Reply To: Raw data analysis, and avoiding self confirmation bias

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Auburn
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These are good questions!

They’re partly addressed here: https://cognitivetypology.com/index.php?title=Motus_Project

Coming Up with a Set of Signals

In brief, the question of whether or not a given selection of signals (i.e. the CT vultology code or someone else’s) was void of bias is something I don’t think human eyes on their own can measure very well — because of how multivariable it is.

I believe that we would need to create a software program that could be fed thousands of videos, and do statistical analysis on those videos in order to naturally and atheoretically derive the most robust repetitive signals, as well as which signals are most statistically linked to each other. I tried to do this myself — it was the very first thing I tried to do in a pre-CT era, but the endeavor is just too labor intensive. So I think computers need to decide that.

Ideally, if a program like this was made, we could see whether CT’s codex is reflected in those results. So you could check CT against that metric. But this doesn’t yet exist, as far as I’m aware, although computer vision is getting awesome lately, so hopefully it will happen soon.

For now

Given that we don’t yet have a purely objective way of knowing whether a codex is a natural representation of human expressivity, we can look instead at the results. For example, do CT’s chosen categories lead to meaningful data parsing when applied across wide populations? If the answer is yes, then we have reason to believe these divisions are not random, or that the CT codex is aligning itself to a natural phenomenon.

I’m working on more and more ways to support this with evidence. You can look at this page for some information: https://cognitivetypology.com/index.php?title=Occupations

What you’ll see in that page is that when these signals are applied to a population of 400+, they produce statistically significant career/occupation clusterings. This is just one of the proofs-of-concepts. Survey data also seems  to support the non-randomness of the signal sets. That’s a pilot study that’s current about halfway done, and I hope to publish that one this month. It shows that Pe-Pi-Je-Pi scores on that survey correspond to people’s vultological typing.

So for the time being I’m taking a pragmatic approach to the efficacy of the vultology code and proposing that there is merit to it, because it works.

It can be tested, and its results align with predictions more often than chance.

  • This reply was modified 1 month, 1 week ago by Auburn.
  • This reply was modified 1 month, 1 week ago by Auburn.
  • This reply was modified 1 month, 1 week ago by Auburn.
  • This reply was modified 1 month, 1 week ago by Auburn.
  • This reply was modified 1 month, 1 week ago by Auburn.
  • This reply was modified 1 month, 1 week ago by Auburn.

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