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Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories
Author(s) -
Martin Jay B.,
Griffiths Thomas L.,
Sanborn Adam N.
Publication year - 2011
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/j.1551-6709.2011.01204.x
Subject(s) - categorization , computer science , affect (linguistics) , natural (archaeology) , markov chain monte carlo , artificial intelligence , set (abstract data type) , markov chain , machine learning , sample (material) , monte carlo method , natural language processing , cognitive psychology , psychology , statistics , mathematics , communication , bayesian probability , chemistry , archaeology , chromatography , history , programming language
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.