Bringing together researchers and students working at the intersection of theory and data to discuss the latest research on efficient neural representations of natural visual stimuli, as well as how active perceptual processes guide selective attention (i.e active vision).

Vision is remarkably efficient, capable of rapidly perceiving rich visual detail despite limited computational resources. To understand this feat, we must consider both how the visual system represents complex natural stimuli efficiently, as well as how active perceptual processes guide selective attention. This workshop brings together leading researchers investigating efficient representation strategies and goal-directed mechanisms of natural vision.

To explore representational frameworks for natural stimuli, this workshop will overview different stimulation and analysis approaches used across visual areas to study complex natural stimuli.  In parallel, it will examine how active perceptual processes shape efficient representation. Talks will explore how the visual system optimizes representation based on motivational drivers and behavioral objectives within natural environments. This goal-directed approach helps vision prioritize the most behaviourally relevant stimuli.

By integrating theory, machine learning, and experimental data, this interdisciplinary workshop aims to advance our conceptualization of vision as both an efficient representational and an active, goal-directed process of sense-making. Achieving a deeper understanding of these fundamental aspects of perception has implications for both neuroscience and developing more human-like computer vision.