Literature DB >> 18031954

Stimulus coding in human associative learning: flexible representations of parts and wholes.

Klaus G Melchers1, David R Shanks, Harald Lachnit.   

Abstract

An enduring theme for theories of associative learning is the problem of explaining how configural discriminations--ones in which the significance of combinations of cues is inconsistent with the significance of the individual cues themselves-are learned. One approach has been to assume that configurations are the basic representational form on which associative processes operate, another has tried in contrast to retain elementalism. We review evidence that human learning is representationally flexible in a way that challenges both configural and elemental theories. We describe research showing that task demands, prior experience, instructions, and stimulus properties all influence whether a particular problem is solved configurally or elementally. Lines of possible future theory development are discussed.

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Year:  2007        PMID: 18031954     DOI: 10.1016/j.beproc.2007.09.013

Source DB:  PubMed          Journal:  Behav Processes        ISSN: 0376-6357            Impact factor:   1.777


  31 in total

1.  CS-US interval determines the transition from overshadowing to potentiation with flavor compounds.

Authors:  W Robert Batsell; Elizabeth Wakefield; Leigh Ann Ulrey; Katie Reimink; Steven L Rowe; Scott Dexheimer
Journal:  Learn Behav       Date:  2012-06       Impact factor: 1.986

2.  Dynamic afferent synapses to decision-making networks improve performance in tasks requiring stimulus associations and discriminations.

Authors:  Mark A Bourjaily; Paul Miller
Journal:  J Neurophysiol       Date:  2012-03-28       Impact factor: 2.714

3.  Generalization decrements: further support for flexibility in stimulus processing.

Authors:  Anna Thorwart; Harald Lachnit
Journal:  Learn Behav       Date:  2010-11       Impact factor: 1.986

4.  Symmetrical generalization decrements: configural stimulus processing in human contingency learning.

Authors:  Anna Thorwart; Harald Lachnit
Journal:  Learn Behav       Date:  2009-02       Impact factor: 1.986

5.  An attention-modulated associative network.

Authors:  Justin A Harris; Evan J Livesey
Journal:  Learn Behav       Date:  2010-02       Impact factor: 1.986

6.  Efficient learning mechanisms hold in the social domain and are implemented in the medial prefrontal cortex.

Authors:  Azade Seid-Fatemi; Philippe N Tobler
Journal:  Soc Cogn Affect Neurosci       Date:  2014-10-17       Impact factor: 3.436

7.  Exploring a latent cause theory of classical conditioning.

Authors:  Samuel J Gershman; Yael Niv
Journal:  Learn Behav       Date:  2012-09       Impact factor: 1.986

8.  Prior beliefs influence symmetrical or asymmetrical generalizations in human causal learning.

Authors:  Ryoji Nishiyama; Takatoshi Nagaishi; Takahisa Masaki
Journal:  Learn Behav       Date:  2017-09       Impact factor: 1.986

Review 9.  On the generality and limits of abstraction in rats and humans.

Authors:  Gonzalo P Urcelay; Ralph R Miller
Journal:  Anim Cogn       Date:  2010-01       Impact factor: 3.084

10.  Explaining compound generalization in associative and causal learning through rational principles of dimensional generalization.

Authors:  Fabian A Soto; Samuel J Gershman; Yael Niv
Journal:  Psychol Rev       Date:  2014-07       Impact factor: 8.934

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