Abstract

Although perceptual grouping has been widely studied, its mechanisms remain poorly understood. We propose a neural model of grouping that, through top-down control of its circuits, implements a grouping strategy involving both a connection strategy (which elements to connect) and a selection strategy (that defines spatiotemporal properties of a selection signal to segment target elements and facilitate identification). We apply the model to a letter discrimination task that investigated relationships among uniform connectedness and the grouping principles of proximity and shape similarity. Participants reported whether small circles formed a global letter E or H, and these circles could be connected by a line or be embedded in a matrix of squares. In the model, a good grouping strategy for this task consists of a connection strategy that connects circles but not squares for all conditions and a selection strategy that uses a selection signal of varying size, depending on whether squares were present. Consistent with empirical results, which were verified in two replication studies, model performance is worse with distractor squares, and line connectors improve performance only in the condition with squares. Rather than relying on abstract grouping principles, we show how the empirical results can be explained in terms of observers implementing a task-dependent grouping strategy that promotes overall performance.