Literature DB >> 17366302

Associationism and cognition: human contingency learning at 25.

David R Shanks1.   

Abstract

A major topic within human learning, the field of contingency judgement, began to emerge about 25 years ago following publication of an article on depressive realism by Alloy and Abramson (1979). Subsequently, associationism has been the dominant theoretical framework for understanding contingency learning but this has been challenged in recent years by an alternative cognitive or inferential approach. This article outlines the key conceptual differences between these approaches and summarizes some of the main methods that have been employed to distinguish between them.

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Year:  2007        PMID: 17366302     DOI: 10.1080/17470210601000581

Source DB:  PubMed          Journal:  Q J Exp Psychol (Hove)        ISSN: 1747-0218            Impact factor:   2.143


  22 in total

1.  Revisiting the role of within-compound associations in cue-interaction phenomena.

Authors:  David Luque; Amanda Flores; Miguel A Vadillo
Journal:  Learn Behav       Date:  2013-03       Impact factor: 1.986

2.  Over-imitation is better explained by norm learning than by distorted causal learning.

Authors:  Ben Kenward; Markus Karlsson; Joanna Persson
Journal:  Proc Biol Sci       Date:  2010-10-13       Impact factor: 5.349

3.  The propositional approach to associative learning as an alternative for association formation models.

Authors:  Jan De Houwer
Journal:  Learn Behav       Date:  2009-02       Impact factor: 1.986

4.  The criterion-calibration model of cue interaction in contingency judgments.

Authors:  Samuel D Hannah; Lorraine G Allan
Journal:  Learn Behav       Date:  2011-05       Impact factor: 1.986

5.  Previously acquired cue-outcome structural knowledge guides new learning: Evidence from the retroactive-interference-between-cues effect.

Authors:  David Luque; Joaquín Morís; Francisco J López; Pedro L Cobos
Journal:  Mem Cognit       Date:  2017-08

6.  The influence of the number of relevant causes on the processing of covariation information in causal reasoning.

Authors:  Kyungil Kim; Arthur B Markman; Tae Hoon Kim
Journal:  Cogn Process       Date:  2016-06-17

7.  Contingency is used to prepare for outcomes: implications for a functional analysis of learning.

Authors:  Fernando Blanco; Helena Matute; Miguel A Vadillo
Journal:  Psychon Bull Rev       Date:  2010-02

8.  Prediction, cognition and the brain.

Authors:  Andreja Bubic; D Yves von Cramon; Ricarda I Schubotz
Journal:  Front Hum Neurosci       Date:  2010-03-22       Impact factor: 3.169

Review 9.  Simple minds: a qualified defence of associative learning.

Authors:  Cecilia Heyes
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2012-10-05       Impact factor: 6.237

10.  The role of learning data in causal reasoning about observations and interventions.

Authors:  Björn Meder; York Hagmayer; Michael R Waldmann
Journal:  Mem Cognit       Date:  2009-04
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