SUSTAIN is a psychological model of human category learning. The purpose of this site is to distribute some example code for using the model. It is our hope that by providing this information freely on the web we can help other researchers, modelers, and students gain insight into the operation of SUSTAIN and assist them in creating simulations using the model.

Recommended Reading:
Love, B.C., Medin, D.L, and Gureckis, T.M (2004) SUSTAIN: A Network Model of Category Learning. Psychological Review, 11, 309-332 [ PDF ]
Click here to DOWNLOAD the SUSTAIN code in PYTHON.
All code provided here was implemented by Todd Gureckis (gureckis@psy.utexas.edu).

This python code applies the SUSTAIN model to the Shepard, Hovland, Jenkins (1961) category learning experiments. The code will output a datafile which represents the learning curves for each of the 6 problems. If you plot this data you should get a figure similar to below with a RMSError of approximately 0.0282 (averaged over 10000 runs). I recommend this as a good benchmark for verifying that the model is operating correctly.





If you know of any others please email me (gureckis@psy.utexas.edu)

Love, B.C. and Gureckis, T.M (2004). The Hippocampus: Where a Cognitive Model meets Cognitive Neuroscience. Proceedings of the 26th Annual Conference of Cognitive Science Society.

Gureckis, T.M and Love, B.C. (2004). Common Mechanisms in Infant and Adult Category Learning. Infancy, vol 5, no.2, 173-198.

Love, B.C., Medin, D.L, and Gureckis, T.M (2004) SUSTAIN: A Network Model of Category Learning. Psychological Review, 11, 309-332

Sakamoto, Y., Matuska, T., & Love, B. C. (2004).  Dimension-wide vs. exemplar-specific attention in category learning and recognition.  In M. Lovett, C. Schunn, C. Lebiere, and P. Munro (Eds.), Proceedings of the International Conference of Cognitive Modeling (pp. 261-266). Mahwah, New Jersey: Lawrence Erlbaum.

Love, B.C. and Gureckis, T.M. (2004). Modeling Learning Under the Influence of Culture. in Doug Medin's festschrift (in press).

Gureckis, T.M and Love, B.C. (2003). Human Unsupervised and Supervised Learning as a Quantitative Distinction. International Journal of Pattern Recognition and Artificial Intelligence, vol. 17, no. 5, 885-901.

Gureckis, T.M and Love, B.C. (2003). Towards a Unified Account of Supervised and Unsupervised Learning. Journal of Experimental and Theoretical Artifical Intelligence, 15, 1-24.

Gureckis, T.M and Love, B.C. (2002). Who says models can only do what you tell them? Unsupervised category learning data, fits, and predictions. In Proceedings of the 24th Annual Conference of the Cognitive Science Society. pgs. 399-404. Hillsdale, NJ: Lawrence Erlbaum.

Gureckis, T.M and Love, B.C. (2002). Modeling Unsupervised Learning with SUSTAIN. In Proceedings of the 15th Annual Florida Artificial Intelligence Research Society (FLAIRS) conference: Special Track: Categorization and Concept Representation: Models and Implications.

Love, B. C., & Medin, D. L. (1998). SUSTAIN: A model of human category learning. Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), USA, 15, 671-676.