Just finished my keynote at @conference_2021 on "Mental health: studying systems instead of syndromes". You can find slides & new preprint here: osf.io/bm6r5/. Really enjoyed making a completely new presentation from scratch.
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The first barrier to progress I talk about is diagnostic literalism and its consequences: while many of us don't believe in MDD or schizophrenia as "natural disease units" in the world, case-control research in our field is often carried out in that way.
I discuss some historical evidence on how arbitrary many of the categories and thresholds we have today in DSM-5 were, and that DSM-5 may look quite different today if minor things had gone differently.
This means diagnostic categories are not natural kinds.
But our field is not alone in having failed its ambitious mission to identify natural kinds: many nosologies are somewhat arbitrary (e.g. biological species, emotions, threshold for high blood pressure). That doesn't make these things less "real" or important.
The second barrier I talk about is reductionism, using the prominent example of biological reductionism. Reductionism is a useful heuristic tool, but has limited value in complex systems such as mental disorders.
Both barriers interact with each other in a vicious cycle: somewhat arbitrary diagnostic categories are reified because we identify (e.g. biological) correlates.
Moving forward, conceptualizing and studying mental disorders as complex systems offer many new opportunities because there is a rich field of complexity science with many theories and methods that may prove to be useful for mental research.
I discuss some features we can study from this perspective, such as emergence, early warning systems, phase transitions, stable states, and so on.
Two days ago, a lawfirm filed a federal antitrust lawsuit against 6 commercial publishers (incl Elsevier & Wiley) in the federal district court in New York.
They allege a 3-part scheme on part of publishers. 🧵
First, an agreement to fix the price of reviewing at 0$ which coerces scholars into providing their labor for nothing.
Second, publishers agreed not to compete with each other by barring researchers to submit manuscripts in multiple jouranls simultaneously.
Third, the scheme entails prohibiting scholars from freely sharing the scientific advancements described in submitted manuscripts while those manuscripts are under peer review, a process that often takes over a year.
The FDA rejected MDMA-assisted therapy for PTSD treatment. This came as a surprise to noone, given that the FDA advisory panel voted 2:9 on efficacy & 1:10 on safety recently.
FDA followed the evidence. If you are angry, direct that at Lykos that carried our low quality trials.
Here a recent @scifri episode on the numerous problems of the Lykos/MAPS studies specifically from the perspective of 1) lived experience, 2) psychotherapy, and 3) clinical trial design.
So in 2007, physicists wrote a paper that made the headlines: according to their calculations, human coin flips aren’t 50/50 - more like 51/49.
Why is that, and did students in Amsterdam really flip 350,000 coins to find out?
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Diaconis et al 2007 showed that coins tend to land with the same side up that the coin started with ().
They were also able to adjust coin flipping machines flip to 100/0, “proving coin flip physics aren’t random”. info.phys.unm.edu/~caves/courses…
Now a group of students in Amsterdam flipped 350,000 coins in a preregistered study & painstakingly recorded the results. In fact, they video taped all coin flips and uploaded them with the paper, so their study is fully reproducible ;).
After careful consideration, the FDA advisory comission voted today 9:2 that MDMA has *not* been shown to be effective for treating PTSD, given massive concerns around validity threats in this literature. They also voted 10:1 that MDMA has *not* shown to be safe.
1/8 New tutorial preprint led by @b_siepe in which we present different descriptive statistics & data visualization techniques with the goal to better understand EMA item functioning.
2/8 EMA data collection is increasing exponentially, but there are many challenges:
🔎 data are complex
🔎 psychometric properties of EMA items often not investigated
🔎 most scales are neither standardized nor validated beyond face validity
So .. how *valid* are our data?
3/8 Validity is a very thorny issue, so instead we decided to write a tutorial on better understanding item *functioning* as a necessary precursor to discussing validity.
In other words: "Look at your data carefully" (a much repeated call over the last century).
1/22 Our new paper led by @ashleylwatts (w @ashlgreene & @wesbonifay) is now published; I view it as the first critical evaluation of the statistical and theoretical p-factor & resulting literature. Here a brief overview of the core arguments in the paper.
We start by clearly differentiating the theoretical p-factor (from here on: P, thought to describe and perhaps cause variation in all forms of psychopathology) from the statistical 'general factor of psychopathology' (from here on: GFP, usually derived via latent variable models)
P has been taken to mean a variety of things, including an (unusually unspecified) causal mechanism, intellectual functioning, disordered thought, negative emotionality, emotion dysregulation, and others.
Authors use GFPs to derive P often don't specify what they mean with P.