Active Inference (AI) is a computational theory that offers a unified explanation for how the brain generates perception and selects actions. This framework proposes that the brain constantly attempts to predict the sensory data it receives from the world. Behavior and sensation are understood as interconnected processes driven by a single imperative: minimizing “surprise,” which refers to unexpected or unlikely sensory outcomes based on the brain’s internal model. AI characterizes the brain not as a passive receiver of information, but as a proactive system that continually tests hypotheses about its environment. This theory provides a comprehensive lens through which to view all cognitive functions, from basic reflexes to complex planning.
The Theoretical Foundation of Active Inference
The core principle governing Active Inference is the minimization of surprise, formally known as the Free Energy Principle (FEP). Biological systems must maintain stable internal states, such as consistent body temperature or pH level, to survive. Any deviation from these preferred, life-sustaining states is considered a surprising outcome that increases the system’s disorder. The FEP suggests that living systems operate to minimize this long-term average of surprise, thereby resisting a natural tendency toward disorder.
The brain achieves this minimization by continuously optimizing an internal representation, or “generative model,” of its world. This model contains the agent’s beliefs and expectations about how external, unobservable causes generate sensory input. The mathematical quantity called variational free energy serves as a quantifiable upper bound on surprise that the brain actively minimizes. Minimizing this free energy maximizes the evidence for the internal model of the world.
How the Brain Updates Internal Models
Perception is the process of updating the internal model to better explain incoming sensory data. The brain generates top-down predictions about what it expects to sense at every level of its hierarchical structure. When sensory input arrives, it is compared against these predictions, generating a “prediction error” signal for any mismatch. This prediction error drives the updating of beliefs, signaling where the current model is inaccurate.
This error signal is passed up the hierarchy, causing higher-level beliefs to adjust until they can explain the unexpected observation. For instance, if the visual cortex predicts a pattern of light but receives a different one, the resulting error forces the internal model to change its hypothesis. Perception is an act of inference, where the brain settles on the most plausible hypothesis to explain its current sensations. The precision, or reliability, assigned to these errors determines how much the internal model is updated.
Action as Evidence Gathering
Active Inference treats action as a second, equally important method for minimizing prediction error, alongside perceptual updating. If the error cannot be sufficiently minimized by changing internal beliefs, the system minimizes it by changing the world itself. The agent acts to make its sensory input conform to its predictions, effectively forcing the world to match its internal model.
This frames motor behavior as “evidence gathering” or “hypothesis testing.” When an agent reaches for a cup, the brain predicts the sequence of proprioceptive (body position) and visual sensations resulting from that movement. The action is executed to eliminate the resulting proprioceptive prediction error, fulfilling the initial prediction. Action is selected based on its expected free energy, which evaluates which sequence of movements, or “policy,” is most likely to lead to the agent’s preferred future states.
Real-World Implications of the Model
The Active Inference framework helps understand complex cognitive phenomena beyond basic perception and action. Planning is conceptualized as an agent simulating future action policies and selecting the one that minimizes expected surprise and maximizes the gathering of novel information. Curiosity and exploration, often termed “epistemic foraging,” are explained as actions taken to reduce uncertainty about the world, thereby improving the generative model for future predictions.
The model also offers insights into neurological and psychiatric conditions by focusing on how the brain weighs the precision of its predictions and errors. Conditions such as anxiety or delusions may be interpreted as a miscalibration of this precision weighting. This means the brain either over-weights or under-weights the reliability of certain prediction errors. Furthermore, Active Inference is used in engineering and artificial intelligence, offering a principled foundation for building adaptive autonomous agents and robots that learn and make decisions efficiently in uncertain environments.