A Unified Account of Implicit and Explicit Learning
Randall K. Jamieson
University of Manitoba

People’s behaviour is often more sophisticated than their explicit knowledge would seem to allow. A popular explanation for the discrepancy is that peoples’ sophistication reflects the work of a specialized learning mechanism that unconsciously extracts and covertly deploys knowledge about structure in the environment (i.e., the theory of implicit learning). I present data from several implicit learning protocols: the artificial grammar task, the serial reaction time task, the speeded identification task, and the implicit associative learning task. I explain those data using a model of human memory designed to explain explicit learning: cued-recall, classification, recognition, and frequency judgement. After I demonstrate that a single model gives a coherent explanation of people’s behaviour in both implicit and explicit learning tasks, I will articulate the theory’s perspective on the nature of intelligence and draw out the implications of that theoretical perspective for the design of artificial cognition.