A foundation model to predict and capture human cognition
10 months ago
- #artificial intelligence
- #neuroscience
- #cognitive modeling
- Centaur is a computational model designed to predict and simulate human behavior across various experiments expressed in natural language.
- The model was fine-tuned on Psych-101, a large-scale dataset with trial-by-trial data from over 60,000 participants and 10,000,000 choices across 160 experiments.
- Centaur outperforms existing cognitive models in predicting held-out participant behavior and generalizes to unseen cover stories, task modifications, and new domains.
- The model's internal representations align more closely with human neural activity after fine-tuning, despite not being explicitly trained for neural alignment.
- Centaur demonstrates human-like characteristics in open-loop simulations, matching human performance in tasks like the horizon-task paradigm and the two-step task.
- The model's robustness was tested through out-of-distribution evaluations, including modified cover stories, structural task changes, and entirely new domains like logical reasoning.
- Centaur's ability to predict human response times and its alignment with neural activity were validated through additional analyses.
- The paper presents a case study using Centaur and Psych-101 for model-guided scientific discovery, improving understanding of human decision-making strategies.
- Future directions include expanding Psych-101 to include more domains and individual differences, and exploring multimodal data formats.
- Centaur represents a significant step towards a unified theory of cognition, demonstrating the potential of data-driven domain-general models.