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Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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Related Experiment Video

Updated: Dec 18, 2025

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

Decoding attention control and selection in visual spatial attention.

Xiangfei Hong^1,2, Ke Bo², Sreenivasan Meyyappan²

¹Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Human Brain Mapping

|June 17, 2020

Summary

This summary is machine-generated.

Multivariate pattern classification of event-related potentials (ERPs) reveals instructional cueing enhances attention control faster than probabilistic cueing. Better attention decoding correlates with stronger neural modulation and faster reaction times.

Keywords:

N1 decoding event-related potential pattern classification reaction time selective attention

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Last Updated: Dec 18, 2025

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Published on: March 18, 2019

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Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control

Published on: July 5, 2015

Area of Science:

Cognitive Neuroscience
Neuroscience
Psychology

Background:

Event-related potentials (ERPs) are crucial for studying attention control.
Univariate ERP analysis has limitations in fully understanding attention mechanisms.
Machine learning offers advanced methods to analyze complex neural data.

Purpose of the Study:

To investigate the impact of instructional versus probabilistic cueing strategies on attention control and selection using multivariate pattern classification.
To examine the neural and behavioral effects of individual differences in attention using advanced analysis techniques.
To compare the efficacy of multivariate pattern classification with traditional univariate ERP approaches.

Main Methods:

Applied multivariate pattern classification to multichannel event-related potentials (ERPs).
Conducted two cued visual spatial attention experiments with 56 participants.
Analyzed decoding accuracy for cue and target identification over time.

Main Results:

Instructional cueing led to earlier (around 80 ms) and higher decoding accuracy compared to probabilistic cueing (around 160 ms).
Earlier and stronger attention selection was observed under instructional cueing for target processing.
Higher cue-related decoding accuracy correlated with greater attentional modulation of N1 amplitude.
Higher target-related decoding accuracy was associated with faster reaction times and larger cueing effects.

Conclusions:

Multivariate pattern classification of multichannel ERPs provides novel insights into attention control and selection.
Instructional cueing facilitates a faster and more distinct attention control set formation.
Individual differences in decoding accuracy reflect variations in attentional modulation and behavioral performance.
Combining multivariate and univariate ERP approaches offers a more comprehensive methodology for studying visual spatial attention.