Literature DB >> 15656732

Retrospective revaluation as simple associative learning.

Stefano Ghirlanda1.   

Abstract

Backward blocking, unovershadowing, and backward conditioned inhibition are examples of retrospective revaluation phenomena that have been suggested to involve more than simple associative learning. Models of these phenomena have thus used additional concepts, for example, appealing to attentional effects or more elaborate learning mechanisms. The author shows that a suitable representation of stimuli, paired with a careful analysis of the discriminations faced by animals, leads to an account of these and other phenomena in terms of a simple elemental model of associative learning, with essentially the same learning mechanism as the R. A. Rescorla and A. R. Wagner (1972) model. The author concludes with a discussion of some implications for theories of learning. Copyright 2005 APA.

Mesh:

Year:  2005        PMID: 15656732     DOI: 10.1037/0097-7403.31.1.107

Source DB:  PubMed          Journal:  J Exp Psychol Anim Behav Process        ISSN: 0097-7403


  9 in total

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2.  How training and testing histories affect generalization: a test of simple neural networks.

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3.  Attention and salience in associative blocking.

Authors:  Stephen E Denton; John K Kruschke
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4.  An instance theory of associative learning.

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

Authors:  Geoff Hollis
Journal:  Mem Cognit       Date:  2019-10

6.  An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective.

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Review 7.  Retrospective revaluation: The phenomenon and its theoretical implications.

Authors:  Ralph R Miller; James E Witnauer
Journal:  Behav Processes       Date:  2015-09-03       Impact factor: 1.777

8.  An elemental model of retrospective revaluation without within-compound associations.

Authors:  Patrick C Connor; Vincent M Lolordo; Thomas P Trappenberg
Journal:  Learn Behav       Date:  2014-03       Impact factor: 1.926

9.  Forget-me-some: General versus special purpose models in a hierarchical probabilistic task.

Authors:  Franziska Bröker; Louise Marshall; Sven Bestmann; Peter Dayan
Journal:  PLoS One       Date:  2018-10-22       Impact factor: 3.240

  9 in total

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