Abstract

Implicit learning, essentially the process of acquiring unconscious (implicit) knowledge, is a fundamental feature of human cognition. Many complex behaviors, including language comprehension and production, music cognition, intuitive decision making, and social interaction, are thought to be largely dependent on implicit knowledge, so it is not surprising that interest in implicit learning spans practically all branches of cognitive science. Research on this topic can be found in two related, yet completely distinct research strands, namely “implicit learning” (Reber, 1967) and “statistical learning” (Saffran, Aslin, & Newport, 1996). In this talk, I will present the results of an experiment that brings together methodological insights from both strands. The objective was to investigate further the role of providing subjects with explicit knowledge prior to artificial language exposure and to identify cognitive variables that predict learning outcomes (working memory, declarative memory, executive function). Subjects were exposed to the artificial language by means of Monaghan et al.’s (2014) cross-situational learning paradigm. The advantage of this innovative paradigm is that it permits tracking of the learning trajectory, as opposed to only relying on post-exposure testing. We used retrospective verbal reports to determine what strategies followed in the learning task and whether they became aware of rules or patterns. Results indicate that provision of prior knowledge significantly boosts learning and that declarative memory accounts for a substantial amount of variance. Awareness of the learning target was not required, as aware and unaware subjects clearly showed a learning effect overall, but it did appear to affect the learning trajectory, with aware subjects performing above chance significantly earlier. Implications for language learning research will be discussed.