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Implicit Learning and Recursion

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Language 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.

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Notes

  1. 1.

    Here we use the convention in the artificial grammar learning literature in experimental psychology, with numbers referring to non-terminals and capital letters as terminals; another convention is to use capital letters for non-terminals and lower-case letters for terminals. The formal definition of a formal grammar is the 4-tuple G = (N, Σ, P, S), for which N represents the set of non-terminal symbols, Σ the set of terminal symbols (surface symbols), P the set of generative production rules and S the starting symbol (from N).

  2. 2.

    Note that the distinction between recursive and iterative algorithms involves a notion of recursion that avoids encompassing all kinds of iteration (cf. Tomalin, 2007).

  3. 3.

    Note also that the distinction between context-free and context-sensitive structures is, in practice, separated by worlds of complexity which are not adequately reflected by the simple distinction of centre-embedded and (mildly context-sensitive) cross-serial structures, e.g. A1A2A3B3B2B1 and A1A2A3B1B2B3.

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Authors and Affiliations

  1. Department of Linguistics & Philosophy, Massachusetts Institute of Technology, Cambridge, MA, USA

    M. Rohrmeier

  2. University of Sussex, Brighton, UK

    Z. Dienes

  3. Shanghai Key Laboratory of Magnetic Resonance and School of Psychology and Cognitive Science, East China Normal University, Shanghai, China

    X. Guo

  4. Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Q. Fu

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  1. M. Rohrmeier
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  3. X. Guo
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  4. Q. Fu
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Correspondence to Z. Dienes .

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  1. Department of Cognitive Sciences, University of Mons, Mons, Belgium

    Francis Lowenthal

  2. Department of Cognitive Sciences, University of Mons, Mons, Belgium

    Laurent Lefebvre

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Rohrmeier, M., Dienes, Z., Guo, X., Fu, Q. (2014). Implicit Learning and Recursion. In: Lowenthal, F., Lefebvre, L. (eds) Language and Recursion. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9414-0_6

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