title: Visual feature learning in artificial grammar classification.
abstract: The Artificial Grammar Learning task has been used extensively to assess individuals' implicit learning capabilities. Previous work suggests that participants implicitly acquire rule-based knowledge as well as exemplar-specific knowledge in this task. This study investigated whether exemplar-specific knowledge acquired in this task is based on the visual features of the exemplars. When a change in the font and case occurred between study and test, there was no effect on sensitivity to grammatical rules in classification judgments. However, such a change did virtually eliminate sensitivity to training frequencies of letter bigrams and trigrams (chunk strength) in classification judgments. Performance of a secondary task during study eliminated this font sensitivity and generally reduced the contribution of chunk strength knowledge. The results are consistent with the idea that perceptual fluency makes a contribution to artificial grammar judgments.
authors.affiliation: Department of Psychology, University of California, Los Angeles, 90095-1563, USA. gychang@ucla.edu
authors.forename: Grace Y
authors.initials: GY
authors.lastname: Chang
authors.forename: Barbara J
authors.initials: BJ
authors.lastname: Knowlton
mesh_terms: D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D014796:Visual Perception