Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study
of implicit learning and the contrast between rules, similarity, and associative learning …

The distinction between rule-based and similarity-based processes in cognition is of
fundamental importance for cognitive science, and has been the focus of a large body of …

This article covers methodological and theoretical issues in artificial grammar learning.
Arguments that such tasks are mediated by abstract knowledge (eg, AS Reber, 1969, 1990) …

Five experiments evaluated the contributions of rule, exemplar, fragment, and episodic
knowledge in artificial grammar learning using memorization versus hypothesis-testing …

Evidence has accrued that tacit knowledge is personal and ineffable as proposed by Polanyi
(1958). Proponents of this view do not explain the mechanism responsible for the …

Cognitive processes are often attributed to statistical or symbolic general-purpose
mechanisms. Here we show that some spontaneous generalizations are driven by …

The authors examine the role of similarity in artificial grammar learning (AGL; AS Reber,
1989). A standard finite-state language was used to create stimuli that were arrangements of …

Despite nearly 20 years of controversy, researchers are still divided over the contributions of
abstract and episodic knowledge to performance on implicit learning tasks, and whether …

Can you learn without knowing it? This controversial and much debated question forms the
basis of this collection of essays as the authors discuss whether the measurable changes in …

We present three artificial-grammar experiments. The first used position constraints, and the
second used sequential constraints. The third varied both the amount of training and the …