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Cited by 351

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Automatic processing of psychological distance: Evidence from a Stroop task.

Open access
Bar-Anan, Yoav, Liberman, Nira, Trope, Yaacov, Algom, Daniel·2007·Journal of Experimental Psychology: General

Abstract

A picture-word version of the Stroop task was used to test the automatic activation of psychological distance by words carrying various senses of psychological distance: temporal (tomorrow, in a year), social (friend, enemy), and hypotheticality (sure, maybe). The pictures implied depth, with the words appearing relatively close to or distant from the observer. The participants classified the spatial distance of words faster when the word's implicit psychological distance matched its spatial distance (e.g., a geographically close word was classified faster when it was "friend" than when it was "enemy"). The findings are consistent with the idea that psychological distance is accessed automatically, even when it is not directly related to people's current goals, and suggest that psychological distance is an important dimension of meaning, common to spatial distance, temporal distance, social distance, and hypotheticality.

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Crossref · 10.1037/0096-3445.136.4.610

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Metadata

Authors
Bar-Anan, Y., Liberman, N., Trope, Y., & Algom, D.
Venue
Journal of Experimental Psychology: General
Published
2007
Volume
136
Issue
4
Pages
610-622
ISSN
  • 0096-3445 (ISSN-L)
  • 1939-2222
OpenAlex
W2023290309
Cited by
351
References
3
FWCI
10.85
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Automatic processing of psychological distance: Evidence from a Stroop task. https://app.cochat.ai/papers/10.1037/0096-3445.136.4.610
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Bar-Anan, Y., Liberman, N., Trope, Y., & Algom, D. (2007). Automatic processing of psychological distance: Evidence from a Stroop task. Journal of Experimental Psychology: General. https://doi.org/10.1037/0096-3445.136.4.610

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.