Marcel Binz

Marcel Binz

@marcelbinz.bsky.social

Natural and artificial general intelligence. https://marcelbinz.github.io/

703 followers600 following26 posts

Marcel Binz reposted

📣 Work with me on AI and behavioral science. I am hiring an Assistant Professor (3 years) at WU Vienna, in collaboration with my team at the MPI for Human Development. Start Jan 2027 or by arrangement. Deadline Nov 11. Please share. jobs.wu.ac.at/Assistant-Pr...

What is a minimal set of assumptions that can explain the majority of human behavior? We investigated this question with large-scale cognitive models 🚀 CORE writes prediction errors in hierarchical episodic memories with context controling encoding, retrieval, and forgetting.

We fit CORE to 7M human choices from 137 experiments and 62 studies, and find that it predicts choices better than a Transformer in all 62 studies and better than Centaur in 47. In simulation, CORE reproduces 75% (50/67) of the reported behavioral effects.

Marcel Binz reposted

1/ 🚨 New opinion piece: "Can we automatize scientific discovery in the cognitive sciences?" We lay out a vision for a fully automated, in-silico science of the mind, where modern AI systems run every stage of the scientific discovery cycle in cognitive science 🧵 #AutomatedDiscovery #AI4Science

check out our GAC on benchmarks at CCN happening later today!

Erin Grant@eringrant.me

Our #CCN2025 GAC debate w/ @gretatuckute.bsky.social, Gemma Roig (www.cvai.cs.uni-frankfurt.de), Jacqueline Gottlieb (gottlieblab.com), Klaus Oberauer, @mschrimpf.bsky.social &‬ @brittawestner.bsky.social asks: 📊 What benchmarks are useful for cognitive science? 💭 2025.ccneuro.org/gac

Marcel Binz reposted

What happens when you train AI on psychological experiments? It behaves a lot like a human mind. Here's my story on Centaur, and the debate about what AI has to offer to cognitive science. Gift link nyti.ms/3ZYqXcg 🧪

nyti.msScientist Use A.I. To Mimic the Mind, Warts and AllTo better understand human cognition, scientists trained a large language model on 10 million psychology experiment questions. It now answers questions much like we do.
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Marcel Binz reposted

In previous work we found that VLMs fall short of human visual cognition. To make them better, we fine-tuned them on visual cognition tasks. We find that while this improves performance on the fine-tuning task, it does not lead to models that generalize to other related tasks:

Marcel Binz reposted

Marcel Binz reposted

Have we built machines that learn and think like people? In our new paper, we find that vision large language models still fall short when it comes to cognitive abilities in the domains of causal reasoning, intuitive physics, and theory of mind. www.nature.com/articles/s42...

nature.comVisual cognition in multimodal large language models - Nature Machine IntelligenceModern vision-based language models face challenges with complex physical interactions, causal reasoning and intuitive psychology. Schulze Buschoff and colleagues demonstrate that while some models ex...

Marcel Binz reposted

Replying to @cccm-bbk.bsky.social

and mark in your calendars the following dates & speakers: David Danks, Jan. 7 Dimitri Coelho Mollo, Jan 14 Raphael Milliere, Jan 21 Ben Bergen, Feb 4 David Garcia, Feb 18 Jay McClelland, Mar 4 Chris Summerfield, Mar 18 Marcel Binz, April 1st Tom Griffiths, April 29 Thomas Icard, May 13

psyc.bbk.ac.ukSeminar Series – Centre for Cognition, Computation and Modelling

Marcel Binz reposted

Preprint alert! We explore 3 exploration tasks, testing if they measure a stable construct & its link to real-world exploration. We find improved robustness of latent factors compared to single-task estimates. With Mirko Thalmann & @ericschulz.bsky.social 🔗https://osf.io/preprints/psyarxiv/tzuey

If you are at NeurIPS, and interested in human alignment, representations, or cognitive modeling, don't miss out Can's Poster tomorrow!

Can Demircan@candemircan.bsky.social

Alignment is more than comparing similarity judgments! How well do pretrained neural networks align with humans in few-shot learning settings? Come check our poster #3904 at #NeurIPS on Wednesday to find out

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