Journal
HUMAN FACTORS
Volume 63, Issue 8, Pages 1324-1341Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/0018720820927687
Keywords
driving; multitasking; task interleaving; computational rationality; reinforcement learning
Funding
- Academy of Finland [310947]
- Finnish Center for Artificial Intelligence
- Academy of Finland project Human Automata
- European Research Council Starting Grant (COMPUTED)
- Academy of Finland (AKA) [310947, 310947] Funding Source: Academy of Finland (AKA)
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The study aims to understand how people adapt multitasking behavior in driving, finding that drivers adjust their strategies based on changes in the task environment, while safe and unsafe behaviors depend on the driver's decisions under uncertainty.
Objective The objective was to better understand how people adapt multitasking behavior when circumstances in driving change and how safe versus unsafe behaviors emerge. Background Multitasking strategies in driving adapt to changes in the task environment, but the cognitive mechanisms of this adaptation are not well known. Missing is a unifying account to explain the joint contribution of task constraints, goals, cognitive capabilities, and beliefs about the driving environment. Method We model the driver's decision to deploy visual attention as a stochastic sequential decision-making problem and propose hierarchical reinforcement learning as a computationally tractable solution to it. The supervisory level deploys attention based on per-task value estimates, which incorporate beliefs about risk. Model simulations are compared against human data collected in a driving simulator. Results Human data show adaptation to the attentional demands of ongoing tasks, as measured in lane deviation and in-car gaze deployment. The predictions of our model fit the human data on these metrics. Conclusion Multitasking strategies can be understood as optimal adaptation under uncertainty, wherein the driver adapts to cognitive constraints and the task environment's uncertainties, aiming to maximize the expected long-term utility. Safe and unsafe behaviors emerge as the driver has to arbitrate between conflicting goals and manage uncertainty about them. Application Simulations can inform studies of conditions that are likely to give rise to unsafe driving behavior.
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