title: Prospective Optimization with Limited Resources.
abstract: The future is uncertain because some forthcoming events are unpredictable and also because our ability to foresee the myriad consequences of our own actions is limited. Here we studied how humans select actions under such extrinsic and intrinsic uncertainty, in view of an exponentially expanding number of prospects on a branching multivalued visual stimulus. A triangular grid of disks of different sizes scrolled down a touchscreen at a variable speed. The larger disks represented larger rewards. The task was to maximize the cumulative reward by touching one disk at a time in a rapid sequence, forming an upward path across the grid, while every step along the path constrained the part of the grid accessible in the future. This task captured some of the complexity of natural behavior in the risky and dynamic world, where ongoing decisions alter the landscape of future rewards. By comparing human behavior with behavior of ideal actors, we identified the strategies used by humans in terms of how far into the future they looked (their "depth of computation") and how often they attempted to incorporate new information about the future rewards (their "recalculation period"). We found that, for a given task difficulty, humans traded off their depth of computation for the recalculation period. The form of this tradeoff was consistent with a complete, brute-force exploration of all possible paths up to a resource-limited finite depth. A step-by-step analysis of the human behavior revealed that participants took into account very fine distinctions between the future rewards and that they abstained from some simple heuristics in assessment of the alternative paths, such as seeking only the largest disks or avoiding the smaller disks. The participants preferred to reduce their depth of computation or increase the recalculation period rather than sacrifice the precision of computation.
authors.affiliation: Institute for Neural Computation, University of California at San Diego, La Jolla, California, United States of America.
authors.forename: Joseph
authors.initials: J
authors.lastname: Snider
authors.affiliation: Institute for Neural Computation, University of California at San Diego, La Jolla, California, United States of America.
authors.forename: Dongpyo
authors.initials: D
authors.lastname: Lee
authors.affiliation: Institute for Neural Computation, University of California at San Diego, La Jolla, California, United States of America; Graduate Program in Neurosciences, University of California at San Diego, La Jolla, California, United States of America.
authors.forename: Howard
authors.initials: H
authors.lastname: Poizner
authors.affiliation: Systems Neurobiology Laboratories, Salk Institute for Biological Studies, La Jolla, California, United States of America.
authors.forename: Sergei
authors.initials: S
authors.lastname: Gepshtein
mesh_terms: D000328:Adult; D000465:Algorithms; D019295:Computational Biology; D003657:Decision Making / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D012201:Reward; D013647:Task Performance and Analysis; D035501:Uncertainty; D055815:Young Adult