Visual Perception in Natural Contexts

Wilson (Bill) Geisler s'intéresse aux domaines généraux de la vision, de la perception visuelle et de l'évolution des systèmes perceptifs. Dans son laboratoire, les questions scientifiques sont souvent abordées avec des techniques multiples : psychophysique (comportement), neurophysiologie (en collaboration avec les professeurs Albrecht et Seidemann), analyse d'images et de scènes, et modélisation mathématique et computationnelle.

Rethinking behavior in the light of evolution

Abstract: In psychology and neuroscience, the human brain is usually described as an information processing system that encodes and manipulates representations of knowledge to produce plans of action. This view leads to a decomposition of brain functions into putative processes such as object recognition, memory, decision-making, action planning, etc., inspiring the search for the neural correlates of these processes. However, neurophysiological data does not support many of the predictions of these classic subdivisions.

Neural and behavioral signatures of resource limitations in visual speed perception

Bayesian inference is an elegant theoretical framework for understanding the characteristic biases and discrimination thresholds in visual speed perception. However, the framework is difficult to validate due to its flexibility and the fact that suitable constraints on the structure of thessensory uncertainty have been missing.

Integration of Personal vs. Social Information for Sustainable Decisions on Climate Action

Some of my past and current research looks at "decisions from  experience,” i.e., decisions based on the personally experienced outcomes of past choices, along the lines of reinforcement learning models and how such learning and updating is related to and differs from the way in which people and other intelligent agents use other sources of information, e.g., vicarious feedback (anecdotal/social and/or in the form of statistical distributions of outcomes) or science- or model-based outcome predictions to make “decisions from description.”  What happens when these different sources of foreca

ANNULÉ - Statistical inference in auditory perception

The brain’s ability to extract statistical regularities from sound is an important tool in auditory scene analysis, necessary for object recognition, structural learning (in speech or music), texture perception, and novel event detection. In this talk, we explore perceptual, neural and computational principles underlying the brain’s ability to collect complex and non-deterministic patterns in echoic memory. These processes underlie statistical inference in auditory perception and guide our ability to delineate salient changes in our acoustic environment.

Le traitement implicite de l’ambiguïté des sons révélé par la réponse pupillaire

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Figure. Après avoir écouté deux sons, les participants devaient dire si le changement de hauteur entre les sons était vers le haut (grave-aigu) ou vers le bas (aigu-grave). Même quand la direction de ce changement est ambiguë, les auditeurs répondent sans hésitation, et sans avoir conscience de l’ambiguïté.
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