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Related Experiment Video

Updated: Oct 18, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

Action planning and control under uncertainty emerge through a desirability-driven competition between parallel

Vince Enachescu^1,2, Paul Schrater^3,4, Stefan Schaal^1,2

¹Department of Neuroscience, University of Southern California, Los Angeles, California, United States of America.

Plos Computational Biology

|October 1, 2021

Summary

Decisions made during actions, even with uncertainty, show correlated reaction times and movement directions. This suggests parallel motor plan competition underlies action control under uncertainty.

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Last Updated: Oct 18, 2025

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Area of Science:

Neuroscience
Cognitive Science
Decision Making

Background:

Most decision-making research separates action selection from action execution.
Real-world decisions often occur during ongoing actions with changing alternatives.
Understanding sensorimotor system's role in dynamic decision-making is crucial.

Purpose of the Study:

To investigate how the brain handles uncertainty in decisions made during ongoing actions.
To decipher the neural mechanisms of action planning and control under evolving uncertainty.

Main Methods:

Trained participants in rapid reaching movements to two potential targets.
Target location was revealed post-movement initiation.
Utilized a neurodynamical framework to model decision-making processes.

Main Results:

Reaction time and initial movement direction were correlated.
Movements to intermediate locations had longer reaction times than direct target approaches.
This correlation was independent of target probability.

Conclusions:

Action planning and control under uncertainty arise from parallel competition between motor plans.
This competition is driven by the desirability of potential outcomes.
The findings offer insights into dynamic decision-making in uncertain environments.