Learning about things that never happened: A critique and refinement of the Rescorla-Wagner update rule when many outcomes are possible.

A vector-based model of discriminative learning is presented. It is demonstrated to learn association strengths identical to the Rescorla–Wagner model under certain parameter settings (Rescorla & Wagner, 1972, Classical Conditioning II: Current Research and Theory, 2, 64–99). For other parameter set...

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Detalles Bibliográficos
Publicado en:Memory & Cognition Vol. 47; no. 7; pp. 1415 - 1431
Autor principal: Hollis, Geoff
Formato: Artículo
Publicado: Springer Nature Oct2019
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2019
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      pub: Springer Nature
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        atl: Learning about things that never happened: A critique and refinement of the Rescorla-Wagner update rule when many outcomes are possible.
      aug:
        au: Hollis, Geoff
        affil: Department of Computing Science, University of Alberta, 3-39 Athabasca Hall, T6G 2E9, Edmonton, AB, Canada
      su:
        Algorithms
        Conceptual structures
        Learning strategies
        Phonological awareness
      sug:
        subj:
          Algorithms
          Conceptual structures
          Learning strategies
          Phonological awareness
      keyword:
        Associative learning
        Discriminative learning
        Language acquisition
        Lexical processing
        Rescorla–Wagner model
        Associative learning
        Discriminative learning
        Language acquisition
        Lexical processing
        Rescorla–Wagner model
      ab: A vector-based model of discriminative learning is presented. It is demonstrated to learn association strengths identical to the Rescorla–Wagner model under certain parameter settings (Rescorla & Wagner, 1972, Classical Conditioning II: Current Research and Theory, 2, 64–99). For other parameter settings, it approximates the association strengths learned by the Rescorla–Wagner model. I argue that the Rescorla–Wagner model has conceptual details that exclude it as an algorithmically plausible model of learning. The vector learning model, however, does not suffer from the same conceptual issues. Finally, we demonstrate that the vector learning model provides insight into how animals might learn the semantics of stimuli rather than just their associations. Results for simulations of language processing experiments are reported.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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