Related Experiment Video
Updated: Jul 2, 2026

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
Published on: July 5, 2015
Sensitivity suppression during attention shifts
Zixiao Zhang1,2, Sheng He1,2,3, Jiedong Zhang1,2
1State Key Laboratory of Cognitive Science and Mental Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
None:
The brain possesses the remarkable ability to suppress undesirable signals generated by our own actions to ensure accurate perception, like the suppression of retinal motion signals during rapid eye movements, known as saccadic suppression. Attention, often referred to as the "mind's eye," undergoes rapid and frequent shifts, often occurring in the absence of explicit motor actions. Is visual processing similarly suppressed during attention shifts? In this study, we employed pupillometry and magnetoencephalography (MEG) to assess visual sensitivity across different attentional states. Our results revealed a reduced or suppressed visual sensitivity during attention shifts through an attentional oscillation paradigm. Such suppression was not due to microsaccades and was absent during rhythmic attentional sampling without spatial shift. MEG data further indicated that the suppression was more pronounced in parietal channels, manifesting at a relatively late stage (150 to 200 ms) of visual processing and suggesting a distinct neural mechanism compared to saccadic suppression. Our findings indicate that suppression mechanisms operate not only when there are spurious signals generated due to the movements of the sensors, but also during transitional states in the allocation of cognitive resources within representational space, thereby supporting stability of visual processing.
More Related Videos
06:46Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity
Published on: March 18, 2019
13:00Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017