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Related Experiment Video

Updated: Jul 15, 2025

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

Rat movements reflect internal decision dynamics in an evidence accumulation task.

Gary A Kane¹, Ryan A Senne², Benjamin B Scott¹

¹Department of Psychological and Brain Sciences and Center for Systems Neuroscience, Boston University, Boston MA.

Biorxiv : the Preprint Server for Biology

|September 25, 2023

Summary

This study reveals how movement dynamics influence perceptual decisions in rats. Incorporating movement data into decision models significantly improves their accuracy in predicting choices.

Keywords:

accumulation decision making motion tracking response time

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Last Updated: Jul 15, 2025

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Published on: May 3, 2018

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

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Published on: August 2, 2018

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

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

Published on: July 17, 2021

Area of Science:

Neuroscience
Cognitive Science
Computational Modeling

Background:

Perceptual decision-making integrates sensory evidence, planning, and motor execution.
The precise interplay between decision processes and motor actions remains incompletely understood.
Existing models differ on whether decisions precede or are concurrent with motor execution.

Approach:

Developed a free-response evidence accumulation task with continuous stimuli.
Introduced a motion-based drift diffusion model (mDDM) integrating movement data.
Utilized video pose estimation to extract movement variables for model constraint.

Key Points:

The mDDM demonstrated superior model fit compared to traditional drift diffusion models for rat decisions.
A distinct period of head immobility preceded choice commitment in trials.
The duration of head immobility correlated with decision bounds and stimulus impact.

Conclusions:

Internal decision dynamics are reflected in observable movements.
Integrating movement parameters enhances the predictive power of diffusion-to-bound decision models.
This work supports models where motor activity is intrinsically linked to ongoing decision-making processes.