We are looking for a postdoctoral researcher who wants to innovate at the intersection of cognitive science, neuroscience, and machine learning. 📍Munich 📅Flexible start: 10/2026–03/2027 ⏳Full consideration by 14/08/2026 💼Funded for 2 years, extension possible Discover, grow, collaborate, eat cake.

LLMs have shown impressive performance in some reasoning tasks, but what internal mechanisms do they use to solve these tasks? In a new preprint, we find evidence that abstract reasoning in LLMs depends on an emergent form of symbol processing arxiv.org/abs/2502.20332 (1/N)

Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models

Many recent studies have found evidence for emergent reasoning capabilities in large language models, but debate persists concerning the robustness of these capabilities, and the extent to which they ...

arxiv.org

Every experience is unique 🌟 light shifts, angles change, yet we recognize objects effortlessly. How do our minds do this? And (how) do they differ from machines? In our new preprint with @ericschulz.bsky.social, we review human generalization and compare it to machine generalization: osf.io/k6ect

About a month late posting this, but here's a new project with @ericschulz.bsky.social, @akjagadish.bsky.social, @marvinmathony.bsky.social and Tobias Ludwig We are using LLMs to propose cognitive models in learning and decision making data. Presenting this work at RLDM! arxiv.org/abs/2502.00879

Towards Automation of Cognitive Modeling using Large Language Models

Computational cognitive models, which formalize theories of cognition, enable researchers to quantify cognitive processes and arbitrate between competing theories by fitting models to behavioral data....

arxiv.org

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:

We are currently building the largest, cross-domain data set of human behavior as part of an open collaborative project. Contributions of any form are welcome, but especially experiments with meta-data from developmental, cross-cultural, or clinical studies. More details: github.com/marcelbinz/P...

GitHub - marcelbinz/Psych-201

Contribute to marcelbinz/Psych-201 development by creating an account on GitHub.

github.com

In the first paper, @candemircan.bsky.social and @tankred-saanum.bsky.social use sparse autoencoders to show that LLMs can implement temporal difference learning in context. This work is together with Akshay Jagadish and @marcelbinz.bsky.social. arxiv.org/abs/2410.01280

Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models

In-context learning, the ability to adapt based on a few examples in the input prompt, is a ubiquitous feature of large language models (LLMs). However, as LLMs' in-context learning abilities continue...

arxiv.org

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

🚨Join our team! We’re hiring a PhD student in Cognitive & Clinical Neuroscience 🧠 🎓 at @uni_wue & @UKW_Wuerzburg! Explore mechanisms of decision-making in healthy people & Parkinson’s using new deep brain stimulation methods. German & English required. Apply by 20 Dec! 🌟🎄 Details: shorturl.at/IcNa0