Learning rational temporal eye movement strategies

Significance In a dynamic world humans not only have to decide where to look but also when to direct their gaze to potentially informative locations in the visual scene. Little is known about how timing of eye movements is related to environmental regularities and how gaze strategies are learned. Here we present behavioral data establishing that humans learn to adjust their temporal eye movements efficiently. Our computational model shows how established properties of the visual system determine the timing of gaze. Surprisingly, a Bayesian learner only incorporating the scalar law of biological timing can fully explain the course of learning these strategies. Thus, humans use temporal regularities learned from observations to adjust the scheduling of eye movements in a nearly optimal way. During active behavior humans redirect their gaze several times every second within the visual environment. Where we look within static images is highly efficient, as quantified by computational models of human gaze shifts in visual search and face recognition tasks. However, when we shift gaze is mostly unknown despite its fundamental importance for survival in a dynamic world. It has been suggested that during naturalistic visuomotor behavior gaze deployment is coordinated with task-relevant events, often predictive of future events, and studies in sportsmen suggest that timing of eye movements is learned. Here we establish that humans efficiently learn to adjust the timing of eye movements in response to environmental regularities when monitoring locations in the visual scene to detect probabilistically occurring events. To detect the events humans adopt strategies that can be understood through a computational model that includes perceptual and acting uncertainties, a minimal processing time, and, crucially, the intrinsic costs of gaze behavior. Thus, subjects traded off event detection rate with behavioral costs of carrying out eye movements. Remarkably, based on this rational bounded actor model the time course of learning the gaze strategies is fully explained by an optimal Bayesian learner with humans’ characteristic uncertainty in time estimation, the well-known scalar law of biological timing. Taken together, these findings establish that the human visual system is highly efficient in learning temporal regularities in the environment and that it can use these regularities to control the timing of eye movements to detect behaviorally relevant events.