CoChat
Sign Up
Cited by 156Personal Information Management and User BehaviorHuman-Automation Interaction and SafetyUsability and User Interface Design

What Makes Interruptions Disruptive?: A Process-Model Account of the Effects of the Problem State Bottleneck on Task Interruption and Resumption

Open access
Borst, Jelmer P., Taatgen, Niels A., van Rijn, Hedderik·2015·Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
DOI 10.1145/2702123.2702156
Open this paper in CoChat — free accountOpen PDF

Abstract

In this paper we present a computational cognitive model of task interruption and resumption, focusing on the effects of the problem state bottleneck. Previous studies have shown that the disruptiveness of interruptions is for an important part determined by three factors: interruption duration, interrupting-task complexity, and moment of interruption. However, an integrated theory of these effects is still missing. Based on previous research into multitasking, we propose a first step towards such a theory in the form of a process model that attributes these effects to problem state requirements of both the interrupted and the interrupting task. Subsequently, we tested two predictions of this model in two experiments. The experiments confirmed that problem state requirements are an important predictor for the disruptiveness of interruptions. This suggests that interfaces should be designed to a) interrupt users at low-problem state moments and b) maintain the problem state for the user when interrupted.

Integrity signals

Registry

Crossref · 10.1145/2702123.2702156

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

12 of 42 cited sources checked · 8 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Longer interruptions lead to longer resumption times for the interrupted task.

    “Several studies have shown that the longer the interruption is, the longer it takes to resume the interrupted task [4,23,35]”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites [4] 4. Altmann, E.M., Trafton, J.G., and Hambrick, D.Z. Momentary interruptions can derail the train of thought. Journal of Experimental Psychology: General 143, 1 (2014), 215-226. source

      Analysis 1 (p. 5) reports a small increase in post-interruption response latency in Experiment 1, whose interruptions averaged 4.4 seconds, but no latency effect in Experiment 2, whose interruptions averaged 2.8 seconds. This is consistent with the claim, but the paper does not establish that resumption time increases as interruption duration increases; the experiments also differed in the interruption task. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [23] 23. Hodgetts, H.M. and Jones, D.M. Interruption of the Tower of London task: support for a goal-activation approach. Journal of Experimental Psychology: General 135, 1 (2006), 103-15. source
    • cites [35] 35. Monk, C.A., Trafton, J.G., and Boehm-Davis, D.A. The effect of interruption duration and demand on resuming suspended goals. Journal of Experimental Psychology: Applied 14, 4 (2008), 299-313. source
  • Premisecitation

    More complex interruptions lead to longer resumption times.

    “In addition, more complex interruptions (e.g., solving 17+36 as compared to 2+3; [23]) also lead to longer resumption times [13,14,20,23,35].”
    Quote in the paperSubstantial claim1 of 5 sources checked
    • cites [13] 13. Cades, D.M., Davis, D.A.B., Trafton, J.G., and Monk, C.A. Does the Difficulty of an Interruption Affect our Ability to Resume? In Proc. HFES. 2007, 234-238.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [14] 14. Cades, D.M., Werner, N., Boehm-Davis, D.A., Trafton, J.G., and Monk, C.A. Dealing with Interruptions can be Complex, but does Interruption Complexity Matter: A Mental Resources Approach to Quantifying Disruptions. In Proc. HFES. 2008, 398-402. source
    • cites [20] 20. Gillie, T. and Broadbent, D.E. What makes interruptions disruptive? A study of length, similarity, and complexity. Psychological Research 50, (1989), 243- 250. source
    • cites [23] 23. Hodgetts, H.M. and Jones, D.M. Interruption of the Tower of London task: support for a goal-activation approach. Journal of Experimental Psychology: General 135, 1 (2006), 103-15. source
    • cites [35] 35. Monk, C.A., Trafton, J.G., and Boehm-Davis, D.A. The effect of interruption duration and demand on resuming suspended goals. Journal of Experimental Psychology: Applied 14, 4 (2008), 299-313. source
  • Premisecitation

    Users take less time to resume a task when interrupted at low-workload moments.

    “These studies confirmed that users take less time to resume a task when interrupted at low-workload moments [25,26] (see [32] for an additional example).”
    Quote in the paperSubstantial claim2 of 3 sources checked
    • cites [25] 25. Iqbal, S.T. and Bailey, B.P. Investigating the effectiveness of mental workload as a predictor of opportune moments for interruption. In Proc. CHI. 2005, 1489-1492. source
    • cites [26] 26. Iqbal, S.T. and Bailey, B.P. Leveraging characteristics of task structure to predict the cost of interruption. In Proc. CHI. 2006, 741-750. source
    • cites [32] 32. McFarlane, D. Comparison of Four Primary Methods for Coordinating the Interruption of People in Human- Computer Interaction. Human-Computer Interaction 17, 1 (2002), 63-139. source
  • Premisecitation

    Subjects were considerably slower and made more errors when the problem state was required for two tasks, as compared to one or none of the tasks.

    “In a series of experiments it was shown that subjects were considerably slower and made more errors when the problem state was required for two tasks, as compared to one or none of the tasks [8,10,11,12,37].”
    Quote in the paperSubstantial claim1 of 5 sources checked
    • cites [8] 8. Borst, J.P., Buwalda, T.A., Van Rijn, H., and Taatgen, N.A. Avoiding the Problem State Bottleneck by Strategic use of the Environment. Acta Psychologica 144, 2 (2013), 373-379. source
    • cites [10] 10. Borst, J.P., Taatgen, N.A., and Van Rijn, H. The Problem State: A Cognitive Bottleneck in Multitasking. Journal of Experimental Psychology: Learning, Memory, & Cognition 36, 2 (2010), 363- 382. source
    • cites [11] 11. Borst, J.P., Taatgen, N.A., and Van Rijn, H. Using a Symbolic Process Model as input for Model-Based fMRI Analysis: Locating the Neural Correlates of Problem State Replacements. NeuroImage 58, 1 (2011), 137-147. source
    • cites [12] 12. Borst, J.P., Taatgen, N.A., Stocco, A., and Van Rijn, H. The Neural Correlates of Problem States: Testing fMRI Predictions of a Computational Model of Multitasking. PLoS ONE 5, 9 (2010), e12966. source
    • cites [37] 37. Nijboer, M., Borst, J.P., Van Rijn, H., and Taatgen, N.A. Single-task fMRI overlap predicts concurrent multitasking interference. NeuroImage 100, (2014), 60-74. source
  • Premisecitation

    The statistical model of [26] showed that Task Level, Difficulty of Next Subtask, and Carry-Over between Subtasks were the most important predictors of interruption disruptiveness.

    “which showed that Task Level, Difficulty of Next Subtask, and Carry-Over between Subtasks, were the most important predictors of interruption disruptiveness.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [26] 26. Iqbal, S.T. and Bailey, B.P. Leveraging characteristics of task structure to predict the cost of interruption. In Proc. CHI. 2006, 741-750. source
  • Classified as probably not a substantial claim. 17
  • May combine two claims. 20
  • The quote only partly states this claim. 2

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Memory-for-goals theory predicted that longer interruptions lead to longer resumption processes, and this was confirmed by several studies.

    “Memory-for-goals theory made the prediction that longer interruptions lead to longer resumption processes, which was confirmed by several studies [23,34,35,45].”
    Quote in the paperNot a substantial claim1 of 4 sources checked
    • cites [23] 23. Hodgetts, H.M. and Jones, D.M. Interruption of the Tower of London task: support for a goal-activation approach. Journal of Experimental Psychology: General 135, 1 (2006), 103-15. source
    • cites [34] 34. Monk, C.A., Boehm-Davis, D.A., and Trafton, J.G. Recovering From Interruptions: Implications for Driver Distraction Research. Human Factors 46, 4 (2004), 650-663. source
    • cites [35] 35. Monk, C.A., Trafton, J.G., and Boehm-Davis, D.A. The effect of interruption duration and demand on resuming suspended goals. Journal of Experimental Psychology: Applied 14, 4 (2008), 299-313. source
    • cites [45] 45. Trafton, J.G., Altmann, E.M., Brock, D.P., and Mintz, F.E. Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal. International Journal of Human-Computer Studies 58, 5 (2003), 583-603. source

  • Classified as probably not a substantial claim.

    Premisecitation

    Memory-for-goals theory assumes that each task has an associated task goal, which is stored in declarative memory when an interruption occurs.

    “Memory-for-goals theory explains these effects by assuming that each task has an associated task goal, which is stored in declarative memory when an interruption occurs [2,3].”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [2] 2. Altmann, E.M. and Trafton, J.G. Memory for goals: An activation-based model. Cognitive Science 26, (2002), 39-83. source
    • cites [3] 3. Altmann, E.M. and Trafton, J.G. Timecourse of recovery from task interruption: data and a model. Psychonomic Bulletin & Review 14, 6 (2007), 1079- 84. source

      The Modeling the Timecourse of Recovery section describes task representations that include goals and subgoals, and the Discussion describes retrieving a disrupted mental context after an interruption (pages unavailable in our copy). Neither passage says that each task has a single associated goal stored in declarative memory when the interruption occurs. (LLM, conf=0.82)

      Experimental — accuracy not yet measured

  • May combine two claims.

    Premisecitation

    In the route-planning task, maintaining information as a problem state during a subtask was related to higher workload [24] and to higher resumption costs when interrupted during the subtask [25].

    “It was shown that this was related to higher workload [24], as well as to higher resumption costs when interrupted during the subtask [25].”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [24] 24. Iqbal, S.T., Adamczyk, P., Zheng, X., and Bailey, B.P. Towards an index of opportunity: understanding changes in mental workload during task execution. In Proc. SIGCHI. 2005, 311-320. source
    • cites [25] 25. Iqbal, S.T. and Bailey, B.P. Investigating the effectiveness of mental workload as a predictor of opportune moments for interruption. In Proc. CHI. 2005, 1489-1492. source

      The ‘Moments for Interruption’ section identifies a high-workload point in route planning between recalling and entering information. The ‘Resumption Lag’ section reports longer resumption times at high-workload (‘Worst’) than low-workload (‘Best’) interruption points, but reports this timing effect across tasks, not specifically for route planning. It does not establish that maintaining a problem state during a subtask caused the higher resumption cost. (LLM, conf=0.84)

      Experimental — accuracy not yet measured

  • The quote only partly states this claim.

    Findingown data

    The memory-for-problem-states model fit Experiment 2 data.

    “Figure 6B shows the fit of a memory-for-problem-states model.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In Experiment 2, ANOVA confirmed main effects of Text-Entry Difficulty, N-Back Difficulty, and Interruption Duration.

    “The ANOVA (Table 2) confirmed these results: besides main effects of Text-Entry Difficulty, N-Back Difficulty, and Interruption Duration”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    In Experiment 1, participant accuracy on the n-back task was above 85% in all conditions.

    “Participant accuracy on the n-back task was above 85% in all conditions.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In Experiment 1, in the hard subtraction/easy n-back condition, resumption costs were slightly higher but did not increase with interruption duration.

    “In the hard subtraction/easy n-back condition the resumption costs were slightly higher, but did not increase with interruption duration either.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    In Experiment 2, the three-way interaction showed a trend towards significance.

    “The threeway interaction showed a trend towards significance.”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Findingown data

    In Experiment 2, the data showed a fixed cost of about 400 ms in all conditions.

    “As the data shows a fixed cost of about 400 ms in all conditions, we added this cost to the model.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    If both tasks require a problem state, the problem state of the primary task is automatically stored in declarative memory during an interruption, where it starts to decay.

    “If both tasks require a problem state, the problem state of the primary task is automatically stored in declarative memory during an interruption, where it starts to decay (analogously to task goals in memory-forgoals).”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: Information in the problem state can be accessed instantly, without incurring a time cost.
    • Rests on an assumption: When a new representation is stored in the problem state, previous contents are automatically encoded in declarative memory.
    • Rests on an assumption: The problem state resource can store at most a single chunk of information at a time.

  • May combine two claims.

    Interpretationreasoning

    The longer the interruption, the further the activation level of the problem state will have decayed, and the longer it takes to retrieve it from memory.

    “As in memory-for-goals, the longer the interruption, the further the activation level of the problem state will have decayed, and the longer it takes to retrieve it from memory -explaining the interruption duration effect.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Information in the problem state can be accessed instantly, without incurring a time cost.
    • Rests on an assumption: When a new representation is stored in the problem state, previous contents are automatically encoded in declarative memory.
    • Rests on an assumption: The problem state resource can store at most a single chunk of information at a time.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Memory-for-problem-states proposes that both the primary and the secondary, interrupting task can have an associated problem state.

    “The main idea of memory-for-problem-states is that both the primary and the secondary, interrupting task can have an associated problem state.”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: Information in the problem state can be accessed instantly, without incurring a time cost.
    • Rests on an assumption: When a new representation is stored in the problem state, previous contents are automatically encoded in declarative memory.
    • Rests on an assumption: The problem state resource can store at most a single chunk of information at a time.

  • May combine two claims.

    Premisecitation

    The problem state acts as a processing bottleneck in multitasking because it can only maintain information for a single task at a time, causing interference when required by multiple tasks concurrently.

    “The problem state acts as a processing bottleneck in multitasking, as it can only maintain information for a single task at a time, and thus causes interference when required by multiple tasks concurrently.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [5] 5. Anderson, J.R. Human symbol manipulation within an integrated cognitive architecture. Cognitive Science 29, (2005), 313-341. source
    • cites [10] 10. Borst, J.P., Taatgen, N.A., and Van Rijn, H. The Problem State: A Cognitive Bottleneck in Multitasking. Journal of Experimental Psychology: Learning, Memory, & Cognition 36, 2 (2010), 363- 382. source

  • May combine two claims.

    Interpretationreasoning

    Memory-for-goals provides a simple and powerful explanation for the interruption duration effect, but its explanation for the interrupting-task complexity effect is less convincing because users often do not seem to rehearse during an interruption.

    “Taken together, memory-for-goals provides a simple and powerful explanation for the interruption duration effect, but its explanation for the interrupting-task complexity effect is less convincing, given that users often do not seem to rehearse during an interruption.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [13] 13. Cades, D.M., Davis, D.A.B., Trafton, J.G., and Monk, C.A. Does the Difficulty of an Interruption Affect our Ability to Resume? In Proc. HFES. 2007, 234-238.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [29] 29. Katidioti, I., Borst, J.P., and Taatgen, N.A. What Happens When We Switch Tasks: Pupil dilation in multitasking. Journal of Experimental Psychology: Applied, (in press).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    In Experiment 1, trial order was random.

    “Trial order was random.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    It is unclear how memory-for-goals accounts for the moment-of-interruption effect.

    “Also, it is unclear how memory-for-goals accounts for the moment-of-interruption effect.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    In Experiment 2, the easy text-entry stimuli were created by scrambling the hard words, controlling for letter-based effects while preventing predictive strategies.

    “By scrambling the words, we controlled for letter-based effects, while preventing the use of strategies to predict the next letter.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    In Experiment 1, after exclusions for task underperformance and health problems, 33 complete data sets remained.

    “Three subjects had to be excluded because they underperformed on the hard subtraction task (< 65% correct or slower than 3,500 ms per response), two subjects for underperforming on the hard nback task (< 80% correct), and one subject because of health problems, leaving 33 complete data sets (23 female, age range 18-27, mean age 20.7).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    If the secondary task does not require a problem state, it will not disrupt the problem state of the primary task, and the primary task will not show an interruption duration effect.

    “Furthermore, if the secondary task does not require a problem state, it will not disrupt the problem state of the primary task, which will therefore again not show an interruption duration effect.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: Information in the problem state can be accessed instantly, without incurring a time cost.
    • Rests on an assumption: When a new representation is stored in the problem state, previous contents are automatically encoded in declarative memory.
    • Rests on an assumption: The problem state resource can store at most a single chunk of information at a time.

  • May combine two claims.

    Interpretationreasoning

    After the interruption, the primary task's problem state has to be retrieved from memory, which takes time and can lead to errors when an incorrect (i.e. older) problem state is retrieved.

    “After the interruption, the primary task's problem state has to be retrieved from memory, which takes time and can lead to errors when an incorrect (i.e.older) problem state is retrieved.”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: Information in the problem state can be accessed instantly, without incurring a time cost.
    • Rests on an assumption: When a new representation is stored in the problem state, previous contents are automatically encoded in declarative memory.
    • Rests on an assumption: The problem state resource can store at most a single chunk of information at a time.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    In the hard condition of Experiment 1, subjects mentally had to keep track of whether a carry was in progress.

    “Thus, in the hard condition subjects mentally had to keep track of whether a 'carry' was in progress (solved columns were masked to ensure that subjects maintained carries mentally, Figure 3).”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    Memory-for-problem-states is based on memory-for-goals theory [2] and on research into the problem state bottleneck [10].

    “Memory-for-problem-states is based on memory-forgoals theory [2] and on research into the problem state bottleneck [10].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [2] 2. Altmann, E.M. and Trafton, J.G. Memory for goals: An activation-based model. Cognitive Science 26, (2002), 39-83. source
    • cites [10] 10. Borst, J.P., Taatgen, N.A., and Van Rijn, H. The Problem State: A Cognitive Bottleneck in Multitasking. Journal of Experimental Psychology: Learning, Memory, & Cognition 36, 2 (2010), 363- 382. source

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Memory-for-problem-states can be regarded as a more specific version of memory-for-goals: instead of focusing on general task goals, it looks in detail at the cognitive requirements of each task.

    “It can be regarded as a more specific version of memory-for-goals: instead of focusing on general task goals, memory-for-problem-states looks in detail at the cognitive requirements of each task.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    In memory-for-problem-states, complexity is all-or-nothing: a secondary task requires a problem state or not.

    “In addition, complexity is all-or-nothing in our framework: a secondary task requires a problem state or not.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    The longer the interruption, the further the activation of problem states in memory will have decayed, and the longer it takes to retrieve them.

    “The longer the interruption, the further their activation in memory will have decayed, and the longer it takes to retrieve them.”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: The problem state(s) associated with the primary task are stored in declarative memory during an interruption and have to be retrieved on resumption.
    • Rests on an assumption: During an interruption, the activation of problem states in declarative memory decays with time.

  • May combine two claims.

    Interpretationown data

    Memory-for-problem-states can account for three important factors in interruption disruptiveness: interruption duration, interrupting-task complexity, and moment of interruption.

    “Memory-for-problem-states can account for three important factors in interruption disruptiveness: interruption duration, interrupting-task complexity, and moment of interruption.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    All three factors (Task Level, Difficulty of Next Subtask, Carry-Over) are clearly related to problem state requirements; especially Carry-Over is an almost identical concept.

    “All three factors are clearly related to problem state requirements; especially Carry-Over is an almost identical concept.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    The model explained the stronger hard/hard effect by two problem states decaying in declarative memory instead of one, and both problem states having to be restored after an interruption.

    “which the model explained by two problem states decaying in declarative memory instead of one, and both problem states having to be restored after an interruption.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Two problem states decay in declarative memory instead of one, and both problem states have to be restored after an interruption.

  • May combine two claims.

    Interpretationreasoning

    Where memory-for-goals predicts an interruption duration effect for every task, memory-for-problem-states specified this to tasks that need a problem state.

    “For example, where memory-for-goals predicts an interruption duration effect for every task, memory-for-problem-states specified this to tasks that need a problem state.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The model predicted an effect of interruption duration when n-back was easy, which was confirmed by the data.

    “Because the model assumes that subjects needed two problem states to perform the hard n-back task, it predicted an effect of interruption duration also when nback was easy, which was confirmed by the data.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: The model assumes that subjects needed two problem states to perform the hard n-back task.

  • May combine two claims.

    Interpretationown data

    Experiment 2 confirmed the second prediction of memory-for-problem-states: if a task requires multiple problem states, the resumption costs will be higher and will always increase with interruption duration, also if the secondary task does not require a problem state.

    “This prediction was confirmed, and a memory-for-problemstates model gave a good account of the data.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: In Experiment 2, the hard text-entry task required two problem states: one for the word and one for the position in the word.

  • May combine two claims.

    Interpretationown data

    In Experiment 2, the effect was stronger in the hard/hard condition, present in both data and model.

    “The effect was even stronger in the hard/hard condition -easy to see in the data (Fig. 6A) but also present in the model (Fig. 6B)”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation

    These costs might be due to general task reorientation costs, for instance related to task-set reconfiguration.

    “These costs might be due to general task reorientation costs, for instance related to task-set reconfiguration (cf.task switching, [36]).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] 36. Monsell, S. Task switching. Trends in Cognitive Sciences 7, 3 (2003), 134-140. source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The term complexity has been inconsistently defined in the literature [35:302].

    “which is necessary given that”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [35:302]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • May combine two claims.

    Interpretationown data

    The two a priori predictions derived from memory-for-problem-states were confirmed by two behavioral experiments, yielding strong support for the theory.

    “As a stronger test, we subsequently derived two a priori predictions from the model.These predictions were confirmed by two behavioral experiments, yielding strong support for our theory.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Experiment 2 confirmed with a new task that there is no effect of interruption duration on resumption costs as long as the primary task does not require a problem state.

    “In addition, Experiment 2 confirmed with a new task that there is no effect of interruption duration on resumption costs as long as the primary task does not require a problem state.”
    Quote in the paperSubstantial claim
36 of 36

  • The paper relies on this without support.

    An integrated theory of the effects of interruption duration, interrupting-task complexity, and moment of interruption is still missing.

    “However, an integrated theory of these effects is still missing.”

  • The authors state this as an assumption.

    The VCR task requires two problem states.

    “Instead, it is assumed that the VCR task requires two problem states, given that it was a complex task with multiple sub-tasks.”

  • The authors state this as an assumption.

    Information in the problem state can be accessed instantly, without incurring a time cost.

    “It is assumed that information in the problem state can be accessed instantly, without incurring a time cost.”

  • The paper relies on this without support.

    When a new representation is stored in the problem state, previous contents are automatically encoded in declarative memory.

    “When a new representation is stored, previous contents are automatically encoded in declarative memory.”

  • The paper relies on this without support.

    The problem state resource can store at most a single chunk of information at a time.

    “The problem state resource is used to maintain intermediate information in a task and can store at most a single chunk of information at a time.”

  • The paper relies on this without support.

    An integrated theory of the effects of interruptions is still absent.

    “Although interruptions are ubiquitous in our society, an integrated theory of their effects is still absent.”

  • The authors state this as an assumption.

    In Experiment 2, the hard text-entry task required two problem states: one for the word and one for the position in the word.

    “We assumed that they needed two problem states for this: one for the word and one for the position in the word.”

  • The authors state this as an assumption.

    The problem state(s) associated with the primary task are stored in declarative memory during an interruption and have to be retrieved on resumption.

    “The interruption duration effect is explained by assuming that the problem state(s) associated with the primary task are stored in declarative memory during an interruption, and have to be retrieved on resumption.”

  • The authors state this as an assumption.

    Complexity can be defined as problem state requirements of the secondary task.

    “The effect of interrupting-task complexity is explained by assuming that complexity can be defined as problem state requirements of the secondary task.”

  • The authors state this as an assumption.

    The model assumes that subjects needed two problem states to perform the hard n-back task.

    “Because the model assumes that subjects needed two problem states to perform the hard n-back task”

  • The paper relies on this without support.

    During an interruption, the activation of problem states in declarative memory decays with time.

    “The longer the interruption, the further their activation in memory will have decayed, and the longer it takes to retrieve them.”

  • The paper relies on this without support.

    Two problem states decay in declarative memory instead of one, and both problem states have to be restored after an interruption.

    “which the model explained by two problem states decaying in declarative memory instead of one, and both problem states having to be restored after an interruption.”
12 of 12

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Initial responses until the first interrupted items were excluded because initial responses can be extreme outliers.

    “Since initial responses can be extreme outliers (Borst et al., 2015) we also excluded the initial responses until the first interrupted items.”

    From Effect of Redundant Haptic Information on Task Performance during Visuo-Tactile Task Interruption and Recovery (2016) · cites this paper as Borst et al., 2015

    The Statistical Procedure (page unavailable) says the authors removed first responses of each trial and extreme response-time outliers. It does not say that first responses were removed because they can be extreme outliers, or that responses through the first interruption were excluded. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Statistics recomputed

Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.

  • ConsistentF(1, 32) = 4.17reported p = .0495 · recomputed p = .049

    “F(1,32) = 4.17, p = .0495”

1 of 1

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Figure 1A is attributed to two different references

    Minor

    The main text identifies Figure 1A as data from Monk et al. [35], while the figure caption attributes those data to [34].

  • The model is said to assign two problem states to the wrong task

    Minor

    The Experiment 2 model section says the hard n-back task needs two problem states. The task description assigns the two problem states to hard text-entry instead, and says they represent the word and the position within it.

2 of 2

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. Anderson J.R., 2007, Oxford University Press
  2. Borst J.P., 2010, Memory, & Cognition 36, 2
  3. Cutrell E., 2001, Proc. INTERACT.
  4. Renaud K, 2000, Expediting Rapid Recovery from Interruptions by Providing a Visualisation of Application Activity, Proc. OZCHI, 348, 355

Cited by

Sorted by citations

Related

Authors
Borst, J. P., Taatgen, N. A., & van Rijn, H.
Affiliations
  • University of Groningen (NL)
  • University of Groningen, Groningen, Netherlands
ORCID
Venue
Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2015-04-18
Pages
2971-2980
DOI
10.1145/2702123.2702156
OpenAlex
W2171022339
Cited by
156
References
50
Citations by year
2026: 3 · 2025: 54 · 2024: 4 · 2023: 6 · 2022: 7 · 2021: 10 · 2020: 16 · 2019: 11 · 2018: 10 · 2017: 9 · 2016: 23 · 2015: 3
FWCI
26.26
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
Funders
ERC (283597)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords
task interruption, task resumption, computational cognitive model, multitasking, interruption duration, interface design, process model

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-11.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-11.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review