The University of Southampton
University of Southampton Institutional Repository

My fair future self: The role of temporal distance and self-enhancement in prediction

Record type: Article

Information people rely on when making self-predictions may be influenced by temporal distance and the self-enhancement motive. We proposed, drawing from Construal Level Theory, that temporally distant (vs. near) predictions reflect the “gist” self-attributes, rather than other attributes (“noise”). Based on the self-enhancement literature, positive (vs. negative) attributes will be perceived as the “gist.rdquo; In three studies, we tested the hypothesis that positive attributes are more prominent in distant predictions. Distant (compared to near) predictions reflect the “gist” attributes, are more positive and confident (Study 1). Such predictions rely on positive (rather than negative) attributes (Study 2). Distant predictions reflect a greater better-than-average effect, better ratings on positive (and not-as-bad on negative) attributes in comparison to peers (Study 3). These tendencies hold true for individuals with varying levels of self-esteem (Studies 1, 3). The studies suggest that temporal distance and motivation to enhance the favorability of self-concept both influence prediction.



Microsoft Word __filestore.soton.ac.uk_Users_gg_mydocuments_constantine publications pdf's_2014_Stephan, Sedikides, Heller, & Shidlovski, 2015, SCeprints.doc - Other
Restricted to Repository staff only
Download (223kB)

Citation

Stephan, Elena, Sedikides, C, Heller, D and Shidlovski, D (2015) My fair future self: The role of temporal distance and self-enhancement in prediction Social Cognition, 33, (2), pp. 149-168. (doi:10.1521/soco.2015.33.2.149).

More information

Published date: 2015

Identifiers

Local EPrints ID: 376426
URI: http://eprints.soton.ac.uk/id/eprint/376426
ISSN: 0278-016X
PURE UUID: 53985b7d-ef75-4325-9028-911f5a67512b

Catalogue record

Date deposited: 28 Apr 2015 12:20
Last modified: 17 Jul 2017 21:09

Export record

Altmetrics

Contributors

Author: Elena Stephan
Author: C Sedikides
Author: D Heller
Author: D Shidlovski

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×