The role of uncertainty and reward on eye movements in a virtual driving task

Abstract

Eye movements during natural tasks are well coordinated with ongoing task demands and many variables could influence gaze strategies. Sprague and Ballard (2003) proposed a gaze-scheduling model that uses a utility-weighted uncertainty metric to prioritize fixations on task-relevant objects and predicted that human gaze should be influenced by both reward structure and task-relevant uncertainties. To test this conjecture, we tracked the eye movements of participants in a simulated driving task where uncertainty and implicit reward (via task priority) were varied. Participants were instructed to simultaneously perform a Follow Task where they followed a lead car at a specific distance and a Speed Task where they drove at an exact speed. We varied implicit reward by instructing the participants to emphasize one task over the other and varied uncertainty in the Speed Task with the presence or absence of uniform noise added to the car's velocity. Subjects' gaze data were classified for the image content near fixation and segmented into looks. Gaze measures, including look proportion, duration and interlook interval, showed that drivers more closely monitor the speedometer if it had a high level of uncertainty, but only if it was also associated with high task priority or implicit reward. The interaction observed appears to be an example of a simple mechanism whereby the reduction of visual uncertainty is gated by behavioral relevance. This lends qualitative support for the primary variables controlling gaze allocation proposed in the Sprague and Ballard model.

A secondary working memory challenge preserves primary place strategies despite overtraining

“…Therefore, we cannot eliminate secondary learning and task difficulty as factors influencing primary strategy recruitment. Indeed, uncertainty has been shown to modify the use of performance strategies (Derusso et al 2010;Sullivan et al 2012). However, it appears that uncertainty, learning phase, and/or task difficulty on the secondary choice are not principal factors driving the effects on primary strategy observed here.…”

Modeling Task Control of Eye Movements

“…This reward uncertainty protocol was developed by Sprague et al [22] to simulate behavior in a walking environment and was shown to be superior to the common round-robin protocol used in robotics. Evidence that gaze allocation in a dynamic, noisy environment is in fact controlled by reduction of visual uncertainty weighted by subjective reward value was obtained by Sullivan et al [36]. This study tracked eye movements of participants in a simulated driving task where uncertainty and implicit reward (via task priority) were varied.…”

“…The data from Sullivan et al [36] was then modeled by Johnson et al [37] using an adaptation of the reward uncertainty protocol to estimate the best reward and noise values for each task directly from the gaze data. Because optimal behavior for driving consisted of staying at a fixed setpoints in distance and speed, they were able to use the simplification of a servo controller for choosing the action, an approximation that works well in this instance.…”

Modeling visual sampling on in-car displays: The challenge of predicting safety-critical lapses of control

“…Uncertainty, event expectancies, internal task state estimates (Johnson et al, 2014;Wickens, et al, 2001;Senders et al, 1967), and saliency, as well as the expected effort and value (reward) of gathering visual information from a particular source (Sullivan et al, 2012;Wickens, et al, 2001) can certainly play a role in driver multitasking behavior and may provide alternative venues or improve the current model for explaining the empirical findings. The current model is based on the theory of threaded cognition in multitasking by Salvucci and Taatgen (2008) and does not require modeling of uncertainty or internal task state estimates (for now at least) or an explicit central executive process in multitasking situations; instead, a straightforward "threading" mechanism suffices to interleave the resource processing between tasks.…”