Thomas Wallis, PhD

Short info

My lab studies visual perception in humans using psychophysics and computational models. A secondary interest is in machine vision systems, including deep neural networks. Topics of current interest include peripheral visibility and scene appearance, eye movements, the interplay between prior knowledge and visual input in determining what we see, segmentation and grouping, explainable AI, and image quality assessment.

Articles

Eicke-Kanani, L., Duan, Y., & Wallis, T. S. (2024).
From visual features of moving objects to subjective impressions of causality. Journal of Vision, 24(10), 823-823.
Proceedings of the 4th Workshop on Tractable Probabilistic Modeling (TPM 2021).

Mahncke, S., Eicke-Kanani, L., Fabritz, O., & Wallis, T. S. (2025).
The visibility of Eidolon distortions in things and stuff.
Journal of Vision, 25(8), 12.

Roth, J., Duan, Y., Mahner, F. P., Kaniuth, P., Wallis, T. S., & Hebart, M. N. (2025).
Ten principles for reliable, efficient, and adaptable coding in psychology and cognitive neuroscience.
Communications Psychology, 3(1), 62.