8 papers
Meta-learning as a principle for human-like visual representations
Can Demircan, Marcel Binz, Alireza Modirshanechi +1
The structure of human visual representations underpins our capacity for adaptive behaviour. While pretrained neural networks model human visual representations with unprecedented…
Meta-learning ecological priors from large language models explains human learning and decision making
Akshay K. Jagadish, Mirko Thalmann, Julian Coda-Forno +2
Human cognition is profoundly shaped by the environments in which it unfolds. Yet, it remains an open question whether learning and decision making can be explained as a principled…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Can we automatize scientific discovery in the cognitive sciences?
Akshay K. Jagadish, Milena Rmus, Kristin Witte +3
The cognitive sciences aim to understand intelligence by formalizing underlying operations as computational models. Traditionally, this follows a cycle of discovery where researche…
metabeta -- A fast neural model for Bayesian mixed-effects regression
Alex Kipnis, Marcel Binz, Eric Schulz
Hierarchical data with multiple observations per group is ubiquitous in empirical sciences and is often analyzed using mixed-effects regression. In such models, Bayesian inference…
Automated scientific minimization of regret
Marcel Binz, Akshay K. Jagadish, Milena Rmus +1
We introduce automated scientific minimization of regret (ASMR) -- a framework for automated computational cognitive science. Building on the principles of scientific regret minimi…