Recurrence affects the geometry of visual representations across the ventral visual stream in the human brain
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| Pubblicato in: | PLoS Biology vol. 23, no. 8 (Aug 2025), p. e3003354-e3003384 |
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| Altri autori: | , , , |
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Public Library of Science
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| Accesso online: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1371/journal.pbio.3003354 |2 doi | |
| 035 | |a 3270865395 | ||
| 045 | 2 | |b d20250801 |b d20250831 | |
| 084 | |a 174830 |2 nlm | ||
| 100 | 1 | |a Xie, Siying | |
| 245 | 1 | |a Recurrence affects the geometry of visual representations across the ventral visual stream in the human brain | |
| 260 | |b Public Library of Science |c Aug 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The human brain orchestrates object vision through an interplay of feedforward processing in concert with recurrent processing. However, where, when, and how recurrent processing contributes to visual processing is incompletely understood due to the difficulties in teasing apart feedforward and recurrent processing. We combined a backward masking paradigm with multivariate analysis on EEG and fMRI data to isolate and characterize the nature of recurrent processing. We find that recurrent processing substantially shapes visual representations across the ventral visual stream, starting early on at around 100 ms in early visual cortex (EVC) and two later phases of around 175 and 300 ms in lateral occipital cortex (LOC), adding persistent rather than transient neural dynamics to visual processing. Using convolutional neural network models for comparison with the brain, we show that recurrence changes the feature format in LOC from predominantly mid-level to more high-level features. Finally, we show that recurrence is mediated by four distinct spectro-temporal neural components, which span the theta to beta frequency range. Together, our results reveal the nature and mechanisms of the effects of recurrent processing on the visual representations in the human brain. | |
| 653 | |a Functional magnetic resonance imaging | ||
| 653 | |a Frequency | ||
| 653 | |a Brain research | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Temporal lobe | ||
| 653 | |a Neural networks | ||
| 653 | |a Multivariate analysis | ||
| 653 | |a Frequency ranges | ||
| 653 | |a Brain | ||
| 653 | |a Visual cortex | ||
| 653 | |a Temporal variations | ||
| 653 | |a Occipital lobe | ||
| 653 | |a Information processing | ||
| 653 | |a Confidence intervals | ||
| 653 | |a Theta rhythms | ||
| 653 | |a Representations | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Singer, Johannes | |
| 700 | 1 | |a Yilmaz, Bati | |
| 700 | 1 | |a Kaiser, Daniel | |
| 700 | 1 | |a Cichy, Radoslaw M | |
| 773 | 0 | |t PLoS Biology |g vol. 23, no. 8 (Aug 2025), p. e3003354-e3003384 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3270865395/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3270865395/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3270865395/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |