
The Fallacy of Seeing Patterns - techchick85
https://blog.clevertap.com/the-fallacy-of-seeing-patterns/
======
jrapdx3
The article starts with, "Human beings try to find patterns to explain the
reason behind almost every phenomenon, but that doesn’t mean that there is a
pattern to rely on." The second part of the sentence is true, on further
observation some patterns prove not to be patterns. However the first half,
the attempt to explain _why_ we "find patterns" isn't convincing.

Instead, replace that phrase with this: "based on brain construction, we
humans are predisposed to find patterns in data we encounter". This idea holds
up to evolutionary scrutiny, organisms have mechanisms biased for self-
protection and finding food and other resources through various forms of
pattern recognition. IOW we find patterns we're neurologically capable of
finding, particularly in regard to survival and reproduction.

Perhaps it's more accurate to assert we "find patterns" useful in making
predictions about our present and future state within the environments we
occupy. The fact that patterns may not turn out to be authentic is simply part
of a process of refinement of pattern-seeking and improving value as
predictors of future states. We may call a pattern an "explanation" but
nothing is actually "explained" as shown by the fact we reserve the right to
enhance or revise the "pattern", or what we insist it predicts, at any given
time.

Edits: grammar and clarity

~~~
Retra
Our brains are basically built to do pattern matching. Though, truthfully, I
find that is too abstract of a way of putting it, and in fact, people tend to
associate "meaning" with "pattern" and thus you end up with statements like
that one.

What I would say the brain is good at is finding patterns in terms of
_identifying what is appropriate_ , with a particularly general understanding
of "appropriate." There's a huge evolutionary drive for this. It's a bit
disjointed to say wolves howl with each other because "brains detect patterns"
and somehow find them useful. It's much clearer when we say that "brains
encode patterns in terms of appropriateness", and thus wolves howl with each
other because their brains know it is appropriate to do so. Just like germ's
biology encodes that it's appropriate to wiggle harder in salty water, or how
a person knows that certain words are correct in specific situations.

So our brains are more like engines for mapping patterned associations to
feelings of appropriateness in context.

~~~
jrapdx3
Appreciate your interesting thoughts on the subject. It certainly does appear
that brain circuits are evolved to respond to patterns in data, for example,
visual neurobiology has been studied extensively re: how visual systems can
identify patterns in visual data.

Not exactly sure how you define "appropriate" in this context. Like an
astronomically complex "neural net", the brain integrates "input" into pattern
recognition, so it's a form of classification filtering that allows
recognition of phenomena. On the basis of experiences, the classified patterns
are bound to probabilistic prediction estimates, in turn informing choice of
actions.

Though I know much less about wolf vs. human behavior, if analogous to human
speech, howling is an action taken in response to evaluation of current
environment, e.g., pattern of other wolf activity, presence of prey, etc. We
might infer howling is "appropriate" under some condition, but really it's
tautology, because it's equivalent to stating we observe howling under some
condition. IOW the latter is a description that's complete and sufficient
regarding what we "know".

It might be clearer to rephrase "appropriate" to "what is and what works",
though "what works" is probably superfluous to understanding the process. IOW
an attempt to "explain" the pattern recognition and response phenomena adds
nothing to our knowledge, and in fact adds a level of indirection that tends
to obscure the nature of process.

I should let it go at that because it's late and I'm tired. Saying more leads
to multi-dimensional consideration of the nature of brain/body operations. If
you are really interested in arcane and slippery meta-level or higher-order
viewpoints I'm happy to say more, but better when it's earlier in the day and
my head is clearer.

~~~
Retra
You're right in that it doesn't seem to add any information. It's possible
that there's nothing more to learn, but it's also possible that the
distinction would manifest in a subtle way that your current formalized
understanding doesn't employ sufficient granularity to capture.

I guess I prefer the term appropriate because it more cleanly handles the case
when the patterns "don't" match: by indicating that such situations don't
exist. There's always a pattern to match, just as there's always some action
that's learned (or instinctually) appropriate to any given situation.

If you're trying to see appropriate as "what works", then you're probably more
strongly aligning the meaning with a rational process than what I intended,
but I don't think you're wrong. 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.

~~~
jrapdx3
> 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.

------
bognition
>As an analyst, one needs to keep in mind that the Journey is more important
than reaching the Destination.

I'm really not sure what to make of the last line. The goal of analysis should
be to produce results that are actionable. In the end it should matter very
little how they are obtained as long as they are accurate.

~~~
apathy
Accurate results are not necessarily actionable.

~~~
bognition
correct, but inaccurate results are never actionable.

~~~
iaw
If only that were true in practice. The stories I could tell about mistakes
I've found...

At the end of the day, your point is 100% correct.

------
iaw
I wish the article was on Apophenia, it would be more fun.

Instead it's not clear to me who the target audience is. The phrasing makes it
appear to be targeted at analysts and not their business partners. Assuming
that to be the case, senior analysts are substantially beyond the level this
article is written at (or should be).

Entry level analysts need close supervision to prevent them from making these,
and other, mistakes. The examples the author draws (specifically cheese vs.
infant mortality and the google flu approximations) don't do a good job at
identifying when this issue arises. For the cheese example it's unclear if the
phenomenon is real or not (the magnitude of the variation in infant death may
actually be significant, if the cheese consumption variation was small then
there would be a different story). The author does nothing to help the reader
resolve this.

In the Google flu example it's only through hindsight (and colossal failure)
that the author identifies the lack of validity in Google's model.

I agree 100% with his point but I don't think the article is providing much
value because essentially the author is simply saying: "be aware, this type of
problem exists out there..." without providing information necessary to
navigate/resolve the problem.

------
auvi
Can anybody reading this comment please enlighten me on good algorithms for
optimum bin sizes for histograms? I have tried DW Scott's (1979) method. But
are there any new better kid in the block?

~~~
Glimjaur
I'm by no means an expert in the area, but i know that the Freedman-Diaconis
rule
([https://en.wikipedia.org/wiki/Freedman%E2%80%93Diaconis_rule](https://en.wikipedia.org/wiki/Freedman%E2%80%93Diaconis_rule))
is used by the seaborn Python plotting library
([https://web.stanford.edu/~mwaskom/software/seaborn/index.htm...](https://web.stanford.edu/~mwaskom/software/seaborn/index.html))
which seems to consistently produce good results.

------
rrecuero
Our brains are wired to believe a plausible story easier than the one told by
the base rate or stats... We need to be aware of the gaps our brain fills by
itself. Definitely not easy, though...

------
T0T0R0
I really thought this was going to be a rant harping on object-oriented
programming.

------
apathy
Ctrl-F "apophenia"

 _0 matches on this page_

Welp, that's pretty sad.

[https://en.wikipedia.org/wiki/Apophenia](https://en.wikipedia.org/wiki/Apophenia)

~~~
liquidise
Excellent link, but no need to poopoo a post because the author's vocab didn't
include a word you expected.

~~~
apathy
Words and concepts exist for a reason. The author of the post could have
turned up the (well researched and imho fascinating) antecedent work very
quickly simply by googling his piece's title. That he did not makes me sad.
Very smart people have previously studied most interesting problems. Ignoring
their work is both arrogant and foolish.

Put differently, you don't learn much by talking, but you learn a lot by
listening. You learn a little by writing, but you and your readers learn much
more if you read up first. (I am not assuming you wrote this. The themes are
general.)

If I wrote a piece that ignored a decades-old, well-known, fundamental result,
not only would editors and colleagues slam me for it, I'd be ashamed of it
myself. I went back and skimmed a few more of this author's posts and I have
to say, they're not of a quality I would suggest to students. If they happen
to read this, I hope they'll talk with someone who has a little formal
training and revise their work.

~~~
jsprogrammer
Does joining the Nazi party qualify you as being very smart?

Wikipedia references work [0] that indicates Konrad's theory could not be
validated empirically.

Do you have any references that validate what you are claiming as a well-
known, fundamental result?

Can you give a definition of the theory?

[0] [https://www.thieme-
connect.com/DOI/DOI?10.1055/s-2007-999113](https://www.thieme-
connect.com/DOI/DOI?10.1055/s-2007-999113)

~~~
apathy
I'm tempted to point out that you went from zero to Godwin in record time. To
assume that Konrad's party affiliation (likely a pragmatic choice at the time)
either validates or invalidates his work is a bit absurd. The issue as I see
it is one of the modern meaning, which is to say, a tendency to see patterns
where none exist.

Nonetheless, let us see whether Konrad's work has been dismantled in more
recent studies. A quick trip to PubMed suggests otherwise:

[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2800156/](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2800156/)

Note that I did not claim Konrad's result was fundamental. I stated that if I
ignored a fundamental result in my writing, I would be pilloried. Konrad's
theory of prodromal schizophrenic ideation beginning with this tendency to see
patterns where none exist is perhaps of interests to psychiatric historians,
but it is not what I would call fundamental. I would claim that if the perfect
term for a concept exists and is established, one should use it.

Apophenia, in its now accepted colloquial meaning, is an exceptionally handy
shorthand for what the writer describes. Like other swell ideas (Gaussian
processes, natural selection) it is so useful that it has accumulated several
names (as a parallel, kriging and clonal evolution are alternate). Either
would work fine here.

~~~
jsprogrammer
I didn't say his party affiliation validates or invalidates his theory. I said
that a published paper was not able to validate it empirically.

The big issue I have is that it seems impossible to define a general concept
of "pattern", such that one could claim it doesn't exist. If such a concept
cannot be defined, then it is unclear what the concept actually means. The
only thing I can imagine that could possibly fit would be if it meant that a
person generates a theory and testable hypothesis that is able to invalidate
the theory, and fails to find any supporting evidence when the hypothesis is
tested, but still claims that the theory was validated in a way that is
demonstrably false. Personally, I don't think psychiatrists are so invested in
their patients that they would actually carry out such tests.

The article that you linked primarily recounts a single anecdote where Konrad
seems to assume very much about his patient (also, it was unclear about the
circumstances; do you know if the anecdote occurred during Nazi rule?). No
mention is made of Konrad attempting to verify any if the patient's claims.

What result are you claiming as fundamental? The entire theory seems to be
self defeating, as a pattern was suggested that has not been able to be
verified.

~~~
apathy
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.

~~~
jsprogrammer
>The fundamental result behind the gambler's ruin is the human tendency to
perceive patterns in genuine randomness.

I don't think pattern recognition drives most gamblers. There are all kinds of
other benefits, perceived or real, that are not accounted for in a purely
monetary payoff grid.

I don't know how you can generate _random_ noise. I assume you are using a
standard, pixelated display to read this message. Even if a random process was
choosing what to display on that screen, there are only a finite number of
configurations. Exactly what you are viewing now could be recreated by such a
random process.

The problem that hasn't been addressed yet is that "pattern" is not well-
defined. If a random display shows a horizontal line pattern, it is still a
pattern by some definition (and you would have no way to distinguish it from a
"intentionally patterned" display that has the same configuration).

~~~
apathy
Of course you could have an intersection between recognizable images and
random noise. The odds of this occurring with any regularity are
infinitesimal, hence the design of experiments. (Recall that there is nothing
like a mathematical proof in the physical world -- at a molecular level, some
water molecules are moving upstream at any given moment, but by reaching into
a stream or tossing some objects into the flow you are taking a large enough
sample to determine where most of them are going).

Since you're not going to get proof one way or another, all a well designed
and experiment can do is give you evidence. This happens to be more valuable
than just about anything else that science has come up with, but it isn't
proof.

Which is why the gold standard for a result is replication in a large sample.
I could have this very page generated by convolving couple of high entropy
random streams. Is it likely to happen repeatedly? Not if the generator is any
good. Same principle for randomized trials. You can end up with unbalanced
arms (I'm proofing a manuscript where we had exactly this problem). But it's
unlikely that they'll be consistently unbalanced across trials with sufficient
sample sizes.

