Perception

Somehow, our minds make sense of sensory data in a way that eludes current machine vision systems. We study visual perception in humans using psychophysics and computational models. A secondary interest is in machine vision systems, including deep neural networks. We compare the inferences of humans and machines to gain stronger insights into both biological and artificial seeing.

Topics of current interest include

  • peripheral visibility and scene appearance
  • eye movements
  • segmentation and grouping, perceptual organisation
  • explainable AI
  • image quality assessment
  • adaptive methods for measuring perception

Ultimately, we aim to understand the computational principles that underlie our brains’ ability to combine sensory inputs to generate holistic perceptual experience.

Why things look as they do

Koffka, 1935

News

July 15 2022: Yunyan Duan joined the lab as a postdoc research scientist. Welcome Yunyan!

July 1 2022: Swantje Mahncke joined the lab as a doctoral student. She will work with Tom and be part of the teaching staff at the Institute of Psychology and Centre for Cognitive Science. Welcome Swantje!

May 15 2022: Lina Eicke-Kanani joined the lab as a doctoral student. She will work with Tom and Benjamin Straube in the context of The Adaptive Mind. Welcome Lina!

Nov 1 2021: Rabea Turon joined the lab as a doctoral student. She will work with Tom and Frank Jäkel on the LOEWE Whitebox project. Welcome Rabea!

March 1, 2021: Tom joined the TU Darmstadt as Professor of Perception.

Representative publications

Zimmermann, R. S., Borowski, J., Geirhos, R., Bethge, M., Wallis, T. S. A., & Brendel, W.. (2021). How Well do Feature Visualizations Support Causal Understanding of CNN Activations? Neural Information Processing Systems (NeurIPS). Link Accepted as a Spotlight (< 3% of submissions).

Funke, C. M., Borowski, J., Stosio, K., Brendel, W., Wallis, T. S. A., & Bethge, M. (2021). Five points to check when comparing visual perception in humans and machines. Journal of Vision, 21(3), 16. Link.

Wallis, T. S. A., Funke, C. M., Ecker, A. S., Gatys, L. A., Wichmann, F. A., & Bethge, M. (2019). Image content is more important than Bouma’s Law for scene metamers. ELife, 8, e42512. Link

Kümmerer, M., Wallis, T.S.A., Gatys, L.A., & Bethge, M. (2017). Understanding Low- and High-Level Contributions to Fixation Prediction. The IEEE International Conference on Computer Vision (ICCV), 2017. Link Model web service

Wallis, T.S.A., Funke, C.M., Ecker, A.S., Gatys, L.A., Wichmann, F.A., & Bethge, M. (2017). A parametric texture model based on deep convolutional features closely matches texture appearance for humans. Journal of Vision, 17(12):5. Link

Lab philosophy

We collaborate honestly: we are comfortable telling each other when we don't understand something, we patiently teach through misunderstandings without judgment, and we challenge each other's ideas when we think they're wrong.

Failures and mistakes are an integral part of the scientific process. Be open and honest, transparently discuss, and learn from these for next time. You should never feel pressure to fudge or falsify data.

We strive to be inclusive and equitable. We do not make inappropriate jokes or comments about others based on gender, race, or other personal factors.

We encourage healthy work/life balance and inclusion of lab members with carer duties through clear separation of work and non-work time, and fostering strong time management skills. Lab members are discouraged from being responsive outside their normal work hours.