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"Simple distribution shifts break DNN models of the visual cortex, consistent with broader findings that the underlying DNNs are brittle to OOD shifts." Cool twist on brain-score-ology from Spandan Madan, Marge Livingstone, Gabriel Kreiman arxiv.org/abs/2406.16935

Check out our latest work introducing the BOLD Moments Dataset (BMD), published a few days ago in Nature Communications! Great effort by Ben Lahner and the rest of the team! Paper: doi.org/10.1038/s41467…; Starter code: github.com/blahner/BOLDMo…

Preprint alert 🚨I am excited about our new paper titled “The representational nature of spatio-temporal recurrent processing in visual object recognition.” 🥳🌟 biorxiv.org/cgi/content/sh…

While I cannot be present at #CCN2024 , the lab of course is - make sure to check out the projects: 🧵

Hey ECVP, please come join me for my talk this afternoon! Using fmri & eyetracking, we (Marius Peelen & Floris de Lange) find preparatory activity in visual cortex can reflect attention-guiding anchor-objects rather than search targets (preprint: biorxiv.org/content/10.110…) #ECVP24

J'ai l'honneur d'apparaître dans la dernière vidéo de MrPhi, qui traite de conscience et d'intelligence artificielle!

Excited about this new paper! Suppose you lost half a field of view, or color vision, would you notice? In a new paper in Trends in Cognitive Sciences, we show that you probably wouldn’t! You can find out more about those striking failures of visual metacognition here: authors.elsevier.com/a/1jsFQ4sIRvTB….

New priprint! When I started my PhD (ages ago), I ran what I thought was a pilot for several iconic memory experiments down the line. This was interesting because iconic memory is (allegedly) linked to rich perceptual experience, and so I had many questions. 1/🧵

Functional responses in the brain to linguistic inputs are spatially organized -- but why? Neil Rathi (at ICLR) Hannes Mehrer @ICLR2025 et al. show that a simple smoothness loss added to language model training explains a range of topographic phenomena in neuroscience

In 2023, Rowan Sommers & I hosted a reading group on “the intersection of Neuroscience and Machine learning” for the UG/PG students Uni Osnabrück. We selected papers trying to maximize our coverage of the field of NeuroAI. Perhaps this list is useful to the broader community 🧵

🧵Join us for a short tour of how a large-scale MEG dataset collected during active vision made us question it all. New work with Carmen Amme Philip Sulewski Eelke Spaak 🦋 @EelkeSpaak.bsky.social Martin Hebart Peter König biorxiv.org/content/10.110…

Neural networks need a dorsal stream to learn structure in natural scenes! really proud of this work led by Jess Thompson

In this new preprint @smfleming and I present a theory of the functions and evolution of conscious vision. This is a big project: osf.io/preprints/psya…. We'd love to get your comments!

🚨 New paper at NeurIPS Conference w/ Michael Lepori! Most work on interpreting vision models focuses on concrete visual features (edges, objects). But how do models represent abstract visual relations between objects? We adapt NLP interpretability techniques for ViTs to find out! 🔍

Manuscript describing the C. elegans dataset on huggingface (quilee et al): arxiv.org/pdf/2411.12091

Structuralist approaches are becoming popular in consciousness science. Marlo Paßler and I propose criteria for which kinds of neural structures can be reliably linked to quality spaces derived from reports.

Very proud to share our ICLR 2026 paper: TopoNets! High-performing vision and language models with brain-like topography! Expertly led by grad student mayukh09 and Mainak Deb! A brief thread..

Next week at #SCIoI: What drives human collective foraging? 🥜 📅 Tue 22/4, 14:00 – Ralf Kurvers How do LLMs align with brain representations? 🧠 📅 Thu 24/4, 10:00 – Adrien Doerig (#ThursdayMorningTalk) 🔗 More info: scienceofintelligence.de/events/

AI vision is insanely good nowadays—but is it really like human vision or something else entirely? In our new pre-print, we pinpoint a fundamental visual mechanism that's trivial for humans yet causes most models to fail spectacularly. Let's dive in👇🧠 [arxiv.org/abs/2504.05253]

🚨 New preprint alert! Our latest review/opinion, led by Drew Linsley, examines how deep neural networks (DNNs) optimized for image categorization align with primate vision, using neural and behavioral benchmarks.