Genetically evolving verbal learner: a computational model based on chunking and evolution

A fundamental issue in cognitive science concerns the interaction of the cognitive “how” operations, the genetic/memetic “why” processes, and by what means this interaction results in constrained variability and individual differences. This study proposes a single GEVL model that combines complex cognitive mechanisms with a genetic programming approach. The model evolves populations of cognitive agents, with each agent learning by chunking and incorporating LTM and STM stores, as well as attention. The model simulates two different verbal learning tasks: one that investigates the effect of stimulus-response (S-R) similarity on the learning rate; and the other, that examines how the learning time is affected by the change in stimuli presentation times. GEVL’s results are compared to both human data and EPAM – a different verbal learning model that utilises hand-crafted task-specific strategies. The automatically evolved GEVL strategies produced good fit to the human data in both studies, improving on EPAM’s scores by as much as factor of two on some of the pattern similarity conditions. These findings offer further support to the mechanisms proposed by chunking theory, connect them to the evolutionary approach, and make further inroads towards a Unified Theory of Cognition (Newell, 1990).