How is multi-tasking different from increased difficulty?


With the advancement of technologies like in-car navigation and smartphones, concerns around how cognitive functioning is influenced by “workload” are increasingly prevalent. Research shows that spreading effort across multiple tasks can impair cognitive abilities through an overuse of resources, and that similar overload effects arise in difficult single-task paradigms. We developed a novel lab-based extension of the Detection Response Task, which measures workload, and paired it with a Multiple Object Tracking Task to manipulate cognitive load. Load was manipulated either by changing within-task difficulty or by the addition of an extra task. Using quantitative cognitive modelling we showed that these manipulations cause similar cognitive impairments through diminished processing rates, but that the introduction of a second task tends to invoke more cautious response strategies that do not occur when only difficulty changes. We conclude that more prudence should be exercised when directly comparing multi-tasking and difficulty-based workload impairments, particularly when relying on measures of central tendency.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Data Availability

The data and materials for all experiments are available at and neither experiment was preregistered.


  1. 1.

    Note: We also applied the Racing Diffusion Model (Tillman & Logan, 2017), which was applied to similar data by previous authors (Tillman et al., 2017). The results of this model were qualitatively the same as the LBA, and the LBA was preferred by WAIC in all cases. Hence, for brevity, we only report the LBA analyses.

  2. 2.

    Note we frame posterior p-values as a test against the direction reported, so that a small p-value corresponds to high certainty. We report this way to be consistent with traditional frequentist p-values.

  3. 3.

    We plot standardized parameter distributions, so the magnitude of changes between drift and threshold is directly comparable.


  1. Antonenko, P., Paas, F., Grabner, R., & Van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425–438.

    Google Scholar 

  2. Ayaz, H., Willems, B., Bunce, B., Shewokis, P. A., Izzetoglu, K., Hah, S., ... Onaral, B. (2010). Cognitive workload assessment of air traffic controllers using optical brain imaging sensors. Advances in understanding human performance: Neuroergonomics, human factors design, and special populations, 21–31.

  3. Brouwer, A.-M., Hogervorst, M. A., Van Erp, J. B., Heffelaar, T., Zimmerman, P. H., & Oostenveld, R. (2012). Estimating workload using EEG spectral power and ERPs in the n-back task. Journal of neural engineering, 9(4), 045008.

    PubMed  Google Scholar 

  4. Brown, S. D., & Heathcote, A. J. (2008). The simplest complete model of choice reaction time: Linear ballistic accumulation. Cognitive Psychology, 57, 153–178.

    PubMed  Google Scholar 

  5. Cavanagh, P., & Alvarez, G. A. (2005). Tracking multiple targets with multifocal attention. Trends in cognitive sciences, 9(7), 349–354.

  6. Compton, R. J., Lin, M., Vargas, G., Carp, J., Fineman, S. L., & Quandt, L. C. (2008). Error detection and post error behavior in depressed undergraduates. Emotion, 8(1), 58.

    PubMed  Google Scholar 

  7. Cooper, J. M., Ingebretsen, H., & Strayer, D. L. (2014). Mental workload of common voice-based vehicle interactions across six different vehicle systems.

  8. Dingus, T. A., Guo, F., Lee, S., Antin, J. F., Perez, M., Buchanan-King, M., & Hankey, J. (2016). Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proceedings of the National Academy of Sciences, 113(10), 2636–2641.

    Google Scholar 

  9. Drews, F. A., Pasupathi, M., & Strayer, D. L. (2008). Passenger and cell phone conversations in simulated driving. Journal of Experimental Psychology: Applied, 14(4), 392.

    PubMed  Google Scholar 

  10. Drews, F. A., Yazdani, H., Godfrey, C. N., Cooper, J. M., & Strayer, D. L. (2009). Text messaging during simulated driving. Human factors, 51(5), 762–770.

    PubMed  Google Scholar 

  11. Drew, T., Horowitz, T. S., & Vogel, E. K. (2013). Swapping or dropping? Electrophysiological measures of difficulty during multiple object tracking. Cognition, 126(2), 213–223.

  12. Eidels, A., Donkin, C., Brown, S. D., & Heathcote, A. (2010). Converging measures of workload capacity. Psychonomic bulletin & review, 17(6), 763–771.

    Google Scholar 

  13. Eidels, A., Houpt, J. W., Altieri, N., Pei, L., & Townsend, J. T. (2011). Nice guys finish fast and bad guys finish last: Facilitatory vs. inhibitory interaction in parallel systems. Journal of mathematical psychology, 55(2), 176–190.

    PubMed  PubMed Central  Google Scholar 

  14. Endres, M. J., Houpt, J. W., Donkin, C., & Finn, P. R. (2015). Working memory capacity and redundant information processing efficiency. Frontiers in psychology, 6, 594.

    PubMed  PubMed Central  Google Scholar 

  15. Evans, N. J. (2019). Assessing the practical differences between model selection methods in inferences about choice response time tasks. Psychonomic Bulletin & Review.

  16. Evans, N. J., & Wagenmakers, E. J. (2019). Evidence accumulation models: Current limitations and future directions. The Quantitative Methods for Psychology.

  17. Evans, N. J., Bennett, A. J., & Brown, S. D. (2019). Optimal or not; depends on the task. Psychonomic Bulletin & Review, 26(3), 1027–1034.

    Google Scholar 

  18. Evans, N. J., Hawkins, G. E., & Brown, S. D. (in press). The role of passing time in decision-making. Journal of Experimental Psychology: Learning, Memory, and Cognition.

  19. Evans, N. J., Steyvers, M., & Brown, S. D. (2018). Modeling the covariance structure of complex datasets using cognitive models: An application to individual differences and the heritability of cognitive ability. Cognitive science, 42(6), 1925–1944.

    Google Scholar 

  20. Franz, E. A., Zelaznik, H. N., Swinnen, S., & Walter, C. (2001). Spatial conceptual influences on the coordination of bimanual actions: When a dual task becomes a single task. Journal of motor behavior, 33(1), 103–112.

    PubMed  Google Scholar 

  21. Garrett, P. M., Howard, Z., Houpt, J. W., Landy, D., & Eidels, A. (2019). Comparative estimation systems perform under severely limited workload capacity. Journal of Mathematical Psychology.

  22. Goldberg, T. E., Berman, K. F., Fleming, K., Ostrem, J., Van Horn, J. D., Esposito, G., ... Weinberger, D. R. (1998). Uncoupling cognitive workload and prefrontal cortical physiology: a pet rcbf study. Neuroimage, 7(4), 296–303.

    PubMed  Google Scholar 

  23. Holm, A., Lukander, K., Korpela, J., Sallinen, M., & Müller, K. M. (2009). Estimating brain load from the eeg. The Scientific World Journal, 9, 639–651.

    PubMed  PubMed Central  Google Scholar 

  24. Howard, Z. L., Garrett, P. G., Little, D. N., Townsend, J., & Eidels, A. (Under Review). Nice Guys Check Twice: No-Signal Processes in Systems Factorial Technology, Psychological Review

  25. Innes, R. J., Howard, Z. L., Evans, N. J., Eidels, A., & Brown, S. (Under Review). A broader application of the detection response task to cognitive tasks and online environments. Human Factors.

  26. International Standards Organization. (2016). Road vehicles–Transport information and control systems–Detection Response Task (DRT) for assessing attentional effects of cognitive load in driving (ISO 17488).

  27. Isreal, J. B., Wickens, C. D., Chesney, G. L., & Donchin, E. (1980). The event-related brain potential as an index of display-monitoring workload. Human factors, 22(2), 211–224.

    PubMed  Google Scholar 

  28. Kahneman, D. (1973). Attention and effort (Vol. 1063). Prentice-Hall Englewood Cliffs, NJ.

  29. Karayanidis, F., Coltheart, M., Michie, P. T., & Murphy, K. (2003). Electrophysiological correlates of anticipatory and post stimulus components of task switching. Psychophysiology, 40(3), 329–348.

    PubMed  Google Scholar 

  30. Knoll, A., Wang, Y., Chen, F., Xu, J., Ruiz, N., Epps, J., & Zarjam, P. (2011). Measuring cognitive workload with low-cost electroencephalograph. In Ifip conference on human-computer interaction (pp. 568–571).

  31. Kok, A. (2001). On the utility of p3 amplitude as a measure of processing capacity. Psychophysiology, 38(3), 557–577.

    PubMed  Google Scholar 

  32. Lin, C.-T., Chen, S.-A., Chiu, T.-T., Lin, H.-Z., & Ko, L.-W. (2011). Spatial and temporal eeg dynamics of dual-task driving performance. Journal of neuroengineering and rehabilitation, 8(1), 11.

    PubMed  PubMed Central  Google Scholar 

  33. Lively, S. E., Pisoni, D. B., Van Summers, W., & Bernacki, R. H. (1993). Effects of cognitive workload on speech production: Acoustic analyses and perceptual consequences. The Journal of the Acoustical Society of America, 93(5), 2962–2973.

    PubMed  PubMed Central  Google Scholar 

  34. Mehler, B., Reimer, B., Coughlin, J., & Dusek, J. (2009). Impact of incremental increases in cognitive workload on physiological arousal and performance in young adult drivers. Transportation Research Record: Journal of the Transportation Research Board(2138), 6–12.

  35. Nourbakhsh, N., Wang, Y., Chen, F., & Calvo, R. A. (2012). Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks. In Proceedings of the 24th australian computer-human interaction conference (pp. 420–423).

  36. Pylyshyn, Z. W., & Storm, R. W. (1988). Tracking multiple independent targets: Evidence for a parallel tracking mechanism. Spatial vision, 3(3), 179–197.

    PubMed  Google Scholar 

  37. Ratcliff, R. (1978). A theory of memory retrieval. Psychological review, 85(2), 59.

    Google Scholar 

  38. Ratcliff, R., Perea, M., Colangelo, A., & Buchanan, L. (2004). A diffusion model account of normal and impaired readers. Brain and Cognition, 55(2), 374–382.

    PubMed  Google Scholar 

  39. Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model: Current issues and history. Trends in cognitive sciences, 20(4), 260–281.

    PubMed  PubMed Central  Google Scholar 

  40. Ratcliff, R., Spieler, D., & Mckoon, G. (2000). Explicitly modeling the effects of aging on response time. Psychonomic Bulletin & Review, 7(1), 1–25.

    Google Scholar 

  41. Ratcliff, R., & Strayer, D. (2014). Modeling simple driving tasks with a one-boundary diffusion model. Psychonomic bulletin & review, 21(3), 577–589.

    Google Scholar 

  42. Rubio, S., Díaz, E., Martín, J., & Puente, J. M. (2004). Evaluation of subjective mental workload: A comparison of swat, nasa-tlx, and workload profile methods. Applied Psychology, 53(1), 61–86.

    Google Scholar 

  43. Salvucci, D. D., Taatgen, N. A., & Borst, J. P. (2009). Toward a unified theory of the multi-tasking continuum: From concurrent performance to task switching, interruption, and resumption. In Proceedings of the sigchi conference on human factors in computing systems (pp. 1819–1828).

  44. Strayer, D. L., Cooper, J. M., Turrill, J., Coleman, J., Medeiros-Ward, N., & Biondi, F. (2013). Measuring cognitive distraction in the automobile.

  45. Strayer, D. L., Cooper, J. M., Turrill, J., Coleman, J. R., & Hopman, R. J. (2015). Measuring cognitive distraction in the automobile iii: A comparison of ten 2015 in-vehicle information systems. AAA Foundation for Traffic Safety.

  46. Strayer, D. L., Cooper, J. M., Turrill, J., Coleman, J. R., & Hopman, R. J. (2017). The smartphone and the driver’s cognitive workload: A comparison of apple, google, and microsoft’s intelligent personal assistants. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, 71(2), 93.

    PubMed  Google Scholar 

  47. Strayer, D. L., Drews, F. A., & Crouch, D. J. (2006). A comparison of the cell phone driver and the drunk driver. Human factors, 48(2), 381–391.

    PubMed  Google Scholar 

  48. Strayer, D. L., Drews, F. A., & Johnston, W. A. (2003). Cell phone-induced failures of visual attention during simulated driving. Journal of experimental psychology: Applied, 9(1), 23.

    PubMed  Google Scholar 

  49. Strayer, D. L., Turrill, J., Coleman, J. R., Ortiz, E. V., & Cooper, J. M. (2014). Measuring cognitive distraction in the automobile ii: Assessing in-vehicle voice-based. Accident Analysis & Prevention, 372, 379.

    Google Scholar 

  50. Strayer, D. L., Watson, J. M., & Drews, F. A. (2011). Cognitive distraction while multi-tasking in the automobile. In Psychology of learning and motivation (Vol. 54, pp. 29–58). Elsevier.

  51. Thorpe, A., Nesbitt, K., & Eidels, A. (2019). Assessing game interface workload and usability: A cognitive science perspective. In Proceedings of the australasian computer science week multiconference (p. 44).

  52. Tillman, G., & Logan, G. D. (2017). The racing diffusion model of speeded decision making.

  53. Tillman, G., Strayer, D., Eidels, A., & Heathcote, A. (2017). Modeling cognitive load effects of conversation between a passenger and driver. Attention, Perception, & Psychophysics, 79(6), 1795–1803.

    Google Scholar 

  54. Townsend, J. T., & Eidels, A. (2011). Workload capacity spaces: A unified methodology for response time measures of efficiency as workload is varied. Psychonomic bulletin & review, 18(4), 659–681.

    Google Scholar 

  55. Tsai, Y.-F., Viirre, E., Strychacz, C., Chase, B., & Jung, T.-P. (2007). Task performance and eye activity: predicting behavior relating to cognitive workload. Aviation, space, and environmental medicine, 78(5), B176–B185.

    PubMed  Google Scholar 

  56. Ulrich, R., & Miller, J. (1993). Information processing models generating lognormally distributed reaction times. Journal of Mathematical Psychology, 37(4), 513–525.

    Google Scholar 

  57. van Ravenzwaaij, D., Dutilh, G., & Wagenmakers, E.-J. (2012). A diffusion model decomposition of the effects of alcohol on perceptual decision making. Psychopharmacology, 219(4), 1017–1025.

    PubMed  Google Scholar 

  58. Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical bayesian model evaluation using leave-one-out cross-validation and waic. Statistics and Computing, 27(5), 1413–1432.

    Google Scholar 

  59. Voss, A., Nagler, M., & Lerche, V. (2013). Diffusion models in experimental psychology. Experimental psychology.

  60. Wagenmakers, E.-J., & Farrell, S. (2004). Aic model selection using akaike weights. Psychonomic bulletin & review, 11(1), 192–196.

    Google Scholar 

  61. Wickens, C. D. (2002). Multiple resources and performance prediction. Theoretical issues in ergonomics science, 3(2), 159–177.

    Google Scholar 

  62. Wickens, C. D. (2008). Multiple resources and mental workload. Human factors, 50(3), 449–455.

    PubMed  Google Scholar 

  63. Williams, P., Howard, Z., Ross, R., & Eidels, A. (2018). Cognitive dysfunction under emotional exposure: When participants with depression symptoms show no cognitive control. Australian Journal of Psychology, 70(4), 378–387.

    Google Scholar 

  64. Young, R. A., Hsieh, L., & Seaman, S. (2013). The tactile detection response task: preliminary validation for measuring the attentional effects of cognitive load.

Download references

Author Note

This research was supported by an Australian Government Research Training Program (RTP) Scholarship awarded to Zachary Howard.

Author information



Corresponding author

Correspondence to Zachary L. Howard.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Howard, Z.L., Evans, N.J., Innes, R.J. et al. How is multi-tasking different from increased difficulty?. Psychon Bull Rev 27, 937–951 (2020).

Download citation


  • Cognitive workload
  • Bayesian modeling
  • Computational models
  • Multitasking