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Reposted by Rachel Ryskin

Kristof Strijkers @strijkers.bsky.social · 5d

The first publication of the #ERC project ‘LaDy’ is a fact and it’s an important one I think:

We show that word processing and meaning prediction is fundamentally different during social interaction compared to using language individually!
👀 short 🧵/1

psycnet.apa.org/fulltext/202...
#OpenAccess

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Reposted by Rachel Ryskin

Anna (Anya) Ivanova @neuranna.bsky.social · 16d

As our lab started to build encoding 🧠 models, we were trying to figure out best practices in the field. So @neurotaha.bsky.social
built a library to easily compare design choices & model features across datasets!

We hope it will be useful to the community & plan to keep expanding it!
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neurotaha @neurotaha.bsky.social · 16d

🚨 Paper alert:
To appear in the DBM Neurips Workshop

LITcoder: A General-Purpose Library for Building and Comparing Encoding Models

📄 arxiv: arxiv.org/abs/2509.091...
🔗 project: litcoder-brain.github.io

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Reposted by Rachel Ryskin

Fernanda Ferreira @fernandaedi.bsky.social · 22d

New paper: We argue that linearization in language production is a foraging process, with speakers navigating semantic and spatial clusters. Lead author: Karina Tachihara, former UC Davis postdoc, now faculty at UIUC!

www.sciencedirect.com/science/arti...

Planning to be incremental: Scene descriptions reveal meaningful clustering in language production

How do speakers plan complex descriptions and then execute those plans? In this work, we attempt to answer this question by asking subjects to describ…

www.sciencedirect.com

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Rachel Ryskin @ryskin.bsky.social · 27d

🚨 Postdoc Opportunity PSA! 🚨

🗓️ UC President’s Postdoctoral Fellowship Program applications are due Nov. 1 (ppfp.ucop.edu/info/)

Open to anyone interested in a postdoc & academic career at a UC campus.

I'm happy to sponsor an applicant if there’s a good fit— please reach out!

University of California | President’s Postdoctoral Fellowship Program

ppfp.ucop.edu

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Reposted by Rachel Ryskin

Matt Goldrick @mattgoldrick.bsky.social · Sep 8

Officially out! "Predicting relative intelligibility from inter-talker distances in a perceptual similarity space for speech" S.E Kim, B. R. Chernyak, @keshet.bsky.social, me, & A. Bradlow link.springer.com/article/10.3.... BONUS: free online similarity calculator so you can join in the fun! 1/

Predicting relative intelligibility from inter-talker distances in a perceptual similarity space for speech - Psychonomic Bulletin & Review

Researchers have generally assumed that listeners perceive speech compositionally, based on the combined processing of local acoustic–phonetic cues associated with individual linguistic units. Yet, th...

link.springer.com

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Reposted by Rachel Ryskin

Greta Tuckute @gretatuckute.bsky.social · Aug 19

Humans largely learn language through speech. In contrast, most LLMs learn from pre-tokenized text.

In our #Interspeech2025 paper, we introduce AuriStream: a simple, causal model that learns phoneme, word & semantic information from speech.

Poster P6, tomorrow (Aug 19) at 1:30 pm, Foyer 2.2!

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Reposted by Rachel Ryskin

Alexander Huth @alexanderhuth.bsky.social · Aug 18

New paper with @rjantonello.bsky.social @csinva.bsky.social, Suna Guo, Gavin Mischler, Jianfeng Gao, & Nima Mesgarani: We use LLMs to generate VERY interpretable embeddings where each dimension corresponds to a scientific theory, & then use these embeddings to predict fMRI and ECoG. It WORKS!

bioRxiv Neuroscience @biorxiv-neursci.bsky.social · Aug 13

Evaluating scientific theories as predictive models in language neuroscience https://www.biorxiv.org/content/10.1101/2025.08.12.669958v1

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Reposted by Rachel Ryskin

Adele Goldberg @adelegoldberg.bsky.social · Aug 18

LLM finds it FAR easier to distinguish b/w DO & PO constructions when the lexical & info structure of instances conform more closely w/ the respective constructions (left 👇). Where's pure syntax? LLM seems to say "🤷‍♀️" (right) @SRakshit
adele.scholar.princeton.edu/sites/g/file...

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Reposted by Rachel Ryskin

Noga Zaslavsky @nogazs.bsky.social · Aug 9

If you missed us at #cogsci2025, my lab presented 3 new studies showing how efficient (lossy) compression shapes individual learners, bilinguals, and action abstractions in language, further demonstrating the extraordinary applicability of this principle to human cognition! 🧵

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Reposted by Rachel Ryskin

Moshe Poliak @moshepoliak.bsky.social · Aug 5

(1)💡NEW PUBLICATION💡
Word and construction probabilities explain the acceptability of certain long-distance dependency structures

Work with Curtis Chen and Ted Gibson

Link to paper: tedlab.mit.edu/tedlab_websi...

In memory of Curtis Chen.

tedlab.mit.edu

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Reposted by Rachel Ryskin

Thomas Hikaru Clark @thomashikaru.bsky.social · Jul 31

1/7 If you're at CogSci 2025, I'd love to see you at my talk on Friday 1pm PDT in Nob Hill A! I'll be talking about our work towards an implemented computational model of noisy-channel comprehension (with @postylem.bsky.social, Ted Gibson, and @rplevy.bsky.social).

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Rachel Ryskin @ryskin.bsky.social · Jul 30

Adaptation to noisy language input in real time: Evidence from ERPs
escholarship.org/uc/item/8cm7...

Adaptation to noisy language input in real time: Evidence from ERPs

Author(s): Li, Jiaxuan; Ortega, Alyssa Viviana; Futrell, Richard; Ryskin, Rachel | Abstract: Language comprehension often deviates from the literal meaning of the input, particularly when errors resem...

escholarship.org

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Rachel Ryskin @ryskin.bsky.social · Jul 30

The Role of Context Gating in Predictive Sentence Processing.
escholarship.org/uc/item/21w8...

The Role of Context Gating in Predictive Sentence Processing

Author(s): Gokcen, Yasemin; Noelle, David C.; Ryskin, Rachel | Abstract: Prediction is a core computation in language, as humans use preceding context to implicitly make predictions about the upcoming...

escholarship.org

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Rachel Ryskin @ryskin.bsky.social · Jul 30

Language experience and prediction across the lifespan: evidence from diachronic fine-tuning of language models.
escholarship.org/uc/item/83b1...

Language experience and prediction across the lifespan: evidence from diachronic fine-tuning of language models

Author(s): Chao, Alton; Cain, Ellis; Ryskin, Rachel | Abstract: Humans predict upcoming language input from context, which depends on prior language experience. This suggests that older adults' predic...

escholarship.org

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Rachel Ryskin @ryskin.bsky.social · Jul 30

Efficient Audience Design in LLMs
escholarship.org/uc/item/6zm2...

Efficient Audience Design in LLMs

Author(s): Ryskin, Rachel; Gawel, Olivia; Tanzer, Owen; Pailo, Viniccius; Kello, Christopher | Abstract: During human communication, speakers balance informativeness and effort by tailoring their lang...

escholarship.org

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Rachel Ryskin @ryskin.bsky.social · Jul 30

Looking forward to seeing everyone at #CogSci2025 this week! Come check out what we’ve been working on in the LInC Lab, along with our fantastic collaborators!

Paper 🔗 in 🧵👇

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Reposted by Rachel Ryskin

Inbal Arnon @inbalarnon.bsky.social · Jul 10

Some happy science news (a small light in times of darkness). New paper out with @luciewolters.bsky.social and Mits Ota: : Skewed distributions facilitate infants’ word segmentation. sciencedirect.com/science/arti...

Skewed distributions facilitate infants' word segmentation

Infants can use statistical patterns to segment continuous speech into words, a crucial task in language acquisition. Experimental studies typically i…

sciencedirect.com

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Rachel Ryskin @ryskin.bsky.social · Jul 8

Thrilled to see this work published — and even more thrilled to have been part of such a great collaborative team!

One key takeaway for me: Webcam eye-tracking w/ jsPsych is awesome for 4-quadrant visual world paradigm studies -- less so for displays w/ smaller ROIs.

Josh de Leeuw @joshdeleeuw.bsky.social · Jul 8

Want to know what kinds of studies webcam-based eye tracking can be used for? Here's our take on the current tech. This certainly isn't the first paper on this topic, but it provides some converging evidence about the viability of eye tracking with online methods. online.ucpress.edu/collabra/art...

What Paradigms Can Webcam Eye-Tracking Be Used For? Attempted Replications of Five Cognitive Science Experiments

Web-based data collection allows researchers to recruit large and diverse samples with fewer resources than lab-based studies require. Recent innovations have expanded the set of methodolgies that are...

online.ucpress.edu

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Reposted by Rachel Ryskin

Ellis Cain @ellscain.bsky.social · Jun 19

New paper w/ @ryskin.bsky.social and Chen Yu: We analyzed parent-child toy play and found that cross-situational learning statistics were present in naturalistic settings!

onlinelibrary.wiley.com/doi/epdf/10....

onlinelibrary.wiley.com

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Reposted by Rachel Ryskin

Emily Morgan @emilynguist.bsky.social · May 30

I'm hiring a postdoc to start this fall! Come work with me? recruit.ucdavis.edu/JPF07123

Post-Doctoral position - Department of Linguistics

University of California, Davis is hiring. Apply now!

recruit.ucdavis.edu

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Greta Tuckute @gretatuckute.bsky.social · May 23

What are the organizing dimensions of language processing?

We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals

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Reposted by Rachel Ryskin

Tom McCoy @rtommccoy.bsky.social · May 20

🤖🧠 Paper out in Nature Communications! 🧠🤖

Bayesian models can learn rapidly. Neural networks can handle messy, naturalistic data. How can we combine these strengths?

Our answer: Use meta-learning to distill Bayesian priors into a neural network!

www.nature.com/articles/s41...

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A schematic of our method. On the left are shown Bayesian inference (visualized using Bayes’ rule and a portrait of the Reverend Bayes) and neural networks (visualized as a weight matrix). Then, an arrow labeled “meta-learning” combines Bayesian inference and neural networks into a “prior-trained neural network”, described as a neural network that has the priors of a Bayesian model – visualized as the same portrait of Reverend Bayes but made out of numbers. Finally, an arrow labeled “learning” goes from the prior-trained neural network to two examples of what it can learn: formal languages (visualized with a finite-state automaton) and aspects of English syntax (visualized with a parse tree for the sentence “colorless green ideas sleep furiously”).

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The Spark Society @sparksociety.bsky.social · Apr 21

Unfortunately, the NSF grant that supports our work has been terminated. This is a setback, but our mission has not changed. We will continue to work hard on making cognitive science a more inclusive field. Stay tuned for upcoming events.

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Thomas Hills @thomhills.bsky.social · Apr 17

Does the mind degrade or become enriched as we grow old? To explain healthy aging effects, the evidence supports enrichment. Indeed, the evidence suggests changes in crystallized (enrichment) and fluid intelligence (slowing) share a common cause. psycnet.apa.org/record/2026-...

APA PsycNet

psycnet.apa.org

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Reposted by Rachel Ryskin

Mike Frank @mcxfrank.bsky.social · Apr 14

Super excited to submit a big sabbatical project this year: "Continuous developmental changes in word
recognition support language learning across early
childhood": osf.io/preprints/ps...

title of paper (in text) plus author list

Time course of word recognition for kids at different ages.

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