Root-letter priming in Maltese visual word recognition
We report on a visual masked priming experiment designed to explore the role of morphology in Maltese visual word recognition. In a lexical decision task, subjects were faster to judge Maltese words of Semitic origin that were primed by triconsonantal letter-strings corresponding to their root-morphemes. In contrast, they were no faster to judge Maltese words of non-Semitic origin that were primed by an equivalent, but non-morphemic, set of three consonant letters, suggesting that morphological overlap, rather than simple form overlap, drives this facilitatory effect. Maltese is unique among the Semitic languages for its orthography: Maltese alone uses the Latin alphabet and requires that all vowels are written, making such triconsonantal strings illegal non-words to which Maltese readers are never exposed, as opposed to other Semitic languages such as Hebrew in which triconsonantal strings often correspond to real words. Under a decomposition-based account of morphological processing, we interpret these results as suggesting that across reading experience Maltese readers have abstracted out and stored root-morphemes for Semitic-origin words lexically, such that these morphemic representations can be activated by exposure to root-letters in isolation and thus prime morphological derivatives.
- Stimulus materials
- Data Analysis
- RT analysis
- Error rate analysis
Published online: 10 August 2018
Amenta, S., and Crepaldi, D.
Bates, D., Kliegl, R., Vasishth, S., and Baayen, H.
(2015) Parsimonious mixed models. arXiv: 1506.04967v1. [https://arxiv.org/abs/1506.04967v1].
Bates, D., Maechler, M., Bolker, B., and Walker, S.
Beyersmann, E., Cavalli, E., Casalis, S., and Colé, P.
Beyersmann, E., and Grainger, J.
Borg, C., Fabri, R., Gatt, A., and Rosner, M.
(2012) Korpus Malti: A corpus of contemporary Maltese, v.2.0. Available at http://mlrs.research.um.edu.mt/.
Boudelaa, S., and Marslen-Wilson, W. D.
Bovingdon, R., and Dalli, A.
Deutsch, A., Frost, R., and Forster, K. I.
Diependaele, K., Brysbaert, M., and Neri, P.
Feldman, L. B., and Bentin, S.
Feldman, L. B., Milin, P., Cho, K. W., Moscoso del Prado Martín, F., and O’Connor, P. A.
Forster, K. I., and Azuma, T.
Forster, K. I., and Davis, C.
Forster, K. I., and Forster, J. C.
Francom, J., Woudstra, D., and Ussishkin, A.
Frost, R., Deutsch, A., and Forster, K. I.
Frost, R., Forster, K. I., and Deutsch, A.
Frost, R., Kugler, T., Deutsch, A., and Forster, K. I.
Grainger, J., Cole, P., and Segui, J.
Jared, D., Jouravlev, O., and Joanisse, M. F.
Kliegl, R., Masson, M. E. J., and Richter, E. M.
Kuznetsova, A., Brockhoff, P. B., and Christensen, R. H. B.
(2016) lmerTest: Tests in linear mixed effects models. [R package v. 2.0-32]. https://CRAN.R-project.org/package=lmerTest.
Longtin, C., Segui, J., and Hallé, P. A.
Luke, S. G.
Marslen-Wilson, W., Tyler, L. K., Waksler, R., and Older, L.
Milin, P., Feldman, L. B., Ramscar, M., Hendrix, P., and Baayen, R. H.
Milin, P., Smolka, E., and Feldman, L. B.
Perea, M., Gatt, A., Moret-Tatay, C., and Fabri, R.
Rastle, K., Davis, M. H., and New, B.
R Core Team
(2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Schmidtke, D., Matsuki, K., and Kuperman, V.
Twist, A. E.
Ussishkin, A., Dawson, C. R., Wedel, A., and Schluter, K.
Velan, H., and Frost, R.
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Nieder, Jessica, Ruben van de Vijver & Holger Mitterer
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