The Scientific Brain Theory: How the brain works in a nutshell.

The following is a summary of the Scientific Brain computational model of the mind. 

Outline

  1. Just like scientists in the research community, the neurons in our brains cooperate and compete following a scheme analogous to the scientific method to identify the causes of the stuff we observe. Once there is such a model of the world, all there is to intelligent behavior is to take whichever plan of action is predicted to yield the sought-after stuff.
  2. * At a lower level, the brain's model of the world includes a probabilistic model, where neural patterns code the probability (intuition) of stuff
  3. At a high level, complementing our low-level probabilistic model, what we can conceive as our conscious knowledge constitutes a logical model, where natural language description spell out some sort of established knowledge of how stuff comes about or what things do we need to do to bring stuff about.
  4. Given that phenomena in this world generally occur on a spectrum of shades of gray, our instinct's probabilistic model is better suited to represent the kind of uncertain pattern associations we observe in real life.
  5. The strings of words of our logical model represent a richer, more versatile and powerful framework to code how stuff comes about or what do we need to do to bring about stuff.


Description

* Just like scientists in the research community, the neurons in our brains cooperate and compete following a scheme analogous to the scientific method to identify the causes of the stuff we observe. Once there is such a model of the world, all there is to intelligent behavior is to take whichever plan of action is predicted to yield the sought-after stuff.

Privileged minds' false narratives aside, neuroscientific research strongly indicates that our sense of consciousness is only a construct. The dominant view among computational neuroscientists is that the brain's objective is to learn a predictive model of how stuff comes about. Moreover, according to the new Scientific Brain Theory Of The Mind, the brain implements a scientific model of how the world works. Not unlike scientists in the research community, the neurons in our brains cooperate and compete following a scheme analogous to the scientific method to identify the causes, reasons and intervening factors for the stuff we observe. Yes, the brain's genius strategy to intelligent behavior is best described by the scientific method: that is, learn a model which replicates how stuff comes about and then take whichever plan of action is predicted to yield the sought-after stuff. The key question then becomes: how such predictive model is built? That is, how does the brain learn what are the causes, reasons and intervening factors for the stuff we observe? 


* At a lower level, the brain's model of the world includes a probabilistic model, where neural patterns code the probability (intuition) of stuff

As a matter of fact, Scientific Brain substantiates that it is actually more precise to speak of two superimposed models, each meant to code different - but complementary - aspects of information: at a lower level, what we can understand as our instinct - comprising both innate and learned 'automatic' behaviors - constitutes a probabilistic model, where neural patterns code the probability (intuition) of stuff. For instance, the Pavlovian Dog's brain follows reinforcement learning to learn that the bell is the cause of the food it is about to receive: that is, once the bell rings, what is the probability that food will be served? 


* At a high level, complementing our low-level probabilistic model, what we can conceive as our conscious knowledge constitutes a logical model, where natural language description spell out some sort of established knowledge of how stuff comes about or what things do we need to do to bring stuff about.

Now, direct pattern associations of this kind are clearly not a suitable coding scheme to represent complex data structures such as long sequences of sophisticated events (e.g., how do we make nice, lofty bread?). Thus, the evolution of a mechanism whereby complex information can be retained, would constitute a formidable evolutionary advantage for any species; even more so if - as is the case with natural language - such a coding scheme is shared among all members of the species. As a matter of fact, a good case has been made that the human brain exhibits the most advanced cognitive capabilities, because it is in humans that the most sophisticated language evolved. We generally see language as a method of communication, but it is first and foremost a knowledge coding scheme. Indeed, it stands to reason that, at a high level, complementing our low-level probabilistic model, what we can conceive as our conscious knowledge constitutes a logical model, where natural language description spell out some sort of established knowledge of how stuff comes about or what things do we need to do to bring stuff about. In the same way that a recipe book represents a logical model of how to prepare foods, our (language-coded) conscious knowledge acts as a logical model for accomplishing all those tasks we carry out consciously (e.g., a bread recipe constitutes the set of causes, reasons and intervening factors for a nice, lofty bread). In fact, the construct responsible for our sense of conscious thought is the best indication, that natural language is the brain's coding scheme for complex knowledge structures.


* Given that phenomena in this world generally occur on a spectrum of shades of gray, our instinct's probabilistic model is better suited to represent the kind of uncertain pattern associations we observe in real life.

Taking together the probabilistic and logical models, the complete design could not be more marvelous, as it combines the best of two worlds: Given that phenomena in this world generally occur on a spectrum of shades of gray, our instinct's probabilistic model is better suited to represent the kind of uncertain pattern associations we observe in real life. For instance, if for once the Pavlovian dog does not get fed after the bell rings, it would not make sense to unlearned the conditioned salivation response altogether (as the discovery of a counterexample requires in formal logic). Rather, it would be smarter to simply adjust the probability that food is served whenever the bell rings.


* The strings of words of our logical model represent a richer, more versatile and powerful framework to code how stuff comes about or what do we need to do to bring about stuff.

However, evidently, raw pattern associations are too basic a coding scheme to efficiently represent complex data structures. Instead, if for example we need to spell out well-proven cooking recipes, strings of words certainly offer a far richer, more versatile and powerful framework. Admittedly, to the extent that words have clear-cut meanings, our language-based logical model does not reflect as colorfully the wide range of hues in the real world as our probabilistic model. For instance, if we are not certain what exactly causes bread dough to rise, it will be rather tortuous to spell out a bread recipe. However, we should all have to agree that it makes little sense to commit a plan of action to paper, before we have it well defined. Definitely, the language-coded logical model constituting our conscious knowledge is meant to represent well-tested, established knowledge.


* In summary, the brain follows two basic coding schemes to implement our knowledge of the world: pattern associations for our instinct's probabilistic model and natural language for our reason's logical model.

In summary, the brain follows two basic coding schemes to implement our knowledge of the world (i.e. what are the causes, reasons and intervening factors for the stuff we observe): at a foundational level, our instinct's probabilistic model represents confidence levels that a certain perceptual stimulus will follow a given cue, or a certain motor response is appropriate to the on-set of a given perceptual stimulus). On the other hand, at a higher level, - in a manner resembling a handbook encyclopedia - natural language complements our probabilistic model with an expandable layer of enhanced logic. This evolution not only made it possible to seamlessly encode all sorts of sophisticated knowledge structures, but - crucially - enabled everyone in a community to share their ideas, which in turn dramatically expanded the groups knowledge of how things work and how stuff can be accomplished. Absolutely, throughout Evolution, just like scientists in the research community or neurons in the brain, humans cooperate and compete with one another, so that at the end of the day, only the most accurate theories survive.

Finally, once the causes for the stuff in the world are all encoded in our brain's model, all there is to intelligent behavior is to run the model from the given situation (cause) and take whichever plan of action is predicted to yield the sought-after stuff. Fascinatingly enough, our conscious experiences will then correspond to any such run of the language-coded logical model.


This article is part of Chapter XIII of "The Scientific Brain Theory of the mind: From Instinct to Reason, How Does The Mind Work". 

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