Bibliographic Details
| Title: |
Spatial Grouping Modulates the Link between Individual Alpha Frequency and Temporal Integration Windows in Crowding. |
| Authors: |
Santoni, Alessia1,2,3 (AUTHOR) alessiasantoni6@gmail.com, Ronconi, Luca2 (AUTHOR), Samaha, Jason3 (AUTHOR) jsamaha@ucsc.edu |
| Source: |
Journal of Cognitive Neuroscience. Aug2026, Vol. 38 Issue 8, p1501-1512. 12p. |
| Subjects: |
Temporal integration, Alpha rhythm, Visual perception, Space perception |
| Abstract: |
Previous research has linked endogenous alpha oscillations (∼7–13 Hz) to temporal integration windows in visual perception, with higher individual alpha frequency predicting improved temporal segregation. Here, we investigated whether alpha-rhythmic temporal integration is a factor in visual crowding and whether this relationship is mediated by spatial grouping mechanisms. Forty-seven participants performed a Vernier discrimination task, in which we manipulated both the stimulus onset asynchrony between flankers and targets, and the spatial configuration of the flankers. Specifically, flankers were arranged to induce either crowding or "uncrowding," through the manipulation of good-Gestalt properties. Our results show that crowding has a temporal integration period of around 170 msec, but this varies substantially across individuals. Importantly, resting-state individual alpha frequency predicted individual variance in temporal integration windows: Individuals with faster endogenous alpha rhythms could begin to segregate targets from distractors at shorter SOAs. Crucially, this effect was specific for crowding-inducing flankers and disappeared when flankers led to uncrowding. These results suggest that top–down spatial grouping can overwrite the temporal integration constraint imposed by alpha oscillations, highlighting both the relevance of alpha for understanding limits on peripheral visual processing and the flexible and context-dependent role of alpha in temporal integration. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Cognitive Neuroscience is the property of MIT Press 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. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |