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Published August 2022 | public
Journal Article

Predictable Effects of Visual Salience in Experimental Decisions and Games


Bottom-up stimulus-driven visual salience is largely automatic, effortless, and independent of a person's "top-down" perceptual goals; it depends only on features of a visual stimulus. Algorithms have been carefully trained to predict stimulus-driven salience values for each pixel in any image. The economic question we address is whether these salience values help explain economic decisions. Our first experimental analysis shows that when people pick between sets of fruits that have artificially induced value, predicted salience (which is uncorrelated with value by design) leads to mistakes. Our second analysis uses evidence from games in which choices are locations in images. When players are trying to cooperatively match locations, predicted salience is highly correlated with the success of matching (r = .57). In competitive hider-seeker location games, players choose salient locations more often than predicted by the unique Nash equilibrium. This tendency creates a disequilibrium "seeker's advantage" (seekers win more often than predicted in equilibrium). The result can be explained by level-k models in which predicted stimulus-driven salience influences level-0 choices and thereby influences overall perceptions, beliefs, and choices of higher-level players. The third analysis shows that there is an effect of visual salience in matrix games, but it is small and statistically weak. Applications to behavioral IO, price and tax salience, nudges and design, and visually influenced beliefs are suggested.

Additional Information

© The Author(s) 2022. Published by Oxford University Press on behalf of the President and Fellows of Harvard College. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). Accepted: 30 May 2022. Published: 11 June 2022. Corrected and typeset: 12 July 2022. Support was provided by the Behavioral and Neuroeconomics Discovery Fund (PI Camerer), Tianqiao and Chrissy Chen Center for Social and Decision Neuroscience, Alfred P. Sloan Foundation (G201811259), and NIMH Conte Center P50MH094258. Thanks to audiences at the Caltech Graduate Proseminar, Sloan/NOMIS Conference on Decision and Cognition, IAREP/SABE (Middlesex), Columbia University, Peking University, UC-Berkeley Behavioral Economics, Virtual Process Tracing Conference, Bocconi IGIER, and the Pitt Behavioral and Experimental Economics seminar. We especially thank Elke Weber, Vince Crawford, Jacqueline Gottlieb, Richard Thaler (for the Schelling list idea), Adam Sanjurjo (for "The Pearl" tip), the editor and several anonymous referees for helpful comments, Luca Polonio and Giorgio Coricelli for sharing the valuable gaze data in games, Anne Karing for a valuable image, Gidi Nave, Xintong Han, and Nina Solovnyeva (SURF 2020). Extra special thanks go to Eskil Forsell, Milica Mormann, and Alec Smith whose energetic prior research on this topic laid the foundation for this article, although their own work was never published. Data Availability. Data and code replicating tables and figures in this article can be found in Li and Camerer (2022) in the Harvard Dataverse, https://doi.org/10.7910/DVN/9LCYKG.

Additional details

August 22, 2023
October 23, 2023