A Predictive Coding Account for Cognition in Human Children and Chimpanzees: A Case Study of Drawing

Abstract

Humans and chimpanzees differ in their cognitive abilities, in particular, in social-cognitive processing; however, the underlying neural mechanisms are still unknown. Based on the theory of predictive coding, we hypothesize that crucial differences in cognitive processing might arise from aberrant reliance on predictions. We test this hypothesis using a recurrent neural network that integrates sensory information with predictions based on the rules of Bayesian inference. Altering a network parameter, we vary how strongly the network relies on its predictions during development. Our model qualitatively replicates findings from a behavioral study on the drawing ability of human children and chimpanzees. Moderate parameter values replicate the ability of human children to complete drawings by adding missing elements. With weak reliance on predictions, the model's behavior is similar to chimpanzees' behaviors: trained networks can follow existing lines but fail to complete drawings. Furthermore, with a strong reliance on predictions, networks learn more abstract representations of drawings and confuse different trained patterns. An analysis of the internal network representations reveals that an aberrant reliance on predictions affects the formation of attractors in the network. Thus, appropriate reliance on their own predictions in humans may be crucial for developing abstract representations and acquiring cognitive skills.