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Related Experiment Video

Updated: May 23, 2026

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

Time-integrated position error accounts for sensorimotor behavior in time-constrained tasks.

Julian J Tramper¹, Bart van den Broek, Wim Wiegerinck

¹Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands. j.tramper@donders.ru.nl

|April 4, 2012

Summary

This summary is machine-generated.

Human motor control models were tested to see if they predict reaching targets before a deadline. A new model, incorporating risk aversion, accurately predicted faster target acquisition in joystick tasks.

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Last Updated: May 23, 2026

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Area of Science:

Neuroscience
Robotics
Biomechanics

Background:

Optimal control theory successfully models human motor behavior by balancing effort and performance.
Existing models predict goal achievement precisely at the final time.
A potential strategy is reaching targets before the deadline to mitigate risk.

Purpose of the Study:

To investigate if minimizing control effort and maximizing performance sufficiently describes human motor behavior in time-constrained tasks.
To compare a standard optimal control model with a new model that penalizes deviations throughout the trial, forcing earlier arrival.

Main Methods:

Two models were tested: a standard model and a new model with an added cost criterion for position deviation.
Subjects used a joystick to steer a ball to a target on a screen before a deadline.
Noise was added to the ball's position to simulate varying levels of uncertainty across two distinct tasks.

Main Results:

The standard model, focusing only on end-time effort-accuracy trade-offs, was insufficient.
The new model accurately predicted that subjects reached target positions well before the final time.
This predictive accuracy held across different noise levels and both tested tasks.

Conclusions:

Minimizing effort and maximizing end-point accuracy alone does not fully explain human motor control in time-constrained tasks.
A model incorporating a penalty for deviations throughout the movement, promoting earlier arrival, better reflects observed human behavior.
This finding suggests that risk aversion, leading to proactive target acquisition, is a key factor in motor control under time pressure.