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.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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).
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).
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).
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).
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).
deBettencourt, M. T., Norman, K. A. & Turk-Browne, N. B. Forgetting from lapses of sustained attention. Psychon. Bull. Rev. 25, 605–611 (2018).
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).
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).
Luck, S. J. & Vogel, E. K. The capacity of visual working memory for features and conjunctions. Nature 390, 279–281 (1997).
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).
Esterman, M., Rosenberg, M. D. & Noonan, S. K. Intrinsic fluctuations in sustained attention and distractor processing. J. Neurosci. 34, 1724–1730 (2014).
Engle, R. W. Working memory capacity as executive attention. Curr. Dir. Psychol. Sci. 11, 19–23 (2002).
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).
Van den Berg, R., Awh, E. & Ma, W. J. Factorial comparison of working memory models. Psychol. Rev. 121, 124–149 (2014).
Rouder, J. N. et al. An assessment of fixed-capacity models of visual working memory. Proc. Natl Acad. Sci. USA 105, 5975–5979 (2008).
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).
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).
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).
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).
Unsworth, N. & Robison, M. K. The influence of lapses of attention on working memory capacity. Mem. Cogn. 44, 188–196 (2016).
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).
Huang, L. Visual working memory is better characterized as a distributed resource rather than discrete slots. J. Vis. 10, 8 (2010).
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).
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).
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).
Zhang, W. & Luck, S. J. Discrete fixed-resolution representations in visual working memory. Nature 453, 233–235 (2008).
Murray, A. M., Nobre, A. C. & Stokes, M. G. Markers of preparatory attention predict visual short-term memory performance. Neuropsychologia 49, 1458–1465 (2011).
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).
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).
Robison, M. K. & Unsworth, N. Pupillometry tracks fluctuations in working memory performance. Atten. Percept. Psychophys. 81, 407–419 (2019).
Peirce, J. W. PsychoPy—psychophysics software in Python. J. Neurosci. Methods 162, 8–13 (2007).
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).
Cowan, N. The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav. Brain Sci. 24, 87–114 (2001).
Suchow, J. W., Brady, T. F., Fougnie, D. & Alvarez, G. A. Modeling visual working memory with the MemToolbox. J. Vis. 13, 9–9 (2013).
Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).
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.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
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
Δ9-Tetrahydrocannabinol (THC) impairs visual working memory performance: a randomized crossover trial
Cognitive Therapy and Research (2020)
Psychonomic Bulletin & Review (2020)
Scientific Reports (2019)