Hostname: page-component-848d4c4894-mwx4w Total loading time: 0 Render date: 2024-06-20T12:28:23.045Z Has data issue: false hasContentIssue false

Going after the bigger picture: Using high-capacity models to understand mind and brain

Published online by Cambridge University Press:  06 December 2023

Hans Op de Beeck
Affiliation:
Leuven Brain Institute, KU Leuven, Leuven, Belgium hans.opdebeeck@kuleuven.be www.hoplab.be
Stefania Bracci
Affiliation:
Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy stefania.bracci@unitn.it https://webapps.unitn.it/du/en/Persona/PER0076943/Curriculum

Abstract

Deep neural networks (DNNs) provide a unique opportunity to move towards a generic modelling framework in psychology. The high representational capacity of these models combined with the possibility for further extensions has already allowed us to investigate the forest, namely the complex landscape of representations and processes that underlie human cognition, without forgetting about the trees, which include individual psychological phenomena.

Type
Open Peer Commentary
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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

Ali, A., Ahmad, N., de Groot, E., van Gerven, M. A. J., & Kietzmann, T. C. (2022). Predictive coding is a consequence of energy efficiency in recurrent neural networks. Patterns, 3(12), 100639.CrossRefGoogle ScholarPubMed
Avberšek, L. K., Zeman, A., & Op de Beeck, H. (2021). Training for object recognition with increasing spatial frequency: A comparison of deep learning with human vision. Journal of Vision, 21(10), 1414.CrossRefGoogle ScholarPubMed
Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94(2), 115.CrossRefGoogle ScholarPubMed
Bracci, S., & Op de Beeck, H. (2016). Dissociations and associations between shape and category representations in the two visual pathways. Journal of Neuroscience, 36(2), 432444.CrossRefGoogle ScholarPubMed
Bracci, S., & Op de Beeck, H. P. (2023). Understanding human object vision: A picture is worth a thousand representations. Annual Review of Psychology, 74, 113135.CrossRefGoogle Scholar
Dobs, K., Martinez, J., Kell, A. J., & Kanwisher, N. (2022). Brain-like functional specialization emerges spontaneously in deep neural networks. Science Advances, 8(11), eabl8913.CrossRefGoogle ScholarPubMed
Doerig, A., Sommers, R. P., Seeliger, K., Richards, B., Ismael, J., Lindsay, G. W., … Kietzmann, T. C. (2023). The neuroconnectionist research programme. Nature Reviews Neuroscience, 24(7), 431450.CrossRefGoogle ScholarPubMed
Duyck, S., Bracci, S., & Op de Beeck, H. (2022). A computational understanding of zoomorphic perception in the human brain. bioRxiv, 2022-09.Google Scholar
Elmoznino, E., & Bonner, M. F. (2022). High-performing neural network models of visual cortex benefit from high latent dimensionality. bioRxiv, 2022-07.Google Scholar
Elsayed, G., Shankar, S., Cheung, B., Papernot, N., Kurakin, A., Goodfellow, I., & Sohl-Dickstein, J. (2018). Adversarial examples that fool both computer vision and time-limited humans. Advances in Neural Information Processing Systems, 31.Google Scholar
Firestone, C. (2020). Performance vs. competence in human–machine comparisons. Proceedings of the National Academy of Sciences of the United States of America, 117(43), 2656226571.CrossRefGoogle ScholarPubMed
Grootswagers, T., & Robinson, A. K. (2021). Overfitting the literature to one set of stimuli and data. Frontiers in Human Neuroscience, 15, 682661.CrossRefGoogle ScholarPubMed
Grossberg, S. (1987). Competitive learning: From interactive activation to adaptive resonance. Cognitive Science, 11(1), 2363.CrossRefGoogle Scholar
Hummel, J. E., & Biederman, I. (1992). Dynamic binding in a neural network for shape recognition. Psychological Review, 99(3), 480.CrossRefGoogle Scholar
Jinsi, O., Henderson, M. M., & Tarr, M. J. (2023). Early experience with low-pass filtered images facilitates visual category learning in a neural network model. PLoS ONE, 18(1), e0280145.CrossRefGoogle Scholar
Kanwisher, N., Gupta, P., & Dobs, K. (2023). CNNs reveal the computational implausibility of the expertise hypothesis. iScience, 105976.CrossRefGoogle ScholarPubMed
Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99(1), 22.CrossRefGoogle ScholarPubMed
Kubilius, J., Bracci, S., & Op de Beeck, H. P. (2016). Deep neural networks as a computational model for human shape sensitivity. PLoS Computational Biology, 12(4), e1004896.CrossRefGoogle ScholarPubMed
McClelland, J. L., Rumelhart, D. E., & PDP Research Group. (1986). Parallel distributed processing (Vol. 2, pp. 2021). MIT Press.Google Scholar
Naselaris, T., Bassett, D. S., Fletcher, A. K., Kording, K., Kriegeskorte, N., Nienborg, H., … Kay, K. (2018). Cognitive computational neuroscience: A new conference for an emerging discipline. Trends in Cognitive Sciences, 22(5), 365367.CrossRefGoogle Scholar
Piloto, L. S., Weinstein, A., Battaglia, P., & Botvinick, M. (2022). Intuitive physics learning in a deep-learning model inspired by developmental psychology. Nature Human Behaviour, 6(9), 12571267.CrossRefGoogle Scholar
Ratan Murty, N. A., Bashivan, P., Abate, A., DiCarlo, J. J., & Kanwisher, N. (2021). Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications, 12(1), 5540.CrossRefGoogle ScholarPubMed
Ritchie, J. B., & Op de Beeck, H. (2019). A varying role for abstraction in models of category learning constructed from neural representations in early visual cortex. Journal of Cognitive Neuroscience, 31(1), 155173.CrossRefGoogle ScholarPubMed
Singer, J. J., Seeliger, K., Kietzmann, T. C., & Hebart, M. N. (2022). From photos to sketches – How humans and deep neural networks process objects across different levels of visual abstraction. Journal of Vision, 22(2), 44.CrossRefGoogle ScholarPubMed
Zeman, A. A., Ritchie, J. B., Bracci, S., & Op de Beeck, H. (2020). Orthogonal representations of object shape and category in deep convolutional neural networks and human visual cortex. Scientific Reports, 10(1), 2453.CrossRefGoogle ScholarPubMed