C9
Dr. de Haas und Dr. Dobs
Beeinflussende Faktoren der kategorialen Gesichtsverarbeitung
Projekt C9 kombiniert Psychophysik, Eye-tracking und neuronale Bildgebung mit Modellierung um die beeinflussenden Faktoren der Gesichtsverarbeitung zu verstehen. Traditionell wurde Gesichtsverarbeitung isoliert und bereichsspezifisch untersucht. Hier werden wir testen, welche facettenreichen Faktoren die Gesichtswahrnehmung beeinflussen, darunter die Plastizität der Gesichtsverarbeitung von künstlichen Merkmalen, und ihre Abhängigkeit von generellen Eigenschaften des Sichtfelds, sowie dem Kontext einer Szene. Basierend auf diesen Ergebnissen werden wir bereichsübergreifende, neuronale Netzwerkmodelle entwickeln, die es uns ermöglichen sollen die menschliche Gesichtsverarbeitung und ihre beeinflussenden Faktoren in der natürlichen Umgebung umfassend vorherzusagen.
Neue Projektrelevante Veröffentlichungen
Borovska, P, de Haas, B (2023). Faces in scenes attract rapid saccades. Journal of Vision, 23(8):11, 1-15. find paper
Broda, M, de Haas, B (2022). Individual fixation tendencies in person viewing generalize from images to videos. iPerception, 13(5), 1–10. find paper
Broda, M, de Haas, B (2022). Individual differences in looking at persons in scenes. Journal of Vision, 22, 9. find paper
Broda, M, de Haas, B (2023). Reading the mind in the nose. iPerception, 14, 2. find paper
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
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.
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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
Kanwisher, N., Gupta, P., & Dobs, K. (2023). CNNs Reveal the Computational Implausibility of the Expertise Hypothesis. iScience, 105976.
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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.
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Kollenda, D., & de Haas, B. (2022). The influence of familiarity on memory for faces and mask wearing. Cogn. Research 7, 45.
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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
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
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
Ältere projektrelevante Veröffentlichungen
*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.
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de Haas, B., & Schwarzkopf, D. S. (2018). Feature-location effects in the Thatcher illusion. Journal of vision, 18(4), 16-16.
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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.
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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.
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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.
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Dobs, K., Bülthoff, I., & Schultz, J. (2016). Identity information content depends on the type of facial movement. Scientific Reports, 6(34301), 1-9.
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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.
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Dobs, K., Isik, L., Pantazis, D., & Kanwisher, N. (2019a). How face perception unfolds over time. Nature Communications, 10, 1258.
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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.
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Dobs, K., Ma, W. J., & Reddy, L. (2017). Near-optimal integration of facial form and motion. Scientific Reports, 7(1):11002, 1-9.
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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.
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