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  5. Lost in Multidimensional Space: Epistemic Motivations Define and Distinguish Negative Affect
 
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Lost in Multidimensional Space: Epistemic Motivations Define and Distinguish Negative Affect

Author(s)
Maher, Paul J.  
Van Tilburg, Wijnand A.P.  
Igou, Eric R.  
Uri
http://hdl.handle.net/10197/10156
Date Issued
2019-03-12
Date Available
2019-04-25T09:16:18Z
Abstract
People’s knowledge of the world is limited and frequently imprecise. Thus, epistemic challenges are commonplace and much research in psychology has investigated their consequences. However, research has not systematically investigated how states of negative affect correspond to the desire for understanding and meaning in life. We investigated the role of epistemic motivations (e.g., meaning search) as features that distinguish forms of negative affect from one another. In three studies, we used multidimensional scaling to model the perceived similarity of negative affect states and then examined to what extent people differentiate these states based on their association with epistemic motivations. These studies revealed that negative states are reliably differentiated through their relation to epistemic pursuits. These findings were verified in a fourth study in which we experimentally induced epistemic affect. Overall, these results indicate that epistemic concerns characterize states of negative affect to a substantial degree.
Sponsorship
Irish Research Council
Type of Material
Journal Article
Publisher
SAGE Publications
Journal
Personality and Social Psychology Bulletin
Volume
45
Issue
9
Start Page
1409
End Page
1424
Copyright (Published Version)
2019 the Society for Personality and Social Psychology
Subjects

Epistemology

Affect

Emotions

Meaning

Sense-making

Appraisal

DOI
10.1177/0146167219833395
Language
English
Status of Item
Peer reviewed
ISSN
0146-1672
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Owning collection
Psychology Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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