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Neural activity predicts individual differences in visual working memory capacity

Author

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  • Edward K. Vogel

    (University of Oregon)

  • Maro G. Machizawa

    (University of Oregon)

Abstract

Contrary to our rich phenomenological visual experience, our visual short-term memory system can maintain representations of only three to four objects at any given moment1,2. For over a century, the capacity of visual memory has been shown to vary substantially across individuals, ranging from 1.5 to about 5 objects3,4,5,6,7. Although numerous studies have recently begun to characterize the neural substrates of visual memory processes8,9,10,11,12, a neurophysiological index of storage capacity limitations has not yet been established. Here, we provide electrophysiological evidence for lateralized activity in humans that reflects the encoding and maintenance of items in visual memory. The amplitude of this activity is strongly modulated by the number of objects being held in the memory at the time, but approaches a limit asymptotically for arrays that meet or exceed storage capacity. Indeed, the precise limit is determined by each individual's memory capacity, such that the activity from low-capacity individuals reaches this plateau much sooner than that from high-capacity individuals. Consequently, this measure provides a strong neurophysiological predictor of an individual's capacity, allowing the demonstration of a direct relationship between neural activity and memory capacity.

Suggested Citation

  • Edward K. Vogel & Maro G. Machizawa, 2004. "Neural activity predicts individual differences in visual working memory capacity," Nature, Nature, vol. 428(6984), pages 748-751, April.
  • Handle: RePEc:nat:nature:v:428:y:2004:i:6984:d:10.1038_nature02447
    DOI: 10.1038/nature02447
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    Cited by:

    1. Chiara Francesca Tagliabue & Debora Brignani & Veronica Mazza, 2019. "Does numerical similarity alter age-related distractibility in working memory?," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-19, September.
    2. Jifan Zhou & Jun Yin & Tong Chen & Xiaowei Ding & Zaifeng Gao & Mowei Shen, 2011. "Visual Working Memory Capacity Does Not Modulate the Feature-Based Information Filtering in Visual Working Memory," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-10, September.
    3. Shaiyan Keshvari & Ronald van den Berg & Wei Ji Ma, 2013. "No Evidence for an Item Limit in Change Detection," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-9, February.
    4. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini, 2023. "Multinomial Logit Processes and Preference Discovery: Inside and Outside the Black Box," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(3), pages 1155-1194.
    5. S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    6. Roth, Volker & Richter, Kai, 2006. "How to fend off shoulder surfing," Journal of Banking & Finance, Elsevier, vol. 30(6), pages 1727-1751, June.
    7. Durk Talsma & Jonne J Sikkens & Jan Theeuwes, 2011. "Stay Tuned: What Is Special About Not Shifting Attention?," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-14, March.
    8. Philipp Singer & Emilio Ferrara & Farshad Kooti & Markus Strohmaier & Kristina Lerman, 2016. "Evidence of Online Performance Deterioration in User Sessions on Reddit," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
    9. Pahor, Anja & JauĊĦovec, Norbert, 2017. "Multifaceted pattern of neural efficiency in working memory capacity," Intelligence, Elsevier, vol. 65(C), pages 23-34.
    10. Carlo Baldassi & Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2020. "A Behavioral Characterization of the Drift Diffusion Model and Its Multialternative Extension for Choice Under Time Pressure," Management Science, INFORMS, vol. 66(11), pages 5075-5093, November.
    11. Zaikin, Oleg & Kushtina, Emma & Rozewski, Przemyslaw, 2006. "Model and algorithm of the conceptual scheme formation for knowledge domain in distance learning," European Journal of Operational Research, Elsevier, vol. 175(3), pages 1379-1399, December.
    12. Veronica Mazza & Alfonso Caramazza, 2012. "Perceptual Grouping and Visual Enumeration," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    13. Daniel Pacheco-Estefan & Marie-Christin Fellner & Lukas Kunz & Hui Zhang & Peter Reinacher & Charlotte Roy & Armin Brandt & Andreas Schulze-Bonhage & Linglin Yang & Shuang Wang & Jing Liu & Gui Xue & , 2024. "Maintenance and transformation of representational formats during working memory prioritization," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    14. J David Timm & Frank Papenmeier, 2019. "Reorganization of spatial configurations in visual working memory: A matter of set size?," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-16, November.
    15. Mohammad Zia Ul Haq Katshu & Giovanni d'Avossa, 2014. "Fine-Grained, Local Maps and Coarse, Global Representations Support Human Spatial Working Memory," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-13, September.
    16. Helen Abadzi, 2006. "Efficient Learning for the Poor : Insights from the Frontier of Cognitive Neuroscience," World Bank Publications - Books, The World Bank Group, number 7023.
    17. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci, 2020. "Multinomial logit processes and preference discovery: outside and inside the black box," Working Papers 663, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

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