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Laura Gwilliams

@lauragwilliams.bsky.social

Language and speech processing - Neuroscience - Assistant Professor at Stanford University - She/Her - 🏳️‍🌈

86 Posts2094 Follower413 Folgt
Marianne de Heer Kloots@mdhk.net · 3mo ago

I'm very happy to share this work that came out of my visit to the GLySN lab! Let's use artificial speech systems to learn new things about the human brain 🧠

Laura Gwilliams@lauragwilliams.bsky.social · 3mo ago

check out our latest preprint led by the one and only @mdhk.net !! using intracranial EEG, we find that distinct neural populations within the temporal lobe encode preceding context, and future context, during naturalistic speech comprehension! www.biorxiv.org/content/10.6...

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David Poeppel@davidpoeppel.bsky.social · 4mo ago

New paper that merits a read (Im totally unbiased...not). Simple, straightforward, impactful message. Prediction a la LLM is nice. Constituent-constrained prediction is nicer. @jiajiezou.bsky.social and Nai Ding show brain, behavioral, MEG, ECoG data. www.nature.com/articles/s41... #neuroskyence

Constituent-constrained word prediction during language comprehension - Nature Neuroscience

Zou et al. reveal a key difference between human brains and large language models (LLMs). While LLMs are optimized to predict the next word, the human brain modulates prediction efficiency by strategi...

nature.com

Laura Gwilliams@lauragwilliams.bsky.social · 5mo ago

thank you @claudia-lopez.bsky.social for this clear and thoughtful coverage in @thetransmitter.bsky.social on some of our recent work on dynamic neural codes in speech processing! 🧠 🌀 ✨ www.thetransmitter.org/language/shi...

Shifting neural code powers speech comprehension

Dynamic coding helps explain how the brain processes multiple features of speech—from the smallest units of sounds to full sentences—simultaneously.

thetransmitter.org

Cliona O'Doherty@clionaod.bsky.social · 6mo ago

1/7 Can infants recognise the world around them? 👶🧠 As part of the FOUNDCOG project, we scanned 134 awake infants using fMRI. Published today in Nature Neuroscience, our research reveals 2-month-old infants already possess complex visual representations in VVC that align with DNNs.

Laura Gwilliams@lauragwilliams.bsky.social · 6mo ago

very happy to share our latest work! building upon our basic science discoveries to better understand language processing in aphasia and healthy older adults

Jill Kries@kriesjill.bsky.social · 6mo ago

Excited to share our new publication “The Spatio-Temporal Dynamics of Phoneme Encoding in Aging and Aphasia”, published in JNeurosci 🧠 ➡️ www.jneurosci.org/content/46/4... with @lauragwilliams.bsky.social & @mvandermosten.bsky.social 🤝 Check out @stanfordbrain.bsky.social ’s summary of it ⬇️

@stanfordbrain.bsky.social · 6mo ago

A new study by @lauragwilliams.bsky.social, @kriesjill.bsky.social, and team suggests that phonetic features are robustly encoded in healthy older adults, but show reduced encoding strength in individuals with post-stroke aphasia during speech comprehension. www.jneurosci.org/content/46/4...

The Spatio-Temporal Dynamics of Phoneme Encoding in Aging and Aphasia

During successful language comprehension, speech sounds (phonemes) are encoded within a series of neural patterns that evolve over time. Here we tested whether these neural dynamics of speech encoding...

jneurosci.org

Marianne de Heer Kloots@mdhk.net · 8mo ago

'Tis the season to preprint BBS commentaries; I'm happy to share ours too! 🎄✨ The textual basis of current LLMs causes trouble, but linguistically relevant insights *can* be found in systems modelling the more natural form of human spoken language: the speech signal itself. arxiv.org/abs/2512.14506

Commentary title: Linguists should learn to love speech-based deep learning models Authors: Marianne de Heer Kloots, Paul Boersma, Willem Zuidema Abstract: Futrell and Mahowald present a useful framework bridging technology-oriented deep learning systems and explanation-oriented linguistic theories. Unfortunately, the target article's focus on generative text-based LLMs fundamentally limits fruitful interactions with linguistics, as many interesting questions on human language fall outside what is captured by written text. We argue that audio-based deep learning models can and should play a crucial role.
Laura Gwilliams@lauragwilliams.bsky.social · 8mo ago

Was a pleasure speaking at the @dataonbrainmind.bsky.social workshop! Plenty of exciting work still to come today - head over to room 10 if you’re are at #NeurIPS!

Data on the Brain & Mind @NeurIPS2025@dataonbrainmind.bsky.social · 8mo ago

For our third talk of the workshop, we are honored to have Prof. @lauragwilliams.bsky.social sharing with us on “Computational architecture of speech comprehension” #NeurIPS2025 📍Location: Upper Level Room 10. 🗓️Schedule: data-brain-mind.github.io