Mild Perceptual Categorization Deficits Follow Bilateral Removal of Anterior Inferior Temporal Cortex in Rhesus Monkeys - Matsumoto, Narihisa (J Neurosci, 2016)
In primates, visual recognition of complex objects depends on the inferior temporal lobe. By extension, categorizing visual stimuli based on similarity ought to depend on the integrity of the same area. We tested three monkeys before and after bilateral anterior inferior temporal cortex (area TE) removal. Although mildly impaired after the removals, they retained the ability to assign stimuli to previously learned categories, e.g., cats versus dogs, and human versus monkey faces, even with trial-unique exemplars. After the TE removals, they learned in one session to classify members from a new pair of categories, cars versus trucks, as quickly as they had learned the cats versus dogs before the removals. As with the dogs and cats, they generalized across trial-unique exemplars of cars and trucks. However, as seen in earlier studies, these monkeys with TE removals had difficulty learning to discriminate between two simple black and white stimuli. These results raise the possibility that TE is needed for memory of simple conjunctions of basic features, but that it plays only a small role in generalizing overall configural similarity across a large set of stimuli, such as would be needed for perceptual categorical assignment. SIGNIFICANCE STATEMENT The process of seeing and recognizing objects is attributed to a set of sequentially connected brain regions stretching forward from the primary visual cortex through the temporal lobe to the anterior inferior temporal cortex, a region designated area TE. Area TE is considered the final stage for recognizing complex visual objects, e.g., faces. It has been assumed, but not tested directly, that this area would be critical for visual generalization, i.e., the ability to place objects such as cats and dogs into their correct categories. Here, we demonstrate that monkeys rapidly and seemingly effortlessly categorize large sets of complex images (cats vs dogs, cars vs trucks), surprisingly, even after removal of area TE, leaving a puzzle about how this generalization is done.