Direct associations or internal transformations? Exploring the mechanisms underlying sequential learning behavior

Todd M. Gureckis, Bradley C. Love

Research output: Contribution to journalArticlepeer-review

Abstract

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predicts differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior.

Original languageEnglish (US)
Pages (from-to)10-50
Number of pages41
JournalCognitive Science
Volume34
Issue number1
DOIs
StatePublished - Jan 2010

Keywords

  • Associative learing
  • Learning constraints
  • Sequence learning
  • Simple recurrent networks
  • Skill acquisition and learning

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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