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Cited by 41Visual perception and processing mechanismsFace Recognition and PerceptionChild and Animal Learning Development

The DOI matches its Crossref record; 255 of the 255 references it cites were found in a registry or an index.

Development of visual object recognition

Ayzenberg, Vladislav, Behrmann, Marlene·2023·Nature Reviews Psychology
DOI 10.1038/s44159-023-00266-w
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Research works printed but not linked: 17

  1. Lake, B., Salakhutdinov, R., Gross, J. & Tenenbaum, J. One shot learning of simple visual concepts. In Proc. Ann. Meet. Cogn. Sci. Soc. (CogSci, 2011).
  2. Kanwisher, N. & Dilks, D. D. in The New Visual Neuroscience (eds Chalupa, L. & J. Werner, J.) 733–748 (MIT Press, 2012).
  3. Zachariou, V., Nikas, C. V., Safiullah, Z. N., Gotts, S. J. & Ungerleider, L. G. Spatial mechanisms within the dorsal visual pathway contribute to the configural processing of faces. Cereb. Cortex 27, 4124–4138 (2017).
  4. Atkinson, J. Human visual development over the first 6 months of life. A review and a hypothesis. Hum. Neurobiol. 3, 61–74 (1983).
  5. Harlow, H. F. The development of learning in the rhesus monkey. Sci. Prog. 12, 239–269 (1959).
  6. Guo, C. et al. Adversarially trained neural representations are already as robust as biological neural representations. In Proc. Int. Conf. Machine Learning Vol. 162 (eds Chaudhuri, K. et al.) 8072–8081 (Proc. Machine Learning Research, 2022).
  7. Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M. & Boyes-Braem, P. in Cognitive Psychology: Key Readings (eds Balota, D. A. & Marsh, E. J.) 448–471 (Psychology Press, 2004).
  8. Tartaglini, A. R., Vong, W. K. & Lake, B. M. A developmentally-inspired examination of shape versus texture bias in machines. Preprint at arXiv https://arxiv.org/abs/1811.12231 (2022). · arXiv
  9. Huber, L. S., Geirhos, R. & Wichmann, F. A. A four-year-old can outperform ResNet-50: out-of-distribution robustness may not require large-scale experience. In 3rd Worksh. on Shared Visual Representations in Human and Machine Intelligence (SVRHM) (NeurIPS, 2021).
  10. Lee, D., Gujarathi, P. & Wood, J. N. Controlled-rearing studies of newborn chicks and deep neural networks. In 3rd Worksh. on Shared Visual Representations in Human and Machine Intelligence (SVRHM) (NeurIPS, 2021).
  11. Orhan, E. A., Gupta, P. V. & Lake, B. M. Self-supervised learning through the eyes of a child. In Advances in Neural Information Processing Systems 116 (NeurIPS, 2021).
  12. Pak, D., Lee, D., Wood, S. M. & Wood, J. N. A newborn embodied Turing test for view-invariant object recognition. Preprint at arXiv https://arxiv.org/abs/2306.05582 (2023). · arXiv
  13. Bakhtiari, S., Mineault, P., Lillicrap, T., Pack, C. & Richards, B. The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning. Adv. Neural Inf. Process. Syst. 34, 25164–25178 (2021).
  14. Zhu, M. & Gupta, S. To prune, or not to prune: exploring the efficacy of pruning for model compression. Preprint at arXiv https://arxiv.org/abs/1710.01878 (2017). · arXiv
  15. Bachevalier, J., Hagger, C. & Mishkin, M. in Alfred Benzon Symposium Vol. 31 Brain Work And Mental Activity (eds Lassen, N. A. et al.) 231–240 (Munksgaard, 1991).
  16. Grotheer, M. et al. Human white matter myelination rate slows down at birth. Preprint at bioRxiv https://doi.org/10.1101/2023.03.02.530800v1 (2023). · DOI
  17. Conwell, C., Prince, J. S., Alvarez, G. A. & Konkle, T. What can 5.17 billion regression fits tell us about artificial models of the human visual system? In 3rd Worksh. on Shared Visual Representations in Human and Machine Intelligence (SVRHM) (NeurIPS, 2021).

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Metadata

Authors
Ayzenberg, V., & Behrmann, M.
Affiliations
  • Carnegie Mellon University (US)
  • University of Pittsburgh (US)
Corresponding author
  • Ayzenberg, Vladislav
  • Behrmann, Marlene
ORCID
Venue
Nature Reviews Psychology
Publisher
Springer Science and Business Media LLC
Published
2023-12-22
Volume
3
Issue
2
Pages
73-90
ISSN
2731-0574 (ISSN-L)
DOI
10.1038/s44159-023-00266-w
OpenAlex
W4390104281
Cited by
41
References
252
Citations by year
2026: 19 · 2025: 15 · 2024: 7
FWCI
1.54
Type
journal article
Language
en
Open access
closed
Licence
Locations
Nature Reviews Psychology (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
  • Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
Keywords
object recognition, infant visual development, ventral stream, infant vision, visual system maturation, child development

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