Literature DB >> 19122057

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

Anna Thorwart1, Harald Lachnit.   

Abstract

Models of associative learning differ in their predictions concerning the symmetry of generalization decrements. Whereas Pearce's (1994) configural model predicts the same response decrement after adding elements to and after removing elements from a previously trained stimulus, elemental models, such as the replaced elements model and Harris's (2006) model, anticipate more of a decrement for removing than for adding elements. In three contingency learning experiments, we manipulated the motion and the spatial arrangement of colored dots in order to induce configural or elemental processing by perceptual grouping. The results reliably showed symmetrical decrements for the added and removed groups. The manipulations of the stimuli had no effect on stimulus processing. This is in line with Pearce's configural model, but it is at variance with the elemental models and previous studies.

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Year:  2009        PMID: 19122057     DOI: 10.3758/LB.37.1.107

Source DB:  PubMed          Journal:  Learn Behav        ISSN: 1543-4494            Impact factor:   1.986


  21 in total

Review 1.  A componential view of configural cues in generalization and discrimination in Pavlovian conditioning.

Authors:  S E Brandon; E H Vogel; A R Wagner
Journal:  Behav Brain Res       Date:  2000-06-01       Impact factor: 3.332

2.  Spatial separation of target and competitor cues enhances blocking of human causality judgements.

Authors:  Steven Glautier
Journal:  Q J Exp Psychol B       Date:  2002-04

3.  Differential effects of adding and removing components of a context on the generalization of conditional freezing.

Authors:  Felisa González; Jennifer J Quinn; Michael S Fanselow
Journal:  J Exp Psychol Anim Behav Process       Date:  2003-01

4.  Elemental representations of stimuli in associative learning.

Authors:  Justin A Harris
Journal:  Psychol Rev       Date:  2006-07       Impact factor: 8.934

Review 5.  Stimulus coding in human associative learning: flexible representations of parts and wholes.

Authors:  Klaus G Melchers; David R Shanks; Harald Lachnit
Journal:  Behav Processes       Date:  2007-10-13       Impact factor: 1.777

6.  HMS: a MATLAB simulator of the Harris model of associative learning.

Authors:  Holger Schultheis; Anna Thorwart; Harald Lachnit
Journal:  Behav Res Methods       Date:  2008-05

7.  The Psychophysics Toolbox.

Authors:  D H Brainard
Journal:  Spat Vis       Date:  1997

8.  Similarity and discrimination: a selective review and a connectionist model.

Authors:  J M Pearce
Journal:  Psychol Rev       Date:  1994-10       Impact factor: 8.934

9.  Contrasting predictive and causal values of predictors and of causes.

Authors:  Oskar Pineño; James C Denniston; Tom Beckers; Helena Matute; Ralph R Miller
Journal:  Learn Behav       Date:  2005-05       Impact factor: 1.986

10.  Outcome and cue properties modulate blocking.

Authors:  Jan De Houwer; Tom Beckers; Steven Glautier
Journal:  Q J Exp Psychol A       Date:  2002-07
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  5 in total

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

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

2.  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

3.  Asymmetrical generalization of conditioning and extinction from compound to element and element to compound.

Authors:  Mark E Bouton; Caleb Doyle-Burr; Drina Vurbic
Journal:  J Exp Psychol Anim Behav Process       Date:  2012-08-27

4.  Three Ways That Non-associative Knowledge May Affect Associative Learning Processes.

Authors:  Anna Thorwart; Evan J Livesey
Journal:  Front Psychol       Date:  2016-12-27

5.  Second-order conditioning and conditioned inhibition: influences of speed versus accuracy on human causal learning.

Authors:  Jessica C Lee; Evan J Livesey
Journal:  PLoS One       Date:  2012-11-28       Impact factor: 3.240

  5 in total

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