S
Prof. Fleming, Ph.D. and Dr. Dobs
Deep learning: Unlocking the potential
The main goal of Project S seeks to promote and support the use of deep learning within the CRC. We will gather tools for analyzing deep neural networks—especially methods for comparing models with behavioral and neural data. We also seek to synthesize findings from across the CRC into a common theoretical framework based on deep learning. Specifically, we will test whether Prediction, Valuation and Categorization can be framed as different learning objectives. We will compare supervised, unsupervised and reward-based learning methods to develop unifying models of the “cardinal mechanisms” of perception.
Neue Projektrelevante Veröffentlichungen
Akbarinia, A. (2025). Exploring the categorical nature of colour perception: Insights from artificial networks. Neural Networks, 181, 106758. find paper
Akbarinia, A., Morgenstern, Y., & Gegenfurtner, K. R. (2023). Contrast sensitivity function in deep networks.Neural Networks, 164, 228-244. 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
Filip, J., Dechterenko, F., Schmidt, F., Lukavsky, J., Kotera, J., Vilimovska, V., & Fleming, R. W. (2025). Material fingerprinting: predicting human perception of material appearance through psychophysical analysis and neural networks. Royal Society Open Science, 12(11), 250513. find paper
Flachot, A., Akbarinia, A., Schütt, H.H., Fleming, R.W., Wichmann, F.A. & Gegenfurtner, K.R. (2022). Deep neural models for color discrimination and color constancy. Journal of Vision, 22(4), 17-17. find paper
Gupta, P., & Dobs, K. (2025). Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition. PLOS Computational Biology, 21(1), e1012751.
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Morimoto, T., Akbarinia, A., Storrs, K., Cheeseman, J. R., Smithson, H. E., Gegenfurtner, K. R., & Fleming, R. W. (2023). Color and gloss constancy under diverse lighting environments. Journal of Vision, 23(7), 8-8. find paper
Ältere projektrelevante Veröffentlichungen
Akbarinia, A., & Gil-Rodríguez, R. (2020). Deciphering image contrast in object classification deep networks. Vision Research, 173, 61-76.
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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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Flachot, A., & Gegenfurtner, K. R. (2021). Color for object recognition: Hue and chroma sensitivity in the deep features of convolutional neural networks. Vision Research, 182(1), 89-100.
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Fleming, R. W., & Storrs, K. R. (2019). Learning to see stuff. Current Opinion in Behavioral Sciences, 30, 100-108.
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Metzger, A., Toscani, M., Akbarinia, A., Valsecchi, M. & Drewing, K. (2021). Deep neural network model of haptic saliency. Scientific Reports, 11(1), 1395.
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DATA
Morgenstern, Y., Hartmann, F., Schmidt, F., Tiedemann, H., Prokott, E., Maiello, G., & Fleming, R. W. (2021). An image-computable model of human visual shape similarity.PLOS Computational Biology, 17(6), e1008981. find paper.
Storrs, K. R., & Fleming, R. W. (2021). Learning about the world by learning about images. Current Directions in Psychological Science, 30(2), 120-128.
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Storrs, K. R., Anderson, B. L., & Fleming, R. W. (2021). Unsupervised learning predicts human perception and misperception of gloss. Nature Human Behaviour, 1-16.
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Van Assen, J. J. R., Nishida, S., & Fleming, R. W. (2020). Visual perception of liquids: Insights from deep neural networks. PLoS Computational Biology, 16(8): e1008018.
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