Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Representation and computation in visual working memory

Abstract

The ability to sustain internal representations of the sensory environment beyond immediate perception is a fundamental requirement of cognitive processing. In recent years, debates regarding the capacity and fidelity of the working memory (WM) system have advanced our understanding of the nature of these representations. In particular, there is growing recognition that WM representations are not merely imperfect copies of a perceived object or event. New experimental tools have revealed that observers possess richer information about the uncertainty in their memories and take advantage of environmental regularities to use limited memory resources optimally. Meanwhile, computational models of visuospatial WM formulated at different levels of implementation have converged on common principles relating capacity to variability and uncertainty. Here we review recent research on human WM from a computational perspective, including the neural mechanisms that support it.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Recall as inference about the past.
Fig. 2: Tools for measuring WM uncertainty.
Fig. 3: Converging models of visual WM.
Fig. 4: Sources of recall error beyond individual features.
Fig. 5: Dynamics of WM representations.

Similar content being viewed by others

References

  1. Wade, N. & Swanston, M. Visual Perception: An Introduction (Psychology Press, 2013).

  2. Knill, D. C. & Pouget, A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712–719 (2004).

    CAS  PubMed  Google Scholar 

  3. Cowan, N. The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav. Brain Sci. 24, 87–114 (2001).

    CAS  PubMed  Google Scholar 

  4. Trommershauser, J., Kording, K. & Landy, M. S. Sensory Cue Integration (Oxford Univ. Press, 2011).

  5. Rademaker, R. L., Tredway, C. H. & Tong, F. Introspective judgments predict the precision and likelihood of successful maintenance of visual working memory. J. Vis. 12, 21 (2012).

    PubMed  PubMed Central  Google Scholar 

  6. Yoo, A. H., Klyszejko, Z., Curtis, C. E. & Ma, W. J. Strategic allocation of working memory resource. Sci. Rep. 8, 16162 (2018).

    PubMed  PubMed Central  Google Scholar 

  7. Honig, M., Ma, W. J. & Fougnie, D. Humans incorporate trial-to-trial working memory uncertainty into rewarded decisions. Proc. Natl Acad. Sci. USA 117, 8391–8397 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Jabar, S. B. et al. Probabilistic and rich individual working memories revealed by a betting game. Sci. Rep. 13, 20912 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Acerbi, L., Vijayakumar, S. & Wolpert, D. M. On the origins of suboptimality in human probabilistic inference. PLoS Comput. Biol. 10, e1003661 (2014).

    PubMed  PubMed Central  Google Scholar 

  10. Keshvari, S., Van den Berg, R. & Ma, W. J. Probabilistic computation in human perception under variability in encoding precision. PLoS ONE 7, e40216 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Yoo, A. H., Acerbi, L. & Ma, W. J. Uncertainty is maintained and used in working memory. J. Vis. 21, 13 (2021).

    PubMed  PubMed Central  Google Scholar 

  12. Devkar, D., Wright, A. A. & Ma, W. J. Monkeys and humans take local uncertainty into account when localizing a change. J. Vis. 17, 4 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. Meyniel, F., Sigman, M. & Mainen, Z. F. Confidence as Bayesian probability: from neural origins to behavior. Neuron 88, 78–92 (2015).

    CAS  PubMed  Google Scholar 

  14. Fleming, S. M. & Daw, N. D. Self-evaluation of decision-making: a general Bayesian framework for metacognitive computation. Psychol. Rev. 124, 91–114 (2017).

    PubMed  PubMed Central  Google Scholar 

  15. Yeon, J. & Rahnev, D. The suboptimality of perceptual decision making with multiple alternatives. Nat. Commun. 11, 3857 (2020).

    PubMed  PubMed Central  Google Scholar 

  16. Oberauer, K. & Lin, H.-Y. An interference model of visual working memory. Psychol. Rev. 124, 21–59 (2017).

    PubMed  Google Scholar 

  17. Swan, G. & Wyble, B. The binding pool: a model of shared neural resources for distinct items in visual working memory. Atten. Percept. Psychophys. 76, 2136–2157 (2014).

    PubMed  Google Scholar 

  18. Schneegans, S. & Bays, P. M. Neural architecture for feature binding in visual working memory. J. Neurosci. 37, 3913–3925 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Bays, P. M. Noise in neural populations accounts for errors in working memory. J. Neurosci. 34, 3632–3645 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Pouget, A., Dayan, P. & Zemel, R. Information processing with population codes. Nat. Rev. Neurosci. 1, 125–132 (2000).

    CAS  PubMed  Google Scholar 

  21. Ohshiro, T., Angelaki, D. E. & DeAngelis, G. C. A normalization model of multisensory integration. Nat. Neurosci. 14, 775–782 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Reynolds, J. H. & Heeger, D. J. The normalization model of attention. Neuron 61, 168–185 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Bays, P. M. A signature of neural coding at human perceptual limits. J. Vis. 16, 4 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. Van den Berg, R., Yoo, A. H. & Ma, W. J.Fechner’s law in metacognition: a quantitative model of visual working memory confidence. Psychol. Rev. 124, 197–214 (2017).

    PubMed  PubMed Central  Google Scholar 

  25. Li, H.-H., Sprague, T. C., Yoo, A. H., Ma, W. J. & Curtis, C. E. Joint representation of working memory and uncertainty in human cortex. Neuron 109, 3699–3712.e6 (2021).

    PubMed  PubMed Central  Google Scholar 

  26. Ma, W. J., Beck, J. M., Latham, P. E. & Pouget, A. Bayesian inference with probabilistic population codes. Nat. Neurosci. 9, 1432–1438 (2006).

    CAS  PubMed  Google Scholar 

  27. Jazayeri, M. & Movshon, J. A. Optimal representation of sensory information by neural populations. Nat. Neurosci. 9, 690–696 (2006).

    CAS  PubMed  Google Scholar 

  28. Van Bergen, R. & Jehee, J. TAFKAP: an improved method for probabilistic decoding of cortical activity. Preprint at bioRxiv https://doi.org/10.1101/2021.03.04.433946 (2021).

  29. Schneegans, S., Taylor, R. & Bays, P. M. Stochastic sampling provides a unifying account of visual working memory limits. Proc. Natl Acad. Sci. USA 117, 20959–20968 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Palmer, J. Attentional limits on the perception and memory of visual information. J. Exp. Psychol. Hum. Percept. Perform. 16, 332–350 (1990).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Sewell, D. K., Lilburn, S. D. & Smith, P. L. An information capacity limitation of visual short-term memory. J. Exp. Psychol. Hum. Percept. Perform. 40, 2214–2242 (2014).

    PubMed  Google Scholar 

  33. Shaw, M. L. in Attention and Performance VIII 277–295 (Taylor & Francis Group, 1980).

  34. Ma, W. J. & Huang, W. No capacity limit in attentional tracking: evidence for probabilistic inference under a resource constraint. J. Vis. 9, 3 (2009).

    Google Scholar 

  35. Vul, E., Alvarez, G., Tenenbaum, J. & Black, M. Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model. Adv. Neural Inf. Process. Syst. 22, 1955–1963 (2009).

  36. Schurgin, M. W., Wixted, J. T. & Brady, T. F. Psychophysical scaling reveals a unified theory of visual memory strength. Nat. Hum. Behav. 4, 1156–1172 (2020).

    PubMed  Google Scholar 

  37. Kriegeskorte, N. & Wei, X.-X. Neural tuning and representational geometry. Nat. Rev. Neurosci. 22, 703–718 (2021).

    CAS  PubMed  Google Scholar 

  38. Tomić, I. & Bays, P. M. Perceptual similarity judgments do not predict the distribution of errors in working memory. J. Exp. Psychol. Learn. Mem. Cogn. 50, 535–549 (2024).

    PubMed  Google Scholar 

  39. Van den Berg, R., Shin, H., Chou, W.-C., George, R. & Ma, W. J. Variability in encoding precision accounts for visual short-term memory limitations. Proc. Natl Acad. Sci. USA 109, 8780–8785 (2012).

    PubMed  PubMed Central  Google Scholar 

  40. Mazyar, H., Van den Berg, R. & Ma, W. J. Does precision decrease with set size? J. Vis. 12, 10 (2012).

    PubMed  PubMed Central  Google Scholar 

  41. Keshvari, S., Van den Berg, R. & Ma, W. J. No evidence for an item limit in change detection. PLoS Comput. Biol. 9, e1002927 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Van den Berg, R., Awh, E. & Ma, W. J. Factorial comparison of working memory models. Psychol. Rev. 121, 124–149 (2014).

    PubMed  PubMed Central  Google Scholar 

  43. Williams, J. R., Robinson, M. M., Schurgin, M., Wixted, J. & Brady, T. You cannot “count” how many items people remember in working memory: the importance of signal detection-based measures for understanding change detection performance. J. Exp. Psychol. Hum. Percept. Perform. 48, 1390–1409 (2022).

    PubMed  PubMed Central  Google Scholar 

  44. Adam, K. C., Vogel, E. K. & Awh, E. Clear evidence for item limits in visual working memory. Cogn. Psychol. 97, 79–97 (2017).

    PubMed  PubMed Central  Google Scholar 

  45. Wilken, P. & Ma, W. J. A detection theory account of change detection. J. Vis. 4, 11 (2004).

    Google Scholar 

  46. Johnson, J. S., Spencer, J. P., Luck, S. J. & Schöner, G. A dynamic neural field model of visual working memory and change detection. Psychol. Sci. 20, 568–577 (2009).

    PubMed  Google Scholar 

  47. Schneegans, S., Spencer, J. P. & Schöner, G. in Dynamic Thinking: A Primer on Dynamic Field Theory 197–226 (Oxford Univ. Press, 2016).

  48. Emrich, S. M., Lockhart, H. A. & Al-Aidroos, N. Attention mediates the flexible allocation of visual working memory resources. J. Exp. Psychol. Hum. Percept. Perform. 43, 1454–1465 (2017).

    PubMed  Google Scholar 

  49. Gorgoraptis, N., Catalao, R. F. G., Bays, P. M. & Husain, M. Dynamic updating of working memory resources for visual objects. J. Neurosci. 31, 8502–8511 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Rajsic, J., Sun, S. Z., Huxtable, L., Pratt, J. & Ferber, S. Pop-out and pop-in: visual working memory advantages for unique items. Psychon. Bull. Rev. 23, 1787–1793 (2016).

    PubMed  Google Scholar 

  51. Klyszejko, Z., Rahmati, M. & Curtis, C. E. Attentional priority determines working memory precision. Vis. Res. 105, 70–76 (2014).

    PubMed  Google Scholar 

  52. Brissenden, J. A., Adkins, T. J., Hsu, Y. T. & Lee, T. G. Reward influences the allocation but not the availability of resources in visual working memory. J. Exp. Psychol. Gen. 152, 1825–1839 (2023).

    PubMed  Google Scholar 

  53. Gong, M. & Li, S. Learned reward association improves visual working memory. J. Exp. Psychol. Hum. Percept. Perform. 40, 841–856 (2014).

    PubMed  Google Scholar 

  54. Carandini, M. & Heeger, D. J. Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2012).

    CAS  Google Scholar 

  55. Lieder, F. & Griffiths, T. L.Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behav. Brain Sci. 43, e1 (2020).

    Google Scholar 

  56. Van den Berg, R. & Ma, W. J. A resource-rational theory of set size effects in human visual working memory. eLife 7, e34963 (2018).

    PubMed  PubMed Central  Google Scholar 

  57. Van den Berg, R., Zou, Q., Li, Y. & Ma, W. J. No effect of monetary reward in a visual working memory task. PLoS ONE 18, e0280257 (2023).

    PubMed  PubMed Central  Google Scholar 

  58. Bengson, J. J. & Luck, S. J. Effects of strategy on visual working memory capacity. Psychon. Bull. Rev. 23, 265–270 (2016).

    PubMed  PubMed Central  Google Scholar 

  59. Mystakidou, M. & van den Berg, R. More motivated but equally good: no effect of gamification on visual working memory performance. Preprint at bioRxiv https://doi.org/10.1101/2020.01.12.903203 (2020).

  60. Yoo, A. H. & Collins, A. G. How working memory and reinforcement learning are intertwined: a cognitive, neural, and computational perspective. J. Cogn. Neurosci. 34, 551–568 (2022).

    PubMed  Google Scholar 

  61. Collins, A. G. & Frank, M. J. How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis. Eur. J. Neurosci. 35, 1024–1035 (2012).

    PubMed  PubMed Central  Google Scholar 

  62. Collins, A. G. The tortoise and the hare: interactions between reinforcement learning and working memory. J. Cogn. Neurosci. 30, 1422–1432 (2018).

    PubMed  Google Scholar 

  63. Brewer, W. F. & Treyens, J. C. Role of schemata in memory for places. Cogn. Psychol. 13, 207–230 (1981).

    Google Scholar 

  64. Bates, C. J. & Jacobs, R. A.Efficient data compression in perception and perceptual memory. Psychol. Rev. 127, 891–917 (2020).

    PubMed  Google Scholar 

  65. Brady, T. F., Konkle, T. & Alvarez, G. A. Compression in visual working memory: using statistical regularities to form more efficient memory representations. J. Exp. Psychol. Gen. 138, 487–502 (2009).

    PubMed  Google Scholar 

  66. Orhan, A. E., Sims, C. R., Jacobs, R. A. & Knill, D. C. The adaptive nature of visual working memory. Curr. Direct. Psychol. Sci. 23, 164–170 (2014).

    Google Scholar 

  67. Sims, C. R., Jacobs, R. A. & Knill, D. C.An ideal observer analysis of visual working memory. Psychol. Rev. 119, 807–830 (2012).

    PubMed  PubMed Central  Google Scholar 

  68. Lew, T. F. & Vul, E. Ensemble clustering in visual working memory biases location memories and reduces the Weber noise of relative positions. J. Vis. 15, 10 (2015).

    PubMed  Google Scholar 

  69. Orhan, A. E. & Jacobs, R. A. A probabilistic clustering theory of the organization of visual short-term memory. Psychol. Rev. 120, 297–328 (2013).

    PubMed  Google Scholar 

  70. Brady, T. F. & Tenenbaum, J. B. A probabilistic model of visual working memory: incorporating higher order regularities into working memory capacity estimates. Psychol. Rev. 120, 85–109 (2013).

    PubMed  Google Scholar 

  71. Girshick, A. R., Landy, M. S. & Simoncelli, E. P. Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat. Neurosci. 14, 926–932 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Huttenlocher, J., Hedges, L. V., Corrigan, B. & Crawford, L. E. Spatial categories and the estimation of location. Cognition 93, 75–97 (2004).

    PubMed  Google Scholar 

  73. Ganguli, D. & Simoncelli, E. P.Eficient sensory encoding and Bayesian inference with heterogeneous neural populations. Neural Comput. 26, 2103–2134 (2014).

    PubMed  PubMed Central  Google Scholar 

  74. Wei, X.-X. & Stocker, A. A. A Bayesian observer model constrained by efficient coding can explain ‘anti-bayesian’ percepts. Nat. Neurosci. 18, 1509–1517 (2015).

    CAS  PubMed  Google Scholar 

  75. Morais, M. & Pillow, J. W. Power-law efficient neural codes provide general link between perceptual bias and discriminability. Adv. Neural Inform. Process. Syst. 31 (2018).

  76. Taylor, R. & Bays, P. M. Efficient coding in visual working memory accounts for stimulus-specific variations in recall. J. Neurosci. 38, 7132–7142 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Luck, S. J. & Vogel, E. K. The capacity of visual working memory for features and conjunctions. Nature 390, 279–281 (1997).

    CAS  PubMed  Google Scholar 

  78. Miller, G. A. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956).

    CAS  PubMed  Google Scholar 

  79. Simon, H. A. How big is a chunk? By combining data from several experiments, a basic human memory unit can be identified and measured. Science 183, 482–488 (1974).

    CAS  PubMed  Google Scholar 

  80. Bae, G.-Y., Olkkonen, M., Allred, S. R. & Flombaum, J. I.Why some colors appear more memorable than others: a model combining categories and particulars in color working memory. J. Exp. Psychol. Gen. 144, 744–763 (2015).

    PubMed  Google Scholar 

  81. Hardman, K. O., Vergauwe, E. & Ricker, T. J. Categorical working memory representations are used in delayed estimation of continuous colors. J. Exp. Psychol. Hum. Percept. Perform. 43, 30–54 (2017).

    PubMed  Google Scholar 

  82. Mathy, F. & Feldman, J. What’s magic about magic numbers? Chunking and data compression in short-term memory. Cognition 122, 346–362 (2012).

    PubMed  Google Scholar 

  83. Norris, D., Kalm, K. & Hall, J. Chunking and redintegration in verbal short-term memory. J. Exp. Psychol. Learn. Mem. Cogn. 46, 872–893 (2020).

    PubMed  Google Scholar 

  84. Ngiam, W. X., Brissenden, J. A. & Awh, E. “Memory compression” effects in visual working memory are contingent on explicit long-term memory. J. Exp. Psychol. Gen. 148, 1373–1385 (2019).

    PubMed  PubMed Central  Google Scholar 

  85. Alvarez, G. A. & Cavanagh, P. The capacity of visual short-term memory is set both by visual information load and by number of objects. Psychol. Sci. 15, 106–111 (2004).

    CAS  PubMed  Google Scholar 

  86. Asp, I. E., Störmer, V. S. & Brady, T. F. Greater visual working memory capacity for visually matched stimuli when they are perceived as meaningful. J. Cogn. Neurosci. 33, 902–918 (2021).

    PubMed  Google Scholar 

  87. Starr, A., Srinivasan, M. & Bunge, S. A. Semantic knowledge influences visual working memory in adults and children. PLoS ONE 15, e0241110 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Brady, T. F. & Störmer, V. S. The role of meaning in visual working memory: real-world objects, but not simple features, benefit from deeper processing. J. Exp. Psychol. Learn. Mem. Cogn. 48, 942–958 (2022).

    PubMed  Google Scholar 

  89. Kaiser, D., Stein, T. & Peelen, M. V. Real-world spatial regularities affect visual working memory for objects. Psychon. Bull. Rev. 22, 1784–1790 (2015).

    PubMed  Google Scholar 

  90. Hu, R. & Jacobs, R. A. Semantic influence on visual working memory of object identity and location. Cognition 217, 104891 (2021).

    PubMed  Google Scholar 

  91. O’Donnell, R. E., Clement, A. & Brockmole, J. R.Semantic and functional relationships among objects increase the capacity of visual working memory. J. Exp. Psychol. Learn. Mem. Cogn. 44, 1151–1158 (2018).

    PubMed  Google Scholar 

  92. Wickens, D. D. Encoding categories of words: an empirical approach to meaning. Psychol. Rev. 77, 1–15 (1970).

    Google Scholar 

  93. Park, I. M. & Pillow, J. W. Bayesian efficient coding. Preprint at bioRxiv https://doi.org/10.1101/178418 (2020).

  94. Weber, A. I., Krishnamurthy, K. & Fairhall, A. L. Coding principles in adaptation. Annu. Rev. Vis. Sci. 5, 427–449 (2019).

    PubMed  Google Scholar 

  95. Benucci, A., Saleem, A. B. & Carandini, M. Adaptation maintains population homeostasis in primary visual cortex. Nat. Neurosci. 16, 724–729 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Shin, H. & Ma, W. J. Visual short-term memory for oriented, colored objects. J. Vis. 17, 12 (2017).

    PubMed  PubMed Central  Google Scholar 

  97. Fougnie, D., Asplund, C. L. & Marois, R. What are the units of storage in visual working memory? J. Vis. 10, 27 (2010).

    PubMed  Google Scholar 

  98. Bays, P. M., Wu, E. Y. & Husain, M. Storage and binding of object features in visual working memory. Neuropsychologia 49, 1622–1631 (2011).

    PubMed  Google Scholar 

  99. Ye, C., Hu, Z., Ristaniemi, T., Gendron, M. & Liu, Q. Retro-dimension-cue benefit in visual working memory. Sci. Rep. 6, 35573 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Park, Y. E., Sy, J. L., Hong, S. W. & Tong, F. Reprioritization of features of multidimensional objects stored in visual working memory. Psychol. Sci. 28, 1773–1785 (2017).

    PubMed  PubMed Central  Google Scholar 

  101. Hajonides, J. E., van Ede, F., Stokes, M. G. & Nobre, A. C. Comparing the prioritization of items and feature-dimensions in visual working memory. J. Vis. 20, 25 (2020).

    PubMed  PubMed Central  Google Scholar 

  102. Palmer, J., Boston, B. & Moore, C. M. Limited capacity for memory tasks with multiple features within a single object. Atten. Percept. Psychophys. 77, 1488–1499 (2015).

    PubMed  PubMed Central  Google Scholar 

  103. Oberauer, K. & Eichenberger, S. Visual working memory declines when more features must be remembered for each object. Mem. Cogn. 41, 1212–1227 (2013).

    Google Scholar 

  104. Hardman, K. O. & Cowan, N. Remembering complex objects in visual working memory: do capacity limits restrict objects or features? J. Exp. Psychol. Learn. Mem. Cogn. 41, 325–347 (2015).

    PubMed  Google Scholar 

  105. Chen, H. & Wyble, B. Attribute amnesia reflects a lack of memory consolidation for attended information. J. Exp. Psychol. Hum. Percept. Perform. 42, 225–234 (2016).

    PubMed  Google Scholar 

  106. Wyble, B., Hess, M., O’Donnell, R. E., Chen, H. & Eitam, B. Learning how to exploit sources of information. Mem. Cogn. 47, 696–705 (2019).

    Google Scholar 

  107. Shin, H. & Ma, W. J. Crowdsourced single-trial probes of visual working memory for irrelevant features. J. Vis. 16, 10 (2016).

    PubMed  PubMed Central  Google Scholar 

  108. Swan, G., Collins, J. & Wyble, B. Memory for a single object has differently variable precisions for relevant and irrelevant features. J. Vis. 16, 32 (2016).

    PubMed  Google Scholar 

  109. Yu, Q. & Shim, W. M. Occipital, parietal, and frontal cortices selectively maintain task-relevant features of multi-feature objects in visual working memory. NeuroImage 157, 97–107 (2017).

    PubMed  Google Scholar 

  110. Bocincova, A. & Johnson, J. S. The time course of encoding and maintenance of task-relevant versus irrelevant object features in working memory. Cortex 111, 196–209 (2019).

    PubMed  Google Scholar 

  111. Marshall, L. & Bays, P. M. Obligatory encoding of task-irrelevant features depletes working memory resources. J. Vis. 13, 21 (2013).

    PubMed  PubMed Central  Google Scholar 

  112. Wang, B., Cao, X., Theeuwes, J., Olivers, C. N. L. & Wang, Z. Location-based effects underlie feature conjunction benefits in visual working memory. J. Vis. 16, 12 (2016).

    PubMed  Google Scholar 

  113. Markov, Y. A., Tiurina, N. A. & Utochkin, I. S. Different features are stored independently in visual working memory but mediated by object-based representations. Acta Psychol. 197, 52–63 (2019).

    Google Scholar 

  114. Brady, T. F., Konkle, T. & Alvarez, G. A. A review of visual memory capacity: beyond individual items and toward structured representations. J. Vis. 11, 4 (2011).

    PubMed  Google Scholar 

  115. Bays, P. M., Catalao, R. F. G. & Husain, M. The precision of visual working memory is set by allocation of a shared resource. J. Vis. 9, 7 (2009).

    Google Scholar 

  116. Huang, L. Distinguishing target biases and strategic guesses in visual working memory. Atten. Percept. Psychophys. 82, 1258–1270 (2020).

    PubMed  Google Scholar 

  117. Pratte, M. S. Swap errors in spatial working memory are guesses. Psychonom. Bull. Rev. 26, 958–966 (2019).

    Google Scholar 

  118. Rajsic, J. & Wilson, D. E. Asymmetrical access to color and location in visual working memory. Atten. Percept. Psychophys. 76, 1902–1913 (2014).

    PubMed  Google Scholar 

  119. Rajsic, J., Swan, G., Wilson, D. E. & Pratt, J. Accessibility limits recall from visual working memory. J. Exp. Psychol. Learn. Mem. Cogn. 43, 1415–1431 (2017).

    PubMed  Google Scholar 

  120. Bays, P. M. Evaluating and excluding swap errors in analogue tests of working memory. Sci. Rep. 6, 19203 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Emrich, S. M. & Ferber, S. Competition increases binding errors in visual working memory. J. Vis. 12, 12 (2012).

    PubMed  Google Scholar 

  122. Rerko, L., Oberauer, K. & Lin, H.-Y. Spatial transposition gradients in visual working memory. Q. J. Exp. Psychol. 67, 3–15 (2014).

    Google Scholar 

  123. Souza, A. S., Rerko, L., Lin, H.-Y. & Oberauer, K. Focused attention improves working memory: implications for flexible-resource and discrete-capacity models. Atten. Percept. Psychophys. 76, 2080–2102 (2014).

    PubMed  Google Scholar 

  124. Sahan, M. I., Dalmaijer, E. S., Verguts, T., Husain, M. & Fias, W. The graded fate of unattended stimulus representations in visuospatial working memory. Front. Psychol. 10, 374 (2019).

    PubMed  PubMed Central  Google Scholar 

  125. Wheeler, M. E. & Treisman, A. M. Binding in short-term visual memory. J. Exp. Psychol. Gen. 131, 48–64 (2002).

    PubMed  Google Scholar 

  126. McMaster, J. M., Tomić, I., Schneegans, S. & Bays, P. M. Swap errors in visual working memory are fully explained by cue-feature variability. Cogn. Psychol. 137, 101493 (2022).

    PubMed  PubMed Central  Google Scholar 

  127. Manohar, S. G., Zokaei, N., Fallon, S. J., Vogels, T. P. & Husain, M. Neural mechanisms of attending to items in working memory. Neurosci. Biobehav. Rev. 101, 1–12 (2019).

    PubMed  PubMed Central  Google Scholar 

  128. Lin, H.-Y. & Oberauer, K. An interference model for visual working memory: applications to the change detection task. Cogn. Psychol. 133, 101463 (2022).

    PubMed  Google Scholar 

  129. Hedayati, S., O’Donnell, R. E. & Wyble, B. A model of working memory for latent representations. Nat. Hum. Behav. 6, 709–719 (2022).

  130. Treisman, A. & Zhang, W. Location and binding in visual working memory. Mem. Cogn. 34, 1704–1719 (2006).

    Google Scholar 

  131. Huang, L. Unit of visual working memory: a Boolean map provides a better account than an object does. J. Exp. Psychol. Gen. 149, 1–30 (2020).

    PubMed  Google Scholar 

  132. Chen, H. & Wyble, B. The location but not the attributes of visual cues are automatically encoded into working memory. Vis. Res. 107, 76–85 (2015).

    PubMed  Google Scholar 

  133. Kondo, A. & Saiki, J. Feature-specific encoding flexibility in visual working memory. PLoS ONE 7, e50962 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Foster, J. J., Bsales, E. M., Jaffe, R. J. & Awh, E. Alpha-band activity reveals spontaneous representations of spatial position in visual working memory. Curr. Biol. 27, 3216–3223.e6 (2017).

    PubMed  PubMed Central  Google Scholar 

  135. Cai, Y., Sheldon, A. D., Yu, Q. & Postle, B. R. Overlapping and distinct contributions of stimulus location and of spatial context to nonspatial visual short-term memory. J. Neurophysiol. 121, 1222–1231 (2019).

    PubMed  PubMed Central  Google Scholar 

  136. Tam, J. & Wyble, B. Location has a privilege, but it is limited: evidence from probing task-irrelevant location. J. Exp. Psychol. Learn. Mem. Cogn. 49, 1051–1067 (2023).

  137. Golomb, J. D., Kupitz, C. N. & Thiemann, C. T. The influence of object location on identity: a ‘spatial congruency bias’. J. Exp. Psychol. Gen. 143, 2262–2278 (2014).

    PubMed  Google Scholar 

  138. Teng, C. & Postle, B. R. Spatial specificity of feature-based interaction between working memory and visual processing. J. Exp. Psychol. Hum. Percept. Perform. 47, 495–507 (2021).

  139. Parra, M. A. et al. Relational and conjunctive binding functions dissociate in short-term memory. Neurocase 21, 56–66 (2015).

    PubMed  Google Scholar 

  140. Piekema, C., Rijpkema, M., Fernández, G. & Kessels, R. P. Dissociating the neural correlates of intra-item and inter-item working-memory binding. PLoS ONE 5, e10214 (2010).

    PubMed  PubMed Central  Google Scholar 

  141. Fougnie, D. & Alvarez, G. A. Object features fail independently in visual working memory: evidence for a probabilistic feature-store model. J. Vis. 11, 3 (2011).

    PubMed  Google Scholar 

  142. Kovacs, O. & Harris, I. M. The role of location in visual feature binding. Atten. Percept. Psychophys. 81, 1551–1563 (2019).

    PubMed  Google Scholar 

  143. Markov, Y. A., Utochkin, I. S. & Brady, T. F. Real-world objects are not stored in holistic representations in visual working memory. J. Vis. 21, 18 (2021).

    PubMed  PubMed Central  Google Scholar 

  144. Schneegans, S., McMaster, J. M. V. & Bays, P. M. Role of time in binding features in visual working memory. Psychol. Rev. 130, 137–154 (2023).

    PubMed  Google Scholar 

  145. Heuer, A. & Rolfs, M. Incidental encoding of visual information in temporal reference frames in working memory. Cognition 207, 104526 (2021).

    PubMed  Google Scholar 

  146. Heuer, A. & Rolfs, M.Temporal and spatial reference frames in visual working memory are defined by ordinal and relational properties. J. Exp. Psychol. Learn. Mem. Cogn. 49, 1361–1375 (2023).

    PubMed  Google Scholar 

  147. Bowman, H. & Wyble, B. The simultaneous type, serial token model of temporal attention and working memory. Psychol. Rev. 114, 38–70 (2007).

    PubMed  Google Scholar 

  148. Sone, H., Kang, M.-S., Li, A. Y., Tsubomi, H. & Fukuda, K. Simultaneous estimation procedure reveals the object-based, but not space-based, dependence of visual working memory representations. Cognition 209, 104579 (2021).

    PubMed  Google Scholar 

  149. Brown, G., Kasem, I., Bays, P. M. & Schneegans, S. Mechanisms of feature binding in visual working memory are stable over long delays. J. Vis. 21, 7 (2021).

    PubMed  PubMed Central  Google Scholar 

  150. Read, C. A., Rogers, J. M. & Wilson, P. H. Working memory binding of visual object features in older adults. Aging Neuropsychol. Cogn. 23, 263–281 (2016).

    Google Scholar 

  151. Rhodes, S., Parra, M. A., Cowan, N. & Logie, R. H. Healthy aging and visual working memory: the effect of mixing feature and conjunction changes. Psychol. Aging 32, 354–366 (2017).

    PubMed  Google Scholar 

  152. Pertzov, Y., Heider, M., Liang, Y. & Husain, M. Effects of healthy ageing on precision and binding of object location in visual short term memory. Psychol. Aging 30, 26–35 (2015).

    PubMed  Google Scholar 

  153. Della Sala, S., Parra, M. A., Fabi, K., Luzzi, S. & Abrahams, S. Short-term memory binding is impaired in AD but not in non-AD dementias. Neuropsychologia 50, 833–840 (2012).

    PubMed  Google Scholar 

  154. Lugtmeijer, S. et al. Consequence of stroke for feature recall and binding in visual working memory. Neurobiol. Learn. Mem. 179, 107387 (2021).

    PubMed  Google Scholar 

  155. Liang, Y. et al. Visual short-term memory binding deficit in familial Alzheimer’s disease. Cortex 78, 150–164 (2016).

    PubMed  PubMed Central  Google Scholar 

  156. Martínez, J. F., Trujillo, C., Arévalo, A., Ibáñez, A. & Cardona, J. F. Assessment of conjunctive binding in aging: a promising approach for Alzheimer’s disease detection. J. Alzheimers Dis. 69, 71–81 (2019).

    PubMed  Google Scholar 

  157. Fornaciai, M. & Park, J. Attractive serial dependence between memorized stimuli. Cognition 200, 104250 (2020).

    PubMed  Google Scholar 

  158. Czoschke, S., Peters, B., Rahm, B., Kaiser, J. & Bledowski, C. Visual objects interact differently during encoding and memory maintenance. Atten. Percept. Psychophys. 82, 1241–1257 (2020).

    PubMed  Google Scholar 

  159. Teng, C., Fulvio, J. M., Jiang, J. & Postle, B. R. Flexible top-down control in the interaction between working memory and perception. J. Vis. 22, 3 (2022).

    PubMed  PubMed Central  Google Scholar 

  160. Webster, M. A. Visual adaptation. Annu. Rev. Vis. Sci. 1, 547–567 (2015).

    PubMed  PubMed Central  Google Scholar 

  161. Cicchini, G. M., Benedetto, A. & Burr, D. C. Perceptual history propagates down to early levels of sensory analysis. Curr. Biol. 31, 1245–1250.e2 (2021).

    PubMed  PubMed Central  Google Scholar 

  162. Kiyonaga, A., Scimeca, J. M., Bliss, D. P. & Whitney, D. Serial dependence across perception, attention, and memory. Trends Cogn. Sci. 21, 493–497 (2017).

    PubMed  PubMed Central  Google Scholar 

  163. Bliss, D. P., Sun, J. J. & D’Esposito, M. Serial dependence is absent at the time of perception but increases in visual working memory. Sci. Rep. 7, 14739 (2017).

    PubMed  PubMed Central  Google Scholar 

  164. Barbosa, J. & Compte, A. Build-up of serial dependence in color working memory. Sci. Rep. 10, 10959 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. Fritsche, M., Mostert, P. & de Lange, F. P. Opposite effects of recent history on perception and decision. Curr. Biol. 27, 590–595 (2017).

    CAS  PubMed  Google Scholar 

  166. Bergen, R. S. V. & Jehee, J. F. M.Probabilistic representation in human visual cortex reflects uncertainty in serial decisions. J. Neurosci. 39, 8164–8176 (2019).

    PubMed  PubMed Central  Google Scholar 

  167. Fritsche, M., Spaak, E. & de Lange, F. P. A Bayesian and efficient observer model explains concurrent attractive and repulsive history biases in visual perception. eLife 9, e55389 (2020).

    PubMed  PubMed Central  Google Scholar 

  168. Cicchini, G. M., Mikellidou, K. & Burr, D. C. The functional role of serial dependence. Proc. R. Soc. B 285, 20181722 (2018).

    PubMed  PubMed Central  Google Scholar 

  169. Bae, G.-Y. & Luck, S. J. Interactions between visual working memory representations. Atten. Percept. Psychophys. 79, 2376–2395 (2017).

    PubMed  PubMed Central  Google Scholar 

  170. Czoschke, S., Fischer, C., Beitner, J., Kaiser, J. & Bledowski, C. Two types of serial dependence in visual working memory. Br. J. Psychol. 110, 256–267 (2019).

    PubMed  Google Scholar 

  171. Kang, M.-S. & Choi, J. Retrieval-induced inhibition in short-term memory. Psychol. Sci. 26, 1014–1025 (2015).

    PubMed  Google Scholar 

  172. Lively, Z., Robinson, M. M. & Benjamin, A. S. Memory fidelity reveals qualitative changes in interactions between items in visual working memory. Psychol. Sci. 32, 1426–1441 (2021).

    PubMed  Google Scholar 

  173. Chunharas, C., Rademaker, R. L., Brady, T. F. & Serences, J. T. An adaptive perspective on visual working memory distortions. J. Exp. Psychol. Gen. 151, 2300–2323 (2022).

    PubMed  PubMed Central  Google Scholar 

  174. Scotti, P. S., Hong, Y., Golomb, J. D. & Leber, A. B. Statistical learning as a reference point for memory distortions: swap and shift errors. Atten. Percept. Psychophys. 83, 1652–1672 (2021).

    PubMed  Google Scholar 

  175. Dubé, C., Zhou, F., Kahana, M. J. & Sekuler, R. Similarity-based distortion of visual short-term memory is due to perceptual averaging. Vis. Res. 96, 8–16 (2014).

    PubMed  Google Scholar 

  176. Brady, T. F. & Alvarez, G. A. Hierarchical encoding in visual working memory: ensemble statistics bias memory for individual items. Psychol. Sci. 22, 384–392 (2011).

    PubMed  Google Scholar 

  177. Papenmeier, F. & Timm, J. D. Do group ensemble statistics bias visual working memory for individual items? A registered replication of Brady and Alvarez (2011). Atten. Percept. Psychophys. 83, 1329–1336 (2021).

    PubMed  Google Scholar 

  178. Sheth, B. R. & Shimojo, S. Compression of space in visual memory. Vis. Res. 41, 329–341 (2001).

    CAS  PubMed  Google Scholar 

  179. Luu, L. & Stocker, A. A. Categorical judgments do not modify sensory representations in working memory. PLoS Comput. Biol. 17, e1008968 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  180. Rademaker, R. L., Park, Y. E., Sack, A. T. & Tong, F. Evidence of gradual loss of precision for simple features and complex objects in visual working memory. J. Exp. Psychol. Hum. Percept. Perform. 44, 925–940 (2018).

    PubMed  PubMed Central  Google Scholar 

  181. Schneegans, S. & Bays, P. M. Drift in neural population activity causes working memory to deteriorate over time. J. Neurosci. 38, 4859–4869 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  182. Shin, H., Zou, Q. & Ma, W. J. The effects of delay duration on visual working memory for orientation. J. Vis. 17, 10 (2017).

    PubMed  PubMed Central  Google Scholar 

  183. Compte, A., Brunel, N., Goldman-Rakic, P. S. & Wang, X.-J. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10, 910–923 (2000).

    CAS  PubMed  Google Scholar 

  184. Wei, Z., Wang, X.-J. & Wang, D.-H. From distributed resources to limited slots in multiple-item working memory: a spiking network model with normalization. J. Neurosci. 32, 11228–11240 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  185. Wimmer, K., Nykamp, D. Q., Constantinidis, C. & Compte, A. Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory. Nat. Neurosci. 17, 431–439 (2014).

    CAS  PubMed  Google Scholar 

  186. Lim, P. C., Ward, E. J., Vickery, T. J. & Johnson, M. R. Not-so-working memory: drift in functional magnetic resonance imaging pattern representations during maintenance predicts errors in a visual working memory task. J. Cogn. Neurosci. 31, 1520–1534 (2019).

    PubMed  Google Scholar 

  187. Wolff, M. J., Jochim, J., Akyürek, E. G., Buschman, T. J. & Stokes, M. G. Drifting codes within a stable coding scheme for working memory. PLoS Biol. 18, e3000625 (2020).

    PubMed  PubMed Central  Google Scholar 

  188. Kuuramo, C., Saarinen, J. & Kurki, I. Forgetting in visual working memory: internal noise explains decay of feature representations. J. Vis. 22, 8 (2022).

    PubMed  PubMed Central  Google Scholar 

  189. Panichello, M. F., DePasquale, B., Pillow, J. W. & Buschman, T. J. Error-correcting dynamics in visual working memory. Nat. Commun. 10, 3366 (2019).

    PubMed  PubMed Central  Google Scholar 

  190. Carroll, S., Josić, K. & Kilpatrick, Z. P. Encoding certainty in bump attractors. J. Comput. Neurosci. 37, 29–48 (2014).

    PubMed  Google Scholar 

  191. Kutschireiter, A., Basnak, M. A., Wilson, R. I. & Drugowitsch, J. Bayesian inference in ring attractor networks. Proc. Natl Acad. Sci. USA 120, e2210622120 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  192. Orhan, A. E. & Ma, W. J. A diverse range of factors affect the nature of neural representations underlying short-term memory. Nat. Neurosci. 22, 275–283 (2019).

    CAS  PubMed  Google Scholar 

  193. Pertzov, Y., Manohar, S. & Husain, M. Rapid forgetting results from competition over time between items in visual working memory. J. Exp. Psychol. Learn. Mem. Cogn. 43, 528–536 (2017).

    PubMed  Google Scholar 

  194. Koyluoglu, O. O., Pertzov, Y., Manohar, S., Husain, M. & Fiete, I. R. Fundamental bound on the persistence and capacity of short-term memory stored as graded persistent activity. eLife 6, e22225 (2017).

    PubMed  PubMed Central  Google Scholar 

  195. Bouchacourt, F. & Buschman, T. J. A flexible model of working memory. Neuron 103, 147���160.e8 (2019).

    PubMed  PubMed Central  Google Scholar 

  196. Almeida, R., Barbosa, J. & Compte, A. Neural circuit basis of visuo-spatial working memory precision: a computational and behavioral study. J. Neurophysiol. 114, 1806–1818 (2015).

    PubMed  PubMed Central  Google Scholar 

  197. Johnson, J. S., van Lamsweerde, A. E., Dineva, E. & Spencer, J. P. Neural interactions in working memory explain decreased recall precision and similarity-based feature repulsion. Sci. Rep. 12, 17756 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  198. Fuster, J. M. & Alexander, G. E. Neuron activity related to short-term memory. Science 173, 652–654 (1971).

    CAS  PubMed  Google Scholar 

  199. Funahashi, S., Bruce, C. J. & Goldman-Rakic, P. S. Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349 (1989).

    CAS  PubMed  Google Scholar 

  200. Hart, E. & Huk, A. C. Recurrent circuit dynamics underlie persistent activity in the macaque frontoparietal network. eLife 9, e52460 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  201. Kamiński, J. et al. Persistently active neurons in human medial frontal and medial temporal lobe support working memory. Nat. Neurosci. 20, 590–601 (2017).

    PubMed  PubMed Central  Google Scholar 

  202. Kornblith, S., Quian Quiroga, R., Koch, C., Fried, I. & Mormann, F. Persistent single-neuron activity during working memory in the human medial temporal lobe. Curr. Biol. 27, 1026–1032 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  203. Brouwer, G. J. & Heeger, D. J. Decoding and reconstructing color from responses in human visual cortex. J. Neurosci. 29, 13992–14003 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  204. Ester, E. F., Anderson, D. E., Serences, J. T. & Awh, E. A neural measure of precision in visual working memory. J. Cogn. Neurosci. 25, 754–761 (2013).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  206. Wolff, M. J., Jochim, J., Akyürek, E. G. & Stokes, M. G. Dynamic hidden states underlying working-memory-guided behavior. Nat. Neurosci. 20, 864–871 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  207. Sreenivasan, K. K., Vytlacil, J. & D’Esposito, M. Distributed and dynamic storage of working memory stimulus information in extrastriate cortex. J. Cogn. Neurosci. 26, 1141–1153 (2014).

    PubMed  PubMed Central  Google Scholar 

  208. Meyers, E. M., Freedman, D. J., Kreiman, G., Miller, E. K. & Poggio, T. Dynamic population coding of category information in inferior temporal and prefrontal cortex. J. Neurophysiol. 100, 1407–1419 (2008).

    PubMed  PubMed Central  Google Scholar 

  209. Cavanagh, S. E., Towers, J. P., Wallis, J. D., Hunt, L. T. & Kennerley, S. W. Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex. Nat. Commun. 9, 3498 (2018).

    PubMed  PubMed Central  Google Scholar 

  210. Coltheart, M. Iconic memory and visible persistence. Percept. Psychophys. 27, 183–228 (1980).

    CAS  PubMed  Google Scholar 

  211. 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 

  212. Postle, B. R. in Mechanisms of Sensory Working Memory 43–58 (Elsevier, 2015).

  213. Baeg, E. et al. Dynamics of population code for working memory in the prefrontal cortex. Neuron 40, 177–188 (2003).

    CAS  PubMed  Google Scholar 

  214. MacDonald, C. J., Lepage, K. Q., Eden, U. T. & Eichenbaum, H. Hippocampal ‘time cells’ bridge the gap in memory for discontiguous events. Neuron 71, 737–749 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  215. Scott, B. B. et al. Fronto-parietal cortical circuits encode accumulated evidence with a diversity of timescales. Neuron 95, 385–398.e5 (2017).

    PubMed  PubMed Central  Google Scholar 

  216. 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 

  217. Parthasarathy, A. et al. Time-invariant working memory representations in the presence of code-morphing in the lateral prefrontal cortex. Nat. Commun. 10, 4995 (2019).

    PubMed  PubMed Central  Google Scholar 

  218. Spaak, E., Watanabe, K., Funahashi, S. & Stokes, M. G. Stable and dynamic coding for working memory in primate prefrontal cortex. J. Neurosci. 37, 6503–6516 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  219. Cueva, C. J. et al. Low-dimensional dynamics for working memory and time encoding. Proc. Natl Acad. Sci. USA 117, 23021–23032 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  220. Oberauer, K. Access to information in working memory: exploring the focus of attention. J. Exp. Psychol. Learn. Mem. Cogn. 28, 411–421 (2002).

    PubMed  Google Scholar 

  221. 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 

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

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  224. 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 

  225. LaRocque, J. J., Riggall, A. C., Emrich, S. M. & Postle, B. R. Within-category decoding of information in different attentional states in short-term memory. Cereb. Cortex 27, 4881–4890 (2017).

    PubMed  Google Scholar 

  226. 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 

  227. 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 

  228. 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 

  229. Bocincova, A., Buschman, T. J., Stokes, M. G. & Manohar, S. G. Neural signature of flexible coding in prefrontal cortex. Proc. Natl Acad. Sci. USA 119, e2200400119 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  230. Masse, N. Y., Yang, G. R., Song, H. F., Wang, X.-J. & Freedman, D. J. Circuit mechanisms for the maintenance and manipulation of information in working memory. Nat. Neurosci. 22, 1159–1167 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  231. Van Loon, A. M., Olmos-Solis, K., Fahrenfort, J. J. & Olivers, C. N. Current and future goals are represented in opposite patterns in object-selective cortex. eLife 7, e38677 (2018).

    PubMed  PubMed Central  Google Scholar 

  232. Yu, Q., Teng, C. & Postle, B. R. Different states of priority recruit different neural representations in visual working memory. PLoS Biol. 18, e3000769 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  233. Wan, Q., Menendez, J. A. & Postle, B. R. Priority-based transformations of stimulus representation in visual working memory. PLoS Comput. Biol. 18, e1009062 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  234. Christophel, T. B., Iamshchinina, P., Yan, C., Allefeld, C. & Haynes, J.-D. Cortical specialization for attended versus unattended working memory. Nat. Neurosci. 21, 494–496 (2018).

    CAS  PubMed  Google Scholar 

  235. Iamshchinina, P., Christophel, T. B., Gayet, S. & Rademaker, R. L. Essential considerations for exploring visual working memory storage in the human brain. Vis. Cogn. 29, 425–436 (2021).

    Google Scholar 

  236. Barbosa, J., Lozano-Soldevilla, D. & Compte, A. Pinging the brain with visual impulses reveals electrically active, not activity-silent, working memories. PLoS Biol. 19, e3001436 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  237. Schneegans, S. & Bays, P. M. Restoration of fMRI decodability does not imply latent working memory states. J. Cogn. Neurosci. 29, 1977–1994 (2017).

    PubMed  PubMed Central  Google Scholar 

  238. Vogel, E. K. & Machizawa, M. G. Neural activity predicts individual differences in visual working memory capacity. Nature 428, 748–751 (2004).

    CAS  PubMed  Google Scholar 

  239. Luria, R., Balaban, H., Awh, E. & Vogel, E. K. The contralateral delay activity as a neural measure of visual working memory. Neurosci. Biobehav. Rev. 62, 100–108 (2016).

    PubMed  PubMed Central  Google Scholar 

  240. Bays, P. M. Reassessing the evidence for capacity limits in neural signals related to working memory. Cereb. Cortex 28, 1432–1438 (2018).

    PubMed  PubMed Central  Google Scholar 

  241. Adam, K. C. S., Vogel, E. K. & Awh, E. Multivariate analysis reveals a generalizable human electrophysiological signature of working memory load. Psychophysiology 57, e13691 (2020).

    PubMed  PubMed Central  Google Scholar 

  242. Emrich, S. M., Riggall, A. C., LaRocque, J. J. & Postle, B. R. Distributed patterns of activity in sensory cortex reflect the precision of multiple items maintained in visual short-term memory. J. Neurosci. 33, 6516–6523 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  243. Sutterer, D. W., Foster, J. J., Adam, K. C. S., Vogel, E. K. & Awh, E. Item-specific delay activity demonstrates concurrent storage of multiple active neural representations in working memory. PLoS Biol. 17, e3000239 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  244. Beukers, A. O., Buschman, T. J., Cohen, J. D. & Norman, K. A. Is activity silent working memory simply episodic memory? Trends Cogn. Sci. 25, 284–293 (2021).

    PubMed  Google Scholar 

  245. Foster, J. J., Vogel, E. K. & Awh, E. in Oxford Handbook of Human Memory (eds Kahana, M. J. & Wagner, A. D.) Ch. 13 (Oxford Univ. Press, 2019).

  246. Riley, M. R. & Constantinidis, C. Role of prefrontal persistent activity in working memory. Front. Syst. Neurosci. 9, 181 (2015).

    PubMed  Google Scholar 

  247. D’Esposito, M. & Postle, B. R. The cognitive neuroscience of working memory. Annu. Rev. Psychol. 66, 115–142 (2015).

    PubMed  Google Scholar 

  248. Xu, Y. Revisit once more the sensory storage account of visual working memory. Vis. Cogn. 28, 433–446 (2020).

    PubMed  PubMed Central  Google Scholar 

  249. Serences, J. T. Neural mechanisms of information storage in visual short-term memory. Vis. Res. 128, 53–67 (2016).

    PubMed  Google Scholar 

  250. Stokes, M. G., Muhle-Karbe, P. S. & Myers, N. E. Theoretical distinction between functional states in working memory and their corresponding neural states. Vis. Cogn. 28, 420–432 (2020).

    PubMed  PubMed Central  Google Scholar 

  251. Cowan, N. The focus of attention as observed in visual working memory tasks: making sense of competing claims. Neuropsychologia 49, 1401–1406 (2011).

    PubMed  PubMed Central  Google Scholar 

  252. Olivers, C. N., 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 

  253. Ort, E., Fahrenfort, J. J. & Olivers, C. N. L. Lack of free choice reveals the cost of multiple-target search within and across feature dimensions. Atten. Percept. Psychophys. 80, 1904–1917 (2018).

    PubMed  Google Scholar 

  254. Beck, V. M., Hollingworth, A. & Luck, S. J. Simultaneous control of attention by multiple working memory representations. Psychol. Sci. 23, 887–898 (2012).

    PubMed  Google Scholar 

  255. Bahle, B., Thayer, D. D., Mordkoff, J. T. & Hollingworth, A. The architecture of working memory: features from multiple remembered objects produce parallel, coactive guidance of attention in visual search. J. Exp. Psychol. Gen. 149, 967–983 (2020).

    PubMed  Google Scholar 

  256. Ort, E., Fahrenfort, J. J., ten Cate, T., Eimer, M. & Olivers, C. N. Humans can efficiently look for but not select multiple visual objects. eLife 8, e49130 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  257. Williams, J. R., Brady, T. F. & Störmer, V. S. Guidance of attention by working memory is a matter of representational fidelity. J. Exp. Psychol. Hum. Percept. Perform. 48, 202–231 (2022).

    PubMed  Google Scholar 

  258. Lundqvist, M., Compte, A. & Lansner, A. Bistable, irregular firing and population oscillations in a modular attractor memory network. PLoS Comput. Biol. 6, e1000803 (2010).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  260. Fiebig, F. & Lansner, A. A spiking working memory model based on Hebbian short-term potentiation. J. Neurosci. 37, 83–96 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  261. Shafi, M. et al. Variability in neuronal activity in primate cortex during working memory tasks. Neuroscience 146, 1082–1108 (2007).

    CAS  PubMed  Google Scholar 

  262. Lundqvist, M., Herman, P. & Miller, E. K. Working memory: delay activity, yes! Persistent activity? Maybe not. J. Neurosci. 38, 7013–7019 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  263. Constantinidis, C. et al. Persistent spiking activity underlies working memory. J. Neurosci. 38, 7020–7028 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  264. Pomper, U. & Ansorge, U. Theta-rhythmic oscillation of working memory performance. Psychol. Sci. 32, 1801–1810 (2021).

    PubMed  Google Scholar 

  265. Cohen, M., Keefe, J. M. & Brady, T. Perceptual awareness occurs along a graded continuum: no evidence of all-or-none failures in continuous reproduction tasks. Psychol. Sci. 34, 1033 (2023).

  266. Taylor, R. & Bays, P. M. Theory of neural coding predicts an upper bound on estimates of memory variability. Psychol. Rev. 127, 700–718 (2020).

    PubMed  PubMed Central  Google Scholar 

  267. Zhou, Y., Curtis, C. E., Sreenivasan, K. & Fougnie, D. Common neural mechanisms control attention and working memory. J. Neurosci. 42, 7110–7120 (2022).

    CAS  PubMed  Google Scholar 

  268. Rademaker, R. L., Chunharas, C. & Serences, J. T. Coexisting representations of sensory and mnemonic information in human visual cortex. Nat. Neurosci. 22, 1336–1344 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  269. Miner, A. E., Schurgin, M. W. & Brady, T. F.Is working memory inherently more ‘precise’ than long-term memory? Extremely high fidelity visual long-term memories for frequently encountered objects. J. Exp. Psychol. Hum. Percept. Perform. 46, 813–830 (2020).

    PubMed  Google Scholar 

  270. Draschkow, D., Kallmayer, M. & Nobre, A. C. When natural behavior engages working memory. Curr. Biol. 31, 869–874.e5 (2021).

    PubMed  PubMed Central  Google Scholar 

  271. Kristjánsson, Á. & Draschkow, D. Keeping it real: looking beyond capacity limits in visual cognition. Atten. Percept. Psychophys. 83, 1375–1390 (2021).

    PubMed  PubMed Central  Google Scholar 

  272. Issen, L. A. & Knill, D. C.Decoupling eye and hand movement control: visual short-term memory influences reach planning more than saccade planning. J. Vis. 12, 3 (2012).

    PubMed  Google Scholar 

  273. 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 

  274. Awh, E., Barton, B. & Vogel, E. K. Visual working memory represents a fixed number of items regardless of complexity. Psychol. Sci. 18, 622–628 (2007).

    PubMed  Google Scholar 

  275. Pratte, M. S. Set size effects on working memory precision are not due to an averaging of slots. Atten. Percept. Psychophys. 82, 2937–2949 (2020).

    PubMed  Google Scholar 

  276. Bays, P. M. Failure of self-consistency in the discrete resource model of visual working memory. Cogn. Psychol. 105, 1–8 (2018).

    PubMed  PubMed Central  Google Scholar 

  277. Devkar, D. T., Wright, A. A. & Ma, W. J. The same type of visual working memory limitations in humans and monkeys. J. Vis. 15, 13 (2015).

    PubMed  PubMed Central  Google Scholar 

  278. Pratte, M. S., Park, Y. E., Rademaker, R. L. & Tong, F.Accounting for stimulus-specific variation in precision reveals a discrete capacity limit in visual working memory. J. Exp. Psychol. Hum. Percept. Perform. 43, 6–17 (2017).

    PubMed  PubMed Central  Google Scholar 

  279. Pashler, H. Familiarity and visual change detection. Percept. Psychophys. 44, 369–378 (1988).

    CAS  PubMed  Google Scholar 

  280. Oostwoud Wijdenes, L., Marshall, L. & Bays, P. M. Evidence for optimal integration of visual feature representations across saccades. J. Neurosci. 35, 10146–10153 (2015).

    PubMed  PubMed Central  Google Scholar 

  281. Wolf, C. & Schütz, A. C. Trans-saccadic integration of peripheral and foveal feature information is close to optimal. J. Vis. 15, 1 (2015).

    PubMed  Google Scholar 

  282. Ganmor, E., Landy, M. S. & Simoncelli, E. P. Near-optimal integration of orientation information across saccades. J. Vis. 15, 8 (2015).

    PubMed  PubMed Central  Google Scholar 

  283. Kong, G., Kroell, L. M., Schneegans, S., Aagten-Murphy, D. & Bays, P. M. Transsaccadic integration relies on a limited memory resource. J. Vis. 21, 24 (2021).

    PubMed  PubMed Central  Google Scholar 

  284. Stewart, E. E. M. & Schütz, A. C. Optimal trans-saccadic integration relies on visual working memory. Vis. Res. 153, 70–81 (2018).

    PubMed  Google Scholar 

  285. Stewart, E. E. M. & Schütz, A. C. Transsaccadic integration benefits are not limited to the saccade target. J. Neurophysiol. 122, 1491–1501 (2019).

    PubMed  PubMed Central  Google Scholar 

  286. Ohl, S. & Rolfs, M. Saccadic eye movements impose a natural bottleneck on visual short-term memory. J. Exp. Psychol. Learn. Mem. Cogn. 43, 736–748 (2017).

    PubMed  Google Scholar 

  287. Udale, R., Tran, M. T., Manohar, S. & Husain, M. Dynamic in-flight shifts of working memory resources across saccades. J. Exp. Psychol. Hum. Percept. Perform. 48, 21–36 (2022).

    PubMed  PubMed Central  Google Scholar 

  288. Shao, N. et al. Saccades elicit obligatory allocation of visual working memory. Mem. Cogn. 38, 629–640 (2010).

    Google Scholar 

  289. Hanning, N. M., Jonikaitis, D., Deubel, H. & Szinte, M. Oculomotor selection underlies feature retention in visual working memory. J. Neurophysiol. 115, 1071–1076 (2016).

    PubMed  Google Scholar 

  290. Heuer, A., Ohl, S. & Rolfs, M. Memory for action: a functional view of selection in visual working memory. Vis. Cogn. 28, 388–400 (2020).

    Google Scholar 

  291. Chen, Y. & Crawford, J. D. Allocentric representations for target memory and reaching in human cortex. Ann. NY Acad. Sci. 1464, 142–155 (2020).

    PubMed  Google Scholar 

  292. Aagten-Murphy, D. & Bays, P. M. Functions of memory across saccadic eye movements. Curr. Top. Behav. Neurosci. 41, 155–183 (2019).

    PubMed  Google Scholar 

  293. Hanning, N. M. & Deubel, H. Independent effects of eye and hand movements on visual working memory. Front. Syst. Neurosci. 12, 37 (2018).

    PubMed  PubMed Central  Google Scholar 

  294. Heuer, A., Crawford, J. D. & Schubö, A. Action relevance induces an attentional weighting of representations in visual working memory. Mem. Cogn. 45, 413–427 (2017).

    Google Scholar 

  295. Heuer, A. & Schubö, A. Separate and combined effects of action relevance and motivational value on visual working memory. J. Vis. 18, 14 (2018).

    PubMed  Google Scholar 

  296. Byrne, P. A. & Crawford, J. D. Cue reliability and a landmark stability heuristic determine relative weighting between egocentric and allocentric visual information in memory-guided reach. J. Neurophysiol. 103, 3054–3069 (2010).

    PubMed  Google Scholar 

  297. Fiehler, K., Wolf, C., Klinghammer, M. & Blohm, G. Integration of egocentric and allocentric information during memory-guided reaching to images of a natural environment. Front. Hum. Neurosci. 8, 636 (2014).

    PubMed  PubMed Central  Google Scholar 

  298. Aagten-Murphy, D. & Bays, P. M. Independent working memory resources for egocentric and allocentric spatial information. PLoS Comput. Biol. 15, e1006563 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

P.M.B. was supported by a personal fellowship from the Wellcome Trust (grant number 106926). T.F.B. was supported by NSF BCS-2141189 and NSF BCS-2146988.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed equally to this work.

Corresponding author

Correspondence to Timothy F. Brady.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Bradley Postle and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bays, P.M., Schneegans, S., Ma, W.J. et al. Representation and computation in visual working memory. Nat Hum Behav 8, 1016–1034 (2024). https://doi.org/10.1038/s41562-024-01871-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-024-01871-2

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing