Inferring source of learning by chimpanzees in cognitive tasks using reinforcement learning theory

Abstract

Reinforcement learning is a mathematical framework for learning better choices through trial-and-error. Recent studies revealed that reinforcement learning is applicable to animal behavior and cognition. However, applying reinforcement learning to animal behavior sometimes encounters difficulties because the information sources utilized by animals to make choices are often unknown, whereas this is identified as the “state” in the reinforcement learning framework. We sought to identify possible state settings including non-standard formulations suitable for explaining data from past chimpanzee studies. Although chimpanzees' performance in a serial learning task was inconsistent with standard reinforcement learning formulations, we found that the combination of state-independent choice making and state-dependent evaluation produced consistent results. Exploration of state settings in reinforcement learning may shed new light on animal learning processes.