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Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
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Neurocomputational Mechanisms of Sense of Agency: Literature Review for Integrating Predictive Coding and Adaptive
1Department of Metabolism and Systems Science, School of Medical Sciences, College of Medicine and Health, University of Birmingham, Birmingham B15 2TT, UK.
Brain Sciences
|May 1, 2025
Summary
Computational models like predictive coding offer a unified framework for understanding the sense of agency (SoA). These models can inform the design of adaptive human-machine interfaces for neurorehabilitation and therapeutic tools.
Area of Science:
- Cognitive Neuroscience
- Computational Psychiatry
- Human-Machine Interaction
Background:
- The sense of agency (SoA) is crucial for cognition, volition, and motor control.
- Understanding SoA disruptions is key for neurorehabilitation and human-machine interface (HMI) design.
- Traditional models struggle with dynamic and uncertain environments.
Purpose of the Study:
- Synthesize computational models (predictive coding, Bayesian inference, optimal control) for a unified SoA framework.
- Align computational models with brain mechanisms for adaptive HMIs and therapeutic tools.
- Inform the design of neurorehabilitation strategies.
Main Methods:
- Literature review of predictive coding, Kalman filtering, LQG control, and active inference.
- Exploration of integration with neurophysiological mechanisms (SCAN system).
- Illustration using case studies, simulations, and XR-based rehabilitation paradigms.
Main Results:
- SoA arises from hierarchical inference combining motor intentions and sensory feedback.
- Predictive coding frameworks offer mechanistic basis for motor learning and control.
- XR interventions can restore SoA by modulating sensory precision and predictions.
Conclusions:
- Predictive coding and active inference provide a framework for understanding and enhancing SoA.
- The SCAN system integrates motor plans with cognitive and affective processes.
- Future research should focus on real-time agency modulation via biofeedback and brain stimulation.
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