The Transfer Effect in Artificial Grammar Learning: Reappraising the Evidence on the Transfer of Sequential Dependencies

Exposure to sequences of elements generated by an artificial grammar enables observers to classify new sequences presented either with the same (within-domain) or different (across-domain) vocabulary elements as being well- or ill-formed according to that grammar (A. S. Reber, 1969). Experiment 1 replicated G. T. M. Altmann, Z. Dienes, and A. Goode's (1995) demonstration of this effect, but inspection of hits and correct rejections revealed that a single cue was used to reject a subset of the ungrammatical sequences. Experiment 2 removed this cue, and participants no longer discriminated between grammatical and ungrammatical sequences in the novel domain. The authors conclude that G. T. M. Altmann et al.'s demonstration of discrimination in the novel domain did not necessitate the application of knowledge pertaining to sequential dependencies. Implications of the data for other studies, showing abstraction of knowledge across test sequences, are also considered.