Are rules and instances subserved by separate systems?

Comparing supervised and unsupervised category learning

Two unsupervised learning modes (incidental and intentional unsupervised learning) and their relation to supervised classification learning are examined. The approach allows for direct comparisons of unsupervised learning data with the Shepard, Hovland, and Jenkins (1961) seminal studies in supervised classification learning. Unlike supervised classification learning, unsupervised learning (especially under incidental conditions) favors linear category structures over compact nonlinear category structures. Unsupervised learning is shown to be multifaceted in that performance varies with task conditions. In comparison with incidental unsupervised learning, intentional unsupervised learning is more rule like, but is no more accurate. The acquisition and application of knowledge is also more laborious under intentional unsupervised learning.

The impact of response mode on implicit and explicit sequence learning

Evidence regarding the influence of response mode on sequence learning in serial reaction time (SRT) tasks has been mixed so far. In the present study, sequence learning was investigated under two different response conditions: manual (button presses) versus verbal (pronunciation of digits). Additionally, participants were divided post hoc into subgroups differing in their degree of explicit knowledge about the sequence. Results showed an interaction between response mode and type of learning (implicit vs. explicit), with explicit learning functioning more effectively under verbal than under manual conditions, whereas implicit learning was unaffected by the variation of the response mode. Implications concerning different underlying learning mechanisms (R-R learning vs. R-S learning) are discussed. Specifically, we suggest that the high response-effect distinctiveness of the verbal responses facilitated R-R learning.