A neurocomputational system for relational reasoning

Abstract

The representation and manipulation of structured relations is central to human reasoning. Recent work in computational modeling and neuroscience has set the stage for developing more detailed neurocomputational models of these abilities. Several key neural findings appear to dovetail with computational constraints derived from a model of analogical processing, 'Learning and Inference with Schemas and Analogies' (LISA). These include evidence that (i) coherent oscillatory activity in the gamma and theta bands enables long-distance communication between the prefrontal cortex and posterior brain regions where information is stored; (ii) neurons in prefrontal cortex can rapidly learn to represent abstract concepts; (iii) a rostral-caudal abstraction gradient exists in the PFC; and (iv) the inferior frontal gyrus exerts inhibitory control over task-irrelevant information.