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Related Experiment Video

Updated: May 14, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

Published on: June 1, 2015

Credit assignment during movement reinforcement learning.

Gregory Dam¹, Konrad Kording, Kunlin Wei

¹Department of Behavioral Sciences, University of Rio Grande, Rio Grande, Ohio, USA.

|February 15, 2013

Summary

This summary is machine-generated.

Humans effectively solve movement credit assignment problems during reinforcement learning by quickly learning implicit reward functions from movement trajectories. A Bayesian model with forgetting accurately predicts this learning.

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Area of Science:

Neuroscience
Motor Control
Machine Learning

Background:

Motor learning often relies on a single performance measure, posing a challenge for the brain to identify which movement aspects to improve.
The credit assignment problem in motor control, particularly within reinforcement learning, remains poorly understood.

Purpose of the Study:

To investigate how humans solve credit assignment problems during a trajectory-learning task.
To determine if participants can infer implicit payoff functions from movement outcomes and rewards.

Main Methods:

Participants performed hand reaches with no explicit target, receiving monetary rewards based on trajectory curvature and direction.
Experimental manipulation of reward structure to test learning.
Comparison of human learning with a Bayesian credit-assignment model incorporating forgetting.

Main Results:

Participants rapidly learned the implicit payoff function governing rewards based on movement trajectories.
Human learning performance closely matched predictions from the Bayesian credit-assignment model.
The model's inclusion of forgetting accurately captured trial-by-trial learning dynamics.

Conclusions:

Humans demonstrate a robust ability to solve the credit assignment problem in motor learning under reinforcement.
A Bayesian framework with forgetting provides a strong predictive model for human motor adaptation.
This study sheds light on neural mechanisms underlying reinforcement learning in movement control.