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Related Experiment Video

Updated: Apr 15, 2026

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

Computational modeling uncovers a dynamic interaction between feature uncertainty and perception-action mapping

Qian Sun¹, Qi Sun²

¹School of Psychology and Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China.

Behavior Research Methods

|April 13, 2026

Summary

This study reveals how internal and external noise interact during visual estimation. Increased sensory noise is balanced by adaptive changes in how we map perception to action.

Keywords:

Bayesian model Efficient coding Noise Optic flow Sensorimotor transformation Visual perception

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Last Updated: Apr 15, 2026

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

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Published on: April 16, 2014

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Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

Cognitive Neuroscience
Computational Vision
Psychophysics

Background:

Visual feature estimation is affected by internal (sensory) and external (physical) noise.
The perception-action mapping process transforms perceptual data into motor responses.
Interactions between noise and perception-action mapping are not well understood.

Purpose of the Study:

To investigate the interplay between internal/external noise and perception-action mapping in visual heading estimation.
To quantify the effects of different noise sources on visual estimation accuracy and variability.
To model the dynamic interactions within the visual perception-action system.

Main Methods:

Two experiments were conducted using 3D dot-cloud optic flow to estimate self-motion direction.
Experiment 1 manipulated internal noise via a color-discrimination task.
Experiment 2 manipulated both internal and external noise using varying levels of noise dots.

Main Results:

Estimation errors and response variability systematically changed across noise conditions.
Computational models indicated that color-discrimination tasks increased internal noise (c).
Noise dots elevated both internal noise (c) and external noise (σE), with a negative correlation to perception-action scaling (a).

Conclusions:

Increased sensory noise (internal or external) is counterbalanced by adaptive scaling of perception-action transformation.
Findings demonstrate a dynamic interaction between sensory noise and sensorimotor mapping in perception.
The developed computational model can dissect sensory uncertainty and action scaling across perceptual domains.