Active inference as a theory of sentient behavior

Area of Science:

• Cognitive Neuroscience
• Artificial Intelligence
• Machine Learning

Background:

• Active inference proposes that sentient behavior relies on internal models for prediction, inference, and action.
• This theory unifies perspectives on action and perception, tracing conceptual roots from Helmholtz to contemporary models.

Purpose of the Study:

• To provide an overview of the historical development and future trajectory of active inference.
• To explore the conceptual foundations, neural underpinnings, and applications of active inference.

Main Methods:

• Review of historical and contemporary literature on active inference.
• Examination of conceptual evolution, including predictive coding and hierarchical generative models.
• Discussion of applications in neuroscience, psychopathology, and emerging fields.

Main Results:

• Active inference provides a unifying framework for understanding action and perception through internal brain models.
• Key developments include predictive coding, sequential models for planning, and hierarchical temporal models.
• Active inference has applications in explaining neurophysiology, psychopathology, and unifying psychological theories.

Conclusions:

• Active inference offers a powerful framework for understanding sentience and brain function.
• Future development is anticipated in neuroscience, robotics, machine learning, and artificial intelligence.
• The theory's broad applicability suggests significant potential beyond biological systems.