Abstract
Our visual environment is relatively stable over time. An optimized visual system could capitalize on this by devoting less representational resources to objects that are physically present. The vividness of subjective experience, however, suggests that externally available (perceived) information is more strongly represented in neural signals than memorized information. To distinguish between these opposing predictions, we use EEG multivariate pattern analysis to quantify the representational strength of task-relevant features in anticipation of a change-detection task. Perceptual availability was manipulated between experimental blocks by either keeping the stimulus available on the screen during a 2-s delay period (perception) or removing it shortly after its initial presentation (memory). We find that task-relevant (attended) memorized features are more strongly represented than irrelevant (unattended) features. More importantly, we find that task-relevant features evoke significantly weaker representations when they are perceptually available compared with when they are unavailable. These findings demonstrate that, contrary to what subjective experience suggests, vividly perceived stimuli elicit weaker neural representations (in terms of detectable multivariate information) than the same stimuli maintained in visual working memory. We hypothesize that an efficient visual system spends little of its limited resources on the internal representation of information that is externally available anyway.
Original language | English |
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Pages (from-to) | 7608-7618 |
Number of pages | 11 |
Journal | Cerebral Cortex |
Volume | 33 |
Issue number | 12 |
Early online date | 31 Mar 2023 |
DOIs | |
Publication status | Published - 15 Jun 2023 |
Bibliographical note
Funding Information:This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement n° 863732).
Funding Information:
We thank Elise B.H. Tans for her help with data collection. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement n◦ 863732).
Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press.
Keywords
- EEG multivariate pattern analysis
- embodied cognition
- perception
- working memory