Artificial intelligence and automation are rapidly transforming scientific practice, where they enable unprecedented scientific discoveries–from generating novel conjectures in mathematics, to identifying novel quantum states in physics, to predicting unprobed protein structures in chemistry. These technologies not only accelerate scientific discovery, but also reshape how science is conducted. Although the fields of AI for science and automated scientific discovery have primarily advanced within the natural and engineering sciences, they are now making significant inroads into psychology and neuroscience–offering approaches for automated scientific discovery in research on mind and brain.

This two-week workshop introduces participants to emerging automated scientific discovery methods for the study of mind and brain. Participants will gain exposure to methods for automating the design and execution of behavioral experiments, the discovery of scientific models, and closed-loop scientific discovery.

Interested? Apply for registration here. 

If you are interested in attending the workshop, please apply for the registration .

Workshop spots are unfortunately limited. Invited registrations will be sent based on availability.

Workshop Topics

Workshop Schedule

The workshop will be divided into two weeks (see syllabus below). 

Week 1 (Sept 23 – Sept 26) will focus on basic methods for automated behavioral research. By the end of Week 1, participants should be able to: 

• Construct and fit models of mind and brain with PsyNeuLink

• Automate the generation of experimental designs with SweetPea

• Automate the generation of web-based behavioral experiments with SweetBean

• Automate web-based data collection with Prolific

• Automate entire behavioral research cycles, by integrating experimental design, experiment generation, and computational modeling into a closed-loop with AutoRA 

Week 2 (Sept 29 – Oct 3) will introduce AI-driven techniques for automated model discovery and large language models for scientific discovery. By the end of Week 2, participants should be able to: 

• Gain a basic understanding of emerging approaches to cognitive model discovery and LLM-based approaches to automating scientific discovery

• Gain hands-on experience with general equation discovery with SINDy

• Automate cognitive equation discovery with SPICE

• Automate cognitive program discovery with Gecko

• Run experiments LLM-based synthetic participants with Centaur and SweetBean

Participants may join in Week 1, Week 2, or both. The workshop aims to equip researchers with the skills to apply automated scientific discovery methods to their own questions. Alongside afternoon and evening lectures (see Schedule below), participants are encouraged to develop individual research projects. To support this work, the organizers and selected speakers will hold office hours during the day, providing guidance on applying the workshop methods to participants’ projects.

Workshop Speakers

Jonathan D. Cohen (Princeton University)

Marcel Binz (Helmholtz Munich)

Victoria Bosch (Osnabrück University)

Alessandra Brondetta (Osnabrück University)

Se-Eun Choi (Osnabrück University)

Peter Clark (Allen Institute for AI)

Nathaniel Daw (Princeton University)

Marina Dubova (Santa Fe Institute)

Matthew Flatt (University of Utah)

Tom Griffiths (Princeton University)

Akshay Kumar Jagadish (Princeton University)

Nathan Kutz (Washington University)

Kyle LaFollette (University of Chicago)

Marcello Mattar (New York University)

Kevin Miller (Google DeepMind, TBC)

Sebastian Musslick (Osnabrück University)

Martyna Plomecka (Google)

Milena Rmus (Helmholz Munich)

Lukas Stelz (Osnabrück University)

Younes Strittmatter (Princeton University)

Muhip Teczan (Osnabrück University)

Daniel Weinhardt (Osnabrück University)

Di Wang (University of Utah)

Covered Methods & Tools

AutoRA: Automated Research Assistant for Closed-Loop Empirical Research

PsyNeuLink: Computational Modeling in Psychology and Neuroscience

SINDy: Sparse Identification of Non-Linear Dynamical Systems 

SPICE: Sparse Identification of Cognitive Equations

SweetBean: Automated Generation of Web-Based Experiments

SweetPea: Automated Experimental Design

Organizers

This workshop is jointly organized by the Laboratory for Automated Scientific Discovery of Mind and Brain at Osnabrück University, the Autonomous Empirical Research Group, and the Department of Psychology at Princeton University.

Organizers: Sebastian Musslick (Osnabrück), Younes Strittmatter (Princeton), Jonathan D. Cohen (Princeton), 

Sponsors

This workshop is made possible through the support of the Lower Saxony Ministry of Science and Culture as well as the Department of Psychology at Princeton University.