SES differences developed prior to 36 months. Moreover, the magnitude of the Black-White vocabulary gap that was already evident by the age of school-entry remained unchanged through the age of 13 years. These authors concluded that by 36 months, SES differences in children's language experience have already led to significant vocabulary disparities, which then widen further in the preschool years and remain constant thereafter. Data from the NICHD Early Childhood Care Research Network also revealed that a substantial achievement gap between low-income Black and White children was already evident by 3 years, and that family as well as school characteristics contributed to maintaining this gap through elementary school ( Burchinal et al., 2011 ). A third recent study with a large, representative sample from the Early Childhood Longitudinal Study, Birth Cohort (ECLS-B) showed that disparities between lower- and higher-SES infants on language and cognitive measures began to emerge by 9 months, and that by 24 months there was a mean difference of .5 SD units between SES groups on the Bayley Cognitive Assessment ( Halle et al. 2009 ).

These large-sample studies of SES disparities in cognitive skills emerging early in life have all been based on standardized assessments of language abilities, using measures which require the child to follow instructions and execute an unambiguous response by speaking or pointing. But given these task demands, such assessments cannot be used effectively with toddlers younger than 2 years. While parent reports of a child's vocabulary can yield valuable data on early language development ( Fenson et al., 2006 ), they do not provide a direct measure of the child's response. Until recently, these methodological limitations made it difficult to investigate the origins of individual differences in language proficiency in infants younger than 24 months. However, refinements in experimental techniques now allow researchers to monitor the time course of language comprehension by very young language learners, providing direct measures of early efficiency in language processing in real time.

Recent experimental studies on language processing in the second and third years have used real-time measures to assess how efficiently children identify the referent of a familiar word in real-time comprehension. In the looking-while-listening (LWL) procedure ( Fernald, Zangl, Portillo & Marchman, 2008 ), children see pictures of two familiar objects as they listen to speech naming one of the objects, and their responses are coded with millisecond-level precision. Cross-sectional studies of both English- and Spanish-learning infants show dramatic gains in the speed and accuracy of language understanding across the second year ( Fernald, Pinto, Swingley, Weinberg & McRoberts, 1998 ; Hurtado, Marchman & Fernald, 2007 ). Moreover, young children, like adults, are able to interpret incoming language incrementally, directing their attention to the appropriate picture as the speech signal unfolds in time ( Fernald, Swingley & Pinto, 2001 ; Swingley, Pinto & Fernald, 1999 ). In a longitudinal study with English-learning toddlers from 15 to 24 months, these online processing measures were found to be stable over time, and processing speed at 24 months was robustly correlated with vocabulary growth over this period ( Fernald et al., 2006 ). Moreover, a follow-up study with the same children six years later showed strong links between processing efficiency in infancy and performance on standardized tests of language and cognitive skills in elementary school ( Marchman & Fernald, 2008 ). These real-time processing measures have revealed consistent concurrent and predictive relations to language outcomes across studies of typically-developing children. They are also high in predictive validity in research with late-talkers, children at increased risk for persistent language delays ( Fernald & Marchman, 2012 ). For these reasons, the LWL task is well suited for investigating both similarities and differences in early language processing skill among infants from different socioeconomic backgrounds.

Research Questions

The main goals in this research were to examine the early development of language processing efficiency in relation to vocabulary learning in English-learning infants from families across a broad demographic range, and to determine whether SES differences in processing efficiency are already evident in infancy, at a younger age than has been reported in previous research. Our previous studies with English-learning children were all conducted at a university laboratory in a prosperous urban area, where almost all the families who volunteer to participate in research are affluent and highly educated (Site 1). To extend beyond this convenience sample of high-SES families, we needed to establish an additional research site in an area where it is possible to recruit equivalent numbers of lower- and middle-SES English-speaking families. Site 2 is located in an urban area comparable in population size to Site 1. However, because these two areas differ substantially in terms of median family income, cost-of-living, and percentage of children living in poverty, as shown in Table 1 , we are able to include a much more diverse sample of English-learning children at Site 2 than is possible at the university lab.

Demographic information on population, median income, cost-of-living index, and poverty rate in the two research sites

Participants

Participants were 48 English-learning children (26 females), recruited through birth records and day care centers at Site 1 ( n = 20) and Site 2 ( n = 28). Exclusionary criteria at time of recruitment included preterm birth, birth complications, hearing/visual impairments, medical issues, or a known developmental disorder. Reported ethnicity of participants was non-Hispanic White (66%), Asian (13%), Alaskan Native/American Indian (10%), Native Hawaiian/Pacific Islander (6%), or African American (4%). After receiving a brochure describing the project, interested parents contacted us by phone, website, or reply card. Parents were then interviewed by phone about their child's language background, health history, and family history of language disorders. Qualifying families were invited to join the study if the child was not regularly exposed to a language other than English. Six additional participants were excluded from final analyses because the families could not attend the 24-month testing session or did not complete both language questionnaires.

Socioeconomic status

Although participants were all typically-developing infants from monolingual English-speaking families, they were diverse in socioeconomic background, as shown in Table 2 . The mothers in these families had about three years of post-high school education, on average, yet spanned a broad range of educational levels: 21% did not finish or were still attending high school, or did not continue their education past high school, 19% had some college, 33% completed a B.A. degree, and another 27% also received some post-B.A. training. Table 2 also shows scores on the Hollingshead Four Factor Index of Socioeconomic Status (HI, Hollingshead, 1975 ). This widely-used index of family SES is based on a weighted average of both parents’ education and occupation, with possible scores ranging from 8 to 66. The HI is divisible into five “strata” of social status: unskilled worker, semi-skilled worker, skilled worker, semi-professional, and major professional. In this sample, parents’ occupations spanned the full range from unskilled worker to major professional. For some analyses, families were divided into Lower- (≤ 45, n = 23) and Higher-SES (> 47, n = 25) sub-groups based on a median split of HI scores, as shown in Table 2 . Both groups included at least one mother with only a high school education, as well as several mothers who had attended college. Nevertheless, the distributions of maternal education levels were substantially different in the two groups. Nearly 90% of the mothers in the Higher-SES group had at least a 4-year college degree, with more than half completing masters or doctoral degrees, while only 30% of the mothers in the lower-SES group had completed college and one had a masters degree. Of the children from families in the Higher-SES group, 19 were recruited at Site 1 and 6 at Site 2. Of those from families in the Lower-SES group, 1 was recruited at Site 1 and 22 at Site 2.

Mean (SD) and range for maternal education and Hollingshead Index for full sample and lower-SES and higher-SES sub-groups

Offline Measures of Vocabulary

Reported expressive vocabulary.

At 18 and 24 months, parents completed the MacArthur-Bates Communicative Development Inventory: Words & Sentences (CDI: Fenson et al., 2006 ). This parent-report instrument ask parents to indicate on a checklist (680 items) which words their child “understands and says”. All parents were told to substitute words on the checklists with variants of those words specific to their family (e.g., nana for grandmother ).

Procedure for Assessing Real-time Language Understanding

Children's real-time comprehension of familiar words was assessed at 18 and 24 months using the looking-while-listening (LWL) procedure ( Fernald et al., 2008 ). The testing apparatus, recording procedures, and verbal and visual stimuli were identical at Sites 1 and 2, and the same two experimenters conducted test sessions at both sites. On each trial, participants viewed two pictures of familiar objects while listening to speech naming one of the pictures. Visual stimuli were colorful pictures (36 × 50 cm) of the target and distracter objects on gray backgrounds, aligned horizontally on a video display. Children sat on the caregiver's lap during the 5-min session, and caregivers wore darkened sunglasses to restrict their view of the images. Each stimulus sentence consisted of a carrier phrase with the target word in final position, followed by an attention-getter (e.g., Where's the car? Do you like it? ). The child's face was video-recorded for later frame-by-frame coding. On each trial, the two pictures were shown simultaneously for 2 s prior to speech onset, remaining on the screen during the auditory stimulus until 1 s after sound offset. Between trials, the screen was blank for approximately 1 s. Each trial lasted approximately 7 s.

Verbal stimuli

A female native speaker of English recorded several tokens of each sentence. Candidate stimuli were acoustically analyzed; final stimulus sentences were selected to be comparable in naturalness and pitch contour and edited so that carrier frames and target words were matched for duration. At 18 months, the mean length of the target noun was 614 ms (range = 604 - 623 ms). At 24 months, mean noun duration was 640 ms (range = 565 -769).

At 18 months, the target nouns were baby, doggy, birdie, kitty, ball, shoe, book , and car , object labels likely to be familiar to English-speaking children at this age ( Dale & Fenson, 1996 ). Each object was presented four times as target and four times as distracter, yielding 32 experimental trials. Interspersed among the critical trials were 4 filler trials (e.g., Do you like those pictures? ). At 24 months, children heard sentences containing the familiar target nouns baby, doggy, birdie, kitty, cookie, book, car , and juice each presented twice as target and twice as distracter, a total of 16 experimental trials. These familiar word trials were interspersed with fillers (4 trials) and trials in which the target word was placed in a carrier frame with an adjective (16 trials) or a semantically-related verb (8 trials). These trials are not analyzed here. Trials on which the parent reported that the child did not understand the target word were excluded from analyses on a child-by-child basis.

Visual stimuli

Pictures corresponding to target words were presented in fixed pairs matched for visual salience, with each object serving equally often as target and distracter. All tokens were judged to represent objects typically familiar to young children. Position of target picture was counterbalanced across trials. Trials were presented in a pseudo-random order such that the same target word never occurred on adjacent trials, and the target picture did not appear on the same side more than two trials in a row.

Video records of children's gaze patterns were analyzed frame-by-frame by highly-trained coders blind to target side and condition. All coding was conducted at Site 1 by coders who were not involved in running the sessions and were blind to testing site. On each frame, coders indicated whether the child was looking at the left picture, right picture, in between the two pictures or away from both. This yielded a high-resolution record of eye movements for each 33-ms interval as the stimulus sentence unfolded, aligned with the onset of the target noun. Trials were later classified as target- or distracter-initial, depending on which picture the child was fixating at target-noun onset. To determine reliability, 25% of sessions were independently re-coded, with inter-observer agreement computed in two ways. First, the mean proportion of frames on which coders agreed on gaze location averaged 98%. Second, the mean proportion of shifts in gaze on which coders agreed within one frame was also calculated, a more conservative measure which also yielded high reliability (97%).

Calculation of accuracy and RT

Two measures of efficiency in real-time speech processing were calculated for each child. First, accuracy was computed as the mean proportion of looking to the named picture on target- and distracter-initial trials, averaged over 300-1800 ms from noun onset. Mean accuracy was based on an average of 22.9 trials ( SD = 5.3) per child at 18 months and 12.2 trials ( SD = 2.9) at 24 months. Second, reaction time (RT) was computed on only those trials on which the child was looking at the distracter picture at the onset of the target word and shifted to the target picture within 300-1800 ms from target word onset. Trials on which the child shifted either within the first 300 ms or later than 1800 ms from target word onset were excluded, since these early and late shifts were less likely to be in response to the stimulus sentence ( Fernald et al., 2008 ). Mean RTs were based on an average of 8.8 trials ( SD = 3.6) at 18 months and 5.0 trials ( SD = 2.1) at 24 months.

Focusing on two crucial aspects of early language proficiency – the development of expressive vocabulary and skill in real-time spoken language processing - this study examined differences and similarities in patterns of developmental change from 18 to 24 months in a diverse group of English-learning children. A central question was how variability in lexical development and real-time processing efficiency would relate to variability in family SES. The scatterplots in Figure 1 show that SES differences were significantly correlated with vocabulary as well as with accuracy and reaction time, our two measures of processing efficiency: 18-month-olds growing up in families with higher HI scores were more advanced in vocabulary, r (48) = .34, p < .02, and were also more accurate, r (48) = .52, p < .001, and faster, r (47) = -.50, p < .001 in spoken word recognition in the LWL task. Correlations between SES and these three language measures were also significant at 24 months: vocabulary: r (48) = .29, p < .05; accuracy, r (48) = .30, p < .05; RT, r (48) = -.45, p < .001. For the next analyses, we divided participants into two SES groups based on a median split of HI scores (see Table 2 ), to compare children from Lower- and Higher-SES families in their patterns of change with age in vocabulary and processing efficiency.

Scatter plots of Vocabulary, Accuracy and RT at 18 months with SES (HI). Dashed vertical line indicates median split of HI values.

Change in Vocabulary from 18 to 24 Months in Lower- and Higher-SES Children

Mean expressive vocabulary scores at 18 and 24 months for Higher- and Lower-SES children are shown in Table 3 and Figure 2 . In a 2 × 2 mixed analysis of variance (ANOVA), with SES group as a between-Ss factor and age as a within-Ss factor, the main effect of age was significant, F (1,46) = 163.5, p < .001, η p 2 = .78, reflecting larger vocabulary scores at 24 months than at 18 months across all children. On average, children's vocabulary size increased by about 225 words over this period. The main effect of SES group was also significant, F (1,46) = 8.6, p < .001, η p 2 = .16, confirming that children in the Higher-SES group were significantly more advanced in vocabulary than those in the Lower-SES group. Indeed, at 18 months, nearly half the children in the Lower-SES group ( n = 12) had fewer than 50 words in their reported vocabulary, while only eight children in the Higher-SES group had scores of 50 words or less. A similar trend was evident at 24 months: Children from Higher-SES families produced nearly 450 words, on average, while children from Lower-SES families produced about 150 fewer words, consistent with previous reports of SES differences in reported vocabulary in this age range (e.g., Arriaga, Fenson, Cronan & Pethick, 1998 ).

Mean number of spoken words reported on the MacArthur/Bates CDI by age and SES (HI). Error bars represent SE of the mean over participants.

Mean (SD) and range of expressive vocabulary a at 18 and 24 months for all participants and by SES sub-group b

An even more striking result was that the pattern of developmental change in vocabulary differed as a function of SES, reflected in a significant age by SES group interaction, F (1,46) = 6.1, p < .02, η p 2 = .12. As illustrated in Figure 2 , a group difference in vocabulary between children from Lower- vs. Higher-SES backgrounds was clearly evident at 18 months, and by 24 months the between-group difference was even larger. Children in the Higher-SES group made significantly greater gains ( M = 268 words, SD = 116) over this period than did children in the Lower-SES group ( M = 180 words, SD = 127), t (46) = 2.5, p < .02.

Changes in Processing Efficiency from 18 to 24 Months in Higher- and Lower-SES Children

Next we compared children at both ages in the two SES groups on two measures of processing efficiency – mean accuracy and mean RT (see Table 4 ) – using 2 (age) × 2 (SES group) mixed ANOVAs.

Mean (SD) of accuracy and reaction time (RT) in the looking-while-listening task at 18 and 24 months for all participants and the lower- and higher-SES sub-groups

Across SES groups, 24-month-olds spent a greater proportion of time looking at the correct picture than did 18-month-olds, F (1, 46) = 31.2, p < .001, η p 2 = .40. There were also significant between-group differences in accuracy: Higher-SES children were more accurate overall than the Lower-SES children, F (1, 46) = 22.8, p < .001, η p 2 = .33. The age × SES interaction was not reliable, p = .69, η p 2 = .003, reflecting comparable relative gains in accuracy from 18 to 24 months for infants in both groups.

The main effect of age is illustrated in Figure 3 , which shows the time course of looking to the target picture in the LWL task for children at 18 and 24 months. This graph plots change over time in the mean proportion of trials on which children overall fixated the target picture, averaged over participants at each 33-msec interval as the sentence unfolds. The proportion of looking to the target picture remained near chance at least halfway through the target noun, when acoustic information potentially enabling identification of the correct referent first became available. After this point, the mean proportion of correct looking began to increase, continuing to rise after the offset of the target noun. Between 18 and 24 months, children increased their proficiency in looking to the named target before the offset of the target noun, reaching a higher level of accuracy at 24 months than six months earlier. It is also important to note that the proportion of looking to the named target picture was significantly above the chance level of .50 chance at 18 months, t (47) = 11.2, p < .0001, and 24 months, t (47) = 15.6, p < .0001, indicating that children overall could correctly identify the referents of familiar object names at both ages.

Mean proportion looking to the target picture as a function of time in ms from noun onset at 18 and 24 months. Error bars represent SE of the mean over participants. The vertical dashed line marks the acoustic offset of the target word.

Although accuracy improved with age for children in both SES groups, there was also a strong and early influence of SES. Figure 4 plots the time course of looking to the correct target picture at 18 and 24 months for the Lower- and Higher-SES groups. The Higher-SES children responded by looking to the named target sooner in the stimulus sentence, and achieved substantially higher levels of accuracy than those in the Lower-SES group. But what is most remarkable about Figure 4 is that the curve for the Lower-SES children at 24 months essentially overlaps with the curve for the Higher-SES children at 18 months. Indeed the mean accuracy for Lower-SES children at 24 months ( M = .69) was identical to that for Higher-SES children at 18 months ( M = .69), indicating that 24-month-olds in the Lower-SES sample were performing at the same level overall as Higher-SES children who were six months younger.

Mean proportion of looking to the target as a function of time in ms from noun onset for Lower-SES and Higher-SES learners. Open squares/circles represent the time course of correct looking at 18 months; filled squares/circles represent the time course of looking in the same children at 24 months. Error bars represent SE of the mean over participants.

Reaction Time

Similar patterns of developmental change were found in analyses of processing speed, shown in Figure 5 . At 24 months, children were about 100 ms faster to initiate a shift from distracter to target picture, on average, than they were at 18 months, a significant main effect of age, F (1,45) = 15.2, p < .001, η p 2 = .25. The main effect of SES on RT was also significant, F (1,45) = 27.5, p < .001, η p 2 = .38, confirming that children in the Higher-SES group were significantly faster overall in familiar word recognition than children in the Lower-SES group. There was no significant age × SES group interaction, p = .27, η p 2 = .03, reflecting parallel gains in response speed with increasing age in both groups of children. However, consistent with the findings for accuracy, the absolute differences in processing speed between the two groups at each age were substantial: the mean RT for Lower-SES children at 24 months was comparable to the mean RT for 18-month-olds in the Higher-SES group.

Mean RT to initiate a shift from the distracter to the target picture at 18 and 24 months for the Higher-SES and Lower-SES learners. Error bars represent SE of the mean over participants.

Relations between Online Processing Skill and Vocabulary in a Diverse Sample of Children

The final analysis explored whether variability in online processing skills aligned with vocabulary knowledge in this diverse sample. First-order correlations between RT and accuracy in real-time comprehension and vocabulary scores at 18 and 24 months are shown in Table 5 . As in previous studies with more homogeneous samples of English-learning children from advantaged families, we found reliable links between performance in the LWL task and expressive vocabulary size at both 18 and 24 months, although links were stronger and more consistent at the later time point. At 24 months, accuracy and RT were correlated with both earlier and concurrent vocabulary scores, accounting for 15 - 23% of the variance. These results echo the recurring finding that those children who are faster and more accurate in real-time interpretation of familiar words tend to be those who are also reported to produce more words ( Fernald et al., 2006 ; Fernald & Marchman, 2012 ; Hurtado et al., 2007 ).

First-order correlations ( r ) between processing efficiency and vocabulary at 18 and 24 months

This research revealed similarities but also striking differences in early language proficiency among infants from advantaged families and from less advantaged families. Our first goal was to track developmental changes in language processing efficiency in relation to vocabulary learning in this diverse sample of English-learning children. Our second goal was to examine SES differences in these crucial aspects of early language development. The most important finding was that significant disparities in language proficiency between infants from higher- and lower-SES families were already evident at 18 months of age, and by 24 months there was a 6-month gap between the two groups.

Similarities and Differences Among Children in Early Processing Efficiency and Vocabulary

Although participants in this study came from very different backgrounds, they showed common patterns of change in the efficiency of real-time language processing from 18 to 24 months. Older children were more likely than younger children to interpret the incoming speech signal incrementally, fixating the target picture as soon as they had enough information to identify the referent. We also found reliable links between skill in early spoken language processing and vocabulary development, replicating results previously shown in children from affluent, highly educated families ( Fernald et al., 2006 ; Fernald & Marchman, 2012 ), but never before in English-learning children from a broader SES range. These results provide further evidence that real-time language processing is aligned with early vocabulary development.

Extending earlier results showing consistent relations between early processing efficiency and vocabulary size to a more diverse group of English-learning children was an important starting point. However, the more surprising outcome of this study was that by the age of 18 months, there were already substantial differences among children as a function of SES. Children from lower-SES families had significantly lower vocabulary scores than children from higher-SES families at the same age, and they were also less efficient in real-time processing. As seen in Table 4 , mean accuracy for the lower-SES children increased from .59 to .69 between the ages of 18 and 24 months; however, mean accuracy for the higher-SES children was already .69 at 18 months, increasing to .77 by 24 months. Measures of processing speed showed a similar pattern: in the lower-SES children, the mean RT at 24 months ( M = 802 ms) was still not as fast as the mean RT at 18 months in the higher-SES children ( M = 746 ms). These differences were equivalent to a six-month disparity between the higher- and lower-SES children, in vocabulary size and in both measures of language processing efficiency.

Exploring Sources of Variability in Young Children's Early Language Proficiency

Where do these substantial differences come from? Variability among individuals in verbal abilities is influenced to some extent by genetic factors ( Oliver & Plomin, 2007 ), but the contributions of early experience to differences in language proficiency are also substantial. Research on language problems in twins has also shown that environmental factors are more powerful than genetic factors in accounting for similarities in language development in children in the same family ( Oliver, Dale & Plomin, 2004 ). Other studies suggest that the contribution of environmental factors to variability in IQ has been underestimated in behavioral genetics studies, which tend to focus on children in middle-class families ( Rowe, Jacobson & Van den Oord, 1999 ; Turkheimer, Haley, Waldron, D'Onofrio & Gottesman, 2003 ). In a study of twins from families diverse in SES, Turkheimer et al. (2003) found that 60% of the variance in cognitive abilities was accounted for by shared environmental factors among children living in poverty, with the genetic contribution close to zero; however, for children in higher-SES families, the opposite pattern of findings emerged. While the power of SES to moderate the heritability of verbal and other cognitive abilities is under debate ( Hanscombe et al., 2012 ), there is consensus that infants’ genetic potentials in these domains can only be realized with appropriate environmental support. In families where adequate resources and support are consistently available, children are more likely to be buffered from adverse circumstances than are children in impoverished families, and so are more likely to be able to achieve their developmental potential.

There are many different experiential factors associated with living in poverty that could contribute to variability in language learning. For example, the physical conditions of everyday life related to safety, sanitation, noise level, and exposure to toxins and dangerous conditions differ dramatically for children in lower- and higher-SES families, as does the access to crucial resources such as adequate nutrition and medical care ( Bradley & Corwyn, 2002 ). Conditions of social and psychological support vary as well, with higher levels of stress and instability in disadvantaged families ( Evans, Gonnella, Marcynyszyn, Gentile & Salpekar, 2005 ). All of these environmental factors are known to have consequences for cognitive and social outcomes in young children (e.g., Evans, 2004 ). There are also well known differences in the quality of parent-child interaction among families differing in SES related to these circumstantial factors. For example, parents under greater stress tend to respond less sensitively to their children ( Mesman, van IJzendoorn, & Bakermans-Kranenburg, 2011 ), and provide less adequate social and cognitive stimulation. This is likely to be one important factor contributing to the well-documented SES differences in the amount and quality of child-directed speech ( Hoff, 2003 ; 2006 ). Hart and Risley (1995) estimated that by 36 months, the children they observed from advantaged families had heard 30 million more words directed to them than those growing up in poverty, a stunning difference that predicted important long-term outcomes ( Walker, et al., 1994 ).

Could variation in early language experience also contribute to individual differences in infants’ real-time processing efficiency, as well as in vocabulary learning? This question was explored in longitudinal research with Spanish-speaking families, examining links between maternal talk, children's processing efficiency, and lexical development ( Hurtado, Marchman, & Fernald, 2008 ). Those infants whose mothers talked with them more at 18 months were those who learned more vocabulary by 24 months. But the most noteworthy finding was that those infants who experienced more and richer language were also more efficient in real-time language processing six months later, compared to those who heard less maternal talk. One interpretation of these findings is that having the opportunity for rich and varied engagement with language from an attentive caretaker provides the infant not only with models for language learning, but also with valuable practice in interpreting language in real time. Thus, child-directed talk sharpens the processing skills used in online comprehension, enabling faster learning of new vocabulary.

Long-term Consequences of Early Differences in Language Skills

How would an advantage in processing efficiency facilitate vocabulary learning? Studies with adults show that faster processing speed can free additional cognitive resources (e.g., Salthouse, 1996 ), which may be particularly beneficial in the early stages of language learning. The infant who can interpret a familiar word more rapidly has more resources available for attending to subsequent words, with advantages for learning new words that come later in the sentence. A slight initial edge in the efficiency of familiar word interpretation could be strengthened through positive-feedback processes, leading to faster growth in vocabulary that in turn leads to further increases in receptive language competence. If rapid lexical access of familiar words facilitates learning new words, then greater efficiency in language processing at 18 and 24 months could have cascading advantages that result in further vocabulary growth.

Results from several studies support the idea that variability in both processing speed and vocabulary could have long-term consequences. In research with adults and children, mean RT across various tasks predicted success on cognitive assessments at every age ( Kail & Salthouse, 1994 ). Because mean RT in adults correlates so consistently with measures of memory, reasoning, language, and fluid intelligence, Salthouse (1996) has argued that gradual increases in processing speed account fundamentally for developmental change with age in cognitive and language functioning. This association has been characterized as a developmental cascade by Fry and Hale (1996) , who proposed that increasing processing speed strengthens working memory, and that stronger working memory then leads to greater cognitive competence. Since vocabulary size also predicts IQ in both adults and children ( Matarazzo, 1972 ; Vance, West & Kutsick, 1989 ), an early advantage in lexical development could have cascading benefits for other aspects of language learning as well (Bates et al., 1988). Vocabulary knowledge also serves as a foundation for later literacy ( Lonigan, Burgess & Anthony, 2000 ), and language proficiency in preschool is predictive of academic success ( Alexander, Entwisle & Horsey, 1997 ). It is clear from these findings that the early emerging differences we found in language proficiency between children from different SES backgrounds have serious implications for their long-term developmental trajectories.

Conclusions

In this research we found significant differences in both vocabulary learning and language processing efficiency that were already present by 18 months, with a six-month gap emerging between higher- and lower-SES toddlers by 24 months. These results mirror findings from new analyses of the ECLS-B data set, which used more global measures to show that reliable differences in cognitive performance between children in lower- and higher-SES families were present by 24 months ( Halle et al., 2009 ; Tucker-Drob, Rhemtulla, Harden, Turkheimer & Fask, 2011 ). What our findings add is the first evidence that SES-related disparities in language skills emerge at an even earlier age. Using high-precision measures of infants’ real-time responses to familiar words, it was not until 24 months that the less advantaged children reached the same levels of speed and accuracy achieved by more advantaged children at 18 months, a six-month gap in the development of processing efficiency. Such a large disparity cannot simply be dismissed as a transitory delay, given that differences among children in trajectories of language growth established by 3 years of age tend to persist and are predictive of later school success or failure ( Burchinal et al., 2011 ; Farkas & Beron, 2004 ).

Because this difference can be characterized as a lag in early processing efficiency with potentially important long-term consequences, it is important to frame this finding in light of scientific discoveries that reveal the weaknesses of the controversial ‘deficit model’ of the 1960's. The view that children from disadvantaged homes were inherently ‘culturally deprived’ ( Riessman, 1962 ) was based on a vague notion of culture as embodied in middle-class practices, institutions, and values. At that time, little was known about the actual activities and practices of parents in different families, with even less scientific evidence on trajectories of cognitive and language development from infancy through childhood. Thus the term ‘deficit’ was used as a global indictment of parenting styles in impoverished families that were simply different from middle-class families - a well-intended but misguided attempt to help teachers understand the difficulties minority children were experiencing in the recently desegregated school system.

There was obfuscating vagueness on both sides of the debate. Advocates of the deficit model proposed a causal account of the effects of children's early life experience on later cognitive development in which both predictor and outcome variables were poorly specified. While many critics of the deficit model raised valid points urging greater respect for different cultural practices (e.g., Heath, 1983 ), others countered with proposals that were simplistic and counterproductive, often reflecting a political agenda. These proposals ranged from calling a halt to research on parenting practices in minority families because it was inherently paternalistic and racist, to focusing on eliminating poverty rather than on ‘blaming the victim’ ( Ryan, 1971 ). The deficit model was incoherent at the time, and the continuing debate on this construct has not led to greater precision or insight ( Gorski, 2006 ).

In an effort to reframe this argument, we end with an example from nutrition, where cognitive consequences can be linked to particular deficits without evoking the reflexive opposition associated with deficit models in social science. Children with iron deficiency anemia (IDA) are typically low in energy and have cognitive difficulties. For many years, the prevailing explanation for these symptoms was that parents treated lethargic children with IDA as if they were younger, which supposedly retarded their cognitive development ( Pollitt, 1993 ). Thus differences among children in global measures of cognitive ability were attributed to ill-defined problems in parenting behavior. However, recent research on IDA has led to a much more precise specification of both causes and consequences. Studies with animal models show that iron deficiency in pre- and postnatal development disrupts the optimal course of myelination, which then reduces efficiency of neural transmission ( Beard, Wiesinger, & Connor, 2003 ). And longitudinal research measuring brain responses to auditory and visual stimuli shows that children with IDA have slower neural transmission, which is very likely to affect the efficiency of cognitive processing ( Algarín, Peirano, Garrido, Pizarro, & Lozoff, 2003 ).

Resting on a foundation of research showing solid relations between a specific causal factor and specific consequences, these discoveries of links between iron deficiency and long-term cognitive difficulties become valuable and highly relevant as public health information. If a mother was told that her child had a “cultural deficit in nutrition,” such a broad, vague claim could only be perceived as a perplexing insult. However, if she heard about new research showing that iron is absolutely critical for optimal brain development in infancy, and that healthy brain development is vital to her child's success in school and in later life, she might be more interested in learning about new ways to provide more iron in her child's diet.

While recent research on nutrition focuses on biological factors that influence early cognitive development, there is increasing scientific evidence that experiential factors also play a critical role in infants’ early language development – by nurturing vocabulary learning ( Hart & Risley, 1995 ; Hoff, 2006 ) as well as strengthening skill in real-time language processing ( Hurtado et al., 2008 ; Weisleder & Fernald, under review). Although the present study was not designed to explore causes of the variability we found among children, our results add to this literature by showing the potential benefits of early processing efficiency for vocabulary growth, and also revealing the potential cost to children with less efficient processing skills, in terms of missed opportunities for learning. From the perspective of basic research and theory in language acquisition, it is essential to investigate not only the typical developmental trajectories of children from privileged families, but also the wide range of variability that becomes apparent when children from more diverse backgrounds are included. We address this goal here by documenting substantial differences between infants from lower- and higher-SES backgrounds that are already evident in the second year of life, using sensitive measures of early language proficiency known to be predictive of later outcomes. The next step is to explore the powerful sources of variability in early experience that contribute to such differences in infants’ emerging language proficiency, and to examine the nature and timing of their influence in larger and more diverse samples of children. From a policy perspective, the ultimate challenge is to frame these discoveries as a public health message ( Knudsen, Heckman, Cameron, & Shonkoff, 2006 ), with the goal of helping caregivers understand the crucial role they can play in enabling infants to build and strengthen skills essential for optimal development.

Acknowledgments

This research was supported by grants from the National Institutes of Health (HD42235 and DC008838). We are grateful to the children and parents who participated in this study, and to our community partners in Northern California who enabled us to conduct this study. Special thanks to Krisa Bruemer, Jillian Maes, Viviana Limón, Lucia Martínez, Nereyda Hurtado, Poornima Bhat, Ricardo Hoffmann Bion, Kyle McDonald, Katherine Adams, Mofeda Dababo, and the staff of the Center for Infant Studies at Stanford University.

• Alexander K, Entwisle D, Horsey C. From first grade forward: Early foundations of high school dropout. Sociology of Education. 1997; 70 :87–107. [ Google Scholar ]
• Algarín C, Peirano P, Garrido M, Pizarro F, Lozoff B. Iron deficiency anemia in infancy: Long-lasting effects on auditory and visual system functioning. Pediatric Research. 2003; 53 :217–223. [ PubMed ] [ Google Scholar ]
• Arnett JJ. The neglected 95%: Why American psychology needs to become less American. American Psychologist. 2008; 63 :602–614. [ PubMed ] [ Google Scholar ]
• Arriaga RI, Fenson L, Cronan T, Pethick SJ. Scores on the MacArthur Communicative Development Inventory of children from low- and middle-incomes families. Applied Psycholinguistics. 1998; 19 :209–223. [ Google Scholar ]
• Bates E, Dale P, Thal D. Individual differences and their implications for theories of language development. In: Fletcher P, MacWhinney B, editors. Handbook of Child Language. Blackwell; Oxford: 1994. [ Google Scholar ]
• Beard JL, Wiesinger JA, Connor JR. Pre- and post-weaning iron deficiency alters myelination in Sprague-Dawley rats. Developmental Neuroscience. 2003; 25 :308–315. [ PubMed ] [ Google Scholar ]
• Bereiter C, Engelmann S. Teaching disadvantaged children in the preschool. Prentice Hall; Englewood Cliffs, NJ: 1966. [ Google Scholar ]
• Bradley RH, Corwyn RF. Socioeconomic status and child development. Annual Review of Psychology. 2002; 53 :371–399. [ PubMed ] [ Google Scholar ]
• Burchinal M, McCartney K, Steinberg L, Crosnoe R, Friedman SL, McLoyd V, Pianta R, NICHD Early Child Care Research Network Examining the Black–White achievement gap among low-income children using the NICHD Study of Early Child Care and Youth Development. Child Development. 2011; 82 :1404–1420. [ PubMed ] [ Google Scholar ]
• Dale PS, Fenson L. Lexical development norms for young children. Behavior Research Methods. 1996; 28 :125–127. [ Google Scholar ]
• Deutsch M, Katz I, Jensen AR. Social class, race, and psychological development. Holt, Rinehart, & Winston; New York, NY: 1968. [ Google Scholar ]
• Dickinson DK, Tabors PO. Beginning literacy with language: Young children learning at home and school. Brookes Publishing; Baltimore, MD: 2001. [ Google Scholar ]
• Evans GW. The environment of childhood poverty. American Psychologist. 2004; 59 :77–92. [ PubMed ] [ Google Scholar ]
• Evans GW, Gonnella C, Marcynyszyn LA, Gentile L, Salpekar N. The role of chaos in poverty and children's socioemotional adjustment. Psychological Science. 2005; 16 :560–565. [ PubMed ] [ Google Scholar ]
• Farkas G, Beron K. The detailed trajectory of oral vocabulary knowledge: Differences by class and race. Social Science Research. 2004; 33 :464–497. [ Google Scholar ]
• Fenson L, Marchman VA, Thal D, Dale PS, Reznick JS, Bates E. 2nd Ed. Brookes Publishing; Baltimore, MD: 2006. MacArthur-Bates Communicative Development Inventories: User's Guide and Technical Manual. [ Google Scholar ]
• Fernald A. Getting beyond the ‘convenience sample’ in research on early cognitive development. Behavioral and Brain Sciences. 2010; 33 :91–92. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fernald A, Marchman VA. Individual differences in lexical processing at 18 months predict vocabulary growth in typically-developing and late-talking toddlers. Child Development. 2012; 83 :203–222. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fernald A, Perfors A, Marchman VA. Picking up speed in understanding: Speech processing efficiency and vocabulary growth across the 2nd year. Developmental Psychology. 2006; 42 :98–116. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fernald A, Pinto J, Swingley D, Weinberg A, McRoberts G. Rapid gains in speed of verbal processing by infants in the 2nd year. Psychological Science. 1998; 9 :72–75. [ Google Scholar ]
• Fernald A, Swingley D, Pinto J. When half a word is enough: Infants can recognize spoken words using partial phonetic information. Child Development. 2001; 72 :1003–1015. [ PubMed ] [ Google Scholar ]
• Fernald A, Weisleder A. Early language experience is vital to developing fluency in understanding. In: Neuman S, Dickinson D, editors. Handbook of Early Literacy Research. Vol. 3. Guilford Publications; New York: 2010. pp. 3–19. [ Google Scholar ]
• Fernald A, Zangl R, Portillo AL, Marchman VA. Looking while listening: Using eye movements to monitor spoken language comprehension by infants and young children. In: Sekerina I, Fernández EM, Clahsen H, editors. Developmental psycholinguistics: Online methods in children's language processing. J. Benjamins; Amsterdam: 2008. pp. 97–135. [ Google Scholar ]
• Fernald LCH, Weber A, Galasso E, Ratsifandrihamanana L. Socioeconomic gradients and child development in a very low income population: Evidence from Madagascar. Developmental Science. 2011; 14 :832–847. [ PubMed ] [ Google Scholar ]
• Fry AF, Hale S. Processing speed, working memory and fluid intelligence: Evidence for a developmental cascade. Psychological Science. 1996; 7 :237–241. [ Google Scholar ]
• Gorski P. The classist underpinnings of Ruby Payne's framework. Teachers College Record. 2006; 12322 Retrieved from http://www.tcrecord.org . [ Google Scholar ]
• Hart B, Risley T. Meaningful differences in the everyday experience of young American children. Brookes Publishing; Baltimore, MD: 1995. [ Google Scholar ]
• Halle T, Forry N, Hair E, Perper K, Wandner L, Wessel J, Vick J. Disparities in early learning and development: Lessons from the Early Childhood Longitudinal Study – Birth Cohort (ECLS-B) Child Trends; Washington, DC: 2009. [ Google Scholar ]
• Hanscombe KB, Trzaskowski M, Haworth CMA, Davis OSP, Dale PS, Plomin PS. Socioeconomic status (SES) and children's intelligence (IQ): In a UK-representative sample: SES Moderates the environmental, not genetic, effect on IQ. PLoS ONE. 2012; 7 (2):e30320. doi:10.1371/journal.pone.0030320. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Heath SB. Ways with words: Language, life, and work in communities and classrooms. Cambridge University Press; New York, NY: 1983. [ Google Scholar ]
• Henrich J, Heine SJ, Norenzayan A. The weirdest people in the world? Behavioral and Brain Sciences. 2010; 33 :61–83. [ PubMed ] [ Google Scholar ]
• Hoff E. The specificity of environmental influence: Socioeconomic status affects early vocabulary development via maternal speech. Child Development. 2003; 74 :1368–1878. [ PubMed ] [ Google Scholar ]
• Hoff E. How social contexts support and shape language development. Developmental Review. 2006; 26 :55–88. [ Google Scholar ]
• Hoff-Ginsberg E. The relation of birth order and socioeconomic status to children's language experience and language development. Applied Psycholinguistics. 1998; 19 :603–629. [ Google Scholar ]
• Hollingshead AB. Four factor index of social status. Yale University; New Haven, CT: 1975. Unpublished manuscript. [ Google Scholar ]
• Hurtado N, Marchman V, Fernald A. Spoken word recognition by Latino children learning Spanish as their first language. Journal of Child Language. 2007; 34 :227–249. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hurtado N, Marchman VA, Fernald A. Does input influence uptake? Links between maternal talk, processing speed and vocabulary size in Spanish-learning children. Developmental Science. 2008; 11 :F31–F39. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Huttenlocher J, Haight W, Bryk A, Seltzer M. Early vocabulary growth: Relation to language input and gender. Developmental Psychology. 1991; 27 :236–248. [ Google Scholar ]
• Kail R, Salthouse T. Processing speed as a mental capacity. Acta Pscyhologica. 1994; 86 :199–225. [ PubMed ] [ Google Scholar ]
• Knudsen EI, Heckman JJ, Cameron JL, Shonkoff JP. Economic, neurobiological, and behavioral perspectives on building America's future workforce. Proceedings of the National Academy of Sciences. 2006; 103 :10155–10162. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lee VE, Burkam DT. Inequality at the starting gate: Social background differences in achievement as children begin school. Economic Policy Institute; Washington, DC: 2002. [ Google Scholar ]
• Lonigan CJ, Burgess SR, Anthony JL. Development of emergent literacy and early reading skills in preschool children: Evidence from a latent variable longitudinal study. Developmental Psychology. 2000; 36 :596–613. [ PubMed ] [ Google Scholar ]
• Marchman VA, Fernald A. Speed of word recognition and vocabulary knowledge in infancy predict cognitive and language outcomes in later childhood. Developmental Science. 2008; 11 :F9–F16. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Matarazzo JD. Wechsler's Measurement and Appraisal of Adult Intelligence. 5th ed. Williams and Wilkins; Baltimore, MD: 1972. [ Google Scholar ]
• Mesman J, van IJzendoorn MH, Bakermans-Kranenburg MJ. Unequal in opportunity, equal in process: Parental sensitivity promotes child development in ethnic minority families. Child Development Perspectives. 2011 doi: 10.1111/j.1750-8606.2011.00223.x. [ Google Scholar ]
• Nelson KE, Welsh JA, Vance Trup EM, Greenberg MT. Language delays of impoverished preschool children in relation to early academic and emotion recognition skills. First Language. 2011; 31 :164–194. [ Google Scholar ]
• Oliver B, Dale PS, Plomin R. Verbal and nonverbal predictors of early language problems: An analysis of twins in early childhood back to infancy. Journal of Child Language. 2004; 31 :609–631. [ PubMed ] [ Google Scholar ]
• Oliver BR, Plomin R. Twins’ Early Development Study (TEDS): A multivariate, longitudinal genetic investigation of language, cognition and behavior problems from childhood through adolescence. Twin Research and Human Genetics. 2007; 10 :96–105. [ PubMed ] [ Google Scholar ]
• Pakulak E, Neville H. Proficiency differences in syntactic processing of monolingual native speakers indexed by event-related potentials. Journal of Cognitive Neuroscience. 2010; 22 :2729–2744. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pollitt E. Iron deficiency and cognitive function. Annual Review of Nutrition. 1993; 13 :521–537. [ PubMed ] [ Google Scholar ]
• Ramey CT, Ramey SL. Early learning and school readiness: Can early intervention make a difference? Merrill-Palmer Quarterly. 2004; 50 :471–491. [ Google Scholar ]
• Riessman F. The Culturally Deprived Child. Harper & Row; New York, NY: 1962. [ Google Scholar ]
• Rowe DC, Jacobson KC, Van den Oord EJCG. Genetic and environmental influences on vocabulary IQ: Parental education level as moderator. Child Development. 1999; 70 :1151–1162. [ PubMed ] [ Google Scholar ]
• Ryan W. Blaming the victim. Pantheon Books; New York, NY: 1971. [ Google Scholar ]
• Salthouse T. The processing-speed theory of adult age differences in cognition. Psychological Review. 1996; 103 :403–428. [ PubMed ] [ Google Scholar ]
• Swingley D, Pinto J, Fernald A. Continuous processing in word recognition at 24 months. Cognition. 1999; 71 :73–108. [ PubMed ] [ Google Scholar ]
• Tucker-Drob EM, Rhemtulla M, Harden KP, Turkheimer E, Fask D. Emergence of a gene-by-socioeconomic status interaction on infant mental ability from 10 months to 2 years. Psychological Science. 2011; 22 :125–133. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Turkheimer E, Haley A, Waldron M, D'Onofrio BD, Gottesman II. Socioeconomic status modifies heritability of IQ in young children. Psychological Science. 2003; 14 :623–628. [ PubMed ] [ Google Scholar ]
• Vance B, West K, Kutsick K. Prediction of Wechsler pre-school and primary scale of intelligence IQ scores for pre-school children using the PPVT Test-R and the expressive one word picture vocabulary test. Journal of Clinical Psychology. 1989; 45 :642–644. [ Google Scholar ]
• Walker D, Greenwood C, Hart B, Carta J. Prediction of school outcomes based on early language production and socioeconomic factors. Child Development. 1994; 65 :606–621. [ PubMed ] [ Google Scholar ]
• Weisleder A, Fernald A. Talking to infants strengthens early language processing abilities and vocabulary learning. under review.

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NSF announces 4 new Engineering Research Centers focused on biotechnology, manufacturing, robotics and sustainability

Engineering innovations transform our lives and energize the economy.  The U.S. National Science Foundation announces a five-year investment of $104 million, with a potential 10-year investment of up to $208 million, in four new NSF Engineering Research Centers (ERCs) to create technology-powered solutions that benefit the nation for decades to come.   

"NSF's Engineering Research Centers ask big questions in order to catalyze solutions with far-reaching impacts," said NSF Director Sethuraman Panchanathan. "NSF Engineering Research Centers are powerhouses of discovery and innovation, bringing America's great engineering minds to bear on our toughest challenges. By collaborating with industry and training the workforce of the future, ERCs create an innovation ecosystem that can accelerate engineering innovations, producing tremendous economic and societal benefits for the nation."  

The new centers will develop technologies to tackle the carbon challenge, expand physical capabilities, make heating and cooling more sustainable and enable the U.S. supply and manufacturing of natural rubber.  

The 2024 ERCs are:  

• NSF ERC for Carbon Utilization Redesign through Biomanufacturing-Empowered Decarbonization (CURB) — Washington University in St. Louis in partnership with the University of Delaware, Prairie View A&M University and Texas A&M University.   CURB will create manufacturing systems that convert CO2 to a broad range of products much more efficiently than current state-of-the-art engineered and natural systems.    
• NSF ERC for Environmentally Applied Refrigerant Technology Hub (EARTH) — University of Kansas in partnership with Lehigh University, University of Hawaii, University of Maryland, University of Notre Dame and University of South Dakota.   EARTH will create a transformative, sustainable refrigerant lifecycle to reduce global warming from refrigerants while increasing the energy efficiency of heating, ventilation and cooling.    
• NSF ERC for Human AugmentatioN via Dexterity (HAND) — Northwestern University in partnership with Carnegie Mellon University, Florida A&M University, and Texas A&M University, and with engagement of MIT.  HAND will revolutionize the ability of robots to augment human labor by transforming dexterous robot hands into versatile, easy-to-integrate tools.     
• NSF ERC for Transformation of American Rubber through Domestic Innovation for Supply Security (TARDISS) — The Ohio State University in partnership with Caltech, North Carolina State University, Texas Tech University and the University of California, Merced.   TARDISS will create bridges between engineering, biology, and agriculture to revolutionize and on-shore alternative natural rubber production from U.S. crops.  

Since its founding in 1985, NSF's ERC program has funded 83 centers (including the four announced today) that receive support for up to 10 years. The centers build partnerships with educational institutions, government agencies and industry stakeholders to support innovation and inclusion in established and emerging engineering research.  

Visit NSF's website and read about NSF Engineering Research Centers .  

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