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Interested in human replay and solid methods? Jointly with @skjerns.de we're releasing a benchmark dataset with known ground-truth neural sequences in MEG & fMRI, for developing & validating replay methods. First test: existing methods show similar effect sizes, but room to improve shorturl.at/6TgIr

So glad to finally share work years in the making! What started with fMRI effect size benchmarks ended up showing that conventional study planning detects ~half of expected brain effects, suggesting more distributed processes than the lit shows (+ new methods for mass univ effect/power estimation)

New MEG preprint out! Huge congrats to postdoc Rohit Yadav. We (among other things…) decoded explore-exploit decisions in the human brain, revealing the lateral frontopolar cortex kicks off the shift to exploration hundreds of milliseconds before the choice itself. www.biorxiv.org/content/10.6...

1/11 Happy to share our TICS paper on using the flexibility of one of the most basic cognitive functions, perception, to understand one of the most complex cognitive dysfunctions, psychiatric conditions (also my first formal work in computational psychiatry 🎉) 📄: www.cell.com/trends/cogni... 🧵 : 👇

1/ Periodic reminder: decodable ≠ causally used. Or, as we put it: Represented Is Not Computed. New paper w/ the excellent Ishita Darade (not on BSky; www.linkedin.com/in/ishita-da...), an undergrad student: arxiv.org/abs/2605.22488 #representation #NeuroAI #neuroscience #interpretability

Just found this intriguing new function by Wadea Abu Dahoud for making a log-scale axis squiggly, mostly to act as a visual cue/speed bump that the axis isn't linear. github.com/wade31985-ar... #rstats #dataviz

Computational models are a key part of science but discovering new ones is hard! DataDIVER discovers concise models from data, which surface new mechanistic ideas and clear predictions for future experiments From Google Deepmind Neuroscience Lab + collaborators www.biorxiv.org/content/10.6...

Perceptual multistability, observed across species and sensory modalities, offers valuable insights into numerous cognitive functions and dysfunctions. For instance, differences in temporal dynamics and information integration during percept formation often distinguish clinical from nonclinical populations. Computational psychiatry can elucidate these variations through two primary approaches: (i) Bayesian modeling, which treats perception as an unconscious inference, and (ii) an active, information-seeking perspective (e.g., reinforcement learning), which frames perceptual switches as internal actions. Our synthesis aims to leverage multistability to bridge these computational psychiatry subfields, linking human and animal studies as well as connecting behavior to underlying neural mechanisms. Perceptual multistability emerges as a promising noninvasive tool for clinical applications, facilitating translational research and enhancing our mechanistic understanding of cognitive processes and their impairments.

Interested in using more of your precious experiment time? ⌛ And giving your participants a good time? 🥳 Join us tomorrow at 8:30 am in HS7 for an exciting symposium on continuous tasks with @lilweb.bsky.social @dominikstrb.bsky.social @ookenfooken.bsky.social at @biodgps-dgpa.bsky.social #PUG2026!

A new #bmm release is available on #CRAN. With this version, #bmm now covers basic response time models. This release contains three new models plus several usability features. Here's what's in it 🧵 venpopov.com/bmm/ #rstats #bayes #openscience

🎉New paper out today in Nature Reviews Psychology🎉 with @mjdahl.bsky.social, @mariamaly.bsky.social, and @thiasmittner.bsky.social. We've been working on a unified framework for attentional states and the dynamics of transitions between them. 🧵