minds as information machines, part 3
a “repasseado”, one of the many portuguese folk dances. this one is particularly fun to dance to. playing is one of the last makers of mirandese pipes, master célio pires.
after the previous definitions, we can move on to bigger structures easily, since the principle is the same. a brain is included in the previous definitions, but it is different in a very important aspect: its feedback loop for thought is done by both reality and its own internal processes: brains (and complex living things) can do work on themselves effectively changing their own information. this has the advantage of not taking hundreds of trials and errors through evolution to produce meaningful responses to environmental pressures. note that DNA can actually act on itself by making proteins that inhibit or change its own structure. overall, this process is more like a spaghetti of loops mixing internal structure and work and external structure and work. the difference i’m focusing on is both how long it takes to cause information to change (the thought ‘clock’), and how much information can be stored (the ‘capacity’). the boundary of “internal” and “external” is artificial, and can be drawn arbitrarily. i tend to prefer a boundary between the part that does work and the part that just provides energy for the work. the first is all things in “motion”, the latter is just the fabric in which they stand.
now, we saw previously that it is not sufficient to account for the information represented, it is also necessary to account for the information reading machine, e.g., knowing a DNA sequence is meaningless without the appropriate cell machinery to process it and an environment that triggers its responses. this additional information should be assumed included in everything i describe as “information”. as i said, it is not sufficient to know the words, it is necessary to know what the words mean. since we generalized the definition of mind and thought, a cell + DNA is actually a small mind with very simple thoughts. its non-random response to the environment signals some sense of understanding of the real world: too hot, too cold, too moist, too dry, stressed, peaceful, all these chemical signals that bathe it are interpreted and processed according to some internal representation of reality. this representation is the chemical structure of the cell itself in its whole: it must include all the parts necessary for the accurate observation and change of reality’s constituents.
the same holds true for a brain of any kind with a big difference. a cell might go through reproduction to change its DNA, but a brain (a brain is a mind made of connected cells we call neurons) can do so on the fly by rewiring itself. a brain, thanks to the capacities of its neurons, can change its observations and its actions easily without necessarily going through the reproductive process (the actions are changed thanks to its relative power over its supporting systems). this allows for it to be a contained self-reorganizing system. but what defines the fitness of this organization is, obviously, reality. the brains that are better at reorganizing themselves to fit an optimal response to reality are favored, and the ones that don’t, aren’t. so it’s plausible to expect that brains are good at observing reality efficiently, otherwise they wouldn’t survive. more on this later.
this representation of information (which we analyzed previously) is quantifiable, like every other before it. but now we have to deal with a self-modifying observation, instead of a passive one (like our planet analogy). a planet has no choice on what information it will store: it is just a consequence of its surroundings. but a mind, through self-modification, can create a subset of information from all the information it gets. both are subjective observers (their observation is a subset of reality), yet the brain can take that subset and change it. this change, sometimes called reflection or learning, can exist in simple and elaborate brains. we like to think that only humans reflect on what they are doing, but i argue that reflection and learning are a consequence of active self-modification.
the reality observed by brains is distorted not only by its location (like the location of a planet), but also by its structural responses and modifications. this means that brains are worse than passive observers at representing reality. this might seem counter intuitive, but as we said before, we may be able to imagine other planets, but this is a long way away from what a dweller of that planet would observe. even a pebble on its surface will know more about the history of the planet than all our telescopes combined. this should be a humbling perspective above all. we must understand that our observations as consequence of our brains are distorted both by being exposed to a limited subset of data and a filtered, biased alteration of the said data. the wrinkles of our skin, the spots and wounds are a much better catalog of what happens to us than our interpretations of what happened to us.
now, there is something very interesting about brains made of neurons, which in my opinion is quite surprising but makes perfect sense. it is impossible to represent reality in a few billion neurons accurately. but, as we saw in our information analysis of abstraction, it is possible to represent increasing quantities of information by creating abstractions. an abstraction is no more than a subset of common information extracted from a large data set. like in my earliest examples, consider that we have the data set {a,b,a,b,a,b}. we can abstract it by saying it is 3 times {a,b}. note that this is not the same as knowing the entire set: it is merely creating an internal observation that has minimal distortion versus the real thing, saving resources, assuming its elements are interchangeable. it is possible, therefore, to represent billions of tons of steel as a mix of a concept of quantity and a concept of steel. note that you won’t be able to point out atom number x in the bulk of steel (therefore your data will not be fully accurate), yet you have a general sense of what it’s made of. this process of pattern recognition and abstraction makes brains naturally scientific. i’m going to get in trouble because of this one i know it. but in this lies a physical flaw in what is abstraction: all the elements of the set abstracted as the same category are considered to be interchangeable and the same, i.e., and iron atom can be swapped by another one with no trouble. while this might turn out to be true for some cases (like chemical elements), it is completely false for other cases (like people). yet our brains are capable of extrapolating data with equal power regardless of the category used. this is not because our brains have elaborate internal representations of both iron atoms and people. it is because the neurons themselves make no distinction between a pattern that is a person and a pattern that is iron. the fact that we can generalize does not come from richness of observation and understanding, instead, comes from a deep lack of understanding of things. this understanding of the differences between things in reality is something that is fine tuned by our interaction with it. to a young child, a puppet is just as alive as a human being: its brain generalized both as living. it is only after much training and learning that these two can be split between a dead puppet and a living being. and what makes the split is the child’s understanding that a puppet can’t move on its own. but the reason why the puppet can’t move on its own comes from the outside world: reality, through the laws of physics, provides that information permanently in every observation. its brain will then, if it’s not deceiving itself, correct its observations to generate a better observation of reality. i hope this is clear because this is essential to understand what will come next.
why is it naturally scientific (actually, i should say naturally philosophic)? let’s go through the process of a mind growing. the mind starts with mind M as it inherited from its ancestors (DNA, cells, etc). this mind M has both observations and actions (as i said before, they are both physical things). if M is faced with a new pattern p and observes and acts on it adequately, then M will prevail. if not, either M corrects itself into a fitter M’ or it will perish. this requires that M is permanently observing reality and testing its internal representation against reality. this is similar to the scientific method: bad ideas are discarded because they don’t work, but whether they work or not is verified by reality. i already provided a definition of the scientific method previously, but now it should become clearer. brains calculate a generative space through abstraction: 3 times {a,b} is a generative space because it can “create” via work a set bigger than itself. abstraction allows us to change the 3 into 4 or 5 and imagine new {a,b} worlds that don’t exist. this is a natural consequence of abstraction. the algorithm used to create the perfect fitting between the abstractions of reality and the observations of reality is the scientific method. its error is the measurement of scientific progress. let’s do a simple example to clarify.
consider miniverse R = {a,b,a,b,a,c}. two competing abstractions exist in minds M1 and M2. M1 = 3 times {a,b} and M2 = 3 times {a}, 2 times {b}, 1 time {c}. which is the one that fits the data best? we generate the extrapolated reality of each, RM1 = {a,b,a,b,a,b} and RM2 = {a,a,a,b,b,c}, and compare them with reality R. we can see that the first mind miscalculated one observation (b instead of c), so it has an error of ~17%. the second calculated all observations correctly. the best choice is, therefore, M2. my formulation of the scientific method is the search for the optimal abstraction whose generated observation space fits reality with 0 error. it is impossible, but an interesting goal. any animal whose understanding of reality is fitter than another will have an advantage over others, and therefore, will thrive. being scientific is no more than being very good at following our survival instinct.
the main point i’m getting at is, however, not how reality as a feedback is key to understanding minds, but actually, how minds themselves represent reality as generative spaces instead of static observations. as we saw above this makes sense if our observation resources are limited to a small quantity of data. it is easier to store the generative space of reality and generate it than to store reality itself. much like my example. but there is a problem. if you accepted my premise that brains store mostly generative spaces instead of real observations, then this means brains can generate realities that do not exist. the existence of brains creates extrapolated realities that do not fit reality. for example, i can imagine an extrapolated crocoduck from my abstractions of living beings, ducks and crocodiles. but this generated observation, when tested, is proven to be false as an existing animal. my abstractions of living beings, ducks and crocodiles are good scientific evidence tested by reality, yet i can generate equally easily completely invalid ones. how can we tell them apart? as i said, for survival reasons, animals with a good sense of “real” should survive better than animals without it. but if survival is out of the picture, then there is no way of stopping fantasy (non-scientific knowledge) from becoming the center actor of a brain’s activity. this hints at the subject of the coming posts. note that even a false idea resides in reality, so it is real only as far as the molecules sustaining its concepts are real. once extrapolated and tested and proved false, it should remain as a hypothetical tale and a proof of the insufficiency of generative spaces.
so far, these seemingly elaborate concepts have been given without any apparent goal. from here on i will begin to build on these concepts and analyze bigger and bigger systems. it’s been very boring, but this sets up the foundation on which i will work, so that ambiguity is removed.