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


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.

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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 (, as well as a GitHub repository (

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 ( along with a GitHub repository (


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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.

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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.

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Correspondence to Megan T. deBettencourt.

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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).

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