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Multitasking in Driving as Optimal Adaptation 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 adapt...
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Published in: | Human factors 2021-12, Vol.63 (8), p.1324-1341 |
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creator | Jokinen, Jussi P. P. Kujala, Tuomo Oulasvirta, Antti |
description | 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. |
doi_str_mv | 10.1177/0018720820927687 |
format | article |
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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.</description><identifier>ISSN: 0018-7208</identifier><identifier>EISSN: 1547-8181</identifier><identifier>DOI: 10.1177/0018720820927687</identifier><identifier>PMID: 32731763</identifier><language>eng</language><publisher>Los Angeles, CA: SAGE Publications</publisher><subject>Adaptation ; Arbitration ; Attention ; Attention task ; Automobile Driving ; Behavior ; Cognition ; Cognitive ability ; Decision making ; Humans ; Multitasking ; Simulation ; Uncertainty ; Visual perception</subject><ispartof>Human factors, 2021-12, Vol.63 (8), p.1324-1341</ispartof><rights>Copyright © 2020, Human Factors and Ergonomics Society</rights><rights>Copyright © 2020, Human Factors and Ergonomics Society 2020 Human Factors and Ergonomics Society</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c462t-a7cda7c948f7adb2832dcb11cc0001fc04de1519262f96f516a0b8b0d1c8d7de3</citedby><cites>FETCH-LOGICAL-c462t-a7cda7c948f7adb2832dcb11cc0001fc04de1519262f96f516a0b8b0d1c8d7de3</cites><orcidid>0000-0001-8222-8540 ; 0000-0002-3024-2209</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,777,781,882,27905,27906,79113</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32731763$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jokinen, Jussi P. P.</creatorcontrib><creatorcontrib>Kujala, Tuomo</creatorcontrib><creatorcontrib>Oulasvirta, Antti</creatorcontrib><title>Multitasking in Driving as Optimal Adaptation Under Uncertainty</title><title>Human factors</title><addtitle>Hum Factors</addtitle><description>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.</description><subject>Adaptation</subject><subject>Arbitration</subject><subject>Attention</subject><subject>Attention task</subject><subject>Automobile Driving</subject><subject>Behavior</subject><subject>Cognition</subject><subject>Cognitive ability</subject><subject>Decision making</subject><subject>Humans</subject><subject>Multitasking</subject><subject>Simulation</subject><subject>Uncertainty</subject><subject>Visual perception</subject><issn>0018-7208</issn><issn>1547-8181</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFRWT</sourceid><recordid>eNp1kUtPxCAUhYnR6PjYuzJN3LipciktdKOZ-E40s3HWhAId0Q4dgZr472UyvhMXPJLz3cO5XIT2AR8DMHaCMXBGMCe4JqzibA2NoKQs58BhHY2Wcr7Ut9B2CE8Y46ouyk20VRBWAKuKETq7H7poowzP1s0y67ILb1-XVxmyySLaueyysZaLKKPtXTZ12vi0K-OjtC6-7aKNVnbB7H2cO2h6dflwfpPfTa5vz8d3uaIViblkSqdVU94yqRvCC6JVA6BUCgWtwlQbKKEmFWnrqi2hkrjhDdaguGbaFDvodOW7GJq50cq46GUnFj4l9G-il1b8Vpx9FLP-VfCyLgrAyeDow8D3L4MJUcxtUKbrpDP9EAShpGacpo9J6OEf9KkfvEvtCVLWjFFaUkgUXlHK9yF4036FASyW0xF_p5NKDn428VXwOY4E5CsgyJn5fvVfw3fAvJdy</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Jokinen, Jussi P. 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P. ; Kujala, Tuomo ; Oulasvirta, Antti</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c462t-a7cda7c948f7adb2832dcb11cc0001fc04de1519262f96f516a0b8b0d1c8d7de3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptation</topic><topic>Arbitration</topic><topic>Attention</topic><topic>Attention task</topic><topic>Automobile Driving</topic><topic>Behavior</topic><topic>Cognition</topic><topic>Cognitive ability</topic><topic>Decision making</topic><topic>Humans</topic><topic>Multitasking</topic><topic>Simulation</topic><topic>Uncertainty</topic><topic>Visual perception</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jokinen, Jussi P. P.</creatorcontrib><creatorcontrib>Kujala, Tuomo</creatorcontrib><creatorcontrib>Oulasvirta, Antti</creatorcontrib><collection>SAGE Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Human factors</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jokinen, Jussi P. P.</au><au>Kujala, Tuomo</au><au>Oulasvirta, Antti</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multitasking in Driving as Optimal Adaptation Under Uncertainty</atitle><jtitle>Human factors</jtitle><addtitle>Hum Factors</addtitle><date>2021-12-01</date><risdate>2021</risdate><volume>63</volume><issue>8</issue><spage>1324</spage><epage>1341</epage><pages>1324-1341</pages><issn>0018-7208</issn><eissn>1547-8181</eissn><abstract>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.</abstract><cop>Los Angeles, CA</cop><pub>SAGE Publications</pub><pmid>32731763</pmid><doi>10.1177/0018720820927687</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0001-8222-8540</orcidid><orcidid>https://orcid.org/0000-0002-3024-2209</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adaptation Arbitration Attention Attention task Automobile Driving Behavior Cognition Cognitive ability Decision making Humans Multitasking Simulation Uncertainty Visual perception |
title | Multitasking in Driving as Optimal Adaptation Under Uncertainty |
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