A Predictive Processing Model of Perception and Action for Self-Other Distinction.

Area of Science:

• Cognitive Neuroscience
• Computational Neuroscience
• Social Cognition

Background:

• The motor system is crucial for processing actions, both self-produced and observed in others.
• Distinguishing between self and other during social interaction is a fundamental cognitive process.
• Current models lack a comprehensive explanation for how the motor system achieves self-other distinction.

Purpose of the Study:

• To propose a hierarchical model of sensorimotor coordination for self-other distinction.
• To investigate the role of perception-action coupling and predictive processing in self-other differentiation.
• To model the sense of agency and its contribution to distinguishing self from other.

Main Methods:

• Developed a hierarchical model of sensorimotor coordination.
• Incorporated principles of perception-action coupling and predictive processing.
• Utilized simulations to test the model across various scenarios.

Main Results:

• The model effectively minimizes free energy during perception and action.
• Simulations demonstrated the model's ability to correctly attribute the sense of agency to self-generated actions.
• The model provides a framework for understanding self-other distinction within sensorimotor processing.

Conclusions:

• Hierarchical sensorimotor coordination, guided by predictive processing, is key to self-other distinction.
• The sense of agency plays a critical role in differentiating self-produced actions from those of others.
• This model offers a computational approach to understanding fundamental aspects of social cognition.