Predicting Reaction Times in Word Recognition by Unsupervised Learning of Morphology

S Virpioja, Minna Lehtonen, A Hultén, R Salmelin, K Lagus

    Research output: Chapter in Book/Conference proceedingChapterScientificpeer-review

    4 Citations (Scopus)

    Abstract

    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.
    Original languageUndefined/Unknown
    Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2011
    EditorsT Honkela, W Duch, M Girolami, S Kaski
    Pages275–282
    Number of pages8
    ISBN (Electronic)978-3-642-21735-7
    DOIs
    Publication statusPublished - 2011
    MoE publication typeA3 Part of a book or another research book

    Cite this