Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-20T04:16:40.565Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimal, resource-rational or sub-optimal? Insights from cognitive development

Published online by Cambridge University Press:  11 March 2020

Vikranth R. Bejjanki Open the ORCID record for Vikranth R. Bejjanki [Opens in a new window]  and
Richard N. Aslin
Vikranth R. Bejjanki
Affiliation:
Department of Psychology and Program in Neuroscience, Hamilton College, Clinton, NY13323bejjanki@hamilton.eduhttps://www.hamilton.edu/academics/our-faculty/directory/faculty-detail/bejjanki-rao
Richard N. Aslin
Affiliation:
Haskins Laboratories, New Haven, CT06511. richard.aslin@yale.eduhttps://haskinslabs.org/people/richard-aslin
Get access Rights & Permissions [Opens in a new window]

Abstract

We agree with the authors regarding the utility of viewing cognition as resulting from an optimal use of limited resources. Here, we advocate for extending this approach to the study of cognitive development, which we feel provides particularly powerful insight into the debate between bounded optimality and true sub-optimality, precisely because young children have limited computational and cognitive resources.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e4
Check for updates
Copyright
Copyright © Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barutchu, A., Crewther, D. P. & Crewther, S. G. (2008) The race that precedes coactivation: Development of multisensory facilitation in children. Developmental Science 12(3):464–73. doi:10.1111/j.1467-7687.2008.00782.x.CrossRefGoogle Scholar
Bejjanki, V. R., Knill, D. C. & Aslin, R. N. (2016) Learning and inference using complex generative models in a spatial localization task. Journal of Vision 16(5):9. doi:10.1167/16.5.9.CrossRefGoogle Scholar
Bejjanki, V. R., Randrup, E. R. & Aslin, R. N. (2019) Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters. Developmental Science e12912. doi:10.1111/desc.12912.Google Scholar
Berniker, M., Voss, M. & Kording, K. (2010) Learning priors for Bayesian computations in the nervous system. PLoS One 5(9):e12686. doi:10.1371/journal.pone.0012686.CrossRefGoogle ScholarPubMed
Best, J. R. & Miller, P. H. (2010) A developmental perspective on executive function. Child Development 81(6):1641–60.CrossRefGoogle ScholarPubMed
Carlson, S. M., Zelazo, P. D. & Faja, S. (2013) Executive function. In: The Oxford handbook of developmental psychology: Vol. 1. Body and mind, ed. Zelazo, P. D., pp. 706–742. Oxford University Press.Google Scholar
Chambers, C., Sokhey, T., Gaebler-Spira, D. & Kording, K. P. (2018) The development of Bayesian integration in sensorimotor estimation. Journal of Vision 18(12):8.CrossRefGoogle ScholarPubMed
Davidson, M. C., Amso, D., Anderson, L. C. & Diamond, A. (2006) Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia 44(11):2037–78.CrossRefGoogle ScholarPubMed
Elman, J. L. (1993) Learning and development in neural networks: The importance of starting small. Cognition 48(1):71–99. doi:10.1016/0010-0277(93)90058-4.CrossRefGoogle ScholarPubMed
Fiser, J. & Aslin, R. N. (2002) Statistical learning of new visual feature combinations by infants. Proceedings of the National Academy of Sciences of the United States of America 99(24):15822–26.CrossRefGoogle ScholarPubMed
Gopnik, A., Sobel, D. M., Schulz, L. E. & Glymour, C. (2001) Causal learning mechanisms in very young children: Two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation. Developmental Psychology 37(5):620–29.CrossRefGoogle ScholarPubMed
Gori, M., Del Viva, M., Sandini, G. & Burr, D. C. (2008) Young children do not integrate visual and haptic form information. Current Biology 18(9):694–98. doi:10.1016/j.cub.2008.04.036.CrossRefGoogle Scholar
Hudson Kam, C. L. & Newport, E. L. (2005) Regularizing unpredictable variation: The roles of adult and child learners in language formation and change. Language Learning and Development 1(2):151–95.CrossRefGoogle Scholar
Jazayeri, M. & Shadlen, M. N. (2010) Temporal context calibrates interval timing. Nature Neuroscience 13(8):1020–26. doi:10.1038/nn.2590.CrossRefGoogle ScholarPubMed
Jusczyk, P. W. & Aslin, R. N. (1995) Infants’ detection of the sound patterns of words in fluent speech. Cognitive Psychology 29(1):1–23. doi:10.1006/cogp.1995.1010.CrossRefGoogle ScholarPubMed
Kersten, A. W. & Earles, J. L. (2001) Less really is more for adults learning a miniature artificial language. Journal of Memory and Language 44(2):250–73.CrossRefGoogle Scholar
Kirkham, N. Z., Slemmer, J. A. & Johnson, S. P. (2002) Visual statistical learning in infancy: Evidence for a domain general learning mechanism. Cognition 83(2):B35–B42.CrossRefGoogle ScholarPubMed
Körding, K. P. & Wolpert, D. M. (2004) Bayesian integration in sensorimotor learning. Nature 427(6971):244–47. doi:10.1038/nature02169.CrossRefGoogle ScholarPubMed
Kuhl, P. K. & Meltzoff, A. N. (1982) The bimodal perception of speech in infancy. Science 218(4577):1138–41.CrossRefGoogle ScholarPubMed
Kwon, O.-S. & Knill, D. C. (2013) The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning. Proceedings of the National Academy of Sciences 110(11):E1064–73. doi:10.1073/pnas.1214869110.CrossRefGoogle ScholarPubMed
Luciana, M. & Nelson, C. A. (1998) The functional emergence of prefrontally-guided working memory systems in four- to eight-year-old children. Neuropsychologia 36(3):273–93.CrossRefGoogle ScholarPubMed
Nardini, M., Bedford, R. & Mareschal, D. (2010) Fusion of visual cues is not mandatory in children. Proceedings of the National Academy of Sciences 107(39):17041–46. doi:10.1073/pnas.1001699107.CrossRefGoogle Scholar
Nardini, M., Jones, P., Bedford, R. & Braddick, O. (2008) Development of cue integration in human navigation. Current Biology 18(9):689–93. doi:10.1016/j.cub.2008.04.021.CrossRefGoogle ScholarPubMed
Neil, P. A., Chee-Ruiter, C., Scheier, C., Lewkowicz, D. J. & Shimojo, S. (2006) Development of multisensory spatial integration and perception in humans. Developmental Science 9(5):454–64.CrossRefGoogle ScholarPubMed
Newport, E. L. (1990) Maturational constraints on language learning. Cognitive Science 14(1):11–28. doi:10.1016/0364-0213(90)90024-Q.CrossRefGoogle Scholar
Petrini, K., Remark, A., Smith, L. & Nardini, M. (2014) When vision is not an option: Children's integration of auditory and haptic information is suboptimal. Developmental Science 17(3):376–87. doi:10.1111/desc.12127.CrossRefGoogle Scholar
Saffran, J. R., Aslin, R. N. & Newport, E. L. (1996) Statistical learning by 8-month-old infants. Science 274(5294):1926–28. doi:10.1126/science.274.5294.1926.CrossRefGoogle ScholarPubMed
Stocker, A. A. & Simoncelli, E. P. (2006) Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience 9(4):578–85.CrossRefGoogle ScholarPubMed
Tassinari, H., Hudson, T. E. & Landy, M. S. (2006) Combining priors and noisy visual cues in a rapid pointing task. The Journal of Neuroscience 26(40):10154–63. doi:10.1523/JNEUROSCI.2779-06.2006.CrossRefGoogle Scholar
Xu, F. & Garcia, V. (2008) Intuitive statistics by 8-month-old infants. Proceedings of the National Academy of Sciences 105(13):5012–15. doi:10.1073/pnas.0704450105.CrossRefGoogle ScholarPubMed
Zelazo, P. D., Anderson, J. E., Richler, J., Wallner-Allen, K., Beaumont, J. L. & Weintraub, S. (2013) NIH toolbox cognition battery (CB): Measuring executive function and attention. Monographs of the Society for Research in Child Development 78(4):16–33.CrossRefGoogle ScholarPubMed