Statistical models of morphology predict eye-tracking measures during visual word recognition

Show simple item record

dc.contributor University of Helsinki, Cognitive Brain Research Unit en
dc.contributor University of Helsinki, Aalto University en
dc.contributor University of Helsinki, Centre for Social Data Science, CSDS en
dc.contributor.author Lehtonen, Minna
dc.contributor.author Varjokallio, Matti
dc.contributor.author Kivikari, Henna
dc.contributor.author Hultén, Annika
dc.contributor.author Virpioja, Sami
dc.contributor.author Hakala, Tero
dc.contributor.author Kurimo, Mikko
dc.contributor.author Lagus, Krista
dc.contributor.author Salmelin, Riitta
dc.date.accessioned 2019-11-08T10:52:04Z
dc.date.available 2019-11-08T10:52:04Z
dc.date.issued 2019-10
dc.identifier.citation Lehtonen , M , Varjokallio , M , Kivikari , H , Hultén , A , Virpioja , S , Hakala , T , Kurimo , M , Lagus , K & Salmelin , R 2019 , ' Statistical models of morphology predict eye-tracking measures during visual word recognition ' , Memory and Cognition , vol. 47 , no. 7 , pp. 1245-1269 . https://doi.org/10.3758/s13421-019-00931-7 en
dc.identifier.issn 1532-5946
dc.identifier.other PURE: 126330858
dc.identifier.other PURE UUID: c9bf317d-c88b-4562-84f6-43759c88c4ac
dc.identifier.other RIS: urn:5B122B5B8EFB1955615200D1C6BF5657
dc.identifier.other WOS: 000491549400001
dc.identifier.other ORCID: /0000-0002-6137-8854/work/64323695
dc.identifier.other ORCID: /0000-0002-0681-4828/work/64324506
dc.identifier.other ORCID: /0000-0002-3568-150X/work/66367323
dc.identifier.uri http://hdl.handle.net/10138/306831
dc.description.abstract We studied how statistical models of morphology that are built on different kinds of representational units, i.e., models emphasizing either holistic units or decomposition, perform in predicting human word recognition. More specifically, we studied the predictive power of such models at early vs. late stages of word recognition by using eye-tracking during two tasks. The tasks included a standard lexical decision task and a word recognition task that assumedly places less emphasis on postlexical reanalysis and decision processes. The lexical decision results showed good performance of Morfessor models based on the Minimum Description Length optimization principle. Models which segment words at some morpheme boundaries and keep other boundaries unsegmented performed well both at early and late stages of word recognition, supporting dual- or multiple-route cognitive models of morphological processing. Statistical models based on full forms fared better in late than early measures. The results of the second, multi-word recognition task showed that early and late stages of processing often involve accessing morphological constituents, with the exception of short complex words. Late stages of word recognition additionally involve predicting upcoming morphemes on the basis of previous ones in multimorphemic words. The statistical models based fully on whole words did not fare well in this task. Thus, we assume that the good performance of such models in global measures such as gaze durations or reaction times in lexical decision largely stems from postlexical reanalysis or decision processes. This finding highlights the importance of considering task demands in the study of morphological processing. en
dc.format.extent 25
dc.language.iso eng
dc.relation.ispartof Memory and Cognition
dc.rights en
dc.subject 6162 Cognitive science en
dc.subject 3112 Neurosciences en
dc.title Statistical models of morphology predict eye-tracking measures during visual word recognition en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.3758/s13421-019-00931-7
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
dc.contributor.pbl
dc.contributor.pbl
dc.contributor.pbl

Files in this item

Total number of downloads: Loading...

Files Size Format View
Lehtonen2019_Ar ... calModelsOfMorphologyP.pdf 688.1Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record