10 papers
Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist
Akshay K. Jagadish, Younes Strittmatter, Nori Jacoby +5
Across the sciences, autonomous systems are increasingly being used in closed-loop discovery, proposing new theories and designing and running experiments to test them. This approa…
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…
Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake +1
Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is verifiable. Yet, many real-world r…
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…
Think-Aloud Reshapes Automated Cognitive Model Discovery Beyond Behavior
Hanbo Xie, Akshay K. Jagadish, Lan Pan +1
Computational cognitive models discovered using large language models have so far relied solely on behavioral data. However, it is well-known that models produced from the behavior…
Automated Adversarial Collaboration for Advancing Theory Building in the Cognitive Sciences
Suyog Chandramouli, George Kachergis, Akshay Jagadish
Cognitive science often evaluates theories through narrow paradigms and local model comparisons, limiting the integration of evidence across tasks and realizations. We introduce an…