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.

Projektrelevante Veröffentlichungen
Akbarinia, A., & Gil-Rodríguez, R. (2020). Deciphering image contrast in object classification deep networks. Vision Research, 173, 61-76. 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., 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
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, 89-100. find paper
Fleming, R. W., & Storrs, K. R. (2019). Learning to see stuff. Current Opinion in Behavioral Sciences, 30, 100-108. find paper
Metzger, A., Toscani, M., Akbarinia, A., Valsecchi, M. & Drewing, K. (2021). Deep neural network model of haptic saliency. Scientific Reports, 11(1), 1395. find paper, 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. DOI
Storrs, K. R., & Fleming, R. W. (2021). Learning about the world by learning about images. Current Directions in Psychological Science, 30(2), 120-128. find paper
Storrs, K. R., Anderson, B. L., & Fleming, R. W. (2021). Unsupervised learning predicts human perception and misperception of gloss. Nature Human Behaviour, 1-16. find paper DOI
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. find paper DOI