TY  - JOUR
PY  - 2020//
TI  - Multitasking in driving as optimal adaptation under uncertainty
JO  - Human factors
A1  - Jokinen, Jussi P. P.
A1  - Kujala, Tuomo
A1  - Oulasvirta, Antti
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0018-7208
UR  - http://dx.doi.org/10.1177/0018720820927687
ID  - ref1
ER  -