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Measuring Model Flexibility With Parameter Space Partitioning: An Introduction and Application Example
Author(s) -
Pitt Mark A.,
Myung Jay I.,
Montenegro Maximiliano,
Pooley James
Publication year - 2008
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 - 10.1080/03640210802477534
Subject(s) - flexibility (engineering) , computer science , connectionism , focus (optics) , trace (psycholinguistics) , space (punctuation) , artificial intelligence , machine learning , mathematics , statistics , artificial neural network , linguistics , physics , philosophy , optics , operating system
A primary criterion on which models of cognition are evaluated is their ability to fit empirical data. To understand the reason why a model yields a good or poor fit, it is necessary to determine the data‐fitting potential (i.e., flexibility) of the model. In the first part of this article, methods for comparing models and studying their flexibility are reviewed, with a focus on parameter space partitioning (PSP), a general‐purpose method for analyzing and comparing all classes of cognitive models. PSP is then demonstrated in the second part of the article in which two connectionist models of speech perception (TRACE and ARTphone) are compared to learn how design differences affect model flexibility.