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
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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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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