Evidence map›Paper›PMID 40083636›Full record

ArticleExperimental economics2024

Individual and contextual effects of attention in risky choice.

Alejandro Hirmas, Jan B Engelmann, Joël van der Weele

Abstract read
In one paragraph

Article in Experimental economics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Alejandro HirmasCenter for Experimental Economics and political Decision Making (CREED), Universiteit van Amsterdam, Amsterdam, The Netherlands.
Jan B EngelmannCenter for Experimental Economics and political Decision Making (CREED), Universiteit van Amsterdam, Amsterdam, The Netherlands.ORCID 0000-0001-6493-8792
Joël van der WeeleCenter for Experimental Economics and political Decision Making (CREED), Universiteit van Amsterdam, Amsterdam, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigate the role of visual attention in risky choice in a rich experimental dataset that includes eye-tracking data. We first show that attention is not reducible to individual and contextual variables, which explain only 20% of attentional variation. We then decompose attentional variation into individual average attention and trial-wise deviations of attention to capture different cognitive processes. Individual average attention varies by individual, and can proxy for individual preferences or goals (as in models of "rational inattention" or goal-directed attention). Trial-wise deviations of attention vary within subjects and depend on contextual factors (as in models of "salience" or stimulus-driven attention). We find that both types of attention predict behavior: average individual attention patterns are correlated with individual levels of loss aversion and capture part of this individual heterogeneity. Adding trial-wise deviations of attention further improves model fit. Our results show that a decomposition of attention into individual average attention and trial-wise deviations of attention can capture separable cognitive components of decision making and provides a useful tool for economists and researchers from related fields interested in decision-making and attention. Supplementary Information: The online version contains supplementary material available at 10.1007/s10683-024-09849-7.

Indexed as

AttentionEye-trackingLoss aversionRandomUtility models

Identifiers

PMID40083636
PMCPMC11897100

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.