Predicting Reaction Times in Word Recognition by Unsupervised Learning of Morphology

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Publication Details

List of Authors: Virpioja S, Lehtonen M, Hultén A, Salmelin R, Lagus K
Editors: Honkela T, Duch W, Girolami M, Kaski S
Publication year: 2011
Journal: Lecture Notes in Computer Science
Book title: Artificial Neural Networks and Machine Learning – ICANN 2011
Journal acronym: LECT NOTES COMPUT SC
Title of series: Lecture Notes in Computer Science
Volume number: 6791
Start page: 275
End page: 282
Number of pages: 8
ISBN: 978-3-642-21734-0
eISBN: 978-3-642-21735-7
ISSN: 0302-9743


A central question in the study of the mental lexicon is how morphologically complex words are processed. We consider this question from the viewpoint of statistical models of morphology. As an indicator of the mental processing cost in the brain, we use reaction times to words in a visual lexical decision task on Finnish nouns. Statistical correlation between a model and reaction times is employed as a goodness measure of the model. In particular, we study Morfessor, an unsupervised method for learning concatenative morphology. The results for a set of inflected and monomorphemic Finnish nouns reveal that the probabilities given by Morfessor, especially the Categories-MAP version, show considerably higher correlations to the reaction times than simple word statistics such as frequency, morphological family size, or length. These correlations are also higher than when any individual test subject is viewed as a model.

Last updated on 2020-04-04 at 07:31