Shravan Vasishth

Keynote speakers in 2026: Dale Barr and Lisa DeBruine.

Applications for the summer school on statistical methods for linguistics and psychology (Potsdam, Germany) are now open:

vasishth.github.io/smlp2026/

a rare sighting of all three authors of The Book, all in one place:

New preprint investigating whether lossy-context surprisal can account for the locality and expectation effects found in Russian, Hindi, and Persian reading data: osf.io/preprints/ps...

We are done with the ninth Statistical Methods for Linguistics and Psychology (SMLP) summer school, Potsdam, Germany. The tenth edition is planned for 24-28 August 2026.

A common disease in academia is that for any position X there is an academic with an opposing position not-X. It's just how academics are. One can't just blindly follow one or another person's recommendation, but develop a good understanding of it oneself, and draw one's own conclusions.

I guess then we should also recommend against using p-values because they are sensitive to the likelihood function assumed? :) Who was it that gave this recommendation? I'm guessing SIngmann but maybe I'm wrong.

Because this is a frequently asked question in summer schools I teach, I am thinking about adding a video lecture on this in my online materials for the book. Do also read the online chapter from our book on this topic:

bruno.nicenboim.me/bayescogsci/...

Also:

Cumming, G. (2014). The new statistics: Why and how. Psychological science, 25(1), 7-29.

Kruschke, J. K. (2011). Bayesian assessment of null values via parameter estimation and model comparison. Perspectives on Psychological Science, 6(3), 299-312.

epub.ub.uni-muenchen.de/74222/

Some other books to read:

Royall, R. (2017). Statistical evidence: a likelihood paradigm. Routledge.

Spiegelhalter, D. (2024). The art of uncertainty: how to navigate chance, ignorance, risk and luck. Random House.

Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2004). Bayesian approaches to clinical trials and health-care evaluation. John Wiley & Sons.

You can use the credible interval to think about whether the pattern is consistent with the predicted effect. Even better if you can compare that interval to a model's a priori predicted interval. Read Spiegelhalter's 2004 book:

That's what the likelihood ratio test (aka anova) does, you compare likelihoods under the null and non-null to decide whether to reject the null. Bayes factors compare marginal likelihoods but the idea is the same. See our BF chapter.

E.g., if my data point is 0, the likelihood of mu being 0 (assuming sd=1) is dnorm(0,0,1). The lik. of mu being 10 is dnorm(0,10,1), which is much smaller than that of dnorm(0,0,1). So I'd favor mu=0 over mu=10. If the data point were 10, the lik. of mu=10 is much higher, and I would reject mu=0.

So with Bayes factors you'd compute the marginal likelihoods under the two models and compare them.

To make a discovery claim, I would have to set up two models, m0 where the effect is 0, and mfull, where it is not. Then I can talk about the relative evidence assuming a null or a non-null effect. 2/2

I would say that both credible intervals in my example are *consistent* with the effect being positive, but I would not go so far as to make a discovery claim. 1/2

The physical book copies have arrived: www.routledge.com/Introduction...

Also see: pure.uva.nl/ws/files/636...

Just a quick question first: If the Bayesian 95% Credible interval is [0.0000001,100] you would reject the null, and if it is [-0.0000001,99], would you fail to reject or even accept the null?

New preprint with @shravanvasishth.bsky.social: We use a computational model to look at how linguistic context affects first-pass attachment and reanalysis. Assuming a tripartite mixture of reanalyzed, non-reanalyzed and inattentive trials outperforms LLM surprisal. lingbuzz.net/lingbuzz/009...

Next week onwards, I'm teaching a five-day introductory course on Bayesian Data Analysis in Gent. Newly recorded video lectures to accompany the course are now online: vasishth.github.io/LecturesIntr...

In person (no streaming/zoom) sentence processing workshop at Potsdam with Tal Linzen, Brian Dillon, Titus von der Malsburg, Oezge Bakay, William Timkey, Pia Schoknecht, Michael Vrazitulis, and Johan Hennert:

vasishth.github.io/sentproc-wor...

Three year postdoc position in Cognitive Science, Potsdam, Germany:

www.uni-potsdam.de/en/verwaltun...

New study on local coherence, led by Pia Schoknecht :

The time course of local coherence effects: Evidence from self-paced reading times and event-related potentials

osf.io/preprints/os...

Choose Europe for Science: ERC welcomes new budget for 'super grants' The Scientific Council of the European Research Council welcomes the offer of substantial additional budget from the European Commission for the development of a new ERC funding instrument offering la...

Scientists wanting to move to Europe from the US take note of this funding opportunity:

erc.europa.eu/news-events/...

@cos.io Are you aware that in restructuring your website you have destroyed many repositories? The new Files directory is empty, and add-ons to github repos no longer work. WTF?

Computational Psycholinguistics Meeting 2025 | Universiteit Utrecht Universiteit Utrecht

Computational Psycholinguistics Meeting 2025

cpl2025.sites.uu.nl

When: December 18–19, 2025

Where: Utrecht, the Netherlands

Abstract submission deadline: June 15, 2025

Organizers: Jakub Dotlačil, Lena Jäger, Bruno Nicenboim, Ece Takmaz

Without question the greatest open courseware in the world, bar none.

At #HSP2025, I'll present work with @tallinzen.bsky.social and @shravanvasishth.bsky.social on modeling garden-pathing in a huge benchmark dataset: hsp2025.github.io/abstracts/29.... Statistically decomposing the effect into subprocesses greatly improves predictive fit over just comparing means!