Felix Wichmann received his B.A. (1994) and DPhil (1999) in Experimental Psychology from the University of Oxford. After post-doctoral research at the University of Leuven, he worked as a research scientist in the Empirical Inference Department at the Max Planck Institute for Biological Cybernetics in Tübingen, followed by an Associate Professorship at the Technical University of Berlin. Since 2011 he is Full Professor at the Eberhard Karls Universität Tübingen.

Title and abstract of his presentation:

Are Deep Neural Networks adequate behavioural models of human visual perception?

Deep neural networks (DNNs) are machine learning algorithms that have revolutionised computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to the suggestion that DNNs may also be good models of human visual perception. In my talk I will review evidence regarding current DNNs as adequate behavioural models of human core object recognition. To this end I will argue that it is important to distinguish between statistical tools and computational models and to understand model quality as a multidimensional concept in which clarity about modelling goals is key. Reviewing a large number of psychophysical and computational explorations of core object recognition performance in humans and DNNs, I will argue that DNNs are highly valuable scientific tools but that, as of today, DNNs should only be regarded as promising---but not yet adequate---computational models of human core object recognition behaviour.

Thursday, December 14, 2023 at 5:00 p.m. // Main Building / Universitätsring 1 - Elise Richter-Saal (1st floor, staircase no. 1)

Felix A. Wichmann: scholar.google.com/citations, www.wichmannlab.org