Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-25T10:18:05.141Z Has data issue: false hasContentIssue false
  • English
  • Français

Observer models of perceptual development

Published online by Cambridge University Press:  10 January 2019

Marko Nardini Open the ORCID record for Marko Nardini [Opens in a new window]  and
Tessa M. Dekker
Marko Nardini
Affiliation:
Department of Psychology, Durham University, Durham DH1 3LE, United Kingdom. [email protected]://community.dur.ac.uk/marko.nardini/
Tessa M. Dekker
Affiliation:
Department of Experimental Psychology and Institute of Ophthalmology, University College London, London WC1E 6BT, United Kingdom. [email protected]://www.ucl.ac.uk/~ucjttb1/
Get access Rights & Permissions [Opens in a new window]

Abstract

We agree with Rahnev & Denison (R&D) that to understand perception at a process level, we must investigate why performance sometimes deviates from idealised decision models. Recent research reveals that such deviations from optimality are pervasive during perceptual development. We argue that a full understanding of perception requires a model of how perceptual systems become increasingly optimised during development.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e238
Copyright
Copyright © Cambridge University Press 2018 

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

Adams, R. J. & Courage, M. L. (2002) Using a single test to measure human contrast sensitivity from early childhood to maturity. Vision Research 42(9):1205–10. Available at: https://doi.org/10.1016/S0042-6989(02)00038-X.Google Scholar
Adams, W. J., Graf, E. W. & Ernst, M. O. (2004) Experience can change the “light-from-above” prior. Nature Neuroscience 7:1057–58. Available at: https://doi.org/10.1038/nn1312.Google Scholar
Blakemore, C. & Van Sluyters, R. C. (1975) Innate and environmental factors in the development of the kitten's visual cortex. Journal of Physiology 248(3):663–716. Available at: https://doi.org/10.1113/jphysiol.1975.sp010995.Google Scholar
Cangelosi, A., Schlesinger, M. & Smith, L. B. (2015) Developmental robotics: From babies to robots. MIT Press.Google Scholar
Dekker, T. M., Ban, H., van der Velde, B., Sereno, M. I., Welchman, A. E. & Nardini, M. (2015) Late development of cue integration is linked to sensory fusion in cortex. Current Biology 25(21): 2856–61. Available at: https://doi.org/10.1016/j.cub.2015.09.043.Google Scholar
Dekker, T. M. & Nardini, M. (2016) Risky visuomotor choices during rapid reaching in childhood. Developmental Science 19(3):427–39. Available at: https://doi.org/10.1111/desc.12322.Google Scholar
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. Available at: https://doi.org/10.1016/j.cub.2008.04.036.Google Scholar
Hadad, B. S., Maurer, D. & Lewis, T. L. (2011) Long trajectory for the development of sensitivity to global and biological motion. Developmental Science 14:1330–39. Available at: https://doi.org/10.1111/j.1467-7687.2011.01078.x.Google Scholar
Jones, P. R. & Dekker, T. M. (2017) The development of perceptual averaging: Learning what to do, not just how to do it. Developmental Science 21:e12584. Available at: https://doi.org/10.1111/desc.12584.Google Scholar
Lawson, R. P., Mathys, C. & Rees, G. (2017) Adults with autism overestimate the volatility of the sensory environment. Nature Neuroscience 20(9):1293–99. Available at: https://doi.org/10.1038/nn.4615.Google Scholar
Manning, C., Dakin, S. C., Tibber, M. S. & Pellicano, E. (2014) Averaging, not internal noise, limits the development of coherent motion processing. Developmental Cognitive Neuroscience 10:44–56. Available at: https://doi.org/10.1016/j.dcn.2014.07.004.Google Scholar
Mondloch, C. J., Le Grand, R. & Maurer, D. (2002) Configural face processing develops more slowly than featural face processing. Perception 31:553–66. Available at: https://doi.org/10.1068/p3339.Google Scholar
Nardini, M., Bedford, R. & Mareschal, D. (2010) Fusion of visual cues is not mandatory in children. Proceedings of the National Academy of Sciences of the United States of America 107(39):17041–46. Available at: https://doi.org/10.1073/pnas.1001699107.Google Scholar
Nardini, M., Jones, P., Bedford, R. & Braddick, O. (2008) Development of cue integration in human navigation. Current Biology 18(9):689–93. Available at: https://doi.org/10.1016/j.cub.2008.04.021.Google Scholar
Ohshiro, T., Angelaki, D. E. & DeAngelis, G. C. (2011) A normalization model of multisensory integration. Nature Neuroscience 14(6):775–82. Available at: https://doi.org/10.1038/nn.2815.Google Scholar
Rosenberg, A., Patterson, J. S. & Angelaki, D. E. (2015) A computational perspective on autism. Proceedings of the National Academy of Sciences of the United States of America 112(30):9158–65. Available at: https://doi.org/10.1073/pnas.1510583112.Google Scholar
Stone, J. V. (2011) Footprints sticking out of the sand. Part 2: Children's Bayesian priors for shape and lighting direction. Perception 40(2):175–90. Available at: https://doi.org/10.1068/p6776.Google Scholar
Sweeny, T. D., Wurnitsch, N., Gopnik, A. & Whitney, D. (2015) Ensemble perception of size in 4–5-year-old children. Developmental Science 18(4):556–68. Available at: https://doi.org/10.1111/desc.12239.Google Scholar
Thomas, R., Nardini, M. & Mareschal, D. (2010) Interactions between “light-from-above” and convexity priors in visual development. Journal of Vision 10:6. Available at: https://doi.org/10.1167/10.8.6.Google Scholar
van Bergen, R. S., Ji Ma, W., Pratte, M. S. & Jehee, J. F. M. (2015) Sensory uncertainty decoded from visual cortex predicts behavior. Nature Neuroscience 18(12):1728–30. Available at: https://doi.org/10.1038/nn.4150.Google Scholar
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D. & Botivnick, M. (2017) Learning to reinforcement learn. ArXiv 1611.05763. Available at: https://arxiv.org/abs/1611.05763.Google Scholar
Wu, S.-W., Delgado, M. R. & Maloney, L. T. (2011) The neural correlates of subjective utility of monetary outcome and probability weight in economic and in motor decision under risk. Journal of Neuroscience 31(24): 8822–31. Available at: https://doi.org/10.1523/JNEUROSCI.0540-11.2011.Google Scholar