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Related Experiment Video

Updated: Jan 21, 2026

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

Distributed attention model of perceptual averaging.

Jongsoo Baek¹, Sang Chul Chong^2,3

¹Yonsei Institute of Convergence Technology, Yonsei University, Incheon, Korea.

Attention, Perception & Psychophysics

|July 27, 2019

Summary

This study introduces a distributed attention model to explain how the visual system averages information, improving perception by reducing noise. The model accurately predicts how performance changes with more items, aiding ensemble perception understanding.

Keywords:

Averaging Distributed attention Noisy percept Observer model

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Related Experiment Videos

Last Updated: Jan 21, 2026

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

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

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Published on: July 5, 2015

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Area of Science:

Cognitive Psychology
Computational Neuroscience
Visual Perception

Background:

The visual system must process complex scenes efficiently despite limited capacity.
Ensemble perception, or summarizing scenes using average information, is a key strategy but its mechanisms are poorly understood.
Internal noise, occurring early or late in visual processing, can affect perception.

Purpose of the Study:

To propose and evaluate a distributed attention model for visual averaging.
To understand how attention modulates averaging and how noise cancellation contributes to ensemble perception.
To explain the relationship between the number of items and performance in averaging tasks.

Main Methods:

A computational model incorporating distributed attention and noise cancellation mechanisms was developed.
A psychophysical experiment measured observers' ability to discriminate average sizes of visual displays.
Model predictions were compared against empirical data from human observers.

Main Results:

The proposed model accurately predicted the observed pattern of averaging performance.
Performance increased with more items but at a decelerating rate, approaching an asymptote.
Attention mechanisms explained performance saturation at small set-sizes and performance increments at large set-sizes.

Conclusions:

The distributed attention model provides a robust framework for understanding visual averaging and ensemble perception.
The model elucidates the roles of attention and noise cancellation in summarizing visual information.
This work offers insights into the computational strategies employed by the human visual system.