Are brains and artificial neural networks converging onto universal representations? There is a seductive idea making the rounds in NeuroAI / machine learning: train systems well enough, and they all converge on the same representation of reality (i.e. a unique world model). We have thoughts™ 1/n

The Umwelt Representation Hypothesis: rethinking Universality

Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on univer...

cell.com

Excited to share our new paper: "Probing the content of semantic representations in body-selective regions" We propose a framework based on object co-occurrence to interpret semantic representations of natural scenes predicted by LLM embeddings. doi.org/10.1162/IMAG...

Probing the content of semantic representations in body-selective regions

Abstract. Recent advances in neural networks trained on natural language have revealed that category-selective regions encode complex semantics and contextual information of natural scenes in addition...

doi.org

Now out in Nature Neuroscience: "Fixation duration on natural scenes is explained by memory encoding not processing demand". www.nature.com/articles/s41... Our eyes don't linger because recognition is hard; they linger to remember. Let me take you on a quick tour. 🧵

Fixation duration on natural scenes is explained by memory encoding not processing demand - Nature Neuroscience

By combining magnetoencephalography and eye tracking, this study sheds light on why people fixate on some parts of natural scenes longer than others. Rather than visual complexity, fixation durations ...

nature.com

Happy to share that our Developmental Visual Diet (DVD) paper was selected as the cover article for the May issue of Nature Machine Intelligence ( @natmachintell.nature.com)! www.nature.com/natmachintel...

Tim Kietzmann @timkietzmann.bsky.social · 5mo ago

Our NeuroAI study made it onto the cover of Nature Machine Intelligence ❤️. In it, we demonstrate that a developmentally-inspired visual diet can drastically improve the robustness of ANN-based vision systems. www.nature.com/articles/s42... open access, open code, open weights, open science.

Now out in Nature Machine Intelligence @NatMachIntell “Adopting a human developmental visual diet yields robust and shape-based AI vision”: doi.org/10.1038/s422.... A wonderful case where brain inspiration improved AI. With @martisamuser.bsky.social, Radek Cichy and @timkietzmann.bsky.social .

Excited about our new preprint: “The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream” arxiv.org/abs/2604.12825 Work with Sushrut Thorat, Anna Mitola, Paolo Papale, Peter König & Tim Kietzmann 🧵 thread below

The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream

In studying primate vision, a large body of work focuses on the first feedforward sweep. During this initial time window, information is thought to pass through ventral stream regions in a stage-like ...

arxiv.org

🚨New Preprint! How can we model natural scene representations in visual cortex? A solution is in active vision: predict the features of the next glimpse! arxiv.org/abs/2511.12715 + @adriendoerig.bsky.social , @alexanderkroner.bsky.social , @carmenamme.bsky.social , @timkietzmann.bsky.social 🧵 1/14

Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex

Scenes are complex, yet structured collections of parts, including objects and surfaces, that exhibit spatial and semantic relations to one another. An effective visual system therefore needs unified ...

arxiv.org

Introducing CorText: a framework that fuses brain data directly into a large language model, allowing for interactive neural readout using natural language. tl;dr: you can now chat with a brain scan 🧠💬 1/n

If you are interested in development and development-inspired NeuroAI, and are coming to CCN this year, come join the workshop with us on 1️⃣1️⃣ Monday, Aug 11 🕒 3:00 – 6:00 pm 📍 Room A2.11 Register here: sites.google.com/view/child2m... (You can also come by my poster to chat!)

CCN 2025 Satellite Event

Background The human visual system is full of optimisations—mechanisms designed to extract the most useful information from a constant stream of incoming data. The field of neuro-AI has made significa...

sites.google.com

Tim Kietzmann @timkietzmann.bsky.social · last yr.

First, @zejinlu.bsky.social will talk about how adopting a human developmental visual diet yields robust, shape-based AI vision. Biological inspiration for the win! Talk Time/Location: Monday, 3-6 pm, Room A2.11 Poster Time/Location: Friday, 2-5 pm, C116 at de Brug & E‑Hall

Not just one, but two fantastic chances to discuss how infant development can inform machine learning and vice-versa at CCN 2025 in Amsterdam!!! Satellite workshop sites.google.com/view/child2m... and Generative Adversarial Collaboration sites.google.com/ccneuro.org/...

CCN 2025 Satellite Event

Background The human visual system is full of optimisations—mechanisms designed to extract the most useful information from a constant stream of incoming data. The field of neuro-AI has made significa...

sites.google.com

🚨 Finally out in Nature Machine Intelligence!! "Visual representations in the human brain are aligned with large language models" 🔗 www.nature.com/articles/s42...

High-level visual representations in the human brain are aligned with large language models - Nature Machine Intelligence

Doerig, Kietzmann and colleagues show that the brain’s response to visual scenes can be modelled using language-based AI representations. By linking brain activity to caption-based embeddings from lar...

nature.com

🚨 Preprint alert! Excited to share my second PhD project: “Adopting a human developmental visual diet yields robust, shape-based AI vision” -- a nice case showing that biology, neuroscience, and psychology can still help AI :)! arxiv.org/abs/2507.03168

Adopting a human developmental visual diet yields robust, shape-based AI vision

Despite years of research and the dramatic scaling of artificial intelligence (AI) systems, a striking misalignment between artificial and human vision persists. Contrary to humans, AI heavily relies ...

arxiv.org

Tim Kietzmann @timkietzmann.bsky.social · last yr.

Exciting new preprint from the lab: “Adopting a human developmental visual diet yields robust, shape-based AI vision”. A most wonderful case where brain inspiration massively improved AI solutions. Work with @zejinlu.bsky.social @sushrutthorat.bsky.social and Radek Cichy arxiv.org/abs/2507.03168

Now out in Nature Human Behaviour @nathumbehav.nature.com : “End-to-end topographic networks as models of cortical map formation and human visual behaviour”. Please check our NHB link: www.nature.com/articles/s41...