1/9 New blog is live! This is part 2 of a series—last time we looked at the Dunning-Kruger effect, now we are digging in to Implicit vs Explicit attitudes and the Implicit Association Test. To start, of course we need a good meme...
haines-lab.com/post/part-2-...
📄 Paper: www.sciencedirect.com/science/arti...
#AcademicSky #CognitiveScience #Psychology #NumericalCognition #MathCognition #IndividualDifferences #Psychometrics #Bayesian #OpenScience #OpenData #NewPaper
This has been a long journey. It started in April 2019 with a real eureka moment. I feel incredibly lucky to have @mattansb.msbstats.info as a major wind and support (and friend!) throughout this work. Years of work with Roi Cohen Kadosh and Avishai Henik, and we're thrilled it's finally out.
We also used a numerical Stroop task and found that stronger numerical bias in CLIP predicts larger Stroop effects when numbers must be ignored. This suggests a "tendency layer": varying attentional processes that predetermine how hard it is to ignore irrelevant information.
These individual differences predicted math fluency and quantitative reasoning, echoing child SFON research.
Hierarchical Bayesian Drift Diffusion modeling lets us combine choice and RT into a single measure, avoiding reliability problems of difference scores. The result: fantastic internal reliability and stable individual differences across all CLIP conditions.
Why this matters: Research on children shows that spontaneous focus on numerosity (SFON) predicts math skills and math development. The CLIP task provides a computerized version that can fit both adults and children! It captures these spontaneous tendencies trial by trial, and works great online!
Our paper is finally out in Cognition! 🎉
We introduce the "CLIP task"—a computerized paradigm for measuring numerical bias in adults: when number and physical size both matter, do you spontaneously rely more on numbers or on physical size?
This has been a long journey. It started in April 2019 with a real eureka moment. I feel incredibly lucky to have @mattansb.msbstats.info as a major wind and support throughout this work. Years of work with Roi Cohen Kadosh and Avishai Henik, and we're thrilled it's finally out.
These individual differences predicted math fluency and quantitative reasoning, echoing child SFON research.
Hierarchical Bayesian Drift Diffusion modeling lets us combine choice and RT into a single measure, avoiding the reliability problems of difference scores. The result: fantastic internal reliability and stable individual differences across all CLIP conditions.
Why this matters: Research on children shows that spontaneous focus on numerosity (SFON) predicts math skills and math development. The CLIP task provides a computerized version that can fit both adults and children! It captures these spontaneous tendencies trial by trial, and works great online!
New preprint with @rogierk.bsky.social @paulbuerkner.com - we introduce "relative measurement uncertainty" - a reliability estimation method that's applicable across a broad class of Bayesian measurement models (e.g., generative-, computational- and item response theory-models osf.io/h54k8
New blog post!
Ever wonder what geom_histogram is actually doing? How about geom_boxplot?
In celebration of the release of #ggplot2 4.0.0 (ggplot8?), I explore the relationships between the “geoms” and “stats” offered by the core {ggplot2} functions.
#rstats
New #rstats blog up!
solomonkurz.netlify.app/blog/2025-07...
This is the first in a new series discussing causal inference with experimental data using multilevel models. My basic case is g-computation is the way to go.
Dive in for code, visuals, and a clearer path through the log-odds fog → cogpsychreserve.netlify.app/posts/logist...
#NLP #Kaggle #marginaleffects #BayesianStatistics #DataScience #SignificantTesting
2/3
• NLP + PCA to capture toxicity/incoherence
• Cohen’s d ➡️ log-odds priors in one line using #brms
• #marginaleffects → 0–100 % probability shifts you can explain
• Inference with HDI-ROPE. It flags which effects are big enough to matter. Great for researchers and anyone shipping spam filters!
1/3 New post up! 📝 I took the workhorse 🔧 of binary modeling—logistic regression—and gave it a Bayesian tune-up using a Kaggle SMS-spam dataset.
Thanks Laura! 🙏 I analyzed vertical-face tasks (6 variants across SOAs) from subjects with mouse responses only. The Preprocessing details are in the post’s collapsible section 😊. Grateful for your work—DM anytime!
Thank you! 😊 While latent correlations are possible in Stan via custom likelihoods (modeling latent Gaussian variables), it's quite involved. For 95% of cases, I recommend the simpler brms approach: model questionnaires as predictors of task effects using condition-by-questionnaire interactions.
6/6 Thanks to @solomonkurz.bsky.social for statistical inspiration, @natehaines.bsky.social for works that influenced my approach, and @almogsi.bsky.social & @mattansb.bsky.social or thoughtful feedback!
#BayesianStatistics #ReliabilityAnalysis #CognitiveScience
5/6 The implications go beyond this single task. Many measures in psychology (and beyond) might be more reliable than we thought—we need to preserve and properly model the information in trial-level data.
4/6 This visualization shows the transformation when the same data is analyzed with trial-level Bayesian methods instead of traditional aggregation:
3/6 I implemented two Bayesian approaches in #brms:
@jeffrouder.bsky.social & @juliaha.bsky.social's variance decomposition
@gangchen6.bsky.social's approach
Both show substantially higher reliability than traditional analyses.
2/6 Recent research by @irenexu.bsky.social claimed the emotional dot-probe task lacks reliability for individual differences research. I wanted to see if more sophisticated analysis methods could tell a different story.
1/6 Hello Bluesky! 👋 Excited to join this community and share my new blog. First post: Using Bayesian hierarchical models to rescue "unreliable" cognitive tasks, with the dot-probe task as my case study. cogpsychreserve.netlify.app/posts/dotpro...
