Dr. de Haas und Dr. Dobs

Factors shaping categorical face processing

Project C9 combines psychophysics, eye tracking and neuroimaging with computational modelling to understand factors shaping face processing. Traditionally, face processing has been investigated in an isolated, domain-specific manner. Here, we will probe multi-facetted factors affecting face processing, such as its plasticity to artificial features and its dependence on domain-general visual field biases and scene context. Based on these findings, we will build multiple-domain computational models allowing for a comprehensive prediction of human face processing and the factors shaping it in natural contexts.

new project-related publications
Borovska, P, de Haas, B (2023). Faces in scenes attract rapid saccades. Journal of Vision, 23(8):11, 1-15. find paper DOI
Broda, M, de Haas, B (2022). Individual fixation tendencies in person viewing generalize from images to videos. iPerception, 13(5), 1–10. find paper DOI
Broda, M, de Haas, B (2022). Individual differences in looking at persons in scenes. Journal of Vision, 22, 9. find paper DOI
Broda, M, de Haas, B (2023). Reading the mind in the nose. iPerception, 14, 2. find paper DOI
Broda, M, Haddad, T, de Haas, B (2023). Quick, eyes! Isolated upper face regions but not artifical features elicit rapid saccades. Journal of Vision, 23, 5. find paper DOI
Broda, M. D., & de Haas, B. (2023). Individual differences in rapid face-directed saccades. Journal of Vision 23, no. 9 (2023): 5451-5451. find paper
de Haas, B. (2022). What's a super-recogniser?. Journal of Vision, 23 (9), 5451-5451. find paper
Dobs, K., Martinez, J., Kell, A. J. E., Kanwisher, N. (2022). 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. find paper
Dobs, K., Yuan, J., Martinez, J., & Kanwisher, N. (2023). Behavioral signatures of face perception emerges in deep neural networks optimized for face recognition. Proceedings of the National Academy of Sciences, 120 (32), e2220642120. find paper DOI
Kanwisher, N., Gupta, P., & Dobs, K. (2023). CNNs Reveal the Computational Implausibility of the Expertise Hypothesis. iScience, 105976. find paper
Kanwisher, N., Khosla, M., & Dobs, K. (2023). Using artificial neural networks to ask ‘why’ questions of minds and brains. Trends in Neurosciences, 46, 72-88. find paper
Kollenda, D., & de Haas, B. (2022). The influence of familiarity on memory for faces and mask wearing. Cogn. Research 7, 45. find paper, DATA
Linka, M, Broda, MD, Alsheimer, T, *de Haas, B, *Ramon, M (2022). Characteristic fixation biases in Super-Recognizers. Journal of Vision, 22, 17. find paper DOI
Linka, M, Özlem, S, Karimpur, H, Schwarzer, G, de Haas, B (2023). Free viewing biases for complex scenes in preschoolers and adults. Sci Rep 13, 11803. find paper DOI
Stoll, S, Infanti, E, de Haas, B, Schwarkopf, DS (2022). Pitfalls in post hoc analyses of population receptive field data. Neuroimage, 263, 119557. find paper DOI
former project-related publications
*Moutsiana, C., *de Haas, B., Papageorgiou, A., Van Dijk, J. A., Balraj, A., Greenwood, J. A., & Schwarzkopf, D. S. (2016). Cortical idiosyncrasies predict the perception of object size. Nature communications, 7(1), 1-12. *shared first autorship. find paper
de Haas, B., & Schwarzkopf, D. S. (2018). Feature-location effects in the Thatcher illusion. Journal of vision, 18(4), 16-16. find paper
de Haas, B., Iakovidis, A. L., Schwarzkopf, D. S., & Gegenfurtner, K. R. (2019). Individual differences in visual salience vary along semantic dimensions. Proceedings of the National Academy of Sciences, 116(24), 11687-11692. find paper, DATA
de Haas, B., Schwarzkopf, D. S., Alvarez, I., Lawson, R. P., Henriksson, L., Kriegeskorte, N., & Rees, G. (2016). Perception and processing of faces in the human brain is tuned to typical feature locations. Journal of Neuroscience, 36(36), 9289-9302. find paper
de Haas, B., Sereno, M. I., & Schwarzkopf, D. S. (2021). Inferior occipital gyrus is organised along common gradients of spatial and face-part selectivity. The Journal of Neuroscience, 41(25), 5511-5521. find paper
Dobs, K., Bülthoff, I., & Schultz, J. (2016). Identity information content depends on the type of facial movement. Scientific Reports, 6(34301), 1-9. find paper
Dobs, K., Bülthoff, I., & Schultz, J. (2018). Use and usefulness of dynamic face stimuli for face perception studies—A review of behavioral findings and methodology. Frontiers in psychology, 9, 1355. find paper
Dobs, K., Isik, L., Pantazis, D., & Kanwisher, N. (2019a). How face perception unfolds over time. Nature Communications, 10, 1258. find paper
Dobs, K., Kell, A., Palmer, I., Cohen, M., & Kanwisher, N. (2019). Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks. Conference on Cognitive Computational Neuroscience (CCN), Berlin, Germany. find paper
Dobs, K., Ma, W. J., & Reddy, L. (2017). Near-optimal integration of facial form and motion. Scientific Reports, 7(1):11002, 1-9. find paper
Dobs, K., Schultz, J., Bülthoff, I., & Gardner, J.L. (2018). Task-dependent enhancement of facial expression and identity representations in human cortex. NeuroImage, 172, 689-702. find paper