Active inference, communication and hermeneutics.

Area of Science:

• Cognitive Science
• Neuroscience
• Psychology

Background:

• Hermeneutics traditionally involves text interpretation but extends to understanding communication.
• Inferring the intended meaning in communication is a key challenge in psychological settings.
• Neural hermeneutics addresses how the brain interprets communicative signals.

Purpose of the Study:

• To propose a solution for neural hermeneutics using the framework of active inference.
• To demonstrate how active inference can model the prediction and updating of internal models during communication.
• To illustrate the role of communication in inducing perceptual learning and model updating.

Main Methods:

• Active inference framework applied to communication.
• Modeling prediction errors and their suppression through perceptual inference and learning.
• Simulation of birdsong communication to illustrate perceptual learning.

Main Results:

• Active inference provides a mechanism for agents to predict and understand each other's communicative intentions.
• Minimizing mutual prediction errors leads to agents adopting shared generative models.
• Communication facilitates long-term changes in generative models, demonstrating perceptual learning.

Conclusions:

• Active inference offers a computational framework for understanding neural hermeneutics.
• Communication acts as a catalyst for perceptual learning by enabling agents to update their internal models.
• This process allows for mutual mind-changing, enhancing inter-agent understanding.