Data and Code for: On agentic behavioral modeling

This repository accompanies a manuscript introducing agentic behavioral modeling (ABM), a framework that links theoretical neuroscience, decision theory, and probabilistic inference by treating artificial agents as generative models of human cognition. The repository contains both the experimental datasets and the code used to analyze them. Code: Python code for the Gabor contrast discrimination and Symmetric bandit learning ABMs Data: Simulated and experimental data for the Gabor contrast discrimination and Symmetric bandit learning ABMs For a full table of contents, please refer to Table-of-Contents.pdf in the main folder unknown