Aligning implicit learning and statistical learning: Two approaches, one phenomenon.
Monaghan and Rebuschat organised Symposium at the annual meeting of the Cognitive Science Society
The past 15-20 years have witnessed a particularly strong interest in our ability to rapidly extract structured information from the environment. This fundamental process of human cognition is widely believed to underpin many complex behaviors – from language development and social interaction to intuitive decision making and music cognition – so this interest spans practically all branches of cognitive science. Research on this topic can be found in two related, yet traditionally distinct research strands, namely "implicit learning" (Reber, 1967) and "statistical learning" (Saffran, Aslin, & Newport, 1996). Both lines of research focus on how we acquire information from complex stimulus domains and both rely heavily on the use of artificial systems (e.g., finite-state grammars, pseudoword lexicons). In typical experiments, participants are initially exposed to stimuli generated by an artificial system and then tested to determine what they have learned. Given these and other significant similarities, Perruchet and Pacton (2006) argue that these distinct lines of research actually represent two approaches to a single phenomenon, and Conway and Christiansen (2006) propose combining the two in name: "implicit-statistical learning". Yet, despite frequent acknowledgements that researchers in implicit learning and statistical learning might essentially be looking at the same phenomenon, there is surprisingly little alignment between the two strands. This symposium seeks to remedy this situation by bringing together leading researchers from both areas in order to promote a shared understanding of research questions and methodologies, to discuss similarities and differences between the two approaches, and to work towards a joint research agenda.