Tessera

@philipparnamets.bsky.social

Cognitive scientist interested in social learning, morality, and preference formation. Research at Karolinska Institutet, Sweden.

9 Posts191 Follower 361 Folgt

Blair Shevlin @bshev.bsky.social · 2mo ago

Now published in eLife: elifesciences.org/articles/103... We find strong evidence that activity in Pre-SMA and vmPFC scales with gaze-weighted accumulated evidence, suggesting attention directly modulates value signals in canonical decision-making circuits! 🧵 on the methods + findings:

Overt visual attention modulates decision-related signals in the frontal cortex

Brain activity in the pre-supplementary motor area and dorsolateral prefrontal cortex represents gaze-weighted accumulated evidence signals in value-based decision-making.

elifesciences.org

Jennifer Bussell PhD @jenbussell.bsky.social · 3mo ago

How does the brain represent the value of knowledge? In our paper--out now at Nature Neuroscience--we identify a representation of intrinsic information value within neural population structures in the orbitofrontal cortex in mice. www.nature.com/articles/s41593-026-02377-y #neuroskyence 🧠🧪 (1/8)

Representations of the intrinsic value of information in mouse orbitofrontal cortex - Nature Neuroscience

Mice are motivated to seek information of no extrinsic value. Recording neural activity in orbitofrontal cortex reveals a representation of the value of information that is distinct from the value of ...

nature.com

Stephan Lewandowsky @lewan.uk · 3mo ago

New in Trends in Cognitive Sciences: misinformation isn't just about false beliefs. Led by Ullrich Ecker & co=authors Emily Spearing & Renee DiResta our piece argues the bigger threat is indirect—the erosion of shared epistemic standards doi.org/10.1016/j.ti... 🧵 1/10

Misinformation as strategy: Epistemic consequences and the undermining of shared truth

Misinformation influences cognition—shaping memory, beliefs, attitudes, reasoning, and decision-making. While intentionally disseminated misinformatio…

doi.org

Michael Muthukrishna @michael.muthukrishna.com · 4mo ago

🚨 New paper in iScience: "Ecological not social factors explain brain size in cephalopods" In 2017, we published the Cultural Brain Hypothesis, a model of human brain evolution. It made an unexpected prediction: non-social animals could evolve big brains too, if their ecology was rich enough. 1/

Magnus Johansson @pgmj.bsky.social · 6mo ago

I've made an R package for Bayesian Rasch #psychometrics with brms models, easyRaschBayes (on CRAN), implementing simple functions to create figures and tables with model fit metrics, etc. Attaching figures from conditional item infit, item-restscore with GK gamma, and the log-likelihood criterion.

@philipparnamets.bsky.social · 8mo ago

Open-minded epistemic attitudes are associated with adherence to public health recommendations and protect against holding false beliefs. New paper with @jayvanbavel.bsky.social @markalfano.bsky.social @robert-m-ross.bsky.social accepted in @sjdm-tweets.bsky.social

Open-mindedness predicts support for public health measures and disbelief in conspiracy theories during the COVID-19 pandemic.
Accepted for publication Judgment and Decision Making.

Riccardo Fusaroli @fusaroli.eurosky.social · 9mo ago

as I'm revising my course materials, I keep stumbling upon cool @mc-stan.org developments. Current favorites: 1. your model has funnels and you exhausted reparametrization ideas: metric = "dense_e" makes your HMC learn about covariance btw parameters. Sloooow, but effective! 1/

Andrew Gelman et al.@statmodeling.bsky.social · 11mo ago

Seven-parameter drift-diffusion pdfs and cdfs now in Stan statmodeling.stat.columbia.edu/2025/12/11/s...

Seven-parameter drift-diffusion pdfs and cdfs now in Stan | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

@philipparnamets.bsky.social · 12mo ago

New paper led by @alexandertagesson.bsky.social Increasing empathy through brief interventions using brief motivational vignette does not work in five both conceptual and direct replications, suggesting limitations to those methods compared to what previous findings may have led us to believe.

Communications Psychology@commspsychol.nature.com · 12mo ago

Contrasting previous offline results, in 5 online studies, motivated empathy interventions for intergroup contexts failed to increase empathy or prosociality with outgroup members. @alexandertagesson.bsky.social @philipparnamets.bsky.social www.nature.com/articles/s44...

@philipparnamets.bsky.social · last yr.

Happy to be part of this project! Check out this new publication :

Niels G. Mede@nielsmede.bsky.social · last yr.

Published today: One of the biggest #science #communication studies to date. We asked 71,922 people in 68 countries how they #engage with information about #science and combined the data with several country-level factors: journals.sagepub.com/doi/10.1177/... #OpenAccess

Jonas Haslbeck @jmbh.bsky.social · last yr.

Two new preprints on multilevel HMMs! Time series data is now pervasive in psychology and new methods are needed to model the dynamics in such data. Hidden Markov Models (HHMs) are powerful models for dynamics in which a system is switching between a number of discrete states.

Colin Camerer @cfcamerer.bsky.social · last yr.

Tour de force review on “Economics of Attention” by Loewenstein just published in J Econ Lit @aeajournals.bsky.social #behavioraleconomics

Julia M. Rohrer @dingdingpeng.the100.ci · last yr.

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.

Mark Rubin @markrubin.bsky.social · last yr.

"I think it is important to dissent when my own group is reasoning badly." Item from the Ingroup Criticism subscale of the new Collected Intergroup Intellectual Humility scale by @philipparnamets.bsky.social, @jayvanbavel.bsky.social, & @markalfano.bsky.social #SocialPsyc #AcademicSky 🧪

Jay Van Bavel, PhD@jayvanbavel.bsky.social · last yr.

We created the Collected Intergroup Intellectual Humility Scale In 10 studies (N~6,000) we combined 10 existing measures into 1 measure that assess how humble people are as individuals & in groups Summary www.centerconflictcooperation-newsletter.com/p/the-collec... Preprint osf.io/preprints/ps...

@philipparnamets.bsky.social · last yr.

Check out our new instrument to measure intellectual humility with an inteegroup touch!

Jay Van Bavel, PhD@jayvanbavel.bsky.social · last yr.

@philipparnamets.bsky.social · 2y ago

Check out our new preprint of social influences on third party moral judgements led by @davidschultner.bsky.social

David Schultner@davidschultner.bsky.social · 2y ago

🚨 New preprint out! 🚨 How do people make moral judgments as third parties? 👀 We show how two motivations—inequality aversion (fair = moral) ⚖️ and the common-is-moral heuristic (frequent = moral 👨‍👩‍👧‍👧—interact to shape evaluations 🧵👇 📄 osf.io/preprints/ps...