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Updated: Nov 4, 2025

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
Published on: July 17, 2021
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, Massachusetts, United States.
New research reveals how movements reflect ongoing decision-making processes. A novel motion-based drift diffusion model (mDDM) better explains animal choices by integrating movement data with evidence accumulation.
Area of Science:
- Neuroscience
- Cognitive Science
- Animal Behavior
Background:
- Perceptual decision-making integrates sensory evidence, planning, and motor execution.
- The interplay between decision processes and motor execution remains incompletely understood.
- Some models posit decisions precede movement, while others suggest movements reflect ongoing decisions.
Purpose of the Study:
- To investigate the relationship between decision dynamics and motor actions leading to a choice.
- To develop and validate a novel model incorporating movement data into decision-making models.
- To enhance the predictive power of decision models by including movement parameters.
Main Methods:
- Utilized a free-response pulse-based evidence accumulation task with rats.
- Developed a motion-based drift diffusion model (mDDM) using video pose estimation.
- Constrained decision parameters trial-by-trial using movement variables.
Main Results:
- The mDDM provided a superior fit to rat choice behavior compared to traditional drift diffusion models.
- A period of head immobility preceding choice was observed and correlated with decision bounds.
- Stimuli presented during the immobility period had the most significant impact on choice selection.
Conclusions:
- Internal decision dynamics are reflected in observable movements.
- Integrating movement parameters into diffusion-to-bound models significantly improves their performance.
- The mDDM offers a more accurate description of animal choice behavior in evidence accumulation tasks.
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