The role of similarity in artificial grammar learning

The authors examine the role of similarity in artificial grammar learning ( AGL; A. S. Reber, 1989). A standard finite-state language was used to creat e stimuli that were arrangements of embedded geometric shapes (Experiment 1 ), connected lines (Experiment 2), and sequences of shapes (Experiment 3). Main effects for well-known predictors from the literature (grammaticality, associative global and anchor chunk strength, novel global and anchor chun k strength, length of items, and edit distance) were observed, thus replica ting previous work. However, the authors extend previous research by using a widely known similarity-based exemplar model of categorization (the gener alized context model; R. M. Nosofsky, 1989) to fit grammaticality judgments , by nested regression analyses. The results suggest that any explanation o f AGL that is based on the existing theories is incomplete without a simila rity process as well. Also, the results provide a foundation for further in terpreting AGL in the wider context of categorization research.