Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories
Article first published online: 4 OCT 2011
Copyright © 2011 Cognitive Science Society, Inc.
Volume 36, Issue 1, pages 150–162, January/February 2012
How to Cite
Martin, J. B., Griffiths, T. L. and Sanborn, A. N. (2012), Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. Cognitive Science, 36: 150–162. doi: 10.1111/j.1551-6709.2011.01204.x
- Issue published online: 11 JAN 2012
- Article first published online: 4 OCT 2011
- Received 19 October 2010; received in revised form 27 February 2011; accepted 3 March 2011
- Emotion classification;
- Markov chain Monte Carlo;
- Reverse correlation;
- Classification images
Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse correlation (RC). We compare RC against an alternative method for inferring the structure of natural categories called Markov chain Monte Carlo with People (MCMCP). Based on an algorithm used in computer science and statistics, MCMCP provides a way to sample from the set of stimuli associated with a natural category. We apply MCMCP and RC to the problem of recovering natural categories that correspond to two kinds of facial affect (happy and sad) from realistic images of faces. Our results show that MCMCP requires fewer trials to obtain a higher quality estimate of people’s mental representations of these two categories.