"Measurements of attributes such as emotions, well-being, or intelligence are widely used for various purposes in society, but it remains a matter of discussion whether psychological measurement is analogous to measurement in the natural sciences, and to what extent it qualifies as measurement at all.' https://doi.org/10.1080/09515089.2023.2300693
Edit: author is here! @mieronen
My initial take: what?! This seems silly. But I'm starting to warm up to it. It's about causality. Consider: "insomnia causes fatigue"; no one disputes it. But there's not a physical thing in the world called insomnia that causes a physical thing in the world, fatigue billiard-ball-style. Rather, the physical causal chain happens by way of a lack of sleep causing the brain state that leads to the mind state of fatigue (in other words, that word "cause" is doing some heavy lifting in that phrase). The question is: can you meaningfully talk about causality when you have abstracted away from physical interactions?
On one hand, of course - you can develop causal models formulated entirely at the psychological level (rewards, punishments, surprises, mood) that make falsifiable predictions and you can both perturb and measure these things to test those models.
On the other hand, we probably do need to take some care that we aren't confusing ourselves as we throw around that word "cause" interchangeably for things that physically interact and abstractions of those things.
Thoughts? I'm particularly curious about cases in which this type of abstraction has led researchers astray.
@NicoleCRust@mieronen I definitely see the temptation to study physical causation but one has to be careful to avoid unwarranted level chauvinism.
I would argue that some things like subjective psychological states are best understood at their own level, though it’s of course interesting to understand how they relate to other levels too.
@katejjeffery@mieronen
Absolutely! My sense is that the big question is along the lines of: do you need study the physical interactions to (definitively) establish causal relationships (both in principle as well as in practice).
It's very much related to the big question: are mental disorders brain disorders? Some psychologists argue that we'll never gain any traction in understanding or treating mental disorders studying the brain because it's the wrong level. One counterargument here is that one cannot actually establish causal relationships between psychological variables unless one studies them at the physical level (the brain).
@NicoleCRust@mieronen Yes I think unless you pin down the physical interactions you can never know whether you have true causality or just a correlation
Without understanding the biological foundation – the cellular level –, the underlying neural circuits and their operation, indeed there seems to be little hope of making sense of brain function.
You all need to read some Hofstadter 😅
That might help lose the fear of causation at different levels.
Oh, you can also read Alicia Juarrero!
In my humble opinion...
If you mean "Gödel, Escher, Bach" by Douglas Hofstadter, I've read it. Was awesome. Felt not all that new to my programming self, as the book touched upon many points I had visited already when learning to program in Lisp (Clojure, with its structural sharing, binary trees, immutability, functional approach to processing data structures) and in Haskell (monads, folds, levels of indirection, functional programming).
@albertcardona@PessoaBrain@katejjeffery@NicoleCRust@mieronen Hofstadter wrote "I am a strange loop" because he was frustrated that GEB was received well, but had no consequences or follow up in the field. (These are my words how I understood it). When I read GEB I thought it to be really something, but my understanding of his ideas was much helped by the second book.
@MolemanPeter@albertcardona@PessoaBrain@katejjeffery@NicoleCRust@mieronen I'd recommend these books to get a handle on thinking about the brain as a whole. Problem is our own understanding needs multiple perspectives to get a grip. Strange loop says something about the awareness illusion, but less on how brains work.
Most of these books are on history of human brains and thinking by the way
@MolemanPeter@albertcardona@PessoaBrain@katejjeffery@NicoleCRust@mieronen my point is you cant understand things like autism or #anxiety disorder if you dont have a broad idea of the architecture of the human Brain. If our brain Indeed has many specialized relatively decoupled functional parts, measuring in cells doesnt yield causa understanding, since cells are not the relevant causal factor
@MolemanPeter@albertcardona@PessoaBrain@katejjeffery@NicoleCRust@mieronen however, you probably know that being trained in psychology. I am self-thought with a life long personal obsession on understanding brains and intelligence that I know now stems party from autism.
What always amazes me in therapy is how everything is done with questions and tests and language. And nothing with electrodes etc
At the risk of offending vast swathes of research fields: what can be measured defines what can be studied and understood. The old joke about looking for something where there is more light (where it is easier given one's training and knowledge) and not where it is suspected to be applies. We all fool ourselves on a daily basis. Breaking free of these habits is hard.
@PessoaBrain@albertcardona@katejjeffery@mieronen
No fear, just difference. Do you not acknowledge that causality between abstracted (psychological) variables is different than physical ones? It’s not same-same, no?
Taking a complex systems approach is a good start (in a predator/system, do foxes cause rabbits or vice versa? The question doesn't really make sense). The question is: can these things be teased apart at that abstract, non-physical level? Perhaps using some of George Sugihara's tricks? https://deepeco.ucsd.edu/nonlinear-dynamics-research/edm/? Or do we need to go to the physical level where physical causation ultimately happens billiard ball style to really tease these things apart?
@NicoleCRust@katejjeffery@mieronen if we use a modern physical computer as a metaphor for the brain and software as metaphor for the mind, it’s certainly possible to have glitches, bugs, errors, etc due solely to physical hardware problems, software problems, or both. I would be surprised if something similar was not true for mental disorders in people.
Missing for me in this analogy: what’s the equivalent of the program/code? That’s what we need to study. Where is it? Insofar as it exists, it’s in the brain’s computations/algorithms. In this analogy, the mind is what happens when you execute that code.
Is this a good analogy? That’s tbd. Some argue no (like @yoginho). I’m still 🤔
@NicoleCRust@katejjeffery@mieronen@yoginho Yes, analogies can be tricky and the word “mind” here is overloaded, so let me rephrase. A physical computer is analogous to the brain. Their architectures differ, but presumably they are both information processing systems. Software corresponds to the information, knowledge, algorithms that are being computed/processed by the brain. A mental disorder might be purely a software issue (ie corrupted info or algorithm) without any hardware issue.
@NicoleCRust@katejjeffery@mieronen@yoginho Now presumably all software manifests in some way at the hardware level (eg info is stored and retrieved in computer memory/neurons after all), but the hardware can be doing everything it is supposed to do and there can still be problems that manifest at the “software” level.
A good example of this might be PTSD. There, a brain operated as it should have (to learn a bad thing and fear it) and there's nothing broken about the brain per se. However, the individual is suffering by continuously reliving a traumatic experience. That could be chalked up to a software problem? Those fearful memories are stored in the hardware (presumably synaptic weights; perhaps in the amygdala and elsewhere) but the experience of fear is presumed to be a complex pattern of activation that involves cortical loops (https://www.pnas.org/doi/full/10.1073/pnas.1619316114).
@MolemanPeter@katejjeffery@mieronen
I (a brain researcher) definitely don’t agree the brain is not relevant. But I’m on board with the notion that the mind and psychological constructs are also important.
@NicoleCRust@mieronen I guess I'm not ready to grant the leap from "measurement" to "causation." (I'm just conversing here, haven't read the cited paper.) If "measurement" means something like "quantify" in the context of good experimental practice (blinding, controls), then I'm naturally inclined (PS that's a quantifiable thing that we can measure, heh) to resist claims that the quantification or measurement has anything to do with causation.
1/2
@NicoleCRust@mieronen Naively perhaps, I'm thinking of the early days of RNAi when we could harness it and measure its effects and even titrate them, without knowing how on earth the expression of dsRNA in a cell would/could lead to suppression of endogenous expression. I remember thinking about this a lot and found it very interesting.
Is that different from "thought" or "feeling"? I guess, but not (to me) because of any relationship between "measure" and "cause".
Throughout the 20th century, causality did not play a significant role in discussions of psychological measurement ...In the philosophy of science, discussions of measurement have revolved around the representational theory of measurement ... representational relationships between empirical and numerical systems.
However, in recent times causality has started to find its way to discussions of psychological measurement. A key insight underlying these approaches is that in valid measurement the attribute or property that is being measured should cause the measurement outcome. To take an example from physics, in valid temperature measurements, variations in temperature should cause variations in thermometer readings. Similarly, in valid measurements of a psychological attribute such as sad mood, variations in sad mood should cause variations in responses to items measuring sad mood.
More concretely, it has been argued that psychometric measurement models, more specifically latent variable models, should be seen as causal models
@NicoleCRust@mieronen You may already know Alicia Juarrero's work? "Dynamics in Action", from 1999 but particularly her "Context Changes Everything" from last year. She analyses how causation is used in science (particularly psychology in the 1999 book, and the 2023 skews that way).
Her argument is that we need to expand our conception of causality (or more particularly, get away from the really constrained conception we inherited from Newton).
@MarekMcGann@mieronen
Thanks! I should read 1999, I guess. I tried pretty hard with Context Changes Everything and concluded that the book wasn't written for me - I just couldn't follow it. I also saw it induce a lot of confusion in others around me. I'm open to the notion there's something important there but I'm not sure how to access it.
I do share Juarrero's enthusiasm for complex dynamical systems and, for example, the work of Mante/Sussillo/etc that she highlights in one of her later chapters. I also appreciate the need to expand the notion of mechanism and causality beyond physical interactions into the algorithmic space; that came up a few days ago in a discussion here: https://neuromatch.social/@NicoleCRust/111875169510893455.
Again, thanks for the 1999 nudge; perhaps I'll give that a try.
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