Rachel Ryskin

@ryskin.bsky.social

Cognitive scientist @ University of California, Merced | http://raryskin.github.io PI of Language, Interaction, & Cognition (LInC) lab: https://linclab0.github.io/

calling all researchers who collect data from humans 🧠🧪 we are running a very brief survey (<1 min) about jsPsych, the software ecosystem for browser-based data collection. if you use jsPsych, or if you collect data with any other software, please fill it out at tinyurl.com/jspsych-census!

I am writing on behalf of the leadership of jsPsych, the software ecosystem for browser-based data collection used in experiments and surveys from many labs worldwide.

It has been difficult for us to figure out how widespread jsPsych usage is, because the software is freely available online and we do not collect any information about downloads, users, experiments posted online, etc. While the software has been cited in thousands of articles, jsPsych usage is likely more widespread than those citations imply, because not every project using jsPsych leads to a citation, and because there are also often substantial delays between actual jsPsych usage and a publication. As such, we are conducting a very brief census of jsPsych users.

If you collect data from humans using jsPsych or any other type of software, we would appreciate your filling out a very brief survey (<1 min) at tinyurl.com/jspsych-census. This will help us to understand how many people, labs, and institutions are current or former users of jsPsych, information that will help support the long-term sustainability of the jsPsych ecosystem.

We would also appreciate it if you could circulate this message to your lab, your department listserv, and your collaborators, to help ensure that it reaches as many members of the research community as possible. The survey is being distributed by Josh de Leeuw, Melissa Kline Struhl, and myself. Please contact me (sam@auckland.ac.nz) off-list if you have any questions about it.

One of the first things we found was that visual feature representations became progressively less independent, even before detectable damage in visual cortex. Information about one feature (say, curvature) started influencing another (say, contrast). We call this neuronal feature confusion. 5/

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The full BBS treatment from me and @futrell.bsky.social on "How linguistics learned to stop worrying and love the LMs" is now out, with all the commentaries and our response. If you "Save PDF", it will give you the whole target article + commentary + response pdf: www.cambridge.org/core/journal...

How linguistics learned to stop worrying and love the language models | Behavioral and Brain Sciences | Cambridge Core

How linguistics learned to stop worrying and love the language models - Volume 49

cambridge.org

New preprint! Children often beat adults at learning hidden patterns. Using eye-tracking and a method we built in eLife (Hann et al. 2026), we found the edge is not better error detection. It is flexibility: kids update their beliefs, adults cling to old predictions. www.biorxiv.org/content/10.6...

Flexible belief updating drives the childhood advantage in statistical learning

Children often outperform adults in probabilistic statistical learning tasks, yet the mechanisms underlying this developmental advantage remain poorly understood. Here, we used eye-tracking measures o...

biorxiv.org

It is not easy to characterize the features represented by human language cortex. This work is a step toward doing so. Using small, interpretable feature sets, we explain language-network responses and show a shared feature basis across brain regions, with graded variation across individuals.

Michael Lepori@michael-lepori.bsky.social · 2mo ago

🚨New preprint!🚨 We know that LM representations can be used to predict brain responses to language. But what *features* of these representations underlie this alignment? We use SAEs to find out!