Statistical Learning Within and Across Modalities: Abstract versus Stimulus-Specific Representations

When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Artificial grammar learning (AGL) experiments showing transfer of learning from one stimulus set to another has encouraged the view that learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we present data to the contrary. These experiments pit abstract, domain-general processing against stimulus-specific learning. The results show that learning in an AGL task is mediated to a greater extent by stimulus-specific, rather than abstract, representations. They furthermore show that learning can proceed separately and independently (i.e., in parallel) for multiple input streams that occur along separate perceptual dimensions or modalities. We conclude that learning probabilistic structure and generalizing to novel stimuli inherently involves learning mechanisms that are closely tied to perceptual features.