Learning: from parts to wholes

Learning Resource Centre, Edge Hill University (Photo credit: jisc_infonet)

The questions of universals (as in Plato’s forms) and wholeness (as in mereology, holism, synergy, etc.) are unsolvable by logic or reason, but second-nature to physical brains.

Philosophers still struggle to say what makes a table a table, and how or why a whole is greater than the sum of its parts. But a brain knows a thing’s type or a part from a whole when it sees it. The so-called universal or whole is entirely the prerogative of each individual brain based on its history of associations.

My hypothesis about learning may not square with biosemiotics, but it could be called bioinformatics if that term were not already taken by computer science. (My definition would be information processing  BY biology rather than about it.) But anyway, I think the learning process is straightforward. Meno’s Paradox does not apply. The organism doesn’t need a model of what it is searching for–it searches for everything it is physically capable of searching for. This is like Google’s web bots that crawl the entire web building a condensed symbolic representation of the digital “environment”.  The unconscious side of the brain (the bulk of it) does something like that all the time.

Image via CrunchBase

Another part of the unconscious brain does something else like Google — it looks at all the collected data and discovers all the possible patterns (and patterns of patterns) that can be found there. No model is required in advance other than some basic start-up algorithm that defines what a pattern is, and this is genetically encoded in all neurons. It is from the strength and relations of these identified patterns that the brain gradually and progressively builds a knowledge base.

Learning is a combination of data mining and progressive pattern detection without the necessity of pre-existing data models or “objects”. The interesting thing is that this same process is currently turning the scientific method on its head, and machines are beginning to learn the way the brain has been learning all along. Thousands if not millions of scientific discoveries are waiting to be found among the hundreds of exabytes of machine-readable data already online.

IMO the simple concepts of association, correlation, and pattern-detection will prove to be the ultimate foundations of epistemology, a subject that has perplexed philosophers and scientists alike for millennia.

(The above “synapsis” obviously leaves out a lot of intermediate steps from simple associations to neural cooperation and division of labor to increasingly sophisticated pattern-detection capabilities on the assumption that a word to the wise is sufficient…and because we haven’t learned all that stuff yet.)

Poor Richard

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