Tessera
Folgen

micha heilbron

@mheilbron.bsky.social

Group leader at Max Planck Institute for Psycholinguistics @mpi-nl.bsky.social // Assistant Professor of Cognitive AI @UvA_Amsterdam. Cog-sci 🤝 AI 🤝 neurosci

83 Posts977 Follower377 Folgt
micha heilbron@mheilbron.bsky.social · 2w ago

Belated, but still happy to see our paper (with @drhanjones.bsky.social) on fleeting memory transformers is out in TACL! We find that giving language models human-like memory decay *improves* language learning, while, unexpectedly, impairing human reading time prediction Follow up results soon!

Bild
Nicole Rust@nicolecrust.bsky.social · 3w ago

Wonderful to see this! For (controversial?) context. There’s long been an argument that what brains & ANNs are doing cannot be fathomed beyond the meta like (eg) architecture, learning rules and such. And there’s a counter-idea: ”let’s try?”. When we stumbled on the correlates of memorability /1

micha heilbron@mheilbron.bsky.social · 3w ago

What makes some stimuli more memorable than others? In a new paper w/ @davogelsang.bsky.social, we show that the magnitude of a stimulus's ANN representation predicts both image and word memorability Stimuli that activate more features, more strongly, leave a stronger memory trace Out now in JML⬇️

micha heilbron@mheilbron.bsky.social · 3w ago

What makes some stimuli more memorable than others? In a new paper w/ @davogelsang.bsky.social, we show that the magnitude of a stimulus's ANN representation predicts both image and word memorability Stimuli that activate more features, more strongly, leave a stronger memory trace Out now in JML⬇️

Bild
Arvind Narayanan@randomwalker.bsky.social · 3w ago

I had the honor of giving a keynote at the International Conference on Machine Learning last week. I addressed the widespread anxiety about how we should adapt as AI capabilities increase. I was thrilled by the talk’s reception, so I have made my slides available www.cs.princeton.edu/~arvindn/tal...

What will be left for us to work on?

ICML 2026 invited keynote — slides and edited transcript, presented click-by-click as delivered. Arvind Narayanan, Princeton University.

cs.princeton.edu

Sebastiaan Mathôt@cogsci.nl · 4mo ago

Ik heb er zin in! Morgen zijn @mheilbron.bsky.social en ik bij @maastrichtu.bsky.social voor een avond vol #wetenschap, #biologie, #psychologie en #AI! 🧠🐝🌿🤖 Meld je aan via www.maastrichtuniversity.nl/nl/events/ee... #maastricht

Een wereld vol denkers: mens, dier, plant en AI - Agenda - Maastricht University

maastrichtuniversity.nl

Sebastiaan Mathôt@cogsci.nl · 4mo ago

23 april geven @mheilbron.bsky.social en ik een lezing in het mooie #Maastricht over Een wereld vol denkers. Een avond vol verhalen over het denken en doen van mens, dier, plant en AI! 🧠🐝🌿🤖 Ik hoop jullie daar te zien! www.maastrichtuniversity.nl/nl/events/ee... #wetenschap #psychologie #biologie

micha heilbron@mheilbron.bsky.social · 4mo ago

Nijmegen friends: Tomorrow (10–12) I'll be debating Pim Haselager at a Donders Session on the thesis: "Artificial neural network models are adequate mechanistic models of the mind" I'm defending, he's opposing. Should be fun. Come join us! www.ru.nl/en/donders-i... @dondersinst.bsky.social

Donders Session - 16 April | Radboud University

Donders Debate with Micha Heilbron and Pim Haselager: Artificial neural network models are adequate mechanistic models of the mind

ru.nl

micha heilbron@mheilbron.bsky.social · 4mo ago

Classic predictive coding: V1 predicts low-level features, higher areas high-level. But recent studies + AI models suggest prediction happens at higher levels of abstraction. Who's right? In new work w/ @wiegerscheurer.bsky.social we find that both are – distinct regimes across the visual field

Wieger Scheurer@wiegerscheurer.bsky.social · 4mo ago

New preprint! w/ @mheilbron.bsky.social We found that, even during simple natural scene viewing, human visual cortex predicts—hierarchically in central vision and at higher levels peripherally—reconciling classical predictive coding with recent evidence from animal models and AI (e.g. JEPA) (1/10)

Athena Akrami@athenaakrami.bsky.social · 4mo ago

Academic friends, It's beyond heartbreaking to watch what's unfolding in Iran & the region. A few of us drafted an open letter calling for protection of civilians & of educational, research, medical & cultural institutions. Please read & sign if you agree: sites.google.com/view/protect... #IranWar

Protect Academic Life in Iran

We, the undersigned academics and researchers from around the world, express our profound concern over recent military strikes on Iran, the retaliatory responses, and the reported impact on civilian l...

sites.google.com

micha heilbron@mheilbron.bsky.social · 4mo ago

Interested in pursuing a PhD in NLP/cog-sci? Studying language learning in LMs from the perspective of human language acquisition? Few more days to apply!!

micha heilbron@mheilbron.bsky.social · 5mo ago

📢 PhD position in Developmental Language Modelling (PLZ RT) What can human language acquisition teach us about training language models? Join us as a PhD! mpi.nl/career-education/vacancies/vacancy/fully-funded-4-year-phd-position-developmental-language @carorowland.bsky.social @mpi-nl.bsky.social

Andrew Lampinen@lampinen.bsky.social · 6mo ago

What is the relationship between memorization and generalization in AI? Is there a fundamental tradeoff? In infinitefaculty.substack.com/p/memorizati... I’ve reviewed some of the evolving perspectives on memorization & generalization in machine learning, from classic perspectives through LLMs.

Memorization vs. generalization in deep learning: implicit biases, benign overfitting, and more

Or: how I learned to stop worrying and love the memorization

infinitefaculty.substack.com

micha heilbron@mheilbron.bsky.social · 6mo ago

Interesting convergence: The trick that made predictive self-supervised vision models work seems to be what the brain was doing all along w/ @predictivebrain.bsky.social: visual cortex is most sensitive to high-level prediction errors -- even in V1 Now published: journals.plos.org/ploscompbiol...

Higher-level spatial prediction in natural vision across mouse visual cortex

Author summary How does the brain make sense of the constant stream of visual information? A popular theory suggests the brain is not a passive receiver but an active predictor, constantly generating ...

journals.plos.org

micha heilbron@mheilbron.bsky.social · last yr.

New preprint, w/ @predictivebrain.bsky.social ! we've found that visual cortex, even when just viewing natural scenes, predicts *higher-level* visual features The aligns with developments in ML, but challenges some assumptions about early sensory cortex www.biorxiv.org/content/10.1...

Alexander Huth@alexanderhuth.bsky.social · 7mo ago

This paper had a pretty shocking headline result (40% of voxels!), so I dug into it, and I think it is wrong. Essentially: they compare two noisy measures and find that about 40% of voxels have different sign between the two. I think this is just noise!

Eiko Fried@eikofried.bsky.social · 7mo ago

Would love to hear expert views on this paper. It appears to show that the operationalization of brain activity the field has relied on for 3 decades—the BOLD response—is not actually a sensible measure of brain activity. www.nature.com/articles/s41...

Sushrut Thorat@martisamuser.bsky.social · 9mo ago

🚨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

David Amadeus Vogelsang@davogelsang.bsky.social · 11mo ago

New preprint out together with @mheilbron.bsky.social We find that a stimulus' representational magnitude—the L2 norm of its DNN representation—predicts intrinsic memorability not just for images, but for words too. www.biorxiv.org/content/10.1...

Representational magnitude as a geometric signature of image and word memorability

What makes some stimuli more memorable than others? While memory varies across individuals, research shows that some items are intrinsically more memorable, a property quantifiable as “memorability”. ...

biorxiv.org

micha heilbron@mheilbron.bsky.social · 12mo ago

New preprint! w/@drhanjones.bsky.social Adding human-like memory limitations to transformers improves language learning, but impairs reading time prediction This supports ideas from cognitive science but complicates the link between architecture and behavioural prediction arxiv.org/abs/2508.05803

Human-like fleeting memory improves language learning but impairs reading time prediction in transformer language models

Human memory is fleeting. As words are processed, the exact wordforms that make up incoming sentences are rapidly lost. Cognitive scientists have long believed that this limitation of memory may, para...

arxiv.org