Associative Attitude Learning: A Closer Look at Evidence and How It Relates to Attitude Models.

associative attitude leaning attitude automaticity dual-process models evaluative conditioning learning

Journal

Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc
ISSN: 1532-7957
Titre abrégé: Pers Soc Psychol Rev
Pays: United States
ID NLM: 9703164

Informations de publication

Date de publication:
05 2019
Historique:
pubmed: 27 3 2018
medline: 18 12 2019
entrez: 27 3 2018
Statut: ppublish

Résumé

Associative attitude learning is typically viewed as a low-level process that automatically registers mere co-occurrences between stimuli, independent of their validity and relational meaning. This view invites to critically examine how attitude formation conforms to four operating conditions (i.e., unawareness, efficiency, goal independence, and uncontrollability) and two operating principles (i.e., unqualified registration of mere co-occurrences between stimuli and formation of direct stimulus-response links), which is the main purpose of the present contribution. The general discussion examines how contemporary attitude models endorse these conditions and principles. Overall, this contribution calls for (a) a nuanced understanding of the nature and scope of associative attitude learning, (b) a fine-grained understanding of how contemporary attitude models endorse conditions and principles reviewed here and find them relevant to their theorization of attitude formation, (c) a clarification of how direct and indirect evaluative measures relate to these conditions and principles, and (d) enhanced efforts in specifying contemporary attitude formation models.

Identifiants

pubmed: 29575983
doi: 10.1177/1088868318763261
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

161-189

Auteurs

Olivier Corneille (O)

1 Université catholique de Louvain, Louvain-la-Neuve, Belgium.

Christoph Stahl (C)

2 University of Cologne, Germany.

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Classifications MeSH