The Fallacy of Seeing Patterns

The fundamental result behind the gambler's ruin is the human tendency to perceive patterns in genuine randomness. Rorschach blots, lotteries, slot machines, most betting endeavors all work because some humans will always find patterns in randomness. The result is easy enough as are the experiments (generate random noise from Uniform(0,1), project it onto a suitable manifold, hire some undergrads or local homeless people to look at them). That's not at all what I claimed Konrad originated. If you want an origin for this type of thing, de Finetti or Laplace or Descartes might be some candidates.

Konrad did give the phenomenon a catchy name and proposed that an increase in this tendency is an initial step in developing schizophrenia. If someone could actually establish this at a neurogenetic level that would be impressive and fundamental; I'm not aware of anyone doing so. I'd expect it to show up in a CNS journal and NIMH or WT to make a big deal if someone did.

The contrast between epiphany and apopheny is so striking, though, and so relevant to this topic, that it annoys me to no end when it is ignored. At the base of all of statistics is a desire to quantify how much of each is present in an observation, experiment, or cyclic series.

As you probably guessed, I am an applied statistician, not a neuroscientist. (I have serious issues with the way statistics are misused in neuroscience, for whatever that's worth). I do not, and cannot, claim that Konrad's theory is fundamental to that field. I do claim that anyone attempting to explain statistical reasoning to a lay public ought to internalize the contrast he proposed. Its setting as a proposed turn towards insanity is just a happy historical note.

> Either way, it bears keeping in mind that our day-to-day language isn't really optimized for discussing these kinds of things, so there's bound to be multiple layers of confusion.

After decades-long study of human behavioral phenomena, I'm striving to articulate what I've learned in coherent written form. It's proving difficult to transform a non-linear multi-dimensional model into ordinary English prose that readers can comprehend. So I absolutely agree with your comment about limitations of ability to reduce mental models to common language.

The issues you bring up concerning formalized models that allow mapping behavior to determining factors are indeed of central importance. A model must permit sufficient granularity of analysis, at the same time covering sufficient generality without contradiction of the granular level. The hard part is describing the interactivity of this whole range of "levels", because the immediate and the distant elements are in fact occurring simultaneously and affecting the system under observation in real time. It gets convoluted when we realize the observation itself has effects on the observed behavior.

The problem I have with "appropriate" is the term's ambiguity. OTOH "pattern" implies there's a "match" or there isn't. (I know, patterns can be iffy, but then they're not quite a pattern.) Encountering a situation that's unclear, where no "matched" pattern is evident, immediately arouses alarm. Then we proceed with caution until observing enough that something "familiar" is gleaned, or observe/interact enough to establish a new pattern.

This state of "I don't know" is constantly implicit, patterns never match perfectly, details always vary. Most of the time that's overlooked because we accept a "close enough fit" to established patterns, that is, categorical classification is an abstraction that works adequately most of the time.

For example, often it's good enough to say "that's a tree" without saying what kind of tree. But other times it's important to distinguish a fir from a pine from a hemlock. Patterns are infinitely divisible, ultimately no two trees are identical, at some level of refinement abstractions break down and no longer apply. A thing is no more or less than its actual attributes. Though indispensable for human existence, abstraction is just a tool, pattern recognition is a built-in mechanism of abstraction, best to remember all tools have their limits.

I certainly would never say there's no more to learn, just that defining terms is only a tool for communication, not to be confused with the information we attempt to share. We get confused when we think we are "explaining" phenomena that we observe. In reality, it's less confusing and more informative to simply describe what we observe. Curiously, thoroughly observed phenomena are the things we tend to call self-evident or self-explaining, which suggests an explanation is only an expression of uncertainty about patterns yet to be adequately elucidated.

I believe Popper thought you might be able to find a metric to judge how close to a "truth" one theory might be compared to others.