# Toward an automated science of the mind

arXiv: https://arxiv.org/abs/2603.20988 · HTML: https://mlsanity.com/paper/2603.20988/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 22 Mar 2026.

## Builds on (most cited first, 3 of 3)

- [OMNI: Open-endedness via Models of human Notions of Interestingness](https://mlsanity.com/paper/2306.01711/) (2023)
- [Automating the Practice of Science -- Opportunities, Challenges, and Implications](https://mlsanity.com/paper/2409.05890/) (2024)
- [Generating Computational Cognitive Models using Large Language Models](https://mlsanity.com/paper/2502.00879/) (2025)

## Built on by (most cited first, 5 of 5)

- [Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist](https://mlsanity.com/paper/2606.26448/) (2026)
- [ATLAS: Active Theory Learning for Automated Science](https://mlsanity.com/paper/2606.12386/) (2026)
- [auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation](https://mlsanity.com/paper/2606.26460/) (2026)
- [Automated Adversarial Collaboration for Advancing Theory Building in the Cognitive Sciences](https://mlsanity.com/paper/2604.25521/) (2026)
- [Sparks of In Silico Cognitive Science: Theories from Simulated Data Can Generalize to Humans](https://mlsanity.com/paper/2609.08003/) (2026)

## Abstract (arXiv)

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: representing experiments, generating synthetic behavior, synthesizing models, and closing the loop to discover psychological theories.