Education
Liège University, MSc, Experimental Psychology
Louvain-la-Neuve University, MSc, Computer Science
Brown University, PhD, Cognitive Science
About
Philippe G. Schyns’ research lies at the intersection of experimental psychology, visual neuroscience, computational vision, and artificial intelligence. His early work showed that scene-specific information can support recognition independently of object information, helping to establish the field of rapid scene categorization. He developed Hybrid Images—including the Einstein/Monroe illusion—to reveal how different spatial scales support different percepts over time. Building on the observation that the same visual input can support multiple judgments, he proposed the framework of diagnostic recognition, which places the observer’s task at the centre of perception and explains how visual information is selected strategically. This framework led to new psychophysical methods, including Bubbles and Superstitious Perception, for identifying the information that supports specific categorizations. His more recent work extends this approach through generative models of visual stimuli, dynamic neuroimaging with MEG, and deep neural networks to determine how task-relevant information is represented, communicated, and computed across brain and artificial systems.
Research Focus
Face, object and scene perception and recognition; dynamic neuroimaging; equivalence of brain and deep neural network models
Publication Highlight
1. Yan, Y, Zhan, J., Garrod, O.G.B., Ince, R.A.A., Jack, R.E. & Schyns, P.G. (2025). The brain computes dynamic facial movements for emotion categorization using a third pathway. Proceedings of the National Academy of Sciences of the United States of America, 122, e2423560122. https://www.pnas.org/doi/10.1073/pnas.2423560122
2. Duan, Y., Zhan, J., Gross, J., Ince, R.A.A. & Schyns, P.G. (2024). Pre-frontal cortex guides dimension-reducing transformations in the occipito-ventral pathway for categorization behaviors. Current Biology, 34, 1-13. https://www.cell.com/current-biology/fulltext/S0960-9822(24)00834-0
3. Schyns, P.G, Snoek, L. & Daube, C. (2022). Degrees of algorithmic equivalence between the brain and its DNN models. Trends in Cognitive Sciences, 26, 1090-1102. https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613%2822%2900220-0