We are currently in the process of open-sourcing our toolbox. Please stay tuned for updates regarding tutorials and documentation.
Musslick, S. (2021). Recovering quantitative models of human information processing with differentiable architecture search. In Proceedings of the 43rd Annual Meeting of the Cognitive Science Society (pp. 348–354). Vienna, AT. https://arxiv.org/abs/2103.13939
Experimental design is a key ingredient of reproducible empirical research. Yet, given the increasing complexity of experimental designs, researchers often struggle to implement ones that allow them to measure their variables of interest without confounds. SweetPea is an open-source declarative language in Python, in which researchers can describe their desired experiment as a set of factors and constraints. The language leverages advances in areas of computer science to sample experiment sequences in an unbiased way. SweetPea is a standalone package. However, the Autonomous Empirical Research Group leverages SweetPea to automate the design of novel experiments.
The SweetPea language is an open-source project in Python. We invite contributions to SweetPea's open-source repository:
In this tutorial, we provide an overview of SweetPea’s capabilities and demonstrate its application to the design of psychological experiments:
Musslick, S., Cherkaev, A., Draut, B., Butt, A. S., Darragh, P., Srikumar, V., ... & Cohen, J. D. (2021). SweetPea: A standard language for factorial experimental design. Behavior Research Methods, 1-25.