title: Humans can adopt optimal discounting strategy under real-time constraints.
abstract: Critical to our many daily choices between larger delayed rewards, and smaller more immediate rewards, are the shape and the steepness of the function that discounts rewards with time. Although research in artificial intelligence favors exponential discounting in uncertain environments, studies with humans and animals have consistently shown hyperbolic discounting. We investigated how humans perform in a reward decision task with temporal constraints, in which each choice affects the time remaining for later trials, and in which the delays vary at each trial. We demonstrated that most of our subjects adopted exponential discounting in this experiment. Further, we confirmed analytically that exponential discounting, with a decay rate comparable to that used by our subjects, maximized the total reward gain in our task. Our results suggest that the particular shape and steepness of temporal discounting is determined by the task that the subject is facing, and question the notion of hyperbolic reward discounting as a universal principle.
authors.affiliation: Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, United States of America. schweigh@usc.edu
authors.forename: N
authors.initials: N
authors.lastname: Schweighofer
authors.forename: K
authors.initials: K
authors.lastname: Shishida
authors.forename: C E
authors.initials: CE
authors.lastname: Han
authors.forename: Y
authors.initials: Y
authors.lastname: Okamoto
authors.forename: S C
authors.initials: SC
authors.lastname: Tanaka
authors.forename: S
authors.initials: S
authors.lastname: Yamawaki
authors.forename: K
authors.initials: K
authors.lastname: Doya
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D002755:Choice Behavior / Q000502:physiology*; D003198:Computer Simulation; D003199:Computer Systems; D003657:Decision Making / Q000502:physiology*; D005716:Game Theory; D006801:Humans; D008954:Models, Biological; D012201:Reward; D013647:Task Performance and Analysis; D013997:Time Factors