title: Learning where to look for a hidden target.
abstract: Survival depends on successfully foraging for food, for which evolution has selected diverse behaviors in different species. Humans forage not only for food, but also for information. We decide where to look over 170,000 times per day, approximately three times per wakeful second. The frequency of these saccadic eye movements belies the complexity underlying each individual choice. Experience factors into the choice of where to look and can be invoked to rapidly redirect gaze in a context and task-appropriate manner. However, remarkably little is known about how individuals learn to direct their gaze given the current context and task. We designed a task in which participants search a novel scene for a target whose location was drawn stochastically on each trial from a fixed prior distribution. The target was invisible on a blank screen, and the participants were rewarded when they fixated the hidden target location. In just a few trials, participants rapidly found the hidden targets by looking near previously rewarded locations and avoiding previously unrewarded locations. Learning trajectories were well characterized by a simple reinforcement-learning (RL) model that maintained and continually updated a reward map of locations. The RL model made further predictions concerning sensitivity to recent experience that were confirmed by the data. The asymptotic performance of both the participants and the RL model approached optimal performance characterized by an ideal-observer theory. These two complementary levels of explanation show how experience in a novel environment drives visual search in humans and may extend to other forms of search such as animal foraging.
authors.affiliation: Institute for Neural Computation, University of California at San Diego, La Jolla, CA 92093, USA.
authors.forename: Leanne
authors.initials: L
authors.lastname: Chukoskie
authors.forename: Joseph
authors.initials: J
authors.lastname: Snider
authors.forename: Michael C
authors.initials: MC
authors.lastname: Mozer
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Krauzlis
authors.forename: Terrence J
authors.initials: TJ
authors.lastname: Sejnowski
mesh_terms: D000818:Animals; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D011340:Problem Solving / Q000502:physiology*; D018794:Problem-Based Learning; D014796:Visual Perception / Q000502:physiology*
keywords: ideal observer; oculomotor; reinforcement learning; saccades