Sequential Effects in Reaching Reveal Efficient Coding in Motor Planning.

Area of Science:

• Neuroscience
• Motor Control
• Computational Neuroscience

Background:

• The nervous system uses prior information to improve perception and action accuracy.
• Bayesian models are prevalent in motor control, integrating past observations into current plans, but often assume stable environments.
• Real-world motor goals change unpredictably, questioning the applicability of traditional Bayesian integration.

Purpose of the Study:

• To investigate if efficient coding, an alternative model, operates in motor planning under dynamic conditions.
• To compare predictions of Bayesian and efficient coding models in unpredictable motor tasks.
• To understand how the motor system maintains accuracy and flexibility in changing environments.

Main Methods:

• Center-out reaching tasks were used with rapidly changing, unpredictable motor goals.
• Sequential effects on motor planning were analyzed to differentiate between Bayesian and efficient coding predictions.
• The influence of intrinsic motor variability on movement biases was examined.

Main Results:

• Movement biases were repulsive, opposing the direction of previous movements, consistent with efficient coding.
• Repulsive biases were amplified by intrinsic motor variability.
• Movement variability decreased for successive reaches with similar goals, suggesting adaptive resource allocation.

Conclusions:

• Findings support the presence of efficient coding in motor planning, a mechanism distinct from traditional Bayesian integration.
• Efficient coding allows the motor system to maintain high accuracy and flexibility in dynamic, unpredictable environments.
• This adaptive mechanism dynamically tunes resource allocation based on environmental statistics.