Rachel Ryskin

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/

571 followers398 following46 posts

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We also discuss open Qs & broader implications (e.g., algorithms, neural mechanisms, development, language evolution, & LLMs). Feedback very welcome! 🙏

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The same inference can mislead us: “The mother gave the candle the daughter” is often read as if a “to” was dropped. This unifies explanations of phenomena from phonemic restoration to illusions (“No head injury is too trivial to be ignored”) to real-time processing effects (via noisy memory).

Noise models are also calibrated to experience. They adapt to context (readers expect more errors from an error-prone typist) and plausibly differ across populations, including older adults, people with aphasia, children, and L2 learners. 🧪 This opens up lots of new, testable predictions!

When you hear “Can you demonstrate what it’s like to brush our 𝘵𝘦𝘦𝘵𝘩’𝘴 𝘱𝘦𝘵?” you probably infer that “pet’s teeth” was intended. You get there by combining prior expectations about what people are likely to say with a model of how messages get corrupted along the way.

We distinguish three sources of noise: errors in the speaker’s production (slips, typos), noise in the environment (a cough, a dropped call), and noise inside the comprehender (perception, attention, memory). Each has its own signature, and comprehenders seem to model all three.

Rachel Ryskin reposted

Imagine two people standing in the same spot, looking at the same Goya. They inspect exactly the same parts of the painting, but in a different order. Later, they talk about what they saw. Ignoring all other differences: are the memories they formed identical?

Rachel Ryskin reposted

Are you: * A cognitive science (or adjacent) PhD (current or graduated, or equivalent research exp.)? * Interested in studying LMs abilities for conceptual reasoning about cog sci, and helping to improve them? If so, apply to our new conceptual reasoning fellows program! Link below:

Rachel Ryskin reposted

DINOv2 has never seen a caption, and Qwen3 has never seen an image. We still aligned their embedding spaces without a single image-caption pair. It even works when the images and the captions come from different datasets. Project page: dominik-schnaus.github.io/unpaired-rosetta ⬇️

🧵 New preprint! Speech is noisy, yet we usually understand it. I used eye-tracking + a self-supervised speech model (HuBERT) to show that speech comprehension unfolds as real-time Bayesian "noisy-channel" inference & that listeners tune their noise model to their environment 👇 osf.io/preprints/ps...

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Finally, after the Noisy block, some participants saw "FATAL ERROR: Audio files corrupted," and the experimenter "fixed" it. These listeners tended to show lower τ in the next block than those given no explanation. The noise can be "explained away!"

🧠 Takeaway: we understand speech by reasoning in real time over a generative model—noise included—updated by implicit stats & explicit causal beliefs. 🤖 HuBERT's representational geometry offers a window into that noise model & a test of AI speech models vs. human gaze. 💬 Feedback very welcome!

🧵 New preprint! Speech is noisy, yet we usually understand it. I used eye-tracking + a self-supervised speech model (HuBERT) to show that speech comprehension unfolds as real-time Bayesian "noisy-channel" inference & that listeners tune their noise model to their environment 👇 osf.io/preprints/ps...

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This simple model captures the classic gaze patterns: Early competitors draw looks, then drop off after disambiguation. Late competitors attract looks above unrelated distractors later, indicating that listeners maintain uncertainty about the past input.

To test adaptation of the noise model, in "Noisy" blocks, 2/3 of fillers had noise spliced in. Listeners' τ was: - higher in Noisy vs. Intact blocks - still elevated in Intact blocks after Noisy ones - lower in Noisy blocks after Intact ones Listeners track noise statistics over time.

🧵 New preprint! Speech is noisy, yet we usually understand it. I used eye-tracking + a self-supervised speech model (HuBERT) to show that speech comprehension unfolds as real-time Bayesian "noisy-channel" inference & that listeners tune their noise model to their environment 👇 osf.io/preprints/ps...

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Open Qs: What does that noise model look like, and how flexible is it? I investigate this w/ eye tracking data from a classic visual world paradigm study (Allopenna et al., 1998): 👂"Click on the beaker" & 👁️ display that might also include a beetle (early sound overlap) or a speaker (late overlap).

Gaze is modeled as incremental Bayesian belief updating: the posterior at one moment becomes the prior for the next. The noise likelihood comes from acoustic similarity in HuBERT's representation space, scaled by an uncertainty parameter τ.

🧵 New preprint! Speech is noisy, yet we usually understand it. I used eye-tracking + a self-supervised speech model (HuBERT) to show that speech comprehension unfolds as real-time Bayesian "noisy-channel" inference & that listeners tune their noise model to their environment 👇 osf.io/preprints/ps...

osf.ioOSF

According to noisy-channel accounts, listeners infer what a speaker meant by combining prior expectations with a model of how the message might get corrupted (e.g., speech error, loud noise, lapse in attention). If you heard "hand me the pork" with only a fork in sight, you would hand over the fork.

Rachel Ryskin reposted

Rachel Ryskin reposted

What’s role of fun in linguistics? Do you do linguistics/language research where you collect data from people? Looking for language researchers across subfields to do a short survey about how they think about the role of fun in collecting data! Survey here: ucsb.co1.qualtrics.com/jfe/form/SV_...

ucsb.co1.qualtrics.comQualtrics Survey | Qualtrics Experience ManagementThe most powerful, simple and trusted way to gather experience data. Start your journey to experience management and try a free account today.

Rachel Ryskin reposted

🚨New paper alert! 🚨 The visual system tunes itself to the statistics of its input. How do individual experiences differently tune vision? In this work, @bjbalas.bsky.social and I examined how one individual difference—height—changes the visual diet and how this affects perception. 1/

Rachel Ryskin reposted

📣📣📣 Preprint Alert! 📣📣📣 Do predictive processes shape brain representations during learning? We investigated the effect of Predictive Coding by training identical recurrent networks with different optimization procedures and then comparing them to human EEG under the same visual learning task.

Rachel Ryskin reposted

since I'm getting many pokes about grad school applications, I wanted to re-up some previous public advice on grad school applications. looking back at it a year on, I still think all this is right, but let me update the 'research statement' part to include a bit on genAI:

Tomer Ullman@tomerullman.bsky.social

It’s grad school application season, and I wanted to give some public advice. Caveats: -*-*-*-* 
> These are my opinions, based on my experiences, they are not secret tricks or guarantees 
> They are general guidelines, not meant to cover a host of idiosyncrasies and special cases

Rachel Ryskin reposted

We uncovered spontaneous evolution of new languages in populations of AI agents. This creates extraordinary scientific opportunities but also safety risks. New blog post with about how we created a platform for studying this safely. www.schmidtsciences.org/glossogen/

schmidtsciences.orgAI Agents Evolve Their Own LanguagesSchmidt Sciences’ new AI Agents Evolving Communication and Coordination pilot program works toward advancing foundational research on multi-agent communication and coordination, and building an open-s...

Rachel Ryskin reposted

UCSD Cognitive Science Department is hiring an Assistant Professor incorporating novel computational approaches in the study of biological and/or artificial intelligence. I have had the privilege of working in this department for 10 years and the intellectual environment really is unique. 1/3

Rachel Ryskin reposted

Can self-interested, self-improving, self-replicating agents learn to cooperate? Our new paper, Tapes Together Strong, shows they can: when social behavior, computation, and reproduction share one energy budget, cooperation evolves from scratch. arxiv.org/abs/2609.10817 🧵

Rachel Ryskin reposted

How does a speech plan become fluent movement? In Nature Human Behaviour, led by Kumar Duraivel: Intracranial recordings reveal how hierarchical speech plans become continuous motor sequences across planning, articulation, and monitoring networks. doi.org/10.1038/s415... 1/5

doi.orgDistinct neural processes link speech planning and execution - Nature Human BehaviourDirect recordings from the human brain reveal a hierarchy in planning to speak, with neural activity organizing whole syllables before the individual sounds that compose them are sequenced.

Rachel Ryskin reposted

Rachel Ryskin reposted

🤖🧠NEW PAPER🧠🤖 (The result of an 8-year project!) LLMs seem very different from symbolic systems. Yet LLMs excel in symbolic domains (e.g., language/code/math). How do they do it? Our finding: LLM representations have implicit symbolic structure! Link in thread ⬇️ 1/n

Rachel Ryskin reposted

If this makes you mad, submit your cognitive science research to @openmindjournal.bsky.social No APCs, no subscription fees. It's funded by university libraries, at low cost.

Eric Schares@eschares.bsky.social

🚨Update: Our analysis of global APC expenditure has now been expanded to cover more publishers (7 -> 14) and more years (2019-2025). We estimate $15B in APCs paid over seven years, $3.7B in 2025 alone. Elsevier crosses the $1B threshold by itself in 2025. arxiv.org/abs/2608.16322 #ScholComm

Rachel Ryskin reposted

Chimpanzees usually fail a simple pointing task that human infants and dogs pass. In our new iScience paper, we provide a possible explanation: once their attention is engaged, chimpanzees use human pointing to find hidden food 73% of the time. Attention may be the missing piece.

Rachel Ryskin reposted

Exciting update! You can run Image Based Meta-Analyses (IBMA) on our platform 🚀 We've known for ~TWO decades that images are better than coordinates, but data has never been shared/organized properly to make that an accessible reality. We did it, enjoy: compose.neurosynth.org

Very clear demonstration of the remarkable preservation of the language network in the brains of older adults (in contrast to the MD network)! 🧠 👵

Anne Billot, PhD@annebillot.bsky.social

📣 New study out today! We show that the brain's language network stays remarkably stable with age — same topography, lateralization, selectivity and connectivity as in younger adults — while the domain-general multiple demand network showed altered activity patterns. www.nature.com/articles/s41...

@salonisameernaik.bsky.social did a fantastic job leading this—impressive since she isn't even in grad school yet (applying this fall!). Super fun to collab w/ @tylermarghetis.bsky.social on the role of perceptual processes in seemingly abstract math reasoning. See thread & preprint for details!👇

Saloni Sameer Naik@salonisameernaik.bsky.social

Excited to share this preprint with my amazing co-authors @ryskin.bsky.social & @tylermarghetis.bsky.social ! 🎉 "Eye-tracking reveals the perceptual basis of algebraic expertise" How does becoming good at algebra change the way you look at equations? + × 👁️ Preprint: osf.io/preprints/ps...