We measure and study visual salience in two-player games, in which players both prefer to match choices of locations or one prefers match and the other mismatch (hide-and-seek). Visual salience is predicted a priori from a computational algorithm based on principles from theoretical neuroscience and previously calibrated by human free gaze data. Salience is a strong predictor of choices, which results in a matching rate of 64% in two samples. Both seekers and hiders choose salient locations more often, though seekers also choose low-salience locations. The result is a “seeker’s advantage” in which seekers win about 9% of the games, compared to a mixed-Nash benchmark of 7%. A salience-perturbed cognitive hierarchy (SCH) model is estimated from the hide-and-seek data. Those estimated parameters accurately predict the choice-salience relation in the matching games.
Speaker:
Colin Camerer, Robert Kirby Professor of Behavioral Finance and Economics, California Institute of Technology
