Recent theoretical models propose that working memory is mediated by rapid transitions in 'activity-silent' neural states (for example, short-term synaptic plasticity). According to the dynamic coding framework, such hidden state transitions flexibly configure memory networks for memory-guided behavior and dissolve them equally fast to allow forgetting. We developed a perturbation approach to measure mnemonic hidden states in an electroencephalogram. By 'pinging' the brain during maintenance, we show that memory-item-specific information is decodable from the impulse response, even in the absence of attention and lingering delay activity. Moreover, hidden memories are remarkably flexible: an instruction cue that directs people to forget one item is sufficient to wipe the corresponding trace from the hidden state. In contrast, temporarily unattended items remain robustly coded in the hidden state, decoupling attentional focus from cue-directed forgetting. Finally, the strength of hidden-state coding predicts the accuracy of working-memory-guided behavior, including memory precision.
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We thank E. Spaak, A. Cravo and N. Myers for comments and advice and all our volunteers for their participation. We also thank the Biotechnology & Biological Sciences Research Council (BB/M010732/1 to M.G.S.) and the National Institute for Health Research Oxford Biomedical Research Centre Programme based at the Oxford University Hospitals Trust, Oxford University. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
The authors declare no competing financial interests.
Integrated supplementary information
The cue-specific neural response showed robust decoding for the cued (n = 30, cluster: 142 to 960 ms, p < 0.001, corrected; average: p < 0.001) and uncued item (n = 30, clusters: 158 to 304 ms, p = 0.035, corrected, 328 to 574 ms, p = 0.006, corrected, average: p = 0.006).
Error shading is the 95 % C.I. of the mean. The boxplots and superimposed circles with error-bars (mean and 95 % C.I. of the mean) represent average decoding from 100 to 1,100 ms after cue onset. Significant average decoding is marked by an asterisk (permutation test, n = 30, p < 0.05).
Supplementary Figure 2 Testing the relationship between item decoding during the memory item epoch and WM accuracy.
a. Task accuracy difference between high and low decoding trials of the cued (blue; n = 30, p = 0.673) and uncued (red; n = 30, p = 0.344) item during the memory items epoch (average decoding from 100 to 1,050 ms relative to memory items onset) in Experiment 1. b. Early accuracy difference between high and low decoding trials of the early-tested item (blue; n = 19, p = 0.865), and late accuracy difference between high and low decoding trials of the late-tested (red; n = 19, p = 0.978) during the memory items epoch (average decoding from 100 to 1,200 ms relative to memory items onset) in Experiment 2. Circles and error-bars superimposed on the boxplots represent mean and 95% C.I. of the mean.
Supplementary Figure 3 Testing the relationship between alpha-lateralization and item decoding after the first impulse in Experiment 2.
Both attended and unattended memory items were decodable after the first impulse in Experiment 2; however, it remains possible that participants sometimes attended to the less-relevant item, contributing to decoding on some trials. To consider this possibility, we test whether the impulse-specific WM item decoding after impulse 1 presentation covaries with trial-wise fluctuations in spatial attention. Spatial attention was indexed by alpha-power lateralization relative to the location of the early-tested item of each time-point (left, also see Figure 4c and corresponding results), and trialwise item decodability was estimated 100-500ms after impulse 1 onset (middle panel). The correlation time-course (right), where each time-point represents the mean correlation of the averaged item decoding (100 – 500 ms after impulse 1) with the alpha-lateralization of that time-point, shows no evidence for a relationship between item decoding and alpha-lateralization for any time-point (permutation test, n = 19, early-tested item: all p > 0.058; late-tested item: all p > 0.148, uncorrected). Therefore, we find no evidence that the impulse-response varies with the focus of attention, even on a trial-wise basis. Error shadings are 95% C.I. of the mean. Circles and error bars superimposed on the boxplots represent mean and 95% C.I. of the mean. Data points outside the 1.5 * interquartile range are marked as crosses in the boxplots.
a. Two memory items were presented, and participants were instructed to memorize both. A retro-cue indicated which item would be tested at the end of the current trial. The impulse stimulus was presented at varying delays (or not at all) and stayed on screen until the probe was presented. Participants indicated whether the probe was rotated clockwise or anti-clockwise relative to the orientation of the cued item. b. Behavioural performance as a function of impulse-probe SOA. None of the uncorrected paired comparisons between the no-impulse condition (SOA 0 ms) and the other SOA conditions reached significance (permutation test, n = 20). Circles and error bars superimposed on the boxplots represent mean and 95% C.I. of the mean. Data points outside the 1.5 * interquartile range are marked as crosses in the boxplots.
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Wolff, M., Jochim, J., Akyürek, E. et al. Dynamic hidden states underlying working-memory-guided behavior. Nat Neurosci 20, 864–871 (2017). https://doi.org/10.1038/nn.4546
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