Human Brain — A Bayesian model. In the previous post we discussed how…
By: Millennial Talks
Tags: Cognition, Bayesian Statistics, Predictions, Brain, Reverse Engineering

In the previous post we discussed how the human brain makes predictions and the model underneath it to forecast the surrounding world’s behaviour. Now, we’ll dive into the details of our brain’s Bayesian nature.

The fundamental principle of Bayesian probability is that in the light of new data, you change your assumptions. For example, if you see a dog-like entity running towards you, you may be 34 percent in your confidence that it may assault you; if you acknowledge that it is an unmuzzled pit bull, your belief that you will be assaulted will rise to 78 percent; if it continues to bark, 92 percent; if another barking dog passes you from behind the chance that your attack-belief may be valid drops again. This is a vague and incomplete example, but it shows how the obtaining of new facts changes your certainty of a conviction. So, Original Trust + New Proof = Revised Faith.

The Bayes’ law is the mathematical representation of this idea : P(B|E)=P(E|B)*P(B)/P(E), that calculates the conditional likelihood that your conviction B is valid, provided proof E. During predictive analysis, this is what the brain approximates. In fact, in line with Bayes’ theorem, the brain updates its mathematical model of the universe by incorporating prediction errors which leads to the idea that the nature of the human brain would be Bayesian.

Your brain knows about the likelihood P(E|B) of E, given hypothesis B, with the model’s estimation or prior probability P(B) and the lower-level data E inside a wider hypothesis space P(E). This yields a posterior probability P(B|E), which calculates the prediction error, by applying Bayes’ law. The error signal, modulated by attention (an estimated accuracy), then propagates back upwards to the next higher stage where the construct is modified, the probabilistic map of truth is corrected by your brain, and the hypothesis checking begins. Just assume, for example, that you want to cross a street:

Your brain measures a hierarchical cascade of projections P(B) on the basis of a large collection of assumptions B about the present case, like

priors about how to change shapes, colours, and noises (for low-level perceptual inference),

priors about how to change shapes, colours, and noises (for low-level perceptual inference),

priors in motion and adjusting traffic signals for cars (for high-level perceptual inference) and

a prior about you standing on the other side of the street (for high-level active inference).

2. Your brain collects sensory data E, like sensory information E, on what is currently happening on the street and in your body, like

proprioceptive information representing the movements of the eye, head, and leg,

conceptual information representing vehicles and traffic signs, as well as

agentic data representing your relation to the goal state.

3. Your model contextualizes the knowledge to obtain P(E) and, considering your predictions, calculates the probabilities of the data P(E|B).

4. In order to measure the posterior probabilities P(B|E), the brain indirectly applies Bayes’ theorem and calculates prediction errors at the appropriate processing layers by:

matching posteriors and priors regarding shapes, colors, and noises,

matching posteriors and priors regarding your eye, head, and leg movements,

matching posteriors and priors regarding vehicles and traffic lights, and

matching the posterior and the prior regarding your goal state.

5. Weighed by predicted accuracies, the prediction errors spread the hierarchical cascade, level by level, and adjust the resulting collection of hypotheses, thereby updating the model to minimize possible prediction errors; or they are actively reduced to the muscles that shift your eyes, head and legs via motor commands (more about that second option in a second).

You will find yourself on the other side of the street after ample cycles of hypothesis checking and paradigm updating during continuous sensorimotor contact with the universe (if all goes well). It is possible to characterise this dynamic predictive mechanism as the brain constantly executing Bayes’ theorem.

So, what does this explain?

The spectrum is highly optimistic for the Bayesian brain theory. For both neural, emotional, and psychological phenomena, it’s supposed to be a unifying structure. If real, predictive modelling describes everything about the brain and mind at a computational stage, for reasons we’ll see shortly. But let’s first take a look at how the predictive processing paradigm relates to particular cognitive phenomena:

Perception is the prediction of sensory signals and the inferential mechanism of adjusting the internal predictive model to minimize predictive error. This is perceptual inference: by modifying the model so that sensory responses fit previous expectations, reducing prediction error. You make the model more analogous to the environment by interpretation.

Imagination can be a side-function of the predictive machinery of the brain based on high-level forecasts that do not move all the way down the hierarchical cascade and may not fit actual sensory results, such as dramatically decreasing the weighting of accuracy on low-level error signals.

Delusions and hallucinations can arise from improvements in the ability to combine incoming information with perceptual predictions.

2. Action is proprioceptive inference and the inferential mechanism of modifying sensory signals to minimize predictive error. This is successful inference (also called predictive control): by rotating the body so that sensory signals meet previous expectations, decreasing predictive error. You make the world conform to your model through motor action. Although the expected proprioceptive states are not yet possible, activity affects the universe to make them so (which means that all your actions are essentially self-fulfilling prophecies).

At the lowest layer of the active inferential phase, reflex arcs run and work to fulfil proprioceptive predictions before the expected sensory feedback is received. The model parameters are not modified, but held constant, as with perceptual inference.

A high-level forecast of an arbitrary potential target state is deliberate action. When a concrete chance to act occurs, multilevel cascades of lower-level predictions are involved in transforming the environment in a manner that makes the high-level prediction come true (i.e., reaching the target state), thus minimizing errors in long-term prediction. A clear example of predictive modeling working at large spatiotemporal scales is the attainment of conscious goals, since it may take minutes, months or years to accomplish a goal, and it may require one, say, to fly overseas)

3. Emotion is interoceptive prediction and the successful inferential mechanism of inducing physiological modifications to minimise prediction error.

Drive is the active-inferential mechanism by which high-level behaviour minimises interoceptive prediction error. For eg, if the brain senses low blood sugar levels by interoceptive inference and the resulting estimation errors are not corrected through autonomic regulation (e.g., body fat metabolization), you would feel empowered by voluntary intervention to mitigate error signals (e.g., eat sugary food).

Mental disorder may be the inability to reduce interoceptive prediction errors.

4. Attention is expected precision optimisation; it modulates the weight of prediction errors. The effect of focus changes being that the same sensorimotor hypotheses (lower-level priors) can not be held for very long and the universe is an ever-changing place that will destroy you if you don’t change (which you can’t if you never move the mind if you still assign the same weight to the mistakes of similar predictions).

5. Learning is the updating, based on prediction errors, of your internal model so that your forecasts steadily change. The higher your forecasts of the world’s causal, probabilistic nature, the more you can communicate with it more efficiently. This is why, after having practiced the related motor skills, adolescents travel more fluently through space than children, why you are less clumsy when playing a sport or a musical instrument, and why it is important to seek truth — enhanced effectiveness due to better predictions.

6. Memory consists of your inner model’s learned parameters, while the inherent knowledge evolution has genetically developed into your nervous system its non-acquired parameters. Both elements dictate the brain’s predictions.

7. Self-awareness is the inferential approach of minimizing the error of inference by modifying the internal self-model, i.e. the model that makes assumptions on what’s most likely to be “you”.

Agency benefits from a sufficiently strong match between the projections of the self-model and the exteroceptive input. You sound like you’re not in control of your decisions if the forecast error is too high.

Ownership results from a strong enough match between the predictions and proprioceptive feedback of the self-model. You can experience that one of your limbs does not belong to you if the estimation error is too high, or you may have an out-of-body experience.

8. Belief is a hyperprior; a high degree of abstraction of a structural prior; a high-level prediction that requires universal understanding of the universe. Some examples:

Physical beliefs. You expect situations to shift over time, you expect large items to collapse easily, and you expect that you will not be able to switch right and left at the same time.

Physiological beliefs. When you open your eyes, you expect to see something and you expect fire to damage and burn your flesh.

Psychological beliefs. To make you feel proud, you expect a fantastic success and you expect to feel regret when you shy away from a challenge.

Social beliefs. You want happy people to smile, provocative words to spark an answer, and the ability to corrupt you.

Cultural beliefs. When they approach stop signals, you want vehicles to slow down, exclusive sales to be displayed in shops, and handshakes to not last an hour.

What’s more interesting is that this bayesian nature of our brain tends to control some of our deepest ethics and morals -

We value truth because concrete beliefs about our surroundings empower us to make good predictions.

We respect integrity and sincerity so we know what we can expect from truthful, sincere individuals, which in social situations involving them improves our prediction making capabilities.

We respect simplicity because straightforward beliefs allow us to easily produce high-level predictions.

We value wisdom because as mirrored life experience equips us with reasonably stable hyperpriors that reduce long-term error in prediction.

At the same time, human cognition’s predictive existence illustrates why we appear to become enslaved by patterns, feel addicted to comfort zones, and shy away from uncertainty. Our priors and hyperpriors are less accurate outside routines, comforts, and certainty, which causes them to make poorer decisions and get higher prediction errors. Yet high errors in estimation are exactly what the brain tries so hard to avoid. The effort to dramatically redesign our templates to fit novel, unfamiliar situations can be so strong that the brain actually decides to cause feelings of terror, anxiety, or irritation to enable us to adhere to what we can anticipate more accurately, i.e. our normal patterns of action in comfort zones of calming regularity.

If we want to abandon or extend our comfort zones, we have to persuade our minds that at a wider time scale, the instantly resulting high prediction errors are worth it. We may thus conceptualise willpower as a hyperprior, namely as the abstract structural prediction that in some cases, short-term higher prediction errors can lead to long-term lower prediction errors. Thus, self-control can minimise overall errors in prediction.

But then again, how does the average error in estimation over time really matter to a brain that at any given moment continuously has to contend with immediate errors? We’ll get to this part in our next post…