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
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
#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
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.
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).
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...
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.
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).
True. And before meeting Yue Sun I had no idea you could have conversations that last for hours about: syllables.
Big news indeed:)
This was an interesting commentary to write on work by Binder et al. regarding impaired acoustic phonetic perception after unilateral left hemisphere stroke. academic.oup.com/brain/advanc...
documentary with a brief appearance of my PI arguing for basic shared values. the past few months here have been highly strange... (example: how to conduct lab meetings when your PI is not allowed to enter the building? 😶🌫️)
www.youtube.com/watch?v=n5nE...
Please repost to get the word out! @nkgarg.bsky.social and I are excited to present a personalized feed for academics! It shows posts about papers from accounts you’re following bsky.app/profile/pape...
People talk a lot about objects, but what about the softness of a cushion, the greenness of an emerald, or the viscosity of oil? In our work just published @pnas.org, we shed light on how we make sense of the hundreds of materials around us.
www.pnas.org/doi/10.1073/...
20+ years ago, an idea about cortical lateralization of audition was advanced: asymmetric sampling in time (AST). This extensive review/reevaluation by Chantal Oderbolz, me, and Martin Meyer assesses how the idea has fared. #notallwrong
www.sciencedirect.com/science/arti...
One week left to apply!
We'll have so much exciting data for projects: for example, neuropixel data from humans while they listen to sentences courtesy of @shaileejain.bsky.social, Eddie Chang, and his group.
I feel like you're already missing a spotlight here by not providing a link to your paper:)
"Key attributes of successful research institutes" journals.plos.org/plosbiology/... – well-written perspective on what makes research institutes successful. having a lot of resources is not sufficient, if there is no positive research culture or good governance structure.
C, on all dimensions.
Thanks to @davidpoeppel.bsky.social and the members of the Poeppel lab for their support and feedback on this work.
We demonstrate the approach on a dataset collected using a speaker odd-one-out task, where we show that people’s first language can shape how they perceive continuous and categorical aspects of accents.
Item-level fitting, on the other hand, provides an estimate of the information present in the data that is not accounted for by prior knowledge and remains to be explained. We can use the fitted models for exploration and hypothesis generation.
(Q2) However, we show in simulations how to incorporate design matrices in the model fit. This allows us to quantify how well participants' odd-one-out choices can be explained using prior knowledge (here: stimulus categories).
Both the stimulus feature space and individual rater weights are optimized in a combined procedure using backpropagation. The model can be fitted without taking prior knowledge into account.
(Q1) In this task, human raters have to choose the odd-one-out in a triplet of 3 stimuli. In this simulated example two raters disagree on 1 triplet. Our approach assumes a common feature space that describes stimuli, but raters can weigh features differently in their choices.
The approach we propose is based on the odd-one-out task, which was used by @martinhebart.bsky.social to reveal dimensions of object representation.
A bookshelf. The books are not arranged in an apparent order.
(Q2) Sometimes the features underlying people’s similarity judgments are not obvious. How can we combine prior knowledge about stimulus domains with data-driven approaches to gain new insights?
two bookshelves: in one of them the books are sorted by size, in the other they are sorted by color.
In a new preprint with @kleind.bsky.social, we ask two questions: (Q1) People differ in how they perceive the similarity of stimuli in their environment. How can we model the features underlying similarity judgments in arbitrary domains, while accounting for individual differences? osf.io/agpb5_v1 🧵
