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  Predicting reaction times in word recognition by unsupervised learning of morphology

Virpioja, S., Lehtonen, M., Hulten, A., Salmelin, R., & Lagus, K. (2011). Predicting reaction times in word recognition by unsupervised learning of morphology. In W. Honkela, W. Dutch, M. Girolami, & S. Kaski (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2011 (pp. 275-282). Berlin: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-A0A7-1 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-A0AA-C
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prediction_reaction_LNCS_2011.pdf (Publisher version), 183KB
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 Creators:
Virpioja, Sami1, Author
Lehtonen, Minna2, 3, 4, Author
Hulten, Annika3, 4, Author           
Salmelin, Riitta3, Author
Lagus, Krista1, Author
Affiliations:
1Department of Information and Computer Science, Aalto University School of Science, ou_persistent22              
2Cognitive Brain Research Unit, Cognitive Science, Institute of Behavioural Sciences, University of Helsinki, ou_persistent22              
3Brain Research Unit, Low Temperature Laboratory, Aalto University School of Science, ou_persistent22              
4Department of Psychology and Logopedics, Abo Akademi University, ou_persistent22              

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 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.

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 Dates: 2011-06-13
 Publication Status: Issued
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 Rev. Type: Peer
 Identifiers: DOI: 10.1007/978-3-642-21735-7_34
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Title: Artificial Neural Networks and Machine Learning – ICANN 2011
Source Genre: Journal
 Creator(s):
Honkela, W., Editor
Dutch, W., Editor
Girolami, M., Editor
Kaski, S., Editor
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Publ. Info: Berlin : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 275 - 282 Identifier: -

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Title: Lecture Notes in Computer Science
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Pages: - Volume / Issue: 6791 Sequence Number: - Start / End Page: - Identifier: -