41 citations · 53 across the 17 of their papers we have counts for
17 papers
CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
Lance Ying, Jinzhou Wu, Yingshan Susan Wang +53
Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science. As AI systems grow increasingly capable, in what ways…
Sparks of In Silico Cognitive Science: Theories from Simulated Data Can Generalize to Humans
Akshay K. Jagadish, Younes Strittmatter, Nori Jacoby +4
Behavioral foundation models have been proposed as stand-ins for human participants across settings, but it is unclear whether theories discovered on them generalize to humans or m…
More accurate behavioral predictions with hybrid Bayesian-connectionist models
Brenden M. Lake, Akshay K. Jagadish, Guangyuan Jiang
Researchers must often choose between Bayesian or neural network models of behavior, two paradigms with complementary strengths and weaknesses. An ideal paradigm would facilitate t…
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…
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…
Toward an automated science of the mind
Akshay K. Jagadish, Milena Rmus, Kristin Witte +3
Artificial Intelligence is starting to automate scientific discovery, raising the prospect of an automated science of the mind. We chart this field around four challenges: represen…
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…
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…
Generating Computational Cognitive Models using Large Language Models
Milena Rmus, Akshay K. Jagadish, Marvin Mathony +2
Computational cognitive models, which formalize theories of cognition, enable researchers to quantify cognitive processes and arbitrate between competing theories by fitting models…
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models
Can Demircan, Tankred Saanum, Akshay K. Jagadish +2
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 learnin…
Centaur: a foundation model of human cognition
Marcel Binz, Elif Akata, Matthias Bethge +37
Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational model…
In-context learning agents are asymmetric belief updaters
Johannes A. Schubert, Akshay K. Jagadish, Marcel Binz +1
We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology. We find that LLMs update their…
Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks
Akshay K. Jagadish, Julian Coda-Forno, Mirko Thalmann +2
Ecological rationality refers to the notion that humans are rational agents adapted to their environment. However, testing this theory remains challenging due to two reasons: the d…
Inducing anxiety in large language models can induce bias
Julian Coda-Forno, Kristin Witte, Akshay K. Jagadish +3
Large language models (LLMs) are transforming research on machine learning while galvanizing public debates. Understanding not only when these models work well and succeed but also…