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Perception

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Related Experiment Video

Updated: Jun 21, 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

Perception and hierarchical dynamics.

Stefan J Kiebel¹, Jean Daunizeau, Karl J Friston

¹Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.

Frontiers in Neuroinformatics

|August 4, 2009

Summary

This summary is machine-generated.

This study proposes a dynamic systems model where perception arises from hierarchical attractors. This mathematical model demonstrates how neuronal states can track sensory signals, advancing artificial speech recognition.

Keywords:

Bayesian inversion biological movement birdsong dynamic systems theory environment perception recognition speech

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

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Published on: April 16, 2014

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

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Published on: July 17, 2021

Area of Science:

Computational Neuroscience
Artificial Intelligence
Cognitive Science

Background:

Perception is complex, involving processing rapid sensory input.
Understanding the temporal dynamics of sensory signals is crucial.
Existing models may not fully capture hierarchical structures in perception.

Purpose of the Study:

To propose a novel mathematical model for perception based on hierarchical attractors in dynamic systems.
To demonstrate how temporal structures in sensory data can be exploited to infer underlying causes.
To link hierarchical dynamic systems to human action perception, specifically artificial speech recognition.

Main Methods:

Developed a mathematical model of perception using hierarchical attractors.
Exploited the temporal structure of rapid sensory dynamics.
Modeled slowly changing neuronal states encoding faster sensory signal trajectories.

Main Results:

Established a proof of concept that neuronal states can encode sensory signal trajectories.
Demonstrated the utility of hierarchical dynamic systems for robust recognition schemes.
Showcased the model's ability to capture temporal dependencies induced by hierarchical structure.

Conclusions:

Perception can be modeled as recognition dynamics enslaved by hierarchical dynamics in the sensorium.
Hierarchical models of dynamical systems offer a plausible approach for robust recognition.
This computational neuroscience framework provides a foundation for future research in perception and recognition.