title: Incremental implicit learning of bundles of statistical patterns.
abstract: Forming an accurate representation of a task environment often takes place incrementally as the information relevant to learning the representation only unfolds over time. This incremental nature of learning poses an important problem: it is usually unclear whether a sequence of stimuli consists of only a single pattern, or multiple patterns that are spliced together. In the former case, the learner can directly use each observed stimulus to continuously revise its representation of the task environment. In the latter case, however, the learner must first parse the sequence of stimuli into different bundles, so as to not conflate the multiple patterns. We created a video-game statistical learning paradigm and investigated (1) whether learners without prior knowledge of the existence of multiple "stimulus bundles" - subsequences of stimuli that define locally coherent statistical patterns - could detect their presence in the input and (2) whether learners are capable of constructing a rich representation that encodes the various statistical patterns associated with bundles. By comparing human learning behavior to the predictions of three computational models, we find evidence that learners can handle both tasks successfully. In addition, we discuss the underlying reasons for why the learning of stimulus bundles occurs even when such behavior may seem irrational.
authors.affiliation: Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, United States.
authors.forename: Ting
authors.initials: T
authors.lastname: Qian
authors.affiliation: Department of Brain and Cognitive Sciences, University of Rochester, United States; Department of Computer Science, University of Rochester, United States; Department of Linguistics, University of Rochester, United States.
authors.forename: T Florian
authors.initials: TF
authors.lastname: Jaeger
authors.affiliation: Department of Brain and Cognitive Sciences, University of Rochester, United States.
authors.forename: Richard N
authors.initials: RN
authors.lastname: Aslin
mesh_terms: D001499:Bayes Theorem; D003463:Cues; D006801:Humans; D008568:Memory; D008960:Models, Psychological; D010364:Pattern Recognition, Visual; D011337:Probability Learning; D011930:Reaction Time
keywords: Bayesian nonparametric models; Implicit learning; Incremental learning; Memory; Sequential input; Stimulus bundles