Notification, Disruption, and Memory: Effects of Messaging Interruptions on Memory and Performance

By Edward Cutrell, Mary Czerwinski, Eric Horvitz. IFIP TC13 International Conference on Human-Computer Interaction, 2001

455 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aa17d9337793fb6676b4f9aad55374ca64a88159

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Citations in the corpus

Large-scale assessment of mobile notifications (2014, background): “The effect of notiﬁcations created by applications in the desk-top workplace, such as email clients or instant messengers applications, has been studied thoroughly in previous work [4, 5, 13, 16].”
If not now, when?: the effects of interruption at different moments within task execution (2004, background): “Temporal Strategies for Interruption Some studies [10, 11] place moments for interruption towards the beginning, middle, or end of a task.”
An in-situ study of mobile phone notifications (2014, background): “[8] found that the effect is more pronounced when the task is cognitively demanding.”
The Experience Sampling Method on Mobile Devices (2017, background): “Previous work has shown that smartphone notifications often interrupt the ongoing task of the user (Cutrell et al. 2001), and frequent notifications can start to annoy users (Church and de Oliviera 2013). These and similar findings have prompted the development of machine learning classifiers explor”
Disruption and recovery of computing tasks: field study, analysis, and directions (2007, background): “Determining appropriate timing strategies [5, 7, 20], display t echniques [11, 30], and strategies for prioritizing tasks are paramount for the success of future disruption and recovery t ools.”
The Need for Command and Control Instant Message Adaptive Interfaces: Lessons Learned from Tactical Tomahawk Human-in-the-Loop Simulations (2004, background): “Furthermore, instant messaging has been demonstrated to negatively impact faster, stimulus-driven search tasks more than slower search tasks (Cutrell et al., 2001; Czerwinski et al., 2000b).”
Models of attention in computing and communication (2003, background): “Thus, beyond pooling results from prior psychological studies, we need to continue to perform user studies that adapt or extend prior results on attention and memory from cognitive psychology to real-world computing and communication applications [2, 3].”
InterruptMe: designing intelligent prompting mechanisms for pervasive applications (2014, background): “find that irrelevant messages result in prolonged task completion times [9].”
Using context-aware computing to reduce the perceived burden of interruptions from mobile devices (2005, background): “Further, it is possible that an interruption at the beginning of a new task could be more costly than when the user has been engaged in a task for some time [5].”
Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal (2003, methodology): “, an interruption every 30 s) or at random intervals (Gillie and Broadbent, 1989; Zijlstra et al., 1999; Czerwinski et al., 2000; Cutrell et al., 2001).”
Predicting human interruptibility with sensors (2005, methodology): “interruptions on different computer tasks [ 8 , 9, 10].”
Break It Down: A Comparison of Macro- and Microtasks (2015, background): “We also find evidence that microtasks may be more resilient to interruption than macrotasks, suggesting that microtasking could be helpful for information workers, who tend to be interrupted often and have difficulty resuming their tasks afterwards [1].”
Learning and reasoning about interruption (2003, background): “In user studies, researchers have elucidated the effects of interrupting people in various ways in different situations [2,3,10,11], and have probed the workload and availability of people in office settings [6,8,9].”