A Neural Model of Human Image Categorization

Abstract

Although studies of categorization have been a staple of psychological research for decades, there continues to be substantial disagreement about how unique classes of objects are represented in the brain. We present a neural architecture for categorizing visual stimuli based on the Neural Engineering Framework and the manipulation of semantic pointers. The model accounts for how the visual system computes semantic representations from raw images, and how those representations are then manipulated to produce category judgments. All computations of the model are carried out in simulated spiking neurons. We demonstrate that the model matches human performance on two seminal behavioural studies of image-based concept acquisition: Posner and Keele (1968) and Regehr and Brooks (1993).


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