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Investigating infant knowledge with representational similarity analysis

Published online by Cambridge University Press:  27 June 2024

Cameron T. Ellis Open the ORCID record for Cameron T. Ellis [Opens in a new window]
Cameron T. Ellis*
Affiliation:
Department of Psychology, Stanford University, Palo Alto, CA, USA [email protected]
*
*Corresponding author.
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Abstract

Decades of research have pushed us closer to understanding what babies know. However, a powerful approach – representational similarity analysis (RSA) – is underused in developmental research. I discuss the strengths of this approach and what it can tell us about infant conceptual knowledge. As a case study, I focus on numerosity as a domain where RSA can make unique progress.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e126
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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References

Bankson, B. B., Hebart, M. N., Groen, I. I., & Baker, C. I. (2018). The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks. NeuroImage, 178, 172–182.CrossRefGoogle ScholarPubMed
Carlson, T. A., Schrater, P., & He, S. (2003). Patterns of activity in the categorical representations of objects. Journal of Cognitive Neuroscience, 15(5), 704–717.CrossRefGoogle ScholarPubMed
Clearfield, M. W., & Mix, K. S. (1999). Number versus contour length in infants’ discrimination of small visual sets. Psychological Science, 10(5), 408–411.CrossRefGoogle Scholar
Ellis, C. T., Skalaban, L. J., Yates, T. S., Bejjanki, V. R., Córdova, N. I., & Turk-Browne, N. B. (2020). Re-imagining fMRI for awake behaving infants. Nature Communications, 11, 4523.CrossRefGoogle ScholarPubMed
Ellis, C. T., Yates, T. S., Skalaban, L. J., Bejjanki, V. R., Arcaro, M. J., & Turk-Browne, N. B. (2021). Retinotopic organization of visual cortex in human infants. Neuron, 109, 1–11.CrossRefGoogle ScholarPubMed
Hyde, D. C., Boas, D. A., Blair, C., & Carey, S. (2010). Near-infrared spectroscopy shows right parietal specialization for number in pre-verbal infants. NeuroImage, 53(2), 647–652.CrossRefGoogle ScholarPubMed
Hyde, D. C., & Spelke, E. S. (2011). Neural signatures of number processing in human infants: Evidence for two core systems underlying numerical cognition. Developmental Science, 14(2), 360–371.CrossRefGoogle ScholarPubMed
Kosakowski, H. L., Cohen, M. A., Takahashi, A., Keil, B., Kanwisher, N., & Saxe, R. (2022). Selective responses to faces, scenes, and bodies in the ventral visual pathway of infants. Current Biology, 32(2), 265–274.CrossRefGoogle ScholarPubMed
Kriegeskorte, N., Mur, M., & Bandettini, P. A. (2008). Representational similarity analysis-connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 4, 1–28.Google Scholar
Leibovich, T., Katzin, N., Harel, M., & Henik, A. (2017). From “sense of number” to “sense of magnitude”: The role of continuous magnitudes in numerical cognition. Behavioral and Brain Sciences, 40, e164.CrossRefGoogle Scholar
Lyons, I. M., Ansari, D., & Beilock, S. L. (2015). Qualitatively different coding of symbolic and nonsymbolic numbers in the human brain. Human Brain Mapping, 36(2), 475–488.CrossRefGoogle ScholarPubMed
McCrink, K., & Wynn, K. (2004). Large-number addition and subtraction by 9-month-old infants. Psychological Science, 15(11), 776–781.CrossRefGoogle ScholarPubMed
Meck, W. H., & Church, R. M. (1983). A mode control model of counting and timing processes. Journal of Experimental Psychology: Animal Behavior Processes, 9(3), 320.Google ScholarPubMed
Sokolowski, H. M., Fias, W., Ononye, C. B., & Ansari, D. (2017). Are numbers grounded in a general magnitude processing system? A functional neuroimaging meta-analysis. Neuropsychologia, 105, 50–69.CrossRefGoogle Scholar
Spelke, E. S. (2022). What babies know: Core knowledge and composition. Oxford University Press.CrossRefGoogle Scholar
Spriet, C., Abassi, E., Hochmann, J. R., & Papeo, L. (2022). Visual object categorization in infancy. Proceedings of the National Academy of Sciences of the United States of America, 119(8), e2105866119.CrossRefGoogle ScholarPubMed
Starr, A., Libertus, M. E., & Brannon, E. M. (2013). Infants show ratio-dependent number discrimination regardless of set size. Infancy, 18(6), 927–941.CrossRefGoogle ScholarPubMed
Uller, C., Carey, S., Huntley-Fenner, G., & Klatt, L. (1999). What representations might underlie infant numerical knowledge? Cognitive Development, 14(1), 1–36.CrossRefGoogle Scholar
Xie, S., Hoehl, S., Moeskops, M., Kayhan, E., Kliesch, C., Turtleton, B., … Cichy, R. M. (2022). Visual category representations in the infant brain. Current Biology, 32(24), 5422–5432.CrossRefGoogle ScholarPubMed
Xu, F. (2003). Numerosity discrimination in infants: Evidence for two systems of representations. Cognition, 89(1), B15–B25.CrossRefGoogle ScholarPubMed
Yates, T. S., Skalaban, L. J., Ellis, C. T., Bracher, A. J., Baldassano, C., & Turk-Browne, N. B. (2022). Neural event segmentation of continuous experience in human infants. Proceedings of the National Academy of Sciences of the United States of America, 119(43), e2200257119.CrossRefGoogle ScholarPubMed
Yousif, S. R., & Keil, F. C. (2021). How we see area and why it matters. Trends in Cognitive Sciences, 25(7), 554–557.CrossRefGoogle ScholarPubMed