Abstract

A fundamental characteristic of human visual perception is the ability to group together disparate elements in a scene and treat them as a single unit. The mechanisms by which humans create such groupings remain unknown, but grouping seems to play an important role in a wide variety of visual phenomena. I propose a neural model of grouping; through top-down control of its circuits, the model implements a grouping strategy that involves both a connection strategy (which elements to connect) and a selection strategy (spatiotemporal properties of a selection signal that segments target elements to facilitate identification). With computer simulations I explain how the circuits work and show how they can account for a wide variety of Gestalt principles of perceptual grouping. Additionally, I extend the model so that it can simulate visual search tasks. I show that when the model uses particular grouping strategies, simulated results closely match empirical results from replication experiments of three visual search tasks. In these experiments, perceptual grouping was induced by proximity and shape similarity (Palmer & Beck, 2007), by the spacing of irrelevant distractors and size similarity (Vickery, 2008), or by the proximity of dots and the proximity and shape similarity of line figures (Trick & Enns, 1997). Thus, I show that the model accounts for a variety of grouping effects and indicates which grouping strategies were likely used to promote performance in three visual search tasks.