Learning about things that never happened: A critique and refinement of the Rescorla-Wagner update rule when many outcomes are possible
| Autor: | Geoff Hollis |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Semantics (computer science)
Conditioning Classical Experimental and Cognitive Psychology Models Psychological 050105 experimental psychology Discrimination Learning Young Adult 03 medical and health sciences 0302 clinical medicine Arts and Humanities (miscellaneous) Animals Humans 0501 psychology and cognitive sciences Association (psychology) Problem Solving Cognitive science 05 social sciences Association Learning Classical conditioning Language acquisition Associative learning Neuropsychology and Physiological Psychology Rescorla–Wagner model Psychology Algorithms 030217 neurology & neurosurgery Discriminative learning |
| Zdroj: | Memory & Cognition. 47:1415-1430 |
| ISSN: | 1532-5946 0090-502X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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