September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
EEG-based decoding of shapes and their categories in visual working memory
Author Affiliations & Notes
  • Frida Printzlau
    University of Toronto
    University of Toronto Mississauga
  • Olya Bulatova
    University of Toronto Mississauga
  • Michael Mack
    University of Toronto
  • Keisuke Fukuda
    University of Toronto
    University of Toronto Mississauga
  • Footnotes
    Acknowledgements  This research was funded by the University of Toronto Faculty of Arts and Sciences Postdoctoral Fellowship Award to FP; the Natural Sciences and Engineering Research Council (KF and MM); the Connaught New Researcher Award to KL; Brain Canada Future Leaders in Canadian Brain Research Grant to MM.
Journal of Vision September 2024, Vol.24, 938. doi:https://doi.org/10.1167/jov.24.10.938
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      Frida Printzlau, Olya Bulatova, Michael Mack, Keisuke Fukuda; EEG-based decoding of shapes and their categories in visual working memory. Journal of Vision 2024;24(10):938. https://doi.org/10.1167/jov.24.10.938.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Visual working memory (VWM) allows us to store information in a highly accessible format for an upcoming task. Traditionally, VWM studies require participants to keep a precise copy of a stimulus in mind. But in the real world, we might need to store the same information for different types of tasks, such as recognition or categorisation judgements. For example, when deciding if a bike is the exact model you want, or the same brand. In this study, we asked how categorisation modulates VWM representations. Participants first learned to group unfamiliar shapes from the Validated Circular Shape (VCS) space (Li et al., 2020) into two categories based on their visual features. They then completed a shape VWM task that either required delayed match-to-sample or delayed match-to-category judgements on different blocks while we collected electroencephalography (EEG) data. We tracked the emergence of stimulus-, category- and task level information with high temporal resolution using multivariate pattern analyses of EEG. The neural activity pattern over posterior electrodes contained information about the memorised shape for about one second following VWM encoding. Initially, the stimulus code overlapped across the two tasks, but quickly separated according to task. Later in the delay, stimulus coding persisted only for the match-to-category task and was accompanied by a neural category signal, indicating that categorisation may require an active stimulus representation. To our knowledge, this is the first illustration that the VCS space is decodable from EEG, preserving the circular similarity structure. This provides a fruitful avenue for researchers looking to characterise neural representations of unfamiliar visual stimuli with high temporal resolution. The results of this study will help elucidate the neural mechanisms supporting VWM under different task demands.

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