iiinova — where your digital self keeps evolving

Iris Groen @irisgroen.bsky.social · 05/08/2026

Who would’ve thought that a talk on LLMs would involve a deep plunge into wasp nest building? Great keynote bij @alonaf.bsky.social at #CCN2026!

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Iris Groen @irisgroen.bsky.social · 04/08/2026

The GAC on World Models at #CCN2026 was SO good. Clearly very well prepared, clear division in proponents and skeptics, lots of concrete examples, beautiful synthesis at the end (and a poll on how audience opinions moved because of it). I learned a lot!

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Iris Groen @irisgroen.bsky.social · 03/08/2026

Mind-blowing opening keynote by Doris Tsao at #CCN2026, presenting an ambitious research program to understand ánd engineer consciousness…!

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Iris Groen @irisgroen.bsky.social · 02/08/2026

A true @cogcompneuro.bsky.social PRO of course brings last years’s cup to #CCN2026 ☕️😎 First coffee in NYC, soooo good to be back! 🤩

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Iris Groen @irisgroen.bsky.social · 19/05/2026

At the same time @annewzonneveld.bsky.social presents ongoing work testing whether video ANNs show signatures of human-like temporal straightening - and we explore if and how this could be useful for behavioural tasks, such as anomaly detection in video! (Pavilion 420)

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Iris Groen @irisgroen.bsky.social · 19/05/2026

Hey video-aficionados #VSS2026, this morning @sargechris.bsky.social presents our recent ICLR paper on benchmarking video ANNs on human EEG, suggesting an extended phase of mid-level video feature processing beyond the feed-forward sweep! (Pavilion 425) (paper here: openreview.net/pdf?id=bSsNS...)

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Iris Groen @irisgroen.bsky.social · 12/05/2026

EXCITED to travel to #VSS2026 to contribute 2 talks and 2 posters from our lab! Me and @niklasmuller.bsky.social will talk scene encoding models on Fri & Sat, @sargechris.bsky.social and @annewzonneveld.bsky.social will discuss their cool work on video models on Tue (schedule👇for exact times).

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Iris Groen @irisgroen.bsky.social · 14/08/2025

I’ve had 40 birthdays so far, but this is the first one chairing a major international conference 👩‍💻! Thanks to the nearly 1000 attendees of #CCN2025 for coming to my birthday this year 😉

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Iris Groen @irisgroen.bsky.social · 10/08/2025

On Tuesday, @sargechris.bsky.social will present a follow-up on her earlier ICLR paper (openreview.net/pdf?id=LM4PY...), where we performed large-scale benchmarking of video-DNNs agains the BOLD Moments fMRI dataset, to see how well such models are representationally aligned with the human brain;

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Iris Groen @irisgroen.bsky.social · 10/08/2025

After preparing for a full year together with @neurosteven.bsky.social and all other amazing organizers of @cogcompneuro.bsky.social, #CCN2025 is finally here! While I'm proud of the entire program we put together, I'd now like to highlight my own lab's contributions, 6 posters total:

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Iris Groen @irisgroen.bsky.social · 16/06/2025

Can we enhance DNN-human alignment for affordances? We tried three things: direct supervision with affordance labels, linguistic representations via captions, and probing a multi-modal LLM (ChatGPT!). While we saw improvements, none of these perfectly captured locomotive affordance representations.

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Iris Groen @irisgroen.bsky.social · 16/06/2025

Moreover, the models showed quite poor alignment with the fMRI patterns we had measured for these scenes. And the unique affordance-related variance was not ‘explained away’ by the best-aligned DNN. Our second key finding!

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Iris Groen @irisgroen.bsky.social · 16/06/2025

We tested a whole bunch of models, and it turns out that all models showed lower alignment with affordances than objects. This was true for models on various tasks – not just classic object or scene recognition, but also contrastive learning with text, self-supervised tasks, video, etc.

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Iris Groen @irisgroen.bsky.social · 16/06/2025

Then, we measured MRI, and found that brain activity patterns in scene-selective visual regions also contained ‘unique’ variance reflecting locomotive affordance information only. Hence, demonstrating a ‘neural reality of affordances’ (PNAS Editor’s quote!) – our first key finding!

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Iris Groen @irisgroen.bsky.social · 16/06/2025

You might think: but this task is easy because you can just say ‘swimming’ if you see ‘water’, and ‘driving’ when you see ‘road’. But the answers were not trivially explained by such labels - almost 80% of the variation in the locomotive action ratings was unexplained by other scene properties!

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Iris Groen @irisgroen.bsky.social · 16/06/2025

This is a very easy task for humans: they respond in a split second, giving highly consistent answers. And these answers form a highly structured representational space, clearly separating different actions along meaningful dimensions, such as water-based vs. road-based activities.

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Iris Groen @irisgroen.bsky.social · 16/06/2025

We showed participants images from real-world indoor and outdoor environments while they did a simple task: mark 6 ways (walking, driving, biking, swimming, boating, climbing) you could realistically move in this environment – i.e. indicate its locomotive action affordances.

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