Olaf Borghi
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olafborghi.com
Olaf Borghi
@olafborghi.com
420 followers 1.4K following 34 posts
Researching the political mind and how political beliefs develop 🧠 Doctoral candidate in the MSCA Network ippad.eu & Centre for the Politics of Feelings - @rhulpsychology.bsky.social 👥 Previously research assistant @univie.ac.at 🐕
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Olaf Borghi @olafborghi.com · Oct 10
🔍How does young people's anxiety about the future relate to their political attitudes?

Find out in my first PhD paper, just published in the special issue "The Psychology of Pushback" at advances.in/psychology/1...
Facing a dark future: Young people’s future anxiety and political attitudes in the UK and Greece
Study finds future anxiety linked to more conservative views among young men, but not young women, in the UK and Greece.
advances.in
olafborghi.com
What a week at the IP-PAD meeting in Amsterdam, where our doctoral network met for training and a conference on youth politics!! I also presented a poster on my recent work on young people's future anxiety and political views (read more here: shorturl.at/Rpc0y)! Super grateful for the fun time! :)
November 9, 2025 at 6:15 PM
Reposted by Olaf Borghi
olafborghi.com
🔍How does young people's anxiety about the future relate to their political attitudes?

Find out in my first PhD paper, just published in the special issue "The Psychology of Pushback" at advances.in/psychology/1...
Facing a dark future: Young people’s future anxiety and political attitudes in the UK and Greece
Study finds future anxiety linked to more conservative views among young men, but not young women, in the UK and Greece.
advances.in
October 10, 2025 at 12:38 PM
olafborghi.com
🔍How does young people's anxiety about the future relate to their political attitudes?

Find out in my first PhD paper, just published in the special issue "The Psychology of Pushback" at advances.in/psychology/1...
Facing a dark future: Young people’s future anxiety and political attitudes in the UK and Greece
Study finds future anxiety linked to more conservative views among young men, but not young women, in the UK and Greece.
advances.in
October 10, 2025 at 12:38 PM
olafborghi.com
marginaleffects is one of my favourite R packages and this is such a great paper!! extremely recommended, alongside all other papers from the two authors and also the amazing and free Model to Meaning book marginaleffects.com
dingdingpeng.the100.ci Julia M. Rohrer @dingdingpeng.the100.ci · Aug 25
Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...
Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as “counterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models). Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve. A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.
September 11, 2025 at 11:49 AM
Reposted by Olaf Borghi
dingdingpeng.the100.ci
New paper out with @boryslaw.bsky.social 🥳 In which we sketch out how to rethink measurement invariance causally for applied researchers. And provide a causal definition of measurement invariance!

www.sciencedirect.com/science/arti...
Rethinking measurement invariance causally Highlights: It is preferable to work with a causal definition of measurement invariance A violation of measurement invariance is a potentially substantively interesting observation Standard tests for measurement invariance rely on strong assumptions Group differences can be thought of as descriptive results Conceptual graph illustration the central points of the manuscript. A group variable is potentiall connected to a construct of interest which affects items. Measurement invariance is violated if the group variable directly affects the items, for example by modifying the loadings from the construct to the items, or by directly affecting an item To make this less abstract, consider a scenario where students take an exam, R, meant to capture some ability, T, and then are admitted to a program, V, depending on their exam results: R → V. This is sufficient to result in a violation of the statistical definition of measurement invariance. Exam results and admission are not independent given ability because exam results have a direct effect on admission. Even if we know somebody’s ability (e.g., we know it’s very high), learning about their admission status (e.g., they were not admitted) can tell us something about their exam result (e.g., it may have been worse than expected). According to the causal definition, this in itself does not constitute measurement bias, which seems a sensible conclusion here. After all, the scenario does not involve any reason to believe that the measurement process varied systematically by admission status. Admission happens after the exams took place, it cannot retroactively influence the measurement process (and, for example, lead to unfair treatment depending on admission status).
September 11, 2025 at 9:11 AM
olafborghi.com
This is incredible work, such an insanely cool paper and findings! Quite alarming that "[post-training and prompting methods that] increased AI persuasiveness [...] also systematically decreased factual accuracy"
kobihackenburg.bsky.social Kobi Hackenburg @kobihackenburg.bsky.social · Jul 21
Today (w/ @ox.ac.uk @stanford @MIT @LSE) we’re sharing the results of the largest AI persuasion experiments to date: 76k participants, 19  LLMs, 707 political issues.

We examine “levers” of AI persuasion: model scale, post-training, prompting, personalization, & more! 

🧵:
July 22, 2025 at 2:00 PM
Reposted by Olaf Borghi
marianavonmohr.bsky.social
In previous work with Manos Tsakiris @mtsakiris.bsky.social , we showed that interoception can act as a buffer against political stress. We now extend this research to the 2024 U.S. Presidential elections, capturing data before and after

Check out our preprint 👇

osf.io/preprints/ps...
OSF
osf.io
July 17, 2025 at 12:36 PM
Reposted by Olaf Borghi
leonardocarella.bsky.social
I'm glad someone did basic due diligence on the wolf paper. (Although - worth noting - PNAS has an impact factor of 9 and Electoral Studies an impact factor of 2: a familiar pattern with replications of fundamentally flawed findings.) www.sciencedirect.com/science/arti...
The East in wolf’s clothing. Wolf attacks correlate with but do not cause far-right voting
The resurgence of wolves in Germany has sparked intense debate, particularly in rural areas where wolf attacks on livestock are frequent. Prior resear…
www.sciencedirect.com
July 9, 2025 at 9:32 PM
Reposted by Olaf Borghi
marianavonmohr.bsky.social
Sharing our work at #ISPP in beautiful Prague on affective prescription —and how this shapes the kind of political leader we’re drawn to based on their appearance.
Preprint coming soon!
July 8, 2025 at 9:32 AM
olafborghi.com
This book is highly recommended! Bonus is that I genuinely enjoyed working through it when it first came out
mcxfrank.bsky.social Mike Frank @mcxfrank.bsky.social · Jul 1
Experimentology is out today!!! A group of us wrote a free online textbook for experimental methods, available at experimentology.io - the idea was to integrate open science into all aspects of the experimental workflow from planning to design, analysis, and writing.
Experimentology cover: title and curves for distributions.
July 4, 2025 at 7:11 AM
Reposted by Olaf Borghi
marianavonmohr.bsky.social
Great talk by @olafborghi.com on cognitive control and politically motivated reasoning, even in the face of unexpected interference 👇😂 #ISPP2025
July 3, 2025 at 2:28 PM
Reposted by Olaf Borghi
olafborghi.com
(1/6) #ISPP2025 is just one day away and I can't wait to be in Prague! It will be the first conference I attend during my PhD - looking forward to all the interesting sessions, catching up with friends, and meeting new people! 🤩 #PsychSciSky #polisky #CogSci #polpsy
July 2, 2025 at 11:26 AM
Reposted by Olaf Borghi
debruine.bsky.social
As one of my favourite colleagues Etienne Roesch just whispered to me in response to a #MetaScience2025 speaker suggesting AI could act as an additional grant reviewer:

A👏I👏is👏not👏an👏analytic👏tool
July 2, 2025 at 2:51 PM
Reposted by Olaf Borghi
mtsakiris.bsky.social
That’s the correct link :
www.jobs.london.ac.uk/Job/JobDetai...
July 1, 2025 at 4:30 PM
olafborghi.com
(1/6) #ISPP2025 is just one day away and I can't wait to be in Prague! It will be the first conference I attend during my PhD - looking forward to all the interesting sessions, catching up with friends, and meeting new people! 🤩 #PsychSciSky #polisky #CogSci #polpsy
July 2, 2025 at 11:26 AM
Reposted by Olaf Borghi
dingdingpeng.the100.ci
To re-up this, this also applies to latent growth curve models 😭 😭 😭
No, I'm afraid you cannot solve a fundamental identification problem by applying a latent growth curve model.
dingdingpeng.the100.ci Julia M. Rohrer @dingdingpeng.the100.ci · May 27
PSA: Don’t trust anyone who tells you that you can identify age or period or cohort effects simply by applying the right statistical model to the right type of data. This is fundamentally misunderstanding the nature of the age-period-cohort problem!>
Arrested Development meme. People have suggested empirically solving the APC problem just by applying the right model to the right data Well did it work for those people Not, it never does, I mean, these people somehow delude themselves into thinking it might, but... ...but it might work for us.
June 18, 2025 at 1:21 PM
Reposted by Olaf Borghi
lucyfoulkes.bsky.social
I’m in the Guardian today, arguing that we should stop them all-class mental health lessons in schools

I've thought very carefully about ‘going public’ with this, because it's a sensitive argument to make, especially in the face of so many young people struggling.

(cont 🧵)

tinyurl.com/vun92cz7
Mental-health lessons in schools sound like a great idea. The trouble is, they don’t work | Lucy Foulkes
All-class therapy sessions don’t help, and may even make matters worse. The evidence shows we need different solutions, says Dr Lucy Foulkes, an academic psychologist at Oxford University
www.theguardian.com
May 21, 2025 at 8:13 AM
Reposted by Olaf Borghi
lucyfoulkes.bsky.social
This is a *key* new paper in the world of school mental health interventions

A very large trial (N=6388) testing a universal CBT-based app for adolescent depression (13-14y)

No effects found (on depression, anxiety, distress or insomnia)

(🧵)

mentalhealth.bmj.com/content/28/1...
Future Proofing Study: a cluster randomised controlled trial evaluating the effectiveness of a universal school-based cognitive–behavioural programme for adolescent depression
April 25, 2025 at 8:30 AM
Reposted by Olaf Borghi
scanunit.bsky.social
🚨 Come work with us!

3-year fully funded PhD position in Social and Cognitive Neuroscience @univie.ac.at @clauslamm.bsky.social to join our project investigating prosocial behavior under uncertainty.

More info: shorturl.at/1fnb2

Please share widely 🔁
3y_PhDposition_univie_ScanUnit.pdf
shorturl.at
April 16, 2025 at 10:55 AM
Reposted by Olaf Borghi
aecoppock.bsky.social
New visualization tool alert!

The vayr package version 1.0.0 is now on CRAN.

It contains position adjustments for ggplot2 that help with overplotting in pleasing ways. My favorite is position_sunflower().

- install.packages("vayr")
- alexandercoppock.com/vayr

#rstats #ggplot2 #dataviz
position_jitter() position_jitter_ellipse() position_sunflower() position_circlepack()
April 16, 2025 at 7:53 PM
Reposted by Olaf Borghi
dingdingpeng.the100.ci
Proudly presenting the (for now) final version of "Why experiments work." To share the materials in a slightly more professional manner, I added a "Resources" page to my website: juliarohrer.com/resources/.

That was long overdue anyway; now there's also a curated list of my papers and blog posts.
April 12, 2025 at 8:21 AM
Reposted by Olaf Borghi
todorova.bsky.social
🧠🌍 Thrilled to share our latest paper, just out in Current Opinion in Behavioral Sciences:
"Neuroscience and climate action: intersecting pathways for brain and planetary health"
Read here (OA!): www.sciencedirect.com/science/arti...

1/
April 9, 2025 at 2:23 PM
Reposted by Olaf Borghi
scanunit.bsky.social
🔎 We are looking for collaborators for our new manylabs looking at the effects of extreme heat on human cognition and well-being across the globe 🌍

See below for more information and sign up if you’re interested 👇🏼
kimdoell.bsky.social Kim Doell @kimdoell.bsky.social · Mar 27
🌍 We are launching a new #ManyLabs!!! Join the Heat & Cognition project! We're studying how extreme heat affects human thinking, social behavior & well-being — globally.
Contribute & co-author:
🔗 Info: heatandmind.wordpress.com
📋 Sign up: www.soscisurvey.de/HeatandCogni...
#EnvironmentalPsychology
March 27, 2025 at 4:45 PM
Reposted by Olaf Borghi
ericwbridgeford.bsky.social
See our new preprint focused on causal thinking for data analysis in neuroscience #causal #neuroscience #neuroimaging #datascience #poldracklab with Brian Caffo, Maya Mathur, and @russpoldrack.bsky.social (1/4)

arxiv.org/abs/2503.10710
How causal perspectives can inform problems in computational neuroscience
Over the past two decades, considerable strides have been made in advancing neuroscience techniques, yet the translation of these advancements into clinically relevant insights for human mental health...
arxiv.org
March 23, 2025 at 6:49 PM
Reposted by Olaf Borghi
dingdingpeng.the100.ci
In related news, maybe you should stop directly messing around with coefficients in the first place! Just talked to a student who was told by their advisor that they should use a continuous scale for a categorical thing because otherwise “the analyses get too complicated.”>
March 23, 2025 at 4:32 AM