Active Inference | Free Energy Principle Wiki

What is Active Inference?

Active Inference is a key concept within the Free Energy Principle framework, which suggests that biological systems not only passively infer the causes of their sensory input but also actively shape their environment to minimize prediction errors and maintain homeostasis.

In the context of the brain, Active Inference proposes that the brain not only updates its internal model based on sensory data but also generates actions that manipulate the environment to align with its predictions. This process involves a continuous cycle of perception, prediction, and action, with the goal of minimizing free energy (i.e., surprise or uncertainty).

The Active Inference Cycle

graph LR A[Perception] --> B[Prediction] B --> C[Prediction Error] C --> D[Action] D --> A

The Active Inference cycle consists of four main components:

Perception: The brain receives sensory input from the environment.
Prediction: The brain generates predictions about the causes of the sensory input based on its internal generative model.
Prediction Error: The brain computes the difference between the predicted and actual sensory input.
Action: The brain generates actions that aim to minimize prediction errors by changing the environment to match its predictions.

Hierarchical Generative Models

Active Inference relies on hierarchical generative models, which are internal representations of the causal structure of the world. These models are organized in multiple levels, with higher levels encoding more abstract and contextual information, while lower levels deal with more concrete sensory details.

graph TD A[Abstract Representations] --> B[Contextual Information] B --> C[Sensory Predictions] C --> D[Sensory Input]

The brain uses these hierarchical generative models to generate predictions at multiple scales and to infer the hidden causes of sensory input. By minimizing prediction errors at each level of the hierarchy, the brain can continuously update and refine its internal model of the world.

Implications of Active Inference

Active Inference has far-reaching implications for understanding brain function and behavior. Some key implications include:

Perception, cognition, and action are deeply intertwined and serve the common goal of minimizing free energy.
The brain is not a passive recipient of information but an active agent that continuously shapes its environment to match its predictions.
Behavior emerges as a result of the brain's attempt to minimize prediction errors and maintain a stable internal state.
Mental disorders can be understood as disruptions in the Active Inference process, leading to suboptimal predictions and maladaptive behaviors.

By framing brain function in terms of Active Inference, the Free Energy Principle offers a unifying framework for understanding the complex interplay between perception, cognition, and action, and provides new insights into the nature of adaptive behavior and its disorders.