iiinova — where your digital self keeps evolving

Reposted by @philipparnamets.bsky.social

Blair Shevlin @bshev.bsky.social · 20/08/2026

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:

elifesciences.org

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.

1226

Reposted by @philipparnamets.bsky.social

Jennifer Bussell PhD @jenbussell.bsky.social · 30/07/2026

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)

nature.com

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 ...

917965

Reposted by @philipparnamets.bsky.social

Stephan Lewandowsky @lewan.uk · 20/07/2026

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

doi.org

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…

46332

Reposted by @philipparnamets.bsky.social

Michael Muthukrishna @michael.muthukrishna.com · 15/07/2026

🚨 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/

12413

Reposted by @philipparnamets.bsky.social

Magnus Johansson @pgmj.bsky.social · 23/04/2026

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.

24616

philipparnamets.bsky.social @philipparnamets.bsky.social · 16/03/2026

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.

1145

Reposted by @philipparnamets.bsky.social

Riccardo Fusaroli @fusaroli.eurosky.social · 04/02/2026

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/

23316

Reposted by @philipparnamets.bsky.social

Andrew Gelman et al. @statmodeling.bsky.social · 11/12/2025

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

statmodeling.stat.columbia.edu

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

02818

philipparnamets.bsky.social @philipparnamets.bsky.social · 18/11/2025

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.

020

philipparnamets.bsky.social @philipparnamets.bsky.social · 21/10/2025

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

120

Reposted by @philipparnamets.bsky.social

Jonas Haslbeck @jmbh.bsky.social · 22/09/2025

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.

27021

Reposted by @philipparnamets.bsky.social

Colin Camerer @cfcamerer.bsky.social · 30/08/2025

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

815339

Reposted by @philipparnamets.bsky.social

Julia M. Rohrer @dingdingpeng.the100.ci · 25/08/2025

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.

461001285

Reposted by @philipparnamets.bsky.social

Mark Rubin @markrubin.bsky.social · 16/06/2025

"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 🧪

0237

philipparnamets.bsky.social @philipparnamets.bsky.social · 16/06/2025

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

031

philipparnamets.bsky.social @philipparnamets.bsky.social · 14/03/2025

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

0103