Peter Donhauser
@pwdonh.bsky.social
240 followers 380 following 30 posts
Cognitive Neuroscience Researcher | Speech & Audition | MEG Frankfurt, Germany https://scholar.google.com/citations?user=276f1C0AAAAJ
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pwdonh.bsky.social
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 🧵
two bookshelves: in one of them the books are sorted by size, in the other they are sorted by color.
pwdonh.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
pwdonh.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.
pwdonh.bsky.social
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).
pwdonh.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.
pwdonh.bsky.social
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).
pwdonh.bsky.social
True. And before meeting Yue Sun I had no idea you could have conversations that last for hours about: syllables.
Just looked through this paper again - and I still think these results are cool (and less obvious than one might think) :-)

"Syllables and their beginnings have a special role in the mental lexicon" Yue Sun providing a nice perspective on phonology and the lexicon.
www.pnas.org/doi/abs/10.1...
PNAS
Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...
www.pnas.org
Reposted by Peter Donhauser
nschawor.bsky.social
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...
Reposted by Peter Donhauser
sjgreenwood.bsky.social
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...
Reposted by Peter Donhauser
martinhebart.bsky.social
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/...
Reposted by Peter Donhauser
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...
Asymmetric Sampling in Time: Evidence and perspectives
Auditory and speech signals are undisputedly processed in both left and right hemispheres, but this bilateral allocation is likely unequal. The Asymme…
www.sciencedirect.com
Reposted by Peter Donhauser
mtoneva.bsky.social
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.
mtoneva.bsky.social
Really excited for this 3 week NeuroAI summer school in Lisbon!

We have course faculty from a range of neuro domains (language, vision, decision making, memory), and neuro levels (low level to cognitive). There will also be a project advised by our exciting lineup of course faculty!

Come join us!
cajal-training.bsky.social
🚀 Applications are OPEN for the CAJAL NeuroAI course!

Learn how AI & deep learning help us model brain activity & behavior. Work with experts, get hands-on training & join a global network!

📅 Apply by March 7
🔗 loom.ly/xg_uRKE

#NeuroAI #DeepLearning #Neuroscience
pwdonh.bsky.social
I feel like you're already missing a spotlight here by not providing a link to your paper:)
Reposted by Peter Donhauser
nschawor.bsky.social
"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.
pwdonh.bsky.social
C, on all dimensions.
pwdonh.bsky.social
#PsychSciSky #neuroskyence
pwdonh.bsky.social
Thanks to @davidpoeppel.bsky.social and the members of the Poeppel lab for their support and feedback on this work.
pwdonh.bsky.social
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.
pwdonh.bsky.social
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.
pwdonh.bsky.social
(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).
pwdonh.bsky.social
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.
pwdonh.bsky.social
(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.