Visual Feature Learning in Artificial Grammar Classification. |
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Authors: | Chang, Grace Y. Knowlton, Barbara J. |
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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. (PsycINFO Database Record (c) 2010 APA, all rights reserved) |
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Keywords: | artificial grammar classification visual feature learning exemplar specific knowledge |
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