35 citations · 75 across the 19 of their papers we have counts for
6 papers · 1 filter
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…
The Riddle Riddle: Testing Flexible Reasoning in Large Language Models and Humans
Bella Fascendini, Kathryn McGregor, Max D. Gupta +1
Humans flexibly adapt their reasoning strategies to the requirements of a given problem. Large language models (LLMs) have performed well on many cognitive tasks, however, it is un…
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 Cognitive Models to Improve Language Model Simulation of Human Persuasion Games
Zirui Cheng, Zeyu Shen, Thomas L. Griffiths +1
People make decisions differently in strategic interactions. Some update beliefs like a Bayesian; others exhibit biases like motivated reasoning. Although creators of large languag…
Do Large Language Models Mentalize When They Teach?
Sevan K. Harootonian, Mark K. Ho, Thomas L. Griffiths +2
How do LLMs decide what to teach next: by reasoning about a learner's knowledge, or by using simpler rules of thumb? We test this in a controlled task previously used to study huma…