Real-time triggering reveals concurrent lapses of attention and working memory

Abstract

Attention and working memory are clearly intertwined, as shown by co-variations in individual ability and the recruitment of similar neural substrates. Both processes fluctuate over time1,2,3,4,5, and these fluctuations may be a key determinant of individual variations in ability6,7. If these fluctuations are due to the waxing and waning of a common cognitive resource, attention and working memory should co-vary on a moment-to-moment basis. To test this, we developed a hybrid task that interleaved a sustained attention task and a whole-report working memory task. Experiment 1 established that performance fluctuations on these tasks correlated across and within participants: attention lapses led to worse working memory performance. Experiment 2 extended this finding using a real-time triggering procedure that monitored attention fluctuations to probe working memory during optimal (high-attention) or suboptimal (low-attention) moments. In low-attention moments, participants stored fewer items in working memory. Experiment 3 ruled out task-general fluctuations as an explanation for these co-variations by showing that the precision of colour memory was unaffected by variations in attention state. In summary, we demonstrate that attention and working memory lapse together, providing additional evidence for the tight integration of these cognitive processes.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Sustained attention relates to working memory performance in an interleaved task.
Fig. 2: Fluctuations of attention predict working memory performance within participants.
Fig. 3: Real-time triggering of working memory probes.
Fig. 4: Sustained attention and colour memory precision in a continuous report task.

Data availability

The data that support the findings of this study are available online in an Open Science Framework repository (https://osf.io/hfeu8/), as well as a GitHub repository (https://github.com/AwhVogelLab/deBettencourt_rtAttnWM).

Code availability

The experimental design was programmed in Python 2.7 using PsychoPy (versions 1.85 and 1.90). All analyses were conducted using custom scripts in Python 3 and R version 3. All codes for running the experiment and regenerating the results are available online in an Open Science Framework repository (https://osf.io/hfeu8/) along with a GitHub repository (https://github.com/AwhVogelLab/deBettencourt_rtAttnWM).

References

  1. 1.

    Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T. & Yiend, J. ‘Oops!’: performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia 35, 747–758 (1997).

    CAS  Article  Google Scholar 

  2. 2.

    Rosenberg, M., Noonan, S., DeGutis, J. & Esterman, M. Sustaining visual attention in the face of distraction: a novel gradual-onset continuous performance task. Atten. Percept. Psychophys. 75, 426–439 (2013).

    Article  Google Scholar 

  3. 3.

    deBettencourt, M. T., Cohen, J. D., Lee, R. F., Norman, K. A. & Turk-Browne, N. B. Closed-loop training of attention with real-time brain imaging. Nat. Neurosci. 18, 470–475 (2015).

    CAS  Article  Google Scholar 

  4. 4.

    deBettencourt, M. T., Norman, K. A. & Turk-Browne, N. B. Forgetting from lapses of sustained attention. Psychon. Bull. Rev. 25, 605–611 (2018).

    Article  Google Scholar 

  5. 5.

    Adam, K. C. S., Mance, I., Fukuda, K. & Vogel, E. K. The contribution of attentional lapses to individual differences in visual working memory capacity. J. Cogn. Neurosci. 27, 1601–1616 (2015).

    Article  Google Scholar 

  6. 6.

    Fortenbaugh, F. C. et al. Sustained attention across the life span in a sample of 10,000: dissociating ability and strategy. Psychol. Sci. 26, 1497–1510 (2015).

    Article  Google Scholar 

  7. 7.

    Luck, S. J. & Vogel, E. K. The capacity of visual working memory for features and conjunctions. Nature 390, 279–281 (1997).

    CAS  Article  Google Scholar 

  8. 8.

    Esterman, M., Noonan, S. K., Rosenberg, M. & DeGutis, J. In the zone or zoning out? Tracking behavioral and neural fluctuations during sustained attention. Cereb. Cortex 23, 2712–2723 (2013).

    Article  Google Scholar 

  9. 9.

    Esterman, M., Rosenberg, M. D. & Noonan, S. K. Intrinsic fluctuations in sustained attention and distractor processing. J. Neurosci. 34, 1724–1730 (2014).

    CAS  Article  Google Scholar 

  10. 10.

    Engle, R. W. Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11, 19–23 (2002).

    Article  Google Scholar 

  11. 11.

    Fukuda, K., Vogel, E., Mayr, U. & Awh, E. Quantity, not quality: the relationship between fluid intelligence and working memory capacity. Psychon. Bull. Rev. 17, 673–679 (2010).

    Article  Google Scholar 

  12. 12.

    Van den Berg, R., Awh, E. & Ma, W. J. Factorial comparison of working memory models. Psychol. Rev. 121, 124–149 (2014).

    Article  Google Scholar 

  13. 13.

    Rouder, J. N. et al. An assessment of fixed-capacity models of visual working memory. Proc. Natl Acad. Sci. USA 105, 5975–5979 (2008).

    CAS  Article  Google Scholar 

  14. 14.

    Unsworth, N., Schrock, J. C. & Engle, R. W. Working memory capacity and the antisaccade task: individual differences in voluntary saccade control. J. Exp. Psychol. Learn. Mem. Cogn. 30, 1302–1321 (2004).

    Article  Google Scholar 

  15. 15.

    Engle, R. W., Tuholski, S. W., Laughlin, J. E. & Conway, A. R. Working memory, short-term memory, and general fluid intelligence: a latent-variable approach. J. Exp. Psychol. Gen. 128, 309–331 (1999).

    Article  Google Scholar 

  16. 16.

    McVay, J. C. & Kane, M. J. Why does working memory capacity predict variation in reading comprehension? On the influence of mind wandering and executive attention. J. Exp. Psychol. Gen. 141, 302–320 (2012).

    Article  Google Scholar 

  17. 17.

    Kane, M. J. et al. For whom the mind wanders, and when: an experience-sampling study of working memory and executive control in daily life. Psychol. Sci. 18, 614–621 (2007).

    Article  Google Scholar 

  18. 18.

    Unsworth, N. & Robison, M. K. The influence of lapses of attention on working memory capacity. Mem. Cogn. 44, 188–196 (2016).

    Article  Google Scholar 

  19. 19.

    Unsworth, N., Fukuda, K., Awh, E. & Vogel, E. K. Working memory and fluid intelligence: capacity, attention control, and secondary memory retrieval. Cogn. Psychol. 71, 1–26 (2014).

    Article  Google Scholar 

  20. 20.

    Huang, L. Visual working memory is better characterized as a distributed resource rather than discrete slots. J. Vis. 10, 8 (2010).

    Article  Google Scholar 

  21. 21.

    Jonker, T. R., Seli, P., Cheyne, J. A. & Smilek, D. Performance reactivity in a continuous-performance task: implications for understanding post-error behavior. Conscious. Cogn. 22, 1468–1476 (2013).

    Article  Google Scholar 

  22. 22.

    Yeung, N., Botvinick, M. M. & Cohen, J. D. The neural basis of error detection: conflict monitoring and the error-related negativity. Psychol. Rev. 111, 931–959 (2004).

    Article  Google Scholar 

  23. 23.

    Cheyne, J. A., Carriere, J. S. A., Solman, G. J. F. & Smilek, D. Challenge and error: critical events and attention-related errors. Cognition 121, 437–446 (2011).

    Article  Google Scholar 

  24. 24.

    Zhang, W. & Luck, S. J. Discrete fixed-resolution representations in visual working memory. Nature 453, 233–235 (2008).

    CAS  Article  Google Scholar 

  25. 25.

    Murray, A. M., Nobre, A. C. & Stokes, M. G. Markers of preparatory attention predict visual short-term memory performance. Neuropsychologia 49, 1458–1465 (2011).

    Article  Google Scholar 

  26. 26.

    Giesbrecht, B., Weissman, D. H., Woldorff, M. G. & Mangun, G. R. Pre-target activity in visual cortex predicts behavioral performance on spatial and feature attention tasks. Brain Res. 1080, 63–72 (2006).

    CAS  Article  Google Scholar 

  27. 27.

    Myers, N. E., Stokes, M. G., Walther, L. & Nobre, A. C. Oscillatory brain state predicts variability in working memory. J. Neurosci. 34, 7735–7743 (2014).

    CAS  Article  Google Scholar 

  28. 28.

    Robison, M. K. & Unsworth, N. Pupillometry tracks fluctuations in working memory performance. Atten. Percept. Psychophys. 81, 407–419 (2019).

    Article  Google Scholar 

  29. 29.

    Peirce, J. W. PsychoPy—psychophysics software in Python. J. Neurosci. Methods 162, 8–13 (2007).

    Article  Google Scholar 

  30. 30.

    Vogel, E. K., Woodman, G. F. & Luck, S. J. The time course of consolidation in visual working memory. J. Exp. Psychol. Hum. Percept. Perform. 32, 1436–1451 (2006).

    Article  Google Scholar 

  31. 31.

    Cowan, N. The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav. Brain Sci. 24, 87–114 (2001).

    CAS  Article  Google Scholar 

  32. 32.

    Suchow, J. W., Brady, T. F., Fougnie, D. & Alvarez, G. A. Modeling visual working memory with the MemToolbox. J. Vis. 13, 9–9 (2013).

    Article  Google Scholar 

  33. 33.

    Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).

    Article  Google Scholar 

Download references

Acknowledgements

We thank K. C. S. Adam and N. Hakim for feedback on the design and analysis of the whole-report working memory task. This research was supported by National Institute of Mental Health grant R01 MH087214, Office of Naval Research grant N00014-12-1-0972, and F32 MH115597 (to M.T.dB.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

Authors

Contributions

M.T.dB., E.A. and E.K.V. conceived of the study and contributed to the study design. M.T.dB. and P.A.K. collected and analysed the data. M.T.dB. wrote an initial draft of the manuscript, which all authors read and edited.

Corresponding author

Correspondence to Megan T. deBettencourt.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

deBettencourt, M.T., Keene, P.A., Awh, E. et al. Real-time triggering reveals concurrent lapses of attention and working memory. Nat Hum Behav 3, 808–816 (2019). https://doi.org/10.1038/s41562-019-0606-6

Download citation

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing