Tanay Katiyar

In fact, juvenile mortality is quite high in certain small-scale societies. Do we want to revert to that? Prolly not!

But we need to start somewhere.

Re what's the point of this work: it should be viewed more as a response to the public sentiment of blaming everything on the technology itself. I agree that the pendulum shouldn't be swung the other way as well where everything's blamed on industrialisation.

Second, yes, we don't always want to revive the natural baseline and make the naturalistic fallacy. Rather, this should be viewed as an intuition pump for potential interventions, that work for the population on average and that could be implemented in digital spaces. Yes, individual differences ...

And in fact, Nikhil pointed out the exact point you make - harassment can be horrific offline in certain hunter-gatherer groups. So point well taken;

Hi Erin, thanks so much for this thoughtful engagement with our work - in sum, I agree with the main criticisms: first, this might ignore the fact that things are worse off offline that online spaces protect against. We acknowledge this completely in the caveats section (see below):

This is a mammoth piece of measurement work for evolutionary psychiatry, that hopefully triggers more rigorous testing of evolutionary theories of mental health.

Camila et al. have spent several years(!!!) developing this vital piece of work, so do check it out and share

WOOO! Congratulations Marius :)

A key thing to note here is that you need to let the likert scale cutpoints also vary across each item. If I recall correctly, the brms syntax should be Question_Score | thres(gr = Question Type) ~ (1 + Predictor_of_interest | Question Type)

Hopefully, this should clinch it :)

The random intercept will then tell you how similar/distinct each item in the index + the random slope will indicate whether your fixed effect varies strongly across each item in the index

I see. If that is the case, I would just forget the categories and model each question jointly as a multilevel ordinal model:

Question_Score ~ (1 + Predictor_of_interest | Question Type)

where score is 0-3 for each question and ques type is the distinct items in the index....

Of course, this presumes that there is an inherent ordering between the categories, which may or may not appropriate

Depending on the spacing, you could then speculate as to why it is probability of going from 1 to 2 on this index is very very different from the probability of going from 2-3? Paul Burkner has a nice vignette on using monotonic predictors in brms

I guess this depends on whether you want to model this as a dependent variable or as a predictor. If it is the latter, you could just model it is a monotonic predictor and the model will automatically figure out that the spacing between the categories is not equidistant.

Tagging similar other folks: @matti.vuorre.com @mjcrockett.bsky.social @m-b-petersen.bsky.social @danmirea.bsky.social @danielnettle.bsky.social @lucyfoulkes.bsky.social @candiceodgers.bsky.social @machterberg.bsky.social @hshawberry.bsky.social @eckles.bsky.social @mitchprinstein.bsky.social

This is one of the rare papers that completely changed my thinking about the field I work in. Have a read!

Tagging folks who might be interested: @randynesse.bsky.social @ehbea.bsky.social @kristensyme.bsky.social @ehemmott.bsky.social

It’s Easy to Blame Mental Health Issues on Tech. But Is It Fair? A popular narrative says devices make us depressed. Research with remote Amazonians adds more depth to the story.

Also, huge thanks to @hugoreasoning.bsky.social for initially guiding my thinking beyond negative effects of mismatch
+
@manvir.bsky.social whose WIRED article provided initial inspiration for this piece, and without whom I would have never properly discovered evo psychiatry: tinyurl.com/3cah4jwx

To read further, here's the link to the open-access version of the article: doi.org/10.1037/rev0...

Second, it potentially provides design recommendations

(16/n)

@zephoria.bsky.social once quipped: "All too often, it is easier to focus on the technology than on the broader systemic issues that are at play because technical changes are easier to see.

IMO, an evolutionary framing makes these broader systemic issues much much much easier to see!

(15/n)

Relatedly, we also state explicit predictions that follow from our perspective. It should be mentioned that early evidence on phone bans seems to support these predictions

(14/n)

This has ripple effects on the public discourse as well!!!

(13/n)

While complementary, we point out some unique benefits of taking an evo perspective here:

First, it helps establish a theory-driven baseline of human behavior

(12/n)

Now, some will point out that we could reach this end-point without taking an evolutionary perspective. And as we acknowledge in the paper, scholars before us have indeed done so (check out work by @sonialivingstone.bsky.social @zephoria.bsky.social )

(11/n)

We attempt to drive home this point via sketching out 2 examples at length, using evidence from anthropology and the SM-mental health literature

(10/n)

To redress these 2 problems, we propose the following perspective that a priori predicts that there will be mixed mental health effects of SM

(9/n)

Problem #2 - Related to (1), it makes us lose sight of how SM and digital tech can actually rectify some of these pre-existing mismatches.

(8/n)

To appreciate this, check out work by @denizsalali.bsky.social and Nik that neatly outlines how other features of contemporary western industrialised environments, for all its wonders and benefits, can also leave us more vulnerable to mental health problems.

(7/n)

But just conceptualising aspects of SM as a mismatch has 2 problems:

1. We fail to contextualize SM's negative mental health effects within other evolutionarily novel societal changes (e.g. global migration, increased social isolation) that are plausible preexisting evolutionary mismatches
(6/n)

Others have (implicitly and explicitly) documented such mismatches in the literature. Check out brilliant work by my lab-mates @amandaferg.bsky.social, @georgiaturner.bsky.social

(5/n)