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Related Experiment Video

Updated: Oct 25, 2025

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

Bidirectional interaction between visual and motor generative models using Predictive Coding and Active Inference.

Louis Annabi¹, Alexandre Pitti¹, Mathias Quoy¹

¹ETIS UMR 8051, CY University, ENSEA, CNRS, France.

Neural Networks : the Official Journal of the International Neural Network Society

|August 3, 2021

Summary

This summary is machine-generated.

This study introduces a novel neural architecture for motor control, integrating Active Inference and Predictive Coding. Sensory predictions guide motor learning and adaptation in a simulated robotic arm, demonstrating effective handwritten letter reproduction.

Keywords:

Active inference Developmental robotics Embodiment Predictive coding Visuo-motor control

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Last Updated: Oct 25, 2025

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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Published on: May 8, 2021

Area of Science:

Computational Neuroscience
Robotics
Machine Learning

Background:

Active Inference (AIF) and Predictive Coding (PC) provide frameworks for understanding brain function.
Integrating sensory prediction and motor control is crucial for intelligent agents.

Purpose of the Study:

To propose a novel neural architecture combining AIF and PC for motor control.
To investigate the role of sensory predictions in guiding motor learning and adaptation.
To explore bidirectional interactions between motor and visual processing modules.

Main Methods:

Development of a neural architecture with separate generative models for sensory prediction and motor trajectories.
Implementation of sequences of sensory predictions to guide motor learning.
Testing the architecture on a simulated robotic arm performing handwritten letter reproduction.

Main Results:

The proposed architecture successfully learned to control a simulated robotic arm.
Sensory prediction sequences effectively guided the learning, control, and online adaptation of motor trajectories.
Bidirectional interactions between motor and visual modules were investigated.

Conclusions:

The integration of AIF and PC offers a promising approach for developing sophisticated motor control systems.
Sensory prediction plays a vital role in enabling adaptive and precise motor behaviors.
The proposed architecture demonstrates potential for applications in robotics and artificial intelligence.