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.”
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. In other words, the dormitive explanation actually falsifies the true facts of the case but what is, I believe, important is that dormitive explanations still permit abduction. Having enunciated a generality that opium contains a dormitive principle, it is then possible to use this type of phrasing for a very large number of other phenomena. We can say, for example, that adrenaline contains an enlivening principle and reserpine a tranquilizing principle. This will give us, albeit inaccurately and epistemologically unacceptably, handles with which to grab at a very large number of phenomena that appear to be formally comparable. And, indeed, they are formally comparable to this extent, that invoking a principle inside one component is in fact the error that is made in every one of these cases.
How are other nebulous univariate influences stuff like the teamwork factor (Weidmann and Deming 2020) different if at all?
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? 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.
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, but has even less satisfying research so perhaps I can ignore it at this level of engagement.
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.
Carcinisation 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.
Bateson, Gregory. 2002. Mind and Nature: A Necessary Unity. New edition edition. Cresskill, N.J: Hampton Press.
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,” 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.
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,” 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. Working Paper Series. National Bureau of Economic Research. https://doi.org/10.3386/w27071.