dc.contributor.author | Lingler, Alexander | |
dc.contributor.author | Talypova, Dinara | |
dc.contributor.author | Jokinen, Jussi P. P. | |
dc.contributor.author | Oulasvirta, Antti | |
dc.contributor.author | Wintersberger, Philipp | |
dc.contributor.editor | Mueller, Florian Floyd | |
dc.contributor.editor | Kyburz, Penny | |
dc.contributor.editor | Williamson, Julie R. | |
dc.contributor.editor | Sas, Corina | |
dc.contributor.editor | Wilson, Max L. | |
dc.contributor.editor | Dugas, Phoebe Toups | |
dc.contributor.editor | Shklovski, Irina | |
dc.date.accessioned | 2024-05-16T11:00:38Z | |
dc.date.available | 2024-05-16T11:00:38Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Lingler, A., Talypova, D., Jokinen, J. P.P., Oulasvirta, A., & Wintersberger, P. (2024). Supporting Task Switching with Reinforcement Learning. In F. F. Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. T. Dugas, & I. Shklovski (Eds.), <i>CHI '24 : Proceedings of the CHI Conference on Human Factors in Computing Systems</i> (Article 82). ACM. <a href="https://doi.org/10.1145/3613904.3642063" target="_blank">https://doi.org/10.1145/3613904.3642063</a> | |
dc.identifier.other | CONVID_213664585 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/94909 | |
dc.description.abstract | Attention management systems aim to mitigate the negative effects of multitasking. However, sophisticated real-time attention management is yet to be developed. We present a novel concept for attention management with reinforcement learning that automatically switches tasks. The system was trained with a user model based on principles of computational rationality. Due to this user model, the system derives a policy that schedules task switches by considering human constraints such as visual limitations and reaction times. We evaluated its capabilities in a challenging dual-task balancing game. Our results confirm our main hypothesis that an attention management system based on reinforcement learning can significantly improve human performance, compared to humans’ self-determined interruption strategy. The system raised the frequency and difficulty of task switches compared to the users while still yielding a lower subjective workload. We conclude by arguing that the concept can be applied to a great variety of multitasking settings. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | ACM | |
dc.relation.ispartof | CHI '24 : Proceedings of the CHI Conference on Human Factors in Computing Systems | |
dc.rights | CC BY-ND 4.0 | |
dc.subject.other | interruption | |
dc.subject.other | notification | |
dc.subject.other | task switching | |
dc.subject.other | machine learning | |
dc.subject.other | artifact or system | |
dc.subject.other | lab study | |
dc.subject.other | quantitative methods | |
dc.title | Supporting Task Switching with Reinforcement Learning | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-202405163675 | |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.relation.isbn | 979-8-4007-0330-0 | |
dc.type.coar | http://purl.org/coar/resource_type/c_5794 | |
dc.description.reviewstatus | peerReviewed | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2024 Copyright held by the owner/author(s). | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | ACM SIGCHI annual conference on human factors in computing systems | |
dc.subject.yso | keskeyttäminen | |
dc.subject.yso | koneoppiminen | |
dc.subject.yso | vahvistusoppiminen | |
dc.subject.yso | ihmisen ja tietokoneen vuorovaikutus | |
dc.subject.yso | laboratoriotutkimus | |
dc.subject.yso | kvantitatiivinen tutkimus | |
dc.subject.yso | tehtävät | |
dc.subject.yso | tarkkaavaisuus | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p5439 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p21846 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p40315 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p38007 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p6757 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p18834 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p3929 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p9105 | |
dc.rights.url | https://creativecommons.org/licenses/by-nd/4.0/ | |
dc.relation.doi | 10.1145/3613904.3642063 | |
jyx.fundinginformation | This project is supported by the Austrian Science Fund (FWF) under grant Nr.P35976-N (AITentive) and the Research Council of Finland under grant Nrs. 328400, 341763, and 328813. | |
dc.type.okm | A4 | |