The Bayesian Brain: How Life Predicts
The brain isn’t just a passive observer of the world. It doesn’t wait for input and then decide how to respond. Instead, it guesses, anticipates, and constructs a model of the world — constantly updating it as new information comes in.
This predictive process is called Bayesian inference, and it has become one of the most influential models in neuroscience and cognitive science. In the Biogenics framework, Bayesian inference isn't an additional pillar like self-organisation, self-production, or self-correction, but it helps us understand how these functions are realised in the brain.
Think of it as the operating logic of a mind trying to survive in an uncertain world.
From Passive Reactor to Active Predictor
Traditional views cast the brain as a processor: stimulus in, behaviour out. But prediction turns that model inside out.
The Bayesian brain operates on a different sequence:
Make a prior guess about what to expect.
Compare that guess to incoming evidence.
If there’s a mismatch — an error — update the model.
Repeat.
The brain is constantly trying to minimise the gap between what it expects and what it perceives. We don’t just experience the world — we simulate it, verify it, revise it.
Why This Matters for Psychology
This model of the brain as a prediction engine changes how we understand perception, emotion, memory, belief, and behaviour. In this view:
Perception isn’t raw data — it’s inference.
Emotion is not a reaction — it’s feedback on predicted relevance.
Belief is not static — it’s a generative tool for future expectations.
Selfhood isn’t fixed — it’s a steady narrative that updates in real time.
This framework doesn’t compete with Biogenics — it explains the how. If SP, SO, and SC describe the logic of life, Bayesian inference describes one of its core tools.
Mapping the Triad to Prediction
Self-Organisation (SO): The brain uses prediction to create internal coherence. It stabilises perception, attention, and identity across time by maintaining useful priors and filtering noise.
Self-Production (SP): Predictive modelling helps the organism plan, act, and allocate energy efficiently. Every goal-directed action is based on anticipated outcomes.
Self-Correction (SC): When predictions fail, the brain updates its model. This error minimisation is the neural equivalent of adaptive correction — learning in motion.
Not a Competing Theory — A Supporting One
Bayesian inference isn’t a new biogenic function. It’s the substrate. It’s the logic through which biological systems handle uncertainty — not only in brains, but also in evolution, learning, and artificial intelligence.
Not all predictions serve life. But when life aims for persistence, prediction becomes one of its most effective tools.
Priors vs. Neurofictives
An important distinction: not all predictions are equal.
Priors are low-level statistical expectations. Dogs bark. Gravity pulls. These are fast, flexible, and usually subconscious.
Neurofictives are deep, emotionally loaded stories about who we are, what the world means, and how we should behave. They’re slower to change, but more central to identity.
Both operate as predictions. But only one operates as meaning.
Mental Health Through a Predictive Lens
When predictive models go wrong, symptoms emerge:
Anxiety: The brain predicts threat too often and with too much certainty.
Depression: Priors about the self and future become rigid and pessimistic.
Psychosis: The system overweights error signals or builds unstable models of reality.
Therapy becomes a recalibration of priors — adjusting expectations, building tolerance for uncertainty, and restoring flexibility.
The Logic of Uncertainty
Bayesian inference reveals a fundamental reality: life doesn’t possess perfect knowledge. It has to guess, adapt, and revise — swiftly, iteratively, and sometimes imperfectly.
This is not a weakness. It is life’s strength.
In a world of noise, change, and risk, the predictive brain is a gamble — one that usually pays off.