Human Brain — Reverse Engineering
By: Millennial Talks
Tags: Cognitive Bias, Predictions, Brain, Statistics, Engineering

The previous post addressed the foundations of the human brain and it’s Bayesian nature and left us with the question — how does the average calculation error over time actually matter to a brain that constantly needs to deal with acute mistakes at any given moment?

The answer lies in the free energy principle.

Any of the potential states of your body are death and dysfunction, according to the second law of thermodynamics. You are grateful to be alive and able to read this. Sooner or later, for each one of us, entropy will catch up and we will reach the physiologically unexpected but biologically possible and imminent state of death. Yet this is just what we have developed to discourage! We were “designed” by Nature to struggle for life, to fight anarchy, to escape entropy. The human body has adapted to sustain itself within planned, easily predictable states.

We accomplish such maintenance, according to the free energy principle, by suppressing our free energy, the information-theoretical equivalent of the overall (long-term average) prediction error. All we do (and all that every living being does) is done to minimise free energy on average and over time, which correlates to the role of the brain to mitigate predictive error.

And all this in order to remain inside the predicted, comparatively stable states… Why? Since only within a certain set of physiological states can we survive. Too hot and we’re dead, too cold and we’re going to die, too much oxygen and we’re going to die, too little oxygen and we’re going to die, et cetera. We would be destroyed by too much or too little of something, thus the simple biochemical concept of homeostasis.

To make a long story short, the following concepts, while working at varying stages of detail, are hypothesised to be equivalent:

cognitive level: minimise prediction error, maximise model fit

information-theoretic level: suppress free energy, reduce surprisal

physiological level: maintain homeostasis

physical level: resist entropy

biological level: survive

The result here is that predictive processing captures what the brain applies to the evolutionary fitness of the body. Both structural and functional aspects of the brain, including its anatomy, integration, neural physiology, electrophysiology, and psychophysiology, are also explained by the free energy theory. I must admit, however, that there’s still much about free energy I don’t understand, and so much math I haven’t gone through yet, that it would be preposterous for me to keep writing as if I can actually explain everything through this short post.

However, there are several objections to this principle. The major of these objections are as follows -

Sensory Deprivation

Shouldn’t the winning tactic be to inspire us to lock ourselves up in a quiet, silent space, if all our brains want is ‘low prediction errors’? What better way than depriving the body of sensory stimuli to make good decisions about anticipated sensory input? We can’t get high estimation errors in absolute darkness when we expect to see — black, black, and black.

However, while this technique can be successful (and relaxing) in the short term, it is not possible to escape the pressures of the universe and our bodies for long. No matter the degree of exteroceptive deprivation, the brain, being a body organ, never fails to produce interoceptive prediction cascades. We would soon feel bored, starving and motivated to leave the room again, bringing our bodies in increasingly unexpected situations. Locked for a long time in a quiet, silent space, we can’t preserve homeostasis. We have a hyperprior in Bayesian terms that occupying the same state for too long would raise the error in long-term prediction. It is typically more successful to engage with a stimulus-rich atmosphere to maintain within a spectrum of predicted states at all times (except, of course, when we need to sleep).

Human rationality

People are notoriously poor at probabilistic reasoning, at speaking of probability rationally. So if mental processes can barely handle Bayesian inference, how can we assume that the brain, which produces the mind, is Bayesian?

Any homunculus doing complex math does not occupy the Bayesian cortex. Its probabilistic inferences are not explicit, but implicit. Brain cells fire in a way that “naturally” approximates the Bayes laws, much as ants run in a way that “naturally” approximates the distributions of Gaussian and Pareto without the need for a chief ant strategist who determines his colony’s optimum course. Here, what does “naturally” mean? In the case of ants: pheromone secretion. In the case of neurons: Bayesian sampling.

The Bayesian brain is a sampler and not a calculator: it samples probability data from a local landscape (its environment). Through time, behavior and interpretation, related to a nearly infinite number of predictions, produce an essentially infinite number of samples from the unimaginably complex distributions of probability that constitute reality. This immense volume of data makes the brain (asymptotically) adhere to the rules of probability, thus mechanistically understanding the Bayes theorem. Of course, all such samples are not processed by the brain; it just uses them to refine its internal world model until it draws new samples from the actual local environment.

The human mind, by comparison, would rely on cognitive shortcuts (heuristics) to produce predictions from only a finite number of samples. Although this has proved to be evolutionarily adaptive, now it causes them to be cognitively biased and irrational statistically. Explain how sampling generates different reasoning biases in the Bayesian Brain without Probabilities. Note though, that laboratory studies on cognitive biases are conducted in extremely scarce environments. When people make everyday cognitive judgments in rich, familiar, realistic contexts, their reasoning can be much closer to Bayes optimality than with random judgments in a lab context where sampling is radically limited.

In addition, note how the confirmation bias (our propensity to rely on evidence supporting our preconceptions) immediately follows from the theory of the Bayesian brain. After all, for the internal construct our brain uses to make predictions, preconceptions are vital. Our preconceptions, what we notice and rely on, are strongly determined by what we already know. And since predictive processing lets us look for whatever is more probable to validate our predictions, as a technique to mitigate predictive error, it would usually prefer confirmatory proof, rather than shocking evidence.

Offline Cognition

The brain does so much more than perceive the world and command motor actions. Not everything we do is directly linked to perception and action. We plan vacations, make financial decisions, learn about political claims, and ruminate about incidents that occurred years ago. Such cognitive activities happen “offline,” i.e., without immediate real-world interaction. How can predictive processing account for that?

Offline cognition is just a type of high-level computation that does not actually spread forecasts all the way down to the lowest levels of the hierarchical cascade (or suppresses low-level model updates by dramatically decreasing the respective precision weights) — although it might at some point, in which case the timescales are simply much larger than those of “online” cognition, i.e., cognition concerned with immediate sensorimotor interactions in the present environment.

Phenomenology

The universe does not look like a confusing collection of entangled distributions of probability density. It looks vibrant and vivid and unambiguous in general, always breathtaking. So how can we assume that nothing but predictive processing is cognition? Isn’t this overly reductive?

Predictive processing is a hypothesis of how information about the environment is encoded by the brain, not how people perceive it. Phenomenology is simply on a higher level of description than cognitive science, similar to how physics could (on an even lower level and to little avail) describe cognitive processes in terms of molecules, atoms, and electrons. How much psychology can be simplified to predictive processing would be a more fascinating issue.

Falsifiability

If everything about the brain and mind can be clarified by predictive processing, does this not reveal its triviality? Moreover, researchers may retroactively tweak their previous conclusions to make any experimental result fit a Bayesian interpretation post hoc. This will suggest that little is clarified adequately by the hypothesis. What prevents the theory of the Bayesian brain from being trivial and unfalsifiable?

Evolution, too, is an incredibly optimistic theory which, with just a few simple tools, attempts to explain everything about life and biological systems. Of course, this calls for a lot of post-hoc theorizing, especially in the field of evolutionary psychology, where tales are frequently separated from alternate explanations. Yet this doesn’t invalidate the theory immediately. What it suggests is that we have to think deeper to come up with plausible alternate theories and stipulate independent evidence-based priors. “There is independent proof, for instance, that we anticipate light to arrive more or less from above, that things move fairly slowly, and that we expect others to look at us.” Neuroscientific studies should integrate such types of evidence.

It is possible to falsify predictive processing and the free energy theory. This will falsify the hypothesis if descending prediction signals were found not to bear predicted accuracies. Or it may turn out that, in terms of prediction error signalling, certain brain regions are not better represented. Finally, a wholesale falsification, though extremely doubtful, would be the finding of an organism that does not at all function to remain within planned states (to preserve homeostasis).

So in conclusion, predictive modelling provides a paradigm at the computational level to explain all fields of neuroscience and cognitive science. While the Bayesian brain hypothesis is still in its infant stage, from a wide variety of diverse areas, confirmatory data floods in on a weekly basis. And while it gives a widely inclusive and optimistic description of the brain and the cognition of people, it leaves something unknown. We must also interact with the paradigms of evolution for specification (as cognition has an evolutionary history), embodied embeddedness (as cognition is embedded in physical environments), and socio-cultural circumstances (as cognition is situated in social and cultural contexts). And if all layers of research are covered and integrated can we fully understand how humans function.