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dc.contributor.authorJokinen, Jussi P. P.
dc.contributor.authorKujala, Tuomo
dc.contributor.authorOulasvirta, Antti
dc.date.accessioned2020-08-03T07:35:18Z
dc.date.available2020-08-03T07:35:18Z
dc.date.issued2020
dc.identifier.citationJokinen, Jussi P. P.; Kujala, Tuomo; Oulasvirta, Antti (2020). Multitasking in Driving as Optimal Adaptation Under Uncertainty. Human Factors, Early online. DOI: 10.1177/0018720820927687
dc.identifier.otherCONVID_41672486
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/71313
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.format.mimetypeapplication/pdf
dc.languageeng
dc.publisherSAGE Publications
dc.relation.ispartofseriesHuman Factors
dc.rightsCC BY 4.0
dc.subject.otherhuomiokyky
dc.subject.otherdriving
dc.subject.othermultitasking
dc.subject.othertask interleaving
dc.subject.othercomputational rationality
dc.subject.otherreinforcement learning
dc.titleMultitasking in Driving as Optimal Adaptation Under Uncertainty
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202008035461
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineKognitiotiedefi
dc.contributor.oppiaineCognitive Scienceen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn0018-7208
dc.relation.volumeEarly online
dc.type.versionpublishedVersion
dc.rights.copyright© 2020, The Author(s)
dc.rights.accesslevelopenAccessfi
dc.subject.ysokognitiiviset prosessit
dc.subject.ysoajotapa
dc.subject.ysohavainnot
dc.subject.ysoliikennekäyttäytyminen
dc.subject.ysovisuaalinen ympäristö
dc.subject.ysokuljettajat
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p5283
jyx.subject.urihttp://www.yso.fi/onto/yso/p23841
jyx.subject.urihttp://www.yso.fi/onto/yso/p5284
jyx.subject.urihttp://www.yso.fi/onto/yso/p3624
jyx.subject.urihttp://www.yso.fi/onto/yso/p27459
jyx.subject.urihttp://www.yso.fi/onto/yso/p5501
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1177/0018720820927687
jyx.fundinginformationThis research has been supported by the following institutions: Jussi P. P. Jokinen: Academy of Finland (grant 310947); and Antti Oulasvirta: Finnish Center for Artificial Intelligence and the European Research Council Starting Grant (COMPUTED) and Academy of Finland project Human Automata.


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