Previous research from our laboratory which examined the impact of interruptions on performance in a collaborative communication task, found that interruptions from a synthetic agent, occurring either at fixed or random intervals, had a more deleterious effect on task performance than when interruption timing was determined by a human participant monitoring the communication task (Peters, Romigh, Bradley, & Raj, 2017). These results suggest that interruption times initiated by the human interrupters were more appropriate than the machine-generated ones; however, post-hoc analyses revealed no relation between interruption timing and language information prior to the human interruptions. Given these conflicting results and the demonstrated role of language information in other communication interactions, we aim to identify which spoken language components most motivate human interruption decisions, utilizing methods borrowed from the turn-taking literature (De Ruiter, Mitterer, & Enfield, 2006). Results indicate that listeners leverage prosodic and lexical information in making interruption decisions.
Get full access to this article
View all access options for this article.
References
1.
AdamczykP. D.BaileyB. P. (2004). If not now, when?: The effects of interruption at different moments within task execution. Conference on Human Factors in Computing Systems - Proceedings, 6(1), 271–278.
2.
BaayenR. H.DavidsonD. J.BatesD. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412. https://doi.org/10.1016/j.jml.2007.12.005
3.
BaileyB. P.KonstanJ. A. (2006). On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in Human Behavior, 22(4), 685–708. https://doi.org/10.1016/j.chb.2005.12.009
BatesD.MächlerM.BolkerB. M.WalkerS. C. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01
6.
BegoleJ.MatsakisN. E.TangJ. C. (2004). Lilsys: Sensing unavailability. Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, 6(3), 511–514.
CutrellE.CzerwinskiM.HorvitzE. (2001). Notification, disruption, and memory: Effects of messaging interruptions on memory and performance. Human Computer Interaction Interact 2001, 1, 263–269.
9.
CzerwinskiM.CutrellE.HorvitzE. (2000). Instant messaging and interruption: Influence of task type on performance. Proceedings of OZCHI2000, 356–361. https://doi.org/10.1016/S13613723(02)01112-0
10.
De RuiterJ. P.MittererH.EnfieldN. J. (2006). Projecting the end of a speaker’s turn: A cognitive cornerstone of conversation. Language, 82(3), 515–535. https://doi.org/10.1353/lan.2006.0130
11.
FogartyJ.LaiJ.ChristensenJ. (2004). Presence versus availability: The design and evaluation of a context-aware communication client. International Journal of Human Computer Studies, 61(3), 299–317. https://doi.org/10.1016/j.ijhcs.2003.12.016
12.
FoxJ.WeisbergS. (2019). Cox proportional-hazards regression for survival data in R. In An R Companion to Applied Regression (3rd ed., pp. 1–18). https://doi.org/10.1016/j.carbon.2010.02.029
HorvitzE. (1999). Principles of mixed-initiative user interfaces. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, May, 159–166.
16.
HorvitzE.ApacibleJ. (2003). Learning and reasoning about interruption. ICMI’03: Fifth International Conference on Multimodal Interfaces, 20–27. https://doi.org/10.1145/958439.958440
17.
HorvitzE.KadieC. M.JacobsA. (2002). Coordinate : Probabilistic forecasting of presence and availability. Proceedings of the Conference on Uncertainty in Artificial Intelligence, 67, 224–233. https://doi.org/10.1107/S1744309110050219
IqbalS. T.BaileyB. P. (2006). Leveraging characteristics of task structure to predict the cost of interruption. Conference on Human Factors in Computing Systems - Proceedings, 2, 741–750. https://doi.org/10.1145/1124772.1124882
20.
IyerN.ThompsonE. R.EnnisZ. N.WillisA.SimpsonB. (2017). Perception of keywords elicited under various adverse acoustic environments. The Journal of the Acoustical Society of America, 141(5), 3821–3822. https://doi.org/10.1121/1.4988477
21.
McCrickardD. S.ChewarC. M.SomervellJ. P.NdiwalanaA. (2003). A model for notification systems evaluation-assessing user goals for multitasking activity. ACM Transactions on Computer-Human Interaction, 10(4), 312–338. https://doi.org/10.1145/966930.966933
22.
McFarlaneD. C. (1999). Coordinating the interruption of people in human- computer interaction. Human-Computer Interaction, INTERACT’99, 295–303.
23.
McFarlaneD. C.LatorellaK. A. (2002). The scope and importance of human interruption in human-computer interaction design. Human- Computer Interaction, 17(1), 1–61. https://doi.org/10.1207/S15327051HCI1701_1
24.
MonkC. A.Boehm-DavisD. A.TraftonJ. G. (2002). The attentional costs of interrupting task performance at various stages. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 46(22), 1824–1828. https://doi.org/10.1177/154193120204602210
25.
NakagawaS.SchielzethH. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x
26.
PetersN.RajB.RomighG. (2017). Topic and prosodic modeling for interruption management in multi-user multitasking communication interactions. AAAI Fall Symposium - Technical Report, FS-17-01-, 45–53.
27.
PetersN.RomighG.BradleyG.RajB. (2017). When to interrupt: A comparative analysis of interruption timings within collaborative communication tasks. Advances in Intelligent Systems and Computing, 497, 177–187. https://doi.org/10.1007/978-3-319-41956-5_17
28.
R Core Team. (2019). R: A language and environment for statistical computing Version 3.6.1. Retrieved from https://www.r-project.org/
29.
RubinsteinJ. S.MeyerD. E.EvansJ. E. (2001). Executive control of cognitive processes in task switching. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 763–797. https://doi.org/10.1037/0096-1523.27.4.763
30.
SacksH.SchegloffE. A.JeffersonG. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735. https://doi.org/10.2307/412243
van EngenK. J.Baese-BerkM.BakerR. E.ChoiA.KimM.BradlowA. R. (2010). The wildcat corpus of native-and foreign- accented english: Communicative efficiency across conversational dyads with varying language alignment profiles. Language and Speech, 53(4), 510–540. https://doi.org/10.1177/0023830910372495
33.
WalkerM. B.TrimboliC. (1982). Smooth transitions in converational interactions. The Journal of Social Psychology, 117, 305–306.
