Psychometrics

Dimensionality reduction for souls



On measuring the minds of people and possibly even discovering something about them thereby.

Causality and confounding

Some of the attempt to measure people’s minds end up tautological rather than explanatory. Because of my own intellectual history I think of this as what Bateson (2002) calls a dormitive principle problem.

A common form of empty explanation is the appeal to what I have called “dormitive principles,” borrowing the word dormitive from Molière. There is a coda in dog Latin to Molière’s Le Malade Imaginaire, and in this coda, we see on the stage a medieval oral doctoral examination. The examiners ask the candidate why opium puts people to sleep. The candidate triumphantly answers, “Because, learned doctors, it contains a dormitive principle.” 1 How are other nebulous univariate influences stuff like the teamwork factor (Weidmann and Deming 2020) different if at all?

A more common name for dormitive principles is begging the question. It is not universally bad to have circularity, although it does complicate things. One can indeed do causal modeling here. Here is an attempt: Quintana (2020).

the algorithms identified prior achievement, executive functions (in particular, working memory, cognitive flexibility, and attentional focusing), and motivation as direct causes of academic achievement.

Quintana’s reading graph

FWIW intelligence research looks like it is unusually fastidious compared to other psychological disciplines as regards avoiding obviously questionable research practices, although that is a different problem.

Smarts

Various links on the \(g\)-factor kerfuffle, by which I understand is meant the hypothesis that there is a useful, simple, explanatory, heritable, identifiable, falsifiable, scalar, consistent measure of human cognitive capacity/speed/efficiency. Closely coupled with IQ, which is supposed to measure some approximation of it.

I know little about this concept. FWIW I feel intuitively that a minimal more interesting question would be about psychometric nonparametric dimension reduction if you wanted to measure what humans could do, And it would make the task-specific predictive loss function explicit.

But lots of arguments in the public sphere are far from that idea. They tend towards claiming or refuting that human mental capacities are univariate and linear, which is a curiously restricted hypothesis to test in this golden age of machine learning, and of richly parameterised models in every other part of biology. I am sure there must be a reason for that poverty of modeling. To an outsider like myself, without context to explain why the hot topic is such a curiously basic one, it feels I am witnessing a schism in the automotive industry about whether coal-powered cars are better than wood-powered cars. Why this particular modeling choice? What is the context? Is this a question of simplicity, generality, portability, reproducibility, explanatory power, cheapness of the resulting test, rhetorical effect, ease of getting experiments past the ethics board? Or to salvage something from a degenerate research program? A little of all of these?

Maybe the reason for my confusion is that the most prominent voices who argue over this have the least nuanced arguments. In this version arguments gain prominence by a toxoplasma of rage annoyance effect, and the debates I notice are not between automotive engineers but automotive industry PR flacks who wish to sell me the new season’s model? Or between engineers and marketing teams? Maybe if I were to dig deeper I would find more of interest here? The problem is that I care about the question, if at all, as something I might need to understand if I am caught up in a shouting match about it, and that latter eventuality has not lately arisen.

[](/images/smbc-lesion.png

[Saturday Morning Breakfast Cereal - Lesion].{caption}

Shalizi’s \(g\), a Statistical Myth, Dalliard’s rejoinder (perhaps best read with their intro to the background which is written with a true fan’s dedication). Zack Davis’ Univariate fallacy is a useful framing for both of the above. IQ defined. The philosophical coda to M. Taylor Saotome-Westlake’s Book Review: Charles Murray’s Human Diversity: The Biology of Gender, Race, and Class includes an analysis in terms of a co-ordination-on-belief problem, which is another angle on why discourse on \(g\)-factors is vexed. Nassim Taleb is aggressive as usual: IQ is largely a pseudoscientific swindle. Steve Hsu has a different take. I feel like if I had time I might want to take apart those last two articles side by side and see where they talk across each other, because they seem to exemplify a common pattern of cross talk.

No but actually 8 factors

Scott McGreal and Bernard Luskin argue about Gardner’s alternative decomposition of human capabilities into 7 or 8 different intelligences. This discussion does not seem to answer my phenomenological questions about \(g\) factor stuff, and has even less satisfying supporting research so perhaps I can ignore it at my current level of engagement.

Personality tests

Survey instruments

There are a lot of popular models for self-assessment. A curious phenomenon is how much we love to do them. Most popular e quadrant models

DISC

One that is more popular amongst my colleagues is DISC. Kate Roskvist: Four Quadrant Models: The History of DISC, I think intentionally, classes these models as inheritors of ancient elemental theories.

Dan Katz, in Re DISC – How Swedes were fooled by one of the biggest scientific bluffs of our time, really hates the DISC model, and provides an extensive literature review of why it is suspect, although no actual scientific studies of the DISC model per se.

Hogwarts houses

Also popular, and similarly based upon the four element.

For the record, I am Ravenclaw with Slytherin ascending. I did not particularly enjoy Harry Potter.

Big-5 personality traits

How are they supposed to work? How did they choose 5? Would like to know. 🤷‍♂ As an outsider knowing nothing of this area, (except that there are between 4 and 6 personality traits in the big 5) I assume that they are not particularly arbitrary, and have arisen in a natural way from data as identifiable predictive latent variables.

The acronym OCEAN is used for the traits: Openness to experience (sometimes called “intellect” or “imagination” or “open-mindedness”), Conscientiousness, Extraversion (sometimes called “surgency”), Agreeableness, and Neuroticism (sometimes called “negative emotionality” or “emotional stability” reversed).

a literal banana has some snark to the contrary:

Those who are skeptical of the Enneagram are usually Type 6, and those who are skeptical of astrology are usually Tauruses. Similarly, those who criticize the Big Five are typically low on extroversion, high on conscientiousness, low on agreeableness, and high on neuroticism (openness to experience can go either way, I suppose).

It might be worth raiding the list of references there to see what is going on, and also the comments which do go in to detail and offer some rebuttals.

The Big Five exist as a special, scientifically validated property of language and survey methods, and that is one basis for their legitimacy. The other basis for the legitimacy of the Five Factor Model is its replicable correlation with consequential life outcomes. We know that the Big Five are not merely phantoms that fall out of a certain analysis of a certain use of language in WEIRD college students, because these traits are reliably correlated with things we care about.

Astrology

References

Bateson, Gregory. 2002. Mind and Nature: A Necessary Unity. New edition edition. Cresskill, N.J: Hampton Press.
Borsboom, Denny. 2006. “The Attack of the Psychometricians.” Psychometrika 71 (3): 425–40. https://doi.org/10.1007/s11336-006-1447-6.
Duckworth, Angela Lee, Patrick D. Quinn, Donald R. Lynam, Rolf Loeber, and Magda Stouthamer-Loeber. 2011. “Role of Test Motivation in Intelligence Testing.” Proceedings of the National Academy of Sciences 108 (19): 7716–20. https://doi.org/10.1073/pnas.1018601108.
Heene, Moritz. 2008. “A Rejoinder to Mackintosh and Some Remarks on the Concept of General Intelligence.” arXiv:0808.2343 [Stat], August. http://arxiv.org/abs/0808.2343.
Jonas, Eric, and Konrad Paul Kording. 2017. “Could a Neuroscientist Understand a Microprocessor?” PLOS Computational Biology 13 (1): e1005268. https://doi.org/10.1371/journal.pcbi.1005268.
Malabou, Catherine, and Carolyn Shread. 2019. Morphing Intelligence: From IQ Measurement to Artificial Brains. Columbia University Press. https://doi.org/10.7312/mala18736.
Quintana, Rafael. 2020. “The Structure of Academic Achievement: Searching for Proximal Mechanisms Using Causal Discovery Algorithms.” Sociological Methods & Research, June. https://doi.org/10.1177/0049124120926208.
Reiersol, Olav. 1950. “Identifiability of a Linear Relation Between Variables Which Are Subject to Error.” Econometrica 18 (4): 375. https://doi.org/10.2307/1907835.
Visser, Beth A., Michael C. Ashton, and Philip A. Vernon. 2006a. “Beyond g: Putting Multiple Intelligences Theory to the Test.” Intelligence 34 (5): 487–502. https://doi.org/10.1016/j.intell.2006.02.004.
———. 2006b. “G and the Measurement of Multiple Intelligences: A Response to Gardner.” Intelligence 34 (5): 507–10. https://doi.org/10.1016/j.intell.2006.04.006.
Weichwald, Sebastian, and Jonas Peters. 2020. “Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness.” arXiv:2002.06060 [q-Bio, Stat], July. http://arxiv.org/abs/2002.06060.
Weidmann, Ben, and David J Deming. 2020. “Team Players: How Social Skills Improve Group Performance.” Working Paper 27071. National Bureau of Economic Research. https://doi.org/10.3386/w27071.

  1. Aside: he also propounds a solution which looks a bit like a causal graph, “A better answer to the doctors’ question would involve, not the opium alone, but a relationship between the opium and the people.”↩︎


No comments yet. Why not leave one?

GitHub-flavored Markdown & a sane subset of HTML is supported.