Global Image Dissimilarity in Macaque Inferotemporal Cortex Predicts Human Visual Search Efficiency

Abstract

Finding a target in a visual scene can be easy or difficult depending on the nature of the distractors. Research in humans has suggested that search is more difficult the more similar the target and distractors are to each other. However, it has not yielded an objective definition of similarity. We hypothesized that visual search performance depends on similarity as determined by the degree to which two images elicit overlapping patterns of neuronal activity in visual cortex. To test this idea, we recorded from neurons in monkey inferotemporal cortex (IT) and assessed visual search performance in humans using pairs of images formed from the same local features in different global arrangements. The ability of IT neurons to discriminate between two images was strongly predictive of the ability of humans to discriminate between them during visual search, accounting overall for 90% of the variance in human performance. A simple physical measure of global similarity--the degree of overlap between the coarse footprints of a pair of images--largely explains both the neuronal and the behavioral results. To explain the relation between population activity and search behavior, we propose a model in which the efficiency of global oddball search depends on contrast-enhancing lateral interactions in high-order visual cortex.