A predictive coding model of representational drawing in human children and chimpanzees

Abstract

Humans and chimpanzees differ in the way that they draw. Human children from a certain age tend to create representational drawings, that is, drawings which represent objects. Chimpanzees, although equipped with sufficient motor skills, do not improve beyond the stage of scribbling behavior. To investigate the underlying cognitive mechanisms, we propose a computational model of predictive coding which allows us to change the way that sensory information and prior predictions are updated into posterior beliefs during time series prediction. We replicate the results of a study from experimental psychology which examined the ability of children and chimpanzees to complete partial drawings of a face. Our results reveal that typical or stronger reliance on the prior enables the network to perform representational drawings as observed in children. In contrast, too weak reliance on the prior replicates the findings that were observed in chimpanzees: existing lines are traced with high accuracy, but non-existing parts are not added to complete a representational drawing. The ability to perform representational drawings, thus, could be explained by subtle changes in how strongly prior information is integrated with sensory percepts rather than by the presence or absence of a specific cognitive mechanism.