The Perception of Multiple Objects: A Connectionist Approach. [Neural Network Modeling and Connectionism]

The Perception of Multiple Objects describes a neurally inspired computational model of two-dimensional object recognition and spatial attention that can explain many characteristics of human visual perception. The model, called MORSEL (named for its ability to perform Multiple Object Recognition and attentional Selection), is unique in providing a broad and unified explanation for a wide range of experimental psychological data on visual perception and attention. Although it draws on existing theoretical perspectives from cognitive psychology, it is a fully mechanistic account, not just a functional-level theory.

MORSEL has been trained to recognize letters and words in various positions on its "retina." Following training, it can also recognize several items at once, subject to capacity limitations. The model makes predictions about what sorts of information the visual system can process in parallel and what sorts must be processed serially.

Through simulation experiments, chiefly in letter and word perception, MORSEL has been shown to account for a variety of psychological phenomena, including perceptual errors that arise when several items appear simultaneously in the visual field, facilitatory effects of context and redundant information, attentional phenomena, visual search performance, and behaviors exhibited by neurological patients with acquired dyslexia.

Michael C. Mozer is a Professor in the Department of Computer Science and the Institute of Cognitive Science at the University of Colorado, Boulder. In 1990 he received the Presidential Young Investigator Award from the National Science Foundation.