title: Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.
abstract: Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies.
authors.affiliation: Cluster Languages of Emotion, Freie Universität Berlin, Habelschwerdter Allee 45, 14195 Berlin, Germany; Centre for Music and Science, Faculty of Music, University of Cambridge, United Kingdom. Electronic address: mrohrmeier@cantab.net.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Rohrmeier
authors.affiliation: Centre for Music and Science, Faculty of Music, University of Cambridge, United Kingdom. Electronic address: ic108@cam.ac.uk.
authors.forename: Ian
authors.initials: I
authors.lastname: Cross
mesh_terms: D006801:Humans; D020407:Internet; D007858:Learning / Q000502:physiology*; D059032:Learning Curve; D008037:Linguistics; D008960:Models, Psychological; D016571:Neural Networks, Computer; D011337:Probability Learning
keywords: Artificial grammar learning; Competitive chunking; Computational modelling; Implicit learning; Incidental learning; N-gram model; Online learning; Simple recurrent network; Statistical learning; Unsupervised learning