Using Visual Salience in Empirical Game Theory

Abstract

“Bottom-up” visual perception is fast, universal, and does not depend on personal goals or experience. Where exactly bottom-up attention is directed in a visual image-- usually based on contrast, color, feature orientation, and centrality-- can be accurately predicted by machine-learned algorithms. We test whether this type of salience can help explain economic decisions in four experimental analyses (three are new). When people pick between sets of valued fruits, salience can lead to mistakes (lower-value sets are sometimes chosen because they are salient). Two studies show strong and weak influence on choices, respectively, of salience in stock price charts and salient payoffs in game matrices. The central analysis is evidence from games in which choices are locations in images. When players are trying to cooperatively match locations, concentration of salience is associated with the success of matching (r=.57). In competitive hider-seeker location games, all players choose salient locations more often. This fact creates a “seeker’s advantage” (9% wins compared to the 7% predicted by unique equilibrium). The location game data are consistent with cognitive hierarchy and level-k models in which predicted salience influences nonstrategic level-0 choices. Model estimates from hider-seeker games can predict behavior in matching games despite their opposite strategic structures. Looking forward, bottom-up salience could be included as a predictable type of costless involuntary “pre-attention” in expanded models of Bayesian-rational inattention. Novel applications involving prices and taxes, disability, ethnic markers, and visually-influenced beliefs are suggested.