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Will Turner

@renrutmailliw.bsky.social
171 followers 216 following 8 posts

cognitive neuroscience postdoc at stanford bootstrapbill.github.io he/him

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Reposted by Will Turner
Laura Gwilliams @lauragwilliams.bsky.social · 19h
delighted to share my review article, out now in Trends in Cognitive Sciences! i have been thinking a lot about dynamics lately. this article outlines three key principles - persistence, parallelism, and time-stamped encoding - that support human speech comprehension 🧠✨🌀
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sarah tung @sarahtung.bsky.social · 05/10/2026
1/ New preprint! 🧠👶 "Eccentricity, not category, organizes infant ventral temporal cortex." With Emily M Chen, Cleo Tay, Kylie Syzmanski, and @camerontellis.bsky.social www.biorxiv.org/content/10.6...
biorxiv.org
Eccentricity, but not category, organizes infant ventral temporal cortex
Scene- and word-selective regions in adult ventral temporal cortex occupy consistent locations across individuals and align with eccentricity biases for peripheral versus central parts of the visual f...
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Liina Pylkkänen @liinapy.bsky.social · 28/09/2026
NeLLab #SNL2026 menu! See you soon @snlmtg.bsky.social!
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Jill Kries @kriesjill.bsky.social · 28/09/2026
Interested in precise spatial and temporal substrates of language in the developing brain? 🧏🧠➡️👶🧒👩‍🦱 Come check out my poster at #snl2026 @snlmtg.bsky.social, on Thursday Poster session C at 10:45am, poster C24!
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Laura Gwilliams @lauragwilliams.bsky.social · 28/09/2026
the Gwilliams Lab and friends are at SNL 2026!! if you're into speech comprehension, intracranial and non-invasive time series, from single neurons to the whole cortex, across ages, using both classic linguistic approaches and advanced ML techniques - we've got something for you! 🧠 🌀 ✨
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Will Turner @renrutmailliw.bsky.social · 07/08/2026
Congrats @jasminpatelsci.bsky.social!!! Thrilled to be part of this new preprint looking at how the brain represents moving objects when they are temporarily hidden from view 🧠 👀
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Minds, Machines, and Brains (MMB) @mmb-journal.bsky.social · 02/08/2026
Hello world! 👋 We’re Minds, Machines, and Brains (MMB) 👤🤖🧠 a new open access journal from @mitpress.bsky.social exploring the principles of intelligence and cognition across natural and artificial minds. Submissions open this Fall! 🔗 direct.mit.edu/mmb
direct.mit.edu
Minds, Machines, and Brains | MIT Press
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Marianne de Heer Kloots @mdhk.net · 30/06/2026
Let’s study learning trajectories in self-supervised speech models! 🔊 Do they reflect the hierarchical organization of spoken language? We have analyzed a lot of training checkpoints to find out 🌠 Preprint: arxiv.org/abs/2604.02043 ⬇️
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Jean-Rémi King @jeanremiking.bsky.social · 29/06/2026
We’re happy to announce 2 releases today: - 🧠Brain2qwerty v1 is published at @Nature Neuroscience - 🚀 Brain2Qwerty v2 is now publicly released Explore how we decode sentences from non-invasive brain recordings: facebookresearch.github.io/brain2qwerty/ Code, papers, data below:
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Steve Fleming @smfleming.bsky.social · 25/06/2026
Lovely to see the full range of excellent commentaries on our BBS article with @matthiasmichel.bsky.social, together with our response, now out: Target article: www.cambridge.org/core/journal... Commentaries: www.cambridge.org/core/journal... Our response: www.cambridge.org/core/journal...
cambridge.org
Sensory horizons and the functions of conscious vision | Behavioral and Brain Sciences | Cambridge Core
Sensory horizons and the functions of conscious vision - Volume 49
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Jean-Rémi King @jeanremiking.bsky.social · 05/06/2026
We have a new postdoc position to work on Neuro AI and human intracranial recording with Pierre Bourdillon and I: Apply here: docs.google.com/forms/d/e/1F... Lab info: bourdillon-titan-lab.fr feel free to RT ;)
docs.google.com
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William Ngiam | 严祥全 @williamngiam.github.io · 28/05/2026
There's ongoing discourse about whether digital technology is impacting attention spans, but the empirical research does not seem to connect with what is felt by the general public. So, to better understand the perceptions, we examined what was being said on the issue in four relevant subreddits.
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Emily Cheng @emcheng.bsky.social · 06/05/2026
Presenting this at #ICML with @rjantonello.bsky.social and Aditya Vaidya✨ Why do 𝙢𝙞𝙙𝙙𝙡𝙚 layers in LLMs and speech-audio models best predict brain responses to language? We show a peak in the dimensionality of 🤖 activations (left) to track high 🧠 predictivity (right) 🧵(cross-posted from X)
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Jiajie Zou @jiajiezou.bsky.social · 22/04/2026
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 More 👇
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Jean-Rémi King @jeanremiking.bsky.social · 15/04/2026
🧠 the Digital Brain Project is now live: $5M total · up to $500k per selected team Let's open-source the modeling of the human brain brain activity! ➡️Apply on: digitalbrainproject.org
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Khai Loong Aw @khaiaw.bsky.social · 14/04/2026
Children exhibit visual understanding from limited experience, orders of magnitude less than our best models. We introduce the Zero-shot World Model (ZWM). Trained on a single child's visual experience, BabyZWM rapidly generates competence across diverse benchmarks with no task-specific training. 🧵
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Prof. Alex Woolgar @alexwoolgar.bsky.social · 08/04/2026
Excited about this one!
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Tobias Gerstenberg @tobigerstenberg.bsky.social · 03/04/2026
The Causality in Cognition Lab -- a supportive, bluesky-colored team -- is looking for a predoc to join us! Here are infos about the lab (cicl.stanford.edu) and the position (careersearch.stanford.edu/jobs/iriss-p...). The application deadline is May 1st. Please share, thank you 🙏
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Irmak Ergin @irmakergin.bsky.social · 01/04/2026
Excited to share our new publication, “Measuring Naturalistic Speech Comprehension in Real Time”! ➡️ rdcu.be/fa3hk #psynomBRM w/ @kriesjill.bsky.social, Shiven Gupta, Maria Papworth Burrel, & @lauragwilliams.bsky.social 🧵1/11
rdcu.be
Measuring naturalistic speech comprehension in real time
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Arthur Prat @arthurpr4t.bsky.social · 27/03/2026
Just posted an update of this study, where we show how the receptive fields of number-encoding neural populations shift and rescale with the prior — "distributed range adaptation" — in line with (dynamic) efficient coding, and predictive of behavior. Check it out! www.biorxiv.org/content/10.1...
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micha heilbron @mheilbron.bsky.social · 05/03/2026
📢 PhD position in the NeuroAI of Language Why can LLMs predict brain activity so well? We're hiring a PhD student to find out -- AI interpretability meets neuroimaging Deadline March 20 Please RT 🙏 👇 mpi.nl/career-education/vacancies/vacancy/fully-funded-4-year-phd-position-neuroai-language
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stanfordbrain.bsky.social @stanfordbrain.bsky.social · 29/01/2026
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...
jneurosci.org
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...
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Reposted by Will Turner
Jill Kries @kriesjill.bsky.social · 29/01/2026
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 ⬇️
jneurosci.org
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...
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Marianne de Heer Kloots @mdhk.net · 17/12/2025
'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.
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Laura Gwilliams @lauragwilliams.bsky.social · 20/11/2025
proud to share this work, led by the brilliant @ilinabg.bsky.social, now out in Nature! Ilina finds that speech-sound neural processing is VERY similar in a language you know and one you don't. differences only emerge at the level of word boundaries and learnt statistical structure 🧠✨
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Violet Chae @violetchae.bsky.social · 10/11/2025
Our new preprint on the FOODEEG open dataset is out! EEG recordings and behavioural responses on food cognition tasks for 117 participants will be made publicly available 🧠 @danfeuerriegel.bsky.social @tgro.bsky.social www.biorxiv.org/content/10.1...
biorxiv.org
FOODEEG: An open dataset of human electroencephalographic and behavioural responses to food images
Investigating the neurocognitive mechanisms underlying food choices has the potential to advance our understanding of eating behaviour and inform health-targeted interventions and policy. Large, publi...
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Laura Gwilliams @lauragwilliams.bsky.social · 07/11/2025
happy to share our new paper, out now in Neuron! led by the incredible Yizhen Zhang, we explore how the brain segments continuous speech into word-forms and uses adaptive dynamics to code for relative time - www.sciencedirect.com/science/arti...
sciencedirect.com
Human cortical dynamics of auditory word form encoding
We perceive continuous speech as a series of discrete words, despite the lack of clear acoustic boundaries. The superior temporal gyrus (STG) encodes …
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Jill Kries @kriesjill.bsky.social · 05/11/2025
I am so honored to have received an Outstanding PhD Thesis award from the Luxembourg National Research Fund @fnr.lu! 🏆 My PhD research was about how language is processed in the brain, with a focus on patients with a language disorder called aphasia 🧠 Find out more➡️ youtu.be/E-Zww-B1jFQ?...
youtu.be
FNR Awards 2025: Outstanding PhD Thesis - Jill Kries
YouTube video by FNRLux
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Laura Gwilliams @lauragwilliams.bsky.social · 22/10/2025
Delighted to share our new paper, now out in PNAS! www.pnas.org/doi/10.1073/... "Hierarchical dynamic coding coordinates speech comprehension in the brain" with dream team @alecmarantz.bsky.social, @davidpoeppel.bsky.social, @jeanremiking.bsky.social Summary 👇 1/8
pnas.org
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...
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Ian Phillips @ianbphillips.bsky.social · 29/10/2025
Fantastic commentary on @smfleming.bsky.social & @matthiasmichel.bsky.social's BBS paper by @renrutmailliw.bsky.social, @lauragwilliams.bsky.social & Hinze Hogendoorn. Hits lots of nails on the head. As @neddo.bsky.social & I also argue: postdiction doesn't prove consciousness is slow! 1/3
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dorottya hetenyi @dotproduct.bsky.social · 21/10/2025
Super happy to share my very first first-author paper out in @sfnjournals.bsky.social! We show content-specific predictions are represented in an alpha rhythm. It’s been a beautiful, inspiring, yet challenging journey. Huge thanks to everyone, especially @peterkok.bsky.social @jhaarsma.bsky.social
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Peter Kok @peterkok.bsky.social · 21/10/2025
@dotproduct.bsky.social's first first author paper is finally out in @sfnjournals.bsky.social! Her findings show that content-specific predictions fluctuate with alpha frequencies, suggesting a more specific role for alpha oscillations than we may have thought. With @jhaarsma.bsky.social. 🧠🟦 🧠🤖
jneurosci.org
Contents of visual predictions oscillate at alpha frequencies
Predictions of future events have a major impact on how we process sensory signals. However, it remains unclear how the brain keeps predictions online in anticipation of future inputs. Here, we combin...
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Hugo Spiers @hugospiers.bsky.social · 16/10/2025
Age and gender distortion in online media and large language models "Furthermore, when generating and evaluating resumes, ChatGPT assumes that women are younger and less experienced, rating older male applicants as of higher quality." No surprise, but now documented: www.nature.com/articles/s41...
nature.com
Age and gender distortion in online media and large language models - Nature
Stereotypes of age-related gender bias are socially distorted, as evidenced by the age gap in the representations of women and men across various media and algorithms, despite no systematic age differ...
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Laura Gwilliams @lauragwilliams.bsky.social · 01/10/2025
really fun getting to think about the "time to consciousness" with this dream team! we discuss interesting parallels between vision and language processing on phenomena like postdictive perceptual effects, among other things! check it out 😄
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Julian Matthews @quining.bsky.social · 30/09/2025
🚨 Out now in @commspsychol.nature.com 🚨 doi.org/10.1038/s442... Our #RegisteredReport tested whether the order of task decisions and confidence ratings bias #metacognition. Some said decisions → confidence enhances metacognition. If true, decades of findings will be affected.
A picture of our paper's abstract and title: The order of task decisions and confidence ratings has little effect on metacognition.

Task decisions and confidence ratings are fundamental measures in metacognition research, but using these reports requires collecting them in some order. Only three orders exist and are used in an ad hoc manner across studies. Evidence suggests that when task decisions precede confidence, this report order can enhance metacognition. If verified, this effect pervades studies of metacognition and will lead the synthesis of this literature to invalid conclusions. In this Registered Report, we tested the effect of report order across popular domains of metacognition and probed two factors that may underlie why order effects have been observed in past studies: report time and motor preparation. We examined these effects in a perception experiment (n = 75) and memory experiment (n = 50), controlling task accuracy and learning. Our registered analyses found little effect of report order on metacognitive efficiency, even when timing and motor preparation were experimentally controlled. Our findings suggest the order of task decisions and confidence ratings has little effect on metacognition, and need not constrain secondary analysis or experimental design.
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Will Turner @renrutmailliw.bsky.social · 29/09/2025
New BBS article w/ @lauragwilliams.bsky.social and Hinze Hogendoorn, just accepted! We respond to a thought-provoking article by @smfleming.bsky.social & @matthiasmichel.bsky.social, and argue that it's premature to conclude that conscious perception is delayed by 350-450ms: bit.ly/4nYNTlb
bit.ly
OSF
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Bryan M. Li @bryanlimy.bsky.social · 19/09/2025
We present our preprint on ViV1T, a transformer for dynamic mouse V1 response prediction. We reveal novel response properties and confirm them in vivo. With @wulfdewolf.bsky.social, Danai Katsanevaki, @arnoonken.bsky.social, @rochefortlab.bsky.social. Paper and code at the end of the thread! 🧵1/7
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Malcolm Campbell @malcolmgcampbell.bsky.social · 19/09/2025
🚨Our preprint is online!🚨 www.biorxiv.org/content/10.1... How do #dopamine neurons perform the key calculations in reinforcement #learning? Read on to find out more! 🧵
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Monica Ellwood-Lowe @mellwoodlowe.bsky.social · 15/09/2025
I’m hiring!! 🎉 Looking for a full-time Lab Manager to help launch the Minds, Experiences, and Language Lab at Stanford. We’ll use all-day language recording, eye tracking, & neuroimaging to study how kids & families navigate unequal structural constraints. Please share: phxc1b.rfer.us/STANFORDWcqUYo
phxc1b.rfer.us
Research Coordinator, Minds, Experiences, and Language Lab in Graduate School of Education, Stanford, California, United States
The Stanford Graduate School of Education (GSE) seeks a full-time Research Coordinator (acting lab manager) to help launch and coordinate the Minds,.....
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Nadine Dijkstra @nadinedijkstra.bsky.social · 11/09/2025
Looking forward to #ICON2025 next week! We will have several presentations on mental imagery, reality monitoring and expectations: To kick us off, on Tuesday at 15:30, Martha Cottam will present: P2.12 | Presence Expectations Modulate the Neural Signatures of Content Prediction Errors
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Jill Kries @kriesjill.bsky.social · 08/09/2025
In August I had the pleasure to present a poster at the Cognitive Computational Neuroscience (CCN) conference in Amsterdam. My poster was about 𝘁𝗵𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝗮𝗹 𝘁𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝘆 𝗮𝗻𝗱 𝗻𝗲𝘂𝗿𝗼𝗮𝗻𝗮𝘁𝗼𝗺𝗶𝗰𝗮𝗹 𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝘀 𝗼𝗳 𝘀𝗽𝗲𝗲𝗰𝗵 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝗼𝗻 🧒➡️🧑 🧠
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Benjamin Lowe @brainboyben.bsky.social · 19/08/2025
🚨Pre-print of some cool data from my PhD days! doi.org/10.1101/2025... ☝️Did you know that visual surprise is (probably) a domain-general signal and/or operates at the object-level? ✌️Did you also know that the timing of this response depends on the specific attribute that violates an expectation?
doi.org
The Latency of a Domain-General Visual Surprise Signal is Attribute Dependent
Predictions concerning upcoming visual input play a key role in resolving percepts. Sometimes input is surprising, under which circumstances the brain must calibrate erroneous predictions so that perc...
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Greta Tuckute @gretatuckute.bsky.social · 19/08/2025
Humans largely learn language through speech. In contrast, most LLMs learn from pre-tokenized text. In our #Interspeech2025 paper, we introduce AuriStream: a simple, causal model that learns phoneme, word & semantic information from speech. Poster P6, tomorrow (Aug 19) at 1:30 pm, Foyer 2.2!
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Laura Gwilliams @lauragwilliams.bsky.social · 10/08/2025
looking forward to seeing everyone at #CCN2025! here's a snapshot of the work from my lab that we'll be presenting on speech neuroscience 🧠 ✨
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Manikya Alister @manikyaalister.bsky.social · 11/08/2025
We know that a consensus of opinions is persuasive, but how reliable is this effect across people and types of consensus, and are there any kinds of claims where people care less about what other people think? This is what we tested in our new(ish) paper in @psychscience.bsky.social
Screenshot of the article "How Convincing Is a Crowd? Quantifying the Persuasiveness of a Consensus for Different Individuals and Types of Claims"
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Benjamin Lowe @brainboyben.bsky.social · 15/07/2025
I really like this paper. I fear that people think the authors are claiming that the brain isn’t predictive though, which this study cannot (and does not) address. As the title says, the data purely show that evoked responses are not necessarily prediction errors, which makes sense!
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PLOS Biology @plosbiology.org · 27/05/2025
It takes time for the #brain to process information, so how can we catch a flying ball? @renrutmailliw.bsky.social &co reveal a multi-stage #motion #extrapolation occurring in the #HumanBrain, shifting the represented position of moving objects closer to real time @plosbiology.org 🧪 plos.io/3Fm83Fc
Mapping the position of moving stimuli. The top three panels show the three events of interest: stimulus onset, stimulus offset, and stimulus reversal (left to right). The bottom three panels show group-level probabilistic spatio-temporal maps centered around these three events. Diagonal black lines mark the true position of the stimulus. Horizontal dashed lines mark the time of the event of interest (stimulus onset, offset, or reversal). Red indicates high probability regions and blue indicates low probability regions (‘position evidence’ gives the difference between the posterior probability and chance). Note: these maps were generated from recordings at posterior/occipital sites.
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PLOS Biology @plosbiology.org · 27/05/2025
It takes time for the #brain to process information, so how can we catch a flying ball? This study provides evidence of multi-stage #motion #extrapolation occurring in the #HumanBrain, shifting the represented position of moving objects closer to real time @plosbiology.org 🧪 plos.io/3Fm83Fc
Mapping the position of moving stimuli. The top three panels show the three events of interest: stimulus onset, stimulus offset, and stimulus reversal (left to right). The bottom three panels show group-level probabilistic spatio-temporal maps centered around these three events. Diagonal black lines mark the true position of the stimulus. Horizontal dashed lines mark the time of the event of interest (stimulus onset, offset, or reversal). Red indicates high probability regions and blue indicates low probability regions (‘position evidence’ gives the difference between the posterior probability and chance). Note: these maps were generated from recordings at posterior/occipital sites.
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Dan Feuerriegel @danfeuerriegel.bsky.social · 18/05/2025
New preprint from the lab! We used EEG⚡🧠 to map how 12 different food attributes are represented in the brain. 🍎🥦🥪🍙🍮 www.biorxiv.org/content/10.1... Led by Violet Chae in collaboration with @tgro.bsky.social
biorxiv.org
Characterising the neural time-courses of food attribute representations
Dietary decisions involve the consideration of multiple, often conflicting, food attributes that precede the computation of an overall value for a food. The differences in the speed at which attribute...
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Greta Tuckute @gretatuckute.bsky.social · 23/05/2025
What are the organizing dimensions of language processing? We show that voxel responses during comprehension are organized along 2 main axes: processing difficulty & meaning abstractness—revealing an interpretable, topographic representational basis for language processing shared across individuals
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