Representational shifts from feedforward to feedback rhythms index phenomenological integration in naturalistic vision.

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Název: Representational shifts from feedforward to feedback rhythms index phenomenological integration in naturalistic vision.
Autoři: Chen, Lixiang, Cichy, Radoslaw Martin, Kaiser, Daniel
Zdroj: Communications Biology; 4/14/2025, Vol. 8 Issue 1, p1-5, 5p
Témata: COGNITIVE psychology, ELECTROENCEPHALOGRAPHY, RHYTHM, ALPHA rhythm
Abstrakt: How does the brain integrate complex and dynamic visual inputs into phenomenologically seamless percepts? Previous results demonstrate that when visual inputs are organized coherently across space and time, they are more strongly encoded in feedback-related alpha rhythms, and less strongly in feedforward-related gamma rhythms. Here, we tested whether this representational shift from feedforward to feedback rhythms is linked to the phenomenological experience of coherence. In an Electroencephalography (EEG) study, we manipulated the degree of spatiotemporal coherence by presenting two segments from the same video across visual hemifields, either synchronously or asynchronously (with a delay between segments). We asked participants whether they perceived the stimulus as coherent or incoherent. When stimuli were presented at the perceptual threshold (i.e., when the same stimulus was judged as coherent 50% of times), perception co-varied with stimulus coding across alpha and gamma rhythms: When stimuli were perceived as coherent, they were represented in alpha activity; when stimuli were perceived as incoherent, they were represented in gamma activity. Whether the same visual input is perceived as coherent or incoherent thus depends on representational shifts between feedback-related alpha and feedforward-related gamma rhythms. Representational shifts from bottom-up gamma to top-down alpha dynamics drive visual integration, highlighting the crucial role of cortical feedback in the construction of seamless perceptual experiences. [ABSTRACT FROM AUTHOR]
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  Data: Representational shifts from feedforward to feedback rhythms index phenomenological integration in naturalistic vision.
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  Data: Communications Biology; 4/14/2025, Vol. 8 Issue 1, p1-5, 5p
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  Data: How does the brain integrate complex and dynamic visual inputs into phenomenologically seamless percepts? Previous results demonstrate that when visual inputs are organized coherently across space and time, they are more strongly encoded in feedback-related alpha rhythms, and less strongly in feedforward-related gamma rhythms. Here, we tested whether this representational shift from feedforward to feedback rhythms is linked to the phenomenological experience of coherence. In an Electroencephalography (EEG) study, we manipulated the degree of spatiotemporal coherence by presenting two segments from the same video across visual hemifields, either synchronously or asynchronously (with a delay between segments). We asked participants whether they perceived the stimulus as coherent or incoherent. When stimuli were presented at the perceptual threshold (i.e., when the same stimulus was judged as coherent 50% of times), perception co-varied with stimulus coding across alpha and gamma rhythms: When stimuli were perceived as coherent, they were represented in alpha activity; when stimuli were perceived as incoherent, they were represented in gamma activity. Whether the same visual input is perceived as coherent or incoherent thus depends on representational shifts between feedback-related alpha and feedforward-related gamma rhythms. Representational shifts from bottom-up gamma to top-down alpha dynamics drive visual integration, highlighting the crucial role of cortical feedback in the construction of seamless perceptual experiences. [ABSTRACT FROM AUTHOR]
– Name: Abstract
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  Data: <i>Copyright of Communications Biology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 4/14/2025
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