title: Requiem for the max rule?
abstract: In tasks such as visual search and change detection, a key question is how observers integrate noisy measurements from multiple locations to make a decision. Decision rules proposed to model this process have fallen into two categories: Bayes-optimal (ideal observer) rules and ad-hoc rules. Among the latter, the maximum-of-outputs (max) rule has been the most prominent. Reviewing recent work and performing new model comparisons across a range of paradigms, we find that in all cases except for one, the optimal rule describes human data as well as or better than every max rule either previously proposed or newly introduced here. This casts doubt on the utility of the max rule for understanding perceptual decision-making.
authors.affiliation: New York University, New York, NY, United States; Baylor College of Medicine, Houston, TX, United States. Electronic address: weijima@nyu.edu.
authors.forename: Wei Ji
authors.initials: WJ
authors.lastname: Ma
authors.affiliation: Baylor College of Medicine, Houston, TX, United States.
authors.forename: Shan
authors.initials: S
authors.lastname: Shen
authors.affiliation: Baylor College of Medicine, Houston, TX, United States; University of Cambridge, Cambridge, UK.
authors.forename: Gintare
authors.initials: G
authors.lastname: Dziugaite
authors.affiliation: Baylor College of Medicine, Houston, TX, United States; University of Cambridge, Cambridge, UK.
authors.forename: Ronald
authors.initials: R
authors.lastname: van den Berg
mesh_terms: D001288:Attention; D001499:Bayes Theorem; D003198:Computer Simulation; D003657:Decision Making; D006801:Humans; D017603:Signal Detection, Psychological / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*
keywords: Change detection; Computational models; Decision rules; Ideal observer; Visual search