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Memory failure predicted by attention lapsing and media multitasking

Nature volume 587, pages 87–91 (2020)Cite this article

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Abstract

With the explosion of digital media and technologies, scholars, educators and the public have become increasingly vocal about the role that an ‘attention economy’ has in our lives1. The rise of the current digital culture coincides with longstanding scientific questions about why humans sometimes remember and sometimes forget, and why some individuals remember better than others2,3,4,5,6. Here we examine whether spontaneous attention lapses—in the moment7,8,9,10,11,12, across individuals13,14,15 and as a function of everyday media multitasking16,17,18,19—negatively correlate with remembering. Electroencephalography and pupillometry measures of attention20,21 were recorded as eighty young adults (mean age, 21.7 years) performed a goal-directed episodic encoding and retrieval task22. Trait-level sustained attention was further quantified using task-based23 and questionnaire measures24,25. Using trial-to-trial retrieval data, we show that tonic lapses in attention in the moment before remembering, assayed by posterior alpha power and pupil diameter, were correlated with reductions in neural signals of goal coding and memory, along with behavioural forgetting. Independent measures of trait-level attention lapsing mediated the relationship between neural assays of lapsing and memory performance, and between media multitasking and memory. Attention lapses partially account for why we remember or forget in the moment, and why some individuals remember better than others. Heavier media multitasking is associated with a propensity to have attention lapses and forget.

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Fig. 1: Attention, goals and memory in the moment.
Fig. 2: Trait-level differences in sustained attention help to explain why individuals are more prone to remembering or forgetting.
Fig. 3: Trait-level differences in sustained attention partially explain the negative relationship between MMT and memory.

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Data availability

Data that support the findings of this study are publicly available via the Open Science Framework47 with identifier zj7tb (https://osf.io/zj7tb). Data used in the preparation of this manuscript are also publicly available from the National Institute of Mental Health (NIMH) Data Archive (NDA) (https://doi.org/10.15154/1519022)48. The source data underlying all figures are provided as a Source Data file. Source data are provided with this paper.

Code availability

Analytic code that support the findings of this study are publicly available via Open Science Framework47 with identifier zj7tb (https://osf.io/zj7tb).

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Acknowledgements

We thank A. Gonzalez and J. Qi for assistance with various aspects of the study. This research was supported by the National Institute of Mental Health (R56MH111672 to A.D.W.) and the National Institute on Aging (R01AG065255 to A.D.W.; F32AG059341 to K.P.M.). The content is solely the views of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Psychology, Stanford University, Stanford, CA, USA

    Kevin P. Madore, Anna M. Khazenzon, Jiefeng Jiang, Anthony M. Norcia & Anthony D. Wagner

  2. Symbolic Systems Program, Stanford University, Stanford, CA, USA

    Cameron W. Backes

  3. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    Melina R. Uncapher

  4. Neuroscape, University of California, San Francisco, San Francisco, CA, USA

    Melina R. Uncapher

  5. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Anthony M. Norcia & Anthony D. Wagner

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Contributions

K.P.M., A.M.K., M.R.U., A.M.N. and A.D.W. developed the study concept and contributed to study design; K.P.M. and C.W.B. collected data under supervision of A.M.N. and A.D.W.; K.P.M., A.M.K., C.W.B. and J.J. analysed data under supervision of A.M.N. and A.D.W.; K.P.M. and A.D.W. wrote the manuscript, and all authors provided critical revisions.

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Correspondence to Kevin P. Madore or Anthony D. Wagner.

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Peer review information Nature thanks Edward K. Vogel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Experimental design.

a, Schematic of the goal-directed memory task with EEG and pupillometry measurements. b, Schematic of electrode clusters from which alpha or ERP signals were extracted for the respective analyses; electrode clusters are illustrated on a 128-channel net. Pupil diameter from the right eye (top right) was recorded concurrently using an eye-tracking system. L, left; R, right.

Extended Data Fig. 2 Pre-goal attention lapses relate to canonical neural signals of recollection- and familiarity-based memory as assayed by grand-average left-lateralized Parietal Old/New and FN400 ERP effects, respectively.

a, Evidence of a peak Parietal Old/New signal (indicated by the black arrow) in the 500–600-ms post-probe window as a function of memory outcome in conceptual and perceptual source-retrieval trials. b, Trial-level interaction between pre-goal attention lapses and the Parietal Old/New signal during remembered (source hit) and forgotten (miss) trials. c, Evidence of a peak FN400 signal (indicated by the black arrow) in the 400–500-ms post-probe window as a function of memory outcome in novelty-detection trials. d, Trial-level interaction between pre-goal attention lapses and FN400 signal on correctly endorsed new items (hits) compared with misses. For visualization, quintiles are shown for the relationship between pre-goal lapsing and ERP signal; statistics included an interaction term for retrieval goal state (for Parietal Old/New) and treated pre-goal lapsing and the ERP signals continuously in trial-level mixed models. y-axis units are z-scores. Data are mean ± s.e.m. Note that z-scoring within run and time-binning in 0.1-s (100-ms) intervals reduces smaller temporal effects that are sometimes exhibited in grand-average ERP plots (for visualization of grand-average ERP plots downsampled to 0.01-s intervals (10-ms), see Extended Data Fig. 4). CRold, correct rejection of old item; CRnew, correct rejection of new item; FAold, false alarm to old item; FAnew, false alarm to new item. n = 75 participants from a single independent experiment.

Source data

Extended Data Fig. 3 Evidence of mean peak Parietal Old/New signal in the 500–600-ms post-probe window as a function of memory outcome in source retrieval trials.

The mean peak Parietal Old/New signal is indicated by the black arrow. a, b, Data are split by conceptual (a) and perceptual (b) source trials. CRold, correct rejection of old item; FAold, false alarm to old item. For conceptual cuing, hits and misses are for conceptually studied items, and correct rejections and false alarms are for perceptually studied items. For perceptual cuing, hits and misses are for perceptually studied items, and correct rejections and false alarms are for conceptually studied items. n = 75 participants from a single independent experiment.

Source data

Extended Data Fig. 4 Grand-average left-lateralized ERPs revealing recollection-based Parietal Old/New and familiarity-based FN400 memory effects.

Data were down-sampled to 10-ms time-bin intervals. a, b, The same profile of findings is observed as with the 100-ms time-bins (see main text), such that evidence of a peak Parietal Old/New signal (indicated by the black arrow) is exhibited 500–600-ms post-probe onset as a function of memory outcome in conceptual and perceptual source-retrieval trials (a) and evidence of a peak FN400 signal (indicated by the black arrow) is exhibited 400–500-ms post-probe onset as a function of memory outcome in novelty-detection trials (b). y-axis units are within-run z-scores. n = 75 participants from a single independent experiment.

Source data

Extended Data Fig. 5 Trait-level differences in sustained attention at encoding help to explain why individuals are more prone to remembering or forgetting.

a, b, Greater pre-goal attention lapsing at encoding is correlated with greater pre-goal attention lapsing at retrieval (a) and lower d′ on the memory task (b). For visualization, raw scores are plotted; statistics included z-scored assays with Pearson correlations. n = 75 participants for alpha retrieval data and n = 80 participants for all other data from a single independent experiment. These trait differences in attention at encoding do not fully explain the relationship between the trait differences in attention at retrieval and memory ability (Supplementary Information).

Source data

Extended Data Fig. 6 Phasic pupil and memory effects.

Evidence of a phasic pupil old/new effect in novelty-detection trials 300–500 ms post-probe, particularly between correctly rejected old objects versus hits to new objects. The mean peak difference is at 400 ms post-probe (indicated by the black arrow). x-axis units are 100-ms time-bin intervals; y-axis units are within-run z-scores. n = 75 participants from a single independent experiment.

Source data

Extended Data Fig. 7 Key results from extreme group analyses of multitasking, memory and sustained attention for light and heavy media multitaskers.

a–c, Heavy media multitaskers exhibited lower d′ on the memory tasks (a), more attention lapses on the gradCPT (b) and more evidence of attention lapsing (assayed by mean alpha power and pupil variability) on the memory task (c), relative to light media multitaskers. Data are mean ± s.e.m. from a single independent experiment. n = 18 light and n = 18 heavy media multitaskers for alpha data; n = 20 light and n = 20 heavy media multitaskers for all other data. d, Histogram of scores (n = 80) on the MMI, illustrated by the bottom 25% of scores (light media multitaskers), the middle 50% of scores (intermediate media multitaskers) and the top 25% of scores (heavy media multitaskers). LMM, light media multitasker; HMM, heavy media multitasker.

Source data

Extended Data Table 1 Mnemonic rates as a function of retrieval goal
Full size table

Supplementary information

Supplementary Information

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Madore, K.P., Khazenzon, A.M., Backes, C.W. et al. Memory failure predicted by attention lapsing and media multitasking. Nature 587, 87–91 (2020). https://doi.org/10.1038/s41586-020-2870-z

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