In this new paper led by @drcharlotte.bsky.social and myself, we explored phonological representations in monolingual and bilingual neural networks trained on speech recognition: doi.org/10.1073/pnas...
We show that multiple phonological systems are organized through parallel representations, preserving the unique aspects of each language while maintaining shared articulatory features (here e.g. manner of articulation and consonant voicing).
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The remarkable human capacity for bilingual and multilingual acquisition raises fundamental
questions about how the brain develops efficient system...
main goal for this year: find a new job! 🙂
looking for a role with fun & complex technical challenges & within a great community. my main expertise is in signal processing/EEG/MEG, but topic-wise I am quite flexible.
science/industry both great! starting mid-year. nschawor.github.io/cv
New paper 🌟out now in Nat. Neurosci.: www.nature.com/articles/s41.... With advisors @davidpoeppel.bsky.social and @Nai!
We show that, while LLMs are optimized to predict the next word, the human brain modulates prediction efficiency by strategically grouping words into constituents. 1/n
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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
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The development of parallel phonological representations varied based on the timing of language exposure, showing how earlier-learned languages shape the acquisition of subsequent ones.
#PsychSciSky @theneuro.bsky.social
The paper discusses in depth the parallels with and potential insights for the bilingual language learning literature. This was a very fun collaborative project together with bilingualism experts @kleind.bsky.social and @kbyers.bsky.social
