Evolving Cognitive Models: A Novel Approach to Verbal Learning

A common goal in cognitive science involves explaining/predicting human performance in experimental settings. This study proposes a single GEMS computational scientific discovery framework that automatically generates multiple models for verbal learning simulations. GEMS achieves this by combining simple and complex cognitive mechanisms with genetic programming. This approach evolves populations of interpretable cognitive agents, with each agent learning by chunking and incorporating long-term memory (LTM) and short-term memory (STM) stores, as well as attention and perceptual mechanisms. The models simulate two different verbal learning tasks: the first investigates the effect of prior knowledge on the learning rate of stimulus-response (S-R) pairs and the second examines how backward recall is affected by the similarity of the stimuli. The models produced by GEMS are compared to both human data and EPAM – a different verbal learning model that utilises hand-crafted task-specific strategies. The models automatically evolved by GEMS produced good fit to the human data in both studies, improving on EPAM’s measures of fit by almost a factor of three on some of the pattern recall conditions. These findings offer further support to the mechanisms proposed by chunking theory (Simon, 1974), connect them to the evolutionary approach, and make further inroads towards a Unified Theory of Cognition (Newell, 1990).