Multitasking in Driving as Optimal Adaptation Under Uncertainty

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorJokinen, Jussi P.P.
dc.contributor.authorKujala, Tuomo
dc.contributor.authorOulasvirta, Antti
dc.contributor.departmentDepartment of Communications and Networking
dc.contributor.departmentUniversity of Jyväskylä
dc.date.accessioned2020-08-28T08:13:16Z
dc.date.available2020-08-28T08:13:16Z
dc.date.issued2021-12
dc.description| openaire: EC/H2020/637991/EU//COMPUTED
dc.description.abstractObjective: 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.en
dc.description.versionPeer revieweden
dc.format.mimetypeapplication/pdf
dc.identifier.citationJokinen , J P P , Kujala , T & Oulasvirta , A 2021 , ' Multitasking in Driving as Optimal Adaptation Under Uncertainty ' , HUMAN FACTORS , vol. 63 , no. 8 , 0018720820927687 , pp. 1324-1341 . https://doi.org/10.1177/0018720820927687en
dc.identifier.doi10.1177/0018720820927687
dc.identifier.issn0018-7208
dc.identifier.otherPURE UUID: 5c8b83a5-19ad-41c9-9185-99cd8d59dd65
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/5c8b83a5-19ad-41c9-9185-99cd8d59dd65
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85088842527&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/76868713/ELEC_Jokinen_etal_Multitasking_in_Driving_as_Optimal_Adaptation_Human_Factors_2021.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/46274
dc.identifier.urnURN:NBN:fi:aalto-202008285212
dc.language.isoenen
dc.publisherSAGE Publications Inc.
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/637991/EU//COMPUTED
dc.relation.ispartofseriesHUMAN FACTORSen
dc.rightsopenAccessen
dc.subject.keywordcomputational rationality
dc.subject.keyworddriving
dc.subject.keywordmultitasking
dc.subject.keywordreinforcement learning
dc.subject.keywordtask interleaving
dc.titleMultitasking in Driving as Optimal Adaptation Under Uncertaintyen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
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