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Little research has considered the perspective of both the interrupter and interruptee.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>\n                      We tested a computational model of interruption and response decision processes through an experiment where participants (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      = 312) worked as a nurse in a simulated clinical team. We examined how task progress, time remaining, and time pressure influenced decisions and compared the model with an alternative that allowed the effects of time pressure to be non-monotonic.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using Bayesian hierarchical modeling, we found that a non-monotonic model best explained interruption decisions. Participants were biased toward interrupting, with time pressure only influencing decisions when it was very high. Contrastingly, the monotonic model best explained response decisions. Participants were more likely to accept interruptions as the interrupter\u2019s time pressure increased in comparison to their own.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Time pressure has a non-monotonic influence on interruption decisions, but a monotonic influence on response decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Application<\/jats:title>\n                    <jats:p>Findings can inform interventions to consider the interruptions process from the perspective of both the interrupter and interruptee. Interventions could focus on training workers to more accurately assess time pressure when making interruption decisions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00187208251388356","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T11:19:30Z","timestamp":1761218370000},"page":"331-353","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Testing a Computational Model of Interruptions: The Effects of Time Pressure on Interruption and Response Decisions"],"prefix":"10.1177","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1032-4941","authenticated-orcid":false,"given":"Emma B.","family":"Knight","sequence":"first","affiliation":[{"name":"The University of Queensland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hector","family":"Palada","sequence":"additional","affiliation":[{"name":"The University of Queensland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5344-9376","authenticated-orcid":false,"given":"Andrew","family":"Neal","sequence":"additional","affiliation":[{"name":"The University of Queensland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1235-1719","authenticated-orcid":false,"given":"Penelope","family":"Sanderson","sequence":"additional","affiliation":[{"name":"The University of Queensland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8875-4541","authenticated-orcid":false,"given":"Timothy","family":"Ballard","sequence":"additional","affiliation":[{"name":"The University of Queensland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/nop2.1064"},{"key":"e_1_3_3_3_1","volume-title":"Improving the management of controllers\u2019 interruptions through the working awareness interruption tool: WAIT","author":"Alqahtani M.","year":"2014","unstructured":"Alqahtani M. 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