Implicit Learning and Recursion

Abstract

Implicit learning research has focused on learning simple structures, such as chunks, even though such structures do not capture the richness of real-world human accomplishments. In particular, music and language exhibit certain recursive features that cannot be captured by regular grammars, let alone mechanisms that learn only chunks. We show in the domains of music, language, poetry and movement that people can implicitly learn recursive grammars in ways that go beyond learning chunks or mere repetition patterns. This is supported by the fact that participants are found to generalise from training materials to novel sequences following the underlying rules. In this context we further propose a parsimony argument that states that although performance on new test items can always be explained by a catch-all finite-state or chunking mechanism, such explanations can be more complex than postulating learning a supra-finite-state mechanism in that they may postulate considerably more rules or states than necessary to explain learning. This is especially true when the finite-state rather than supra-finite-state mechanism, in order to perform on the test material, needs to acquire states or chunks not required for learning the training material. We highlight both the strength and weakness of our current evidence in this regard.