Dutta Anirban
Department of Metabolism and Systems Science, School of Medical Sciences, College of Medicine and Health, University of Birmingham, Birmingham B15 2TT, UK.
Brain Sci. 2025 Apr 14;15(4):396. doi: 10.3390/brainsci15040396.
The sense of agency (SoA)-the subjective experience of controlling one's own actions and their consequences-is a fundamental aspect of human cognition, volition, and motor control. Understanding how the SoA arises and is disrupted in neuropsychiatric disorders has significant implications for human-machine interface (HMI) design for neurorehabilitation. Traditional cognitive models of agency often fail to capture its full complexity, especially in dynamic and uncertain environments.
This review synthesizes computational models-particularly predictive coding, Bayesian inference, and optimal control theories-to provide a unified framework for understanding the SoA in both healthy and dysfunctional brains. It aims to demonstrate how these models can inform the design of adaptive HMIs and therapeutic tools by aligning with the brain's own inference and control mechanisms.
I reviewed the foundational and contemporary literature on predictive coding, Kalman filtering, the Linear-Quadratic-Gaussian (LQG) control framework, and active inference. I explored their integration with neurophysiological mechanisms, focusing on the somato-cognitive action network (SCAN) and its role in sensorimotor integration, intention encoding, and the judgment of agency. Case studies, simulations, and XR-based rehabilitation paradigms using robotic haptics were used to illustrate theoretical concepts.
The SoA emerges from hierarchical inference processes that combine top-down motor intentions with bottom-up sensory feedback. Predictive coding frameworks, especially when implemented via Kalman filters and LQG control, provide a mechanistic basis for modeling motor learning, error correction, and adaptive control. Disruptions in these inference processes underlie symptoms in disorders such as functional movement disorder. XR-based interventions using robotic interfaces can restore the SoA by modulating sensory precision and motor predictions through adaptive feedback and suggestion. Computer simulations demonstrate how internal models, and hypnotic suggestions influence state estimation, motor execution, and the recovery of agency.
Predictive coding and active inference offer a powerful computational framework for understanding and enhancing the SoA in health and disease. The SCAN system serves as a neural hub for integrating motor plans with cognitive and affective processes. Future work should explore the real-time modulation of agency via biofeedback, simulation, and SCAN-targeted non-invasive brain stimulation.
能动感（SoA）——控制自身行为及其后果的主观体验——是人类认知、意志和运动控制的一个基本方面。了解SoA在神经精神疾病中如何产生和受到干扰，对神经康复的人机界面（HMI）设计具有重要意义。传统的能动认知模型往往无法捕捉其全部复杂性，尤其是在动态和不确定的环境中。
本综述综合了计算模型，特别是预测编码、贝叶斯推理和最优控制理论，为理解健康和功能失调大脑中的SoA提供一个统一的框架。其目的是展示这些模型如何通过与大脑自身的推理和控制机制相结合，为适应性HMI和治疗工具的设计提供信息。
我回顾了关于预测编码、卡尔曼滤波、线性二次高斯（LQG）控制框架和主动推理的基础和当代文献。我探讨了它们与神经生理机制的整合，重点是躯体认知行动网络（SCAN）及其在感觉运动整合、意图编码和能动判断中的作用。使用机器人触觉的案例研究、模拟和基于扩展现实（XR）的康复范式来说明理论概念。
SoA源于将自上而下的运动意图与自下而上的感觉反馈相结合的分层推理过程。预测编码框架，特别是通过卡尔曼滤波器和LQG控制实现时，为运动学习、误差校正和自适应控制建模提供了一个机制基础。这些推理过程的中断是诸如功能性运动障碍等疾病症状的基础。使用机器人界面的基于XR的干预措施可以通过自适应反馈和建议调节感觉精度和运动预测来恢复SoA。计算机模拟展示了内部模型和催眠建议如何影响状态估计、运动执行和能动恢复。
预测编码和主动推理为理解和增强健康和疾病状态下的SoA提供了一个强大的计算框架。SCAN系统是将运动计划与认知和情感过程整合的神经枢纽。未来的工作应探索通过生物反馈、模拟和针对SCAN的非侵入性脑刺激对能动感进行实时调节。
