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<html>
<head>
<title>SUSTAIN: A Network Model of Category Learning</title>
<META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=iso-8859-1">
<link rel="stylesheet" type="text/css" href="http://love.psy.utexas.edu/~gureckis/styles.css" />
</head>
<body bgcolor="#F5F6F0" marginwidth="0" marginheight="0" topmargin="0" leftmargin="0">
<center>
<br><br><br>
<table cellpadding="0" cellspacing="3" border="0" width="80%">
<tr>
<td align="right">
<img src="images/net.gif">
</td>
<td align="left">
<img src="images/sustaintitle.gif">
</td>
</tr>
<tr>
</tr>
<td valign="bottom" align="right"><!--<img src="images/all.gif" height="200">-->
</td>
<td valign="top">
<br><br>
<span class="MainText">
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.
<p>
Recommended Reading:<br>
Love, B.C., Medin, D.L, and Gureckis, T.M (2004) SUSTAIN: A Network Model of Category Learning.
<i>Psychological Review</i>, 11, 309-332 [ <a href="http://love.psy.utexas.edu/~love/papers/love_etal_press.pdf">PDF</a> ]<br />
</p>
<!--<img src="images/all.gif">-->
</span>
</td>
</tr>
<tr>
<td colspan="2"><hr></td>
</tr>
</tr>
<td align="right" valign="top"><img src="images/sustaincode.gif"></td>
<td valign="top">
<span class="MainText">
<a href="sustain.py">Click here to DOWNLOAD the SUSTAIN code in PYTHON.</a><br>
<span class="SmallText">All code provided here was implemented by
<a href="http://love.psy.utexas.edu/~gureckis/">Todd Gureckis</a>
(<a href="[email protected]">[email protected]</a>).
</span>
<br><br>
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.
</span>
<br><br><br>
<img src="shepardsm.gif" border=0>
</br>
<br>
</td>
</tr>
<tr>
<td colspan="2"><hr></td>
</tr>
</tr>
<td align="right" valign="top">
<img src="images/sustainpapers.gif"><br>
<span class="SmallText">
If you know of any others please email me (<a href="[email protected]">[email protected]</a>)
</span>
</td>
<td>
<span class="MainText">
<p>
Love, B.C. and Gureckis, T.M (2004). The Hippocampus: Where a Cognitive Model meets Cognitive Neuroscience.
<i>Proceedings of the 26th Annual Conference of Cognitive Science Society</i>.<br />
</p>
<p>
Gureckis, T.M and Love, B.C. (2004). Common Mechanisms in Infant and Adult Category Learning.
<i>Infancy</i>, vol 5, no.2, 173-198.<br />
</p>
<p>
Love, B.C., Medin, D.L, and Gureckis, T.M (2004) SUSTAIN: A Network Model of Category Learning.
<i>Psychological Review</i>, 11, 309-332<br />
</p>
<p>
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.
</p>
<p>
Love, B.C. and Gureckis, T.M. (2004). Modeling Learning Under the Influence of Culture. in Doug Medin's festschrift (in press).
</p>
<p>
Gureckis, T.M and Love, B.C. (2003). Human Unsupervised and Supervised Learning as a Quantitative Distinction.
<i>International Journal of Pattern Recognition and Artificial Intelligence</i>, vol. 17, no. 5, 885-901.<br />
</p>
<p>
Gureckis, T.M and Love, B.C. (2003). Towards a Unified Account of Supervised and Unsupervised Learning. <i>Journal of
Experimental and Theoretical Artifical Intelligence</i>, <i>15</i>, 1-24.<br />
</p>
<p>
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.<br />
</p>
<p>
Gureckis, T.M and Love, B.C. (2002). Modeling Unsupervised Learning with SUSTAIN. In <i>Proceedings of the
15th Annual Florida Artificial Intelligence Research Society (FLAIRS) conference: Special Track: Categorization and Concept
Representation: Models and Implications.</i> <br />
</p>
<p>
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.
</p>
</span>
</td>
</tr>
</table>
<br><br><br>
</center>
</body>
</html>