Multiple Cognitive Abilities from a Single Cortical Algorithm

Abstract

Abstract One strong claim made by the representational–hierarchical account of cortical function in the ventral visual stream (VVS) is that the VVS is a functional continuum: The basic computations carried out in service of a given cognitive function, such as recognition memory or visual discrimination, might be the same at all points along the VVS. Here, we use a single-layer computational model with a fixed learning mechanism and set of parameters to simulate a variety of cognitive phenomena from different parts of the functional continuum of the VVS: recognition memory, categorization of perceptually related stimuli, perceptual learning of highly similar stimuli, and development of retinotopy and orientation selectivity. The simulation results indicate—consistent with the representational–hierarchical view—that the simple existence of different levels of representational complexity in different parts of the VVS is sufficient to drive the emergence of distinct regions that appear to be specialized for solving a particular task, when a common neurocomputational learning algorithm is assumed across all regions. Thus, our data suggest that it is not necessary to invoke computational differences to understand how different cortical regions can appear to be specialized for what are considered to be very different psychological functions.