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Early morphological decomposition of suffixed words: Masked priming evidence with transposed-letter nonword primes

Published online by Cambridge University Press:  14 March 2012

ELISABETH BEYERSMANN*
Affiliation:
Macquarie University
JON ANDONI DUÑABEITIA
Affiliation:
Basque Centre on Cognition, Brain and Language
MANUEL CARREIRAS
Affiliation:
Basque Centre on Cognition, Brain and Language, and Basque Foundation for Science
MAX COLTHEART
Affiliation:
Macquarie University
ANNE CASTLES
Affiliation:
Macquarie University
*
ADDRESS FOR CORRESPONDENCE Elisabeth Beyersmann, Macquarie Centre for Cognitive Science, Macquarie University, Sydney, NSW 2109, Australia. E-mail: lisi.beyersmann@mq.edu.au
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Abstract

Many studies have previously reported that the recognition of a stem target (e.g., teach) is facilitated by the prior masked presentation of a prime consisting of a derived form of it (e.g., teacher). We conducted two lexical decision experiments to investigate masked morphological priming in Spanish. Experiment 1 showed that equal magnitudes of masked stem-target priming are obtained for both morphologically complex word primes (e.g., doloroso-DOLOR [painful-PAIN]) and morphologically complex nonword primes that included letter transpositions within the stem (e.g., dlooroso-DOLOR). Experiment 2 used morphologically complex nonword primes comprising lexically illegal combinations of stems and suffixes (e.g., total + ito [a little total]). Priming was obtained for morphologically related nonword primes (e.g., totalito-TOTAL), but not for nonword primes that included letter transpositions within the pseudostem (e.g., ttoalito-TOTAL). Our data suggest that morphoorthographic parsing mechanisms benefit from semantic constraints at early stages in the reading system, which we discuss in the context of current morphological processing accounts.

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Articles
Copyright
Copyright © Cambridge University Press 2012 
Figure 0

Table 1. Mean word frequency, bigram frequency, length, and orthographic N for the stimuli in Experiment 1

Figure 1

Table 2. Mean (standard deviation) lexical decision times (ms) and percentage of errors for real word targets in Experiment 1

Figure 2

Table 3. Mean word frequency, bigram frequency, length, and orthographic N for the stimuli in Experiment 2

Figure 3

Table 4. Mean (standard deviation) lexical decision times (ms) and percentage of errors for real word targets in Experiment 2