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Dynamic hidden states underlying working-memory-guided behavior

Nature Neuroscience volume 20, pages 864–871 (2017)Cite this article

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Abstract

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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Figure 1: Experiment 1 task structure, behavioral performance and attention-related alpha-band activity.
Figure 2: Orientation decoding in EEG and pinging hidden states of WM.
Figure 3: Relationship between item-specific impulse decoding and WM accuracy.
Figure 4: Experiment 2 task structure, behavioral performance and attention-related alpha-band activity.
Figure 5: Priority-dependent encoding and maintenance in WM.
Figure 6: Attended and unattended WM items in early and late epochs and their relationship with behavioral performance.

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References

  1. Baddeley, A. Working memory: looking back and looking forward. Nat. Rev. Neurosci. 4, 829–839 (2003).

    CAS  PubMed  Google Scholar 

  2. Curtis, C.E. & D'Esposito, M. Persistent activity in the prefrontal cortex during working memory. Trends Cogn. Sci. 7, 415–423 (2003).

    PubMed  Google Scholar 

  3. Goldman-Rakic, P.S. Cellular basis of working memory. Neuron 14, 477–485 (1995).

    CAS  PubMed  Google Scholar 

  4. Stokes, M.G. 'Activity-silent' working memory in prefrontal cortex: a dynamic coding framework. Trends Cogn. Sci. 19, 394–405 (2015).

    PubMed  PubMed Central  Google Scholar 

  5. Watanabe, K. & Funahashi, S. Neural mechanisms of dual-task interference and cognitive capacity limitation in the prefrontal cortex. Nat. Neurosci. 17, 601–611 (2014).

    CAS  PubMed  Google Scholar 

  6. Watanabe, K. & Funahashi, S. Prefrontal delay-period activity reflects the decision process of a saccade direction during a free-choice ODR task. Cereb. Cortex 17 Suppl 1: i88–i100 (2007).

    PubMed  Google Scholar 

  7. Miller, E.K., Erickson, C.A. & Desimone, R. Neural mechanisms of visual working memory in prefrontal cortex of the macaque. J. Neurosci. 16, 5154–5167 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Barak, O., Tsodyks, M. & Romo, R. Neuronal population coding of parametric working memory. J. Neurosci. 30, 9424–9430 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. LaRocque, J.J., Lewis-Peacock, J.A., Drysdale, A.T., Oberauer, K. & Postle, B.R. Decoding attended information in short-term memory: an EEG study. J. Cogn. Neurosci. 25, 127–142 (2013).

    PubMed  PubMed Central  Google Scholar 

  10. Lewis-Peacock, J.A., Drysdale, A.T., Oberauer, K. & Postle, B.R. Neural evidence for a distinction between short-term memory and the focus of attention. J. Cogn. Neurosci. 24, 61–79 (2012).

    PubMed  Google Scholar 

  11. Sprague, T.C., Ester, E.F. & Serences, J.T. Restoring latent visual working memory representations in human cortex. Neuron 91, 694–707 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Sreenivasan, K.K., Curtis, C.E. & D'Esposito, M. Revisiting the role of persistent neural activity during working memory. Trends Cogn. Sci. 18, 82–89 (2014).

    PubMed  PubMed Central  Google Scholar 

  13. Buonomano, D.V. & Maass, W. State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113–125 (2009).

    CAS  PubMed  Google Scholar 

  14. Barak, O. & Tsodyks, M. Working models of working memory. Curr. Opin. Neurobiol. 25, 20–24 (2014).

    CAS  PubMed  Google Scholar 

  15. Murray, J.D. et al. Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex. Proc. Natl. Acad. Sci. USA 114, 394–399 (2017).

    CAS  PubMed  Google Scholar 

  16. Fujisawa, S., Amarasingham, A., Harrison, M.T. & Buzsáki, G. Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex. Nat. Neurosci. 11, 823–833 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Lundqvist, M. et al. Gamma and beta bursts underlie working memory. Neuron 90, 152–164 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Mongillo, G., Barak, O. & Tsodyks, M. Synaptic theory of working memory. Science 319, 1543–1546 (2008).

    CAS  PubMed  Google Scholar 

  19. Hempel, C.M., Hartman, K.H., Wang, X.-J., Turrigiano, G.G. & Nelson, S.B. Multiple forms of short-term plasticity at excitatory synapses in rat medial prefrontal cortex. J. Neurophysiol. 83, 3031–3041 (2000).

    CAS  PubMed  Google Scholar 

  20. Sugase-Miyamoto, Y., Liu, Z., Wiener, M.C., Optican, L.M. & Richmond, B.J. Short-term memory trace in rapidly adapting synapses of inferior temporal cortex. PLoS Comput. Biol. 4, e1000073 (2008).

    PubMed  PubMed Central  Google Scholar 

  21. Wolff, M.J., Ding, J., Myers, N.E. & Stokes, M.G. Revealing hidden states in visual working memory using electroencephalography. Front. Syst. Neurosci. 9, 123 (2015).

    PubMed  PubMed Central  Google Scholar 

  22. Griffin, I.C. & Nobre, A.C. Orienting attention to locations in internal representations. J. Cogn. Neurosci. 15, 1176–1194 (2003).

    PubMed  Google Scholar 

  23. Landman, R., Spekreijse, H. & Lamme, V.A.F. Large capacity storage of integrated objects before change blindness. Vision Res. 43, 149–164 (2003).

    PubMed  Google Scholar 

  24. Harrison, S.A. & Tong, F. Decoding reveals the contents of visual working memory in early visual areas. Nature 458, 632–635 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Worden, M.S., Foxe, J.J., Wang, N. & Simpson, G.V. Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex. J. Neurosci. 20, RC63 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Saproo, S. & Serences, J.T. Spatial attention improves the quality of population codes in human visual cortex. J. Neurophysiol. 104, 885–895 (2010).

    PubMed  PubMed Central  Google Scholar 

  27. Myers, N.E. et al. Testing sensory evidence against mnemonic templates. eLife 4, e09000 (2015).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Bays, P.M. & Husain, M. Dynamic shifts of limited working memory resources in human vision. Science 321, 851–854 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  31. Larocque, J.J., Lewis-Peacock, J.A. & Postle, B.R. Multiple neural states of representation in short-term memory? It's a matter of attention. Front. Hum. Neurosci. 8, 5 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. Olivers, C.N.L., Peters, J., Houtkamp, R. & Roelfsema, P.R. Different states in visual working memory: when it guides attention and when it does not. Trends Cogn. Sci. 15, 327–334 (2011).

    PubMed  Google Scholar 

  33. Souza, A.S. & Oberauer, K. In search of the focus of attention in working memory: 13 years of the retro-cue effect. Atten. Percept. Psychophys. 78, 1839–1860 (2016).

    PubMed  Google Scholar 

  34. van Ede, F., Niklaus, M. & Nobre, A.C. Temporal expectations guide dynamic prioritization in visual working memory through attenuated α oscillations. J. Neurosci. 37, 437–445 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Rose, N.S. et al. Reactivation of latent working memories with transcranial magnetic stimulation. Science 354, 1136–1139 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Stokes, M.G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Mante, V., Sussillo, D., Shenoy, K.V. & Newsome, W.T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Martínez-García, M., Rolls, E.T., Deco, G. & Romo, R. Neural and computational mechanisms of postponed decisions. Proc. Natl. Acad. Sci. USA 108, 11626–11631 (2011).

    PubMed  PubMed Central  Google Scholar 

  39. Brainard, D.H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).

    Article  CAS  PubMed  Google Scholar 

  40. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).

    PubMed  Google Scholar 

  41. Schneider, D., Mertes, C. & Wascher, E. The time course of visuo-spatial working memory updating revealed by a retro-cuing paradigm. Sci. Rep. 6, 21442 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 156869 (2011).10.1155/2011/156869

    Article  PubMed  Google Scholar 

  43. Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data. J. Neurosci. Methods 164, 177–190 (2007).

    PubMed  Google Scholar 

  44. De Maesschalck, R., Jouan-Rimbaud, D. & Massart, D.L. The Mahalanobis distance. Chemometr. Intell. Lab. Syst. 50, 1–18 (2000).

    CAS  Google Scholar 

  45. Ledoit, O. & Wolf, M. Honey, I shrunk the sample covariance matrix. J. Portfolio Management 30, 110–119 (2004).

    Google Scholar 

  46. Claessens, P.M.E. & Wagemans, J. A Bayesian framework for cue integration in multistable grouping: Proximity, collinearity, and orientation priors in zigzag lattices. J. Vis. 8, 1–23 (2008).

    PubMed  Google Scholar 

  47. King, J.-R. & Dehaene, S. Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18, 203–210 (2014).

    PubMed  PubMed Central  Google Scholar 

  48. Pilat, D. & Fukasaku, Y. OECD principles and guidelines for access to research data from public funding. Data Sci. J. 6, OD4–OD11 (2007).

    Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  1. Department of Experimental Psychology, University of Oxford, Oxford, UK

    Michael J Wolff, Janina Jochim & Mark G Stokes

  2. Department of Experimental Psychology, University of Groningen, Groningen, the Netherlands

    Michael J Wolff & Elkan G Akyürek

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  1. Michael J Wolff
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  2. Janina Jochim
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Contributions

M.J.W., M.G.S., and E.G.A. designed the study. M.J.W. and J.J. collected the data. M.J.W. analyzed the data. M.J.W., M.G.S., E.G.A., and J.J. wrote the manuscript.

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Correspondence to Mark G Stokes.

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Integrated supplementary information

Supplementary Figure 1 Cue-specific item decoding time-course (Experiment 1).

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.

Supplementary Figure 4 Task schematic and results of behavioral experiment.

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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Supplementary Figures 1–4 (PDF 462 kb)

Supplementary Methods Checklist (PDF 449 kb)

Supplementary Software

Custom matlab function “mahalTune_func.m” Custom matlab function used for all main analyses (TXT 3 kb)

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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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