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@philipparnamets.bsky.social

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

144 Followers  |  311 Following  |  5 Posts  |  Joined: 14.03.2025  |  1.5692

Latest posts by philipparnamets.bsky.social on Bluesky

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

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

11.12.2025 21:00 — 👍 28    🔁 18    💬 0    📌 0

Congrats!

03.12.2025 19:26 — 👍 1    🔁 0    💬 0    📌 0

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.

18.11.2025 08:08 — 👍 2    🔁 0    💬 0    📌 0

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

21.10.2025 18:47 — 👍 2    🔁 0    💬 1    📌 0
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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.

22.09.2025 11:10 — 👍 69    🔁 21    💬 2    📌 0
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Tour de force review on “Economics of Attention” by Loewenstein just published in J Econ Lit
@aeajournals.bsky.social

#behavioraleconomics

29.08.2025 23:24 — 👍 153    🔁 39    💬 9    📌 2
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).

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.

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.

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.

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...

25.08.2025 11:49 — 👍 1009    🔁 288    💬 47    📌 22

"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 🧪

16.06.2025 21:48 — 👍 23    🔁 7    💬 0    📌 1

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

16.06.2025 21:12 — 👍 3    🔁 1    💬 0    📌 0

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

14.03.2025 16:40 — 👍 10    🔁 3    💬 0    📌 0

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