Brain & Language 133 (2014) 14–25

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Second language phonology influences first language word naming Kalinka Timmer a,b,c,⇑, Lesya Y. Ganushchak a,b,d, Ilse Ceusters a, Niels O. Schiller a,b a

Leiden University Centre for Linguistics (LUCL), Leiden University, Leiden, The Netherlands Leiden Institute for Brain and Cognition (LIBC), Leiden University, Leiden, The Netherlands c Department of Psychology, York University, Toronto, Canada d Brain and Education Lab, Education and Child Studies, Faculty of Social and Behavioural Sciences, Leiden University, Leiden, The Netherlands b

a r t i c l e

i n f o

Article history: Accepted 10 March 2014

Keywords: Reading aloud Cross-linguistic priming Masked onset priming effect Grapheme-to-phoneme conversion Models of reading aloud

a b s t r a c t The Masked Onset Priming Effect (MOPE) has been reported in speakers’ first languages (L1). The aims of the present study are to investigate whether second language (L2) phonology is active during L1 reading, and to disentangle the contributions of orthography and phonology in reading aloud. To this end, Dutch–English bilinguals read aloud L1 target words primed by L2 words, while electroencephalography (EEG) was recorded. The onset of the primes was manipulated to disentangle the contributions of orthography and phonology (i.e. O+P+: kite – KUNST, ‘art’; O+P: knee – KUNST; OP+: crime – KUNST; OP: mine – KUNST). Phonological but not orthographic overlap facilitated RTs. However, event-related brain potentials (ERPs) revealed both orthographic and phonological priming starting 125 ms after target presentation. Taken together, we gained insights into the time course of cross-linguistic priming and demonstrated that L2 phonology is activated rapidly in an L1 environment. Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction Researchers have been debating how multiple languages are represented in the brain. One view is that of language-specific selection, which assumes that the different languages of a bilingual speaker are represented separately in the brain (e.g. Simple Naming Model of Costa, Miozzo, & Caramazza, 1999). When bilinguals speak in one language, words from the non-target language are not considered and therefore no competition takes place between languages. The opposite view, i.e. language non-specific selection, assumes simultaneous activation of words from different languages (Bilingual Interactive Activation Model (BIA+); Dijkstra & Van Heuven, 2002). This means that competition can take place between languages (Green, 1998). Most current research supports the notion that at least on some level the languages of multilinguals form one lexicon (i.e. non-selective representation). Cross-language competition and language switching costs are in most bilingual models explained by a common lexicon in which words of all the languages of a bilingual are represented together (Dijkstra & Van Heuven, 2002). However, is it only the lexical level where all languages are represented collectively? ⇑ Corresponding author at: Department of Psychology, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada. E-mail addresses: [email protected], [email protected], [email protected] (K. Timmer). http://dx.doi.org/10.1016/j.bandl.2014.03.004 0093-934X/Ó 2014 Elsevier Inc. All rights reserved.

In the present study, we investigate whether sub-lexical phonological priming from the L2 to the L1 is achievable in unbalanced bilinguals. Such priming would suggest co-activation of the sublexical phonologies of both languages. In addition, we investigate the time course of sub-lexical cross-linguistic activation. To this aim the electroencephalography (EEG) is recorded while participants performed a masked priming paradigm. The masked priming paradigm is often used to tap into the early processes of reading. The masked onset priming effect (MOPE) reflects reduced speech onset latencies for prime-target pairs that have a shared initial phonological onset (e.g. kernel [kernEl] – CARPET [kArpEt]) rather than orthographic onset-overlap (e.g. circus [sırkEs] – CARPET [kArpEt]; e.g. Mousikou, Coltheart, & Saunders, 2010; Schiller, 2007; Timmer & Schiller, 2012; Timmer, Vahid-Gharavi, & Schiller, 2012). The MOPE reflects early and automatic sublexical processing. For instance, in previous studies it has been shown that during monolingual masked priming tasks (i.e. when prime-target pairs are in the same language), the orthographic and phonological priming effects are seen in ERPs as early as 80– 120 ms and continue until 280–480 ms after target onset (Timmer & Schiller, 2012; Timmer et al., 2012). MOPE has been typically shown during reading aloud tasks where prime-target pairs were presented to the participants’ L1. However, reading aloud studies have rarely investigated whether the phonology of L2 could also be activated during reading aloud in L1 (Timmer, Ganushchak, Mitlina, & Schiller, 2013). In contrast to the reading aloud

K. Timmer et al. / Brain & Language 133 (2014) 14–25

literature, the behavioral visual word recognition literature has already investigated cross-language phonological masked priming with cognates (Gollan, Forster, & Frost, 1997; Kim & Davis, 2003; Voga & Grainger, 2007), interlingual homophones (Brysbaert, Van Dyck, & Van de Poel, 1999; Duyck, 2005; Kim & Davis, 2003; Van Wijnendaele & Brysbaert, 2002), interlingual pseudohomophones (Brysbaert et al., 1999; Van Wijnendaele & Brysbaert, 2002), and existing words (Dimitropoulou, Duñabeitia, & Carreiras, 2011). This literature is often interpreted to suggest fast and automatic crosslanguage grapheme-to-phoneme conversion (GPC; how letters are matched to their corresponding pronunciation) and hence nonselective phonological activation (Dijkstra & Van Heuven, 2002). However, cognates have a common orthographic, phonological and semantic representation in both languages. Therefore, the cross-language semantic overlap might send feedback to the sublexical orthographic and phonological representation, thereby strengthening the supposedly sub-lexical phonological effects. Cognates are special due to their strong connection between semantics and orthography across languages (Pecher, 2001). Interlingual homophones, in contrast, do not share their meaning across languages, and therefore send no semantic feedback. However, they share a common phonology, and possible partially overlap in orthography which may also increase the sub-lexical phonological priming effect. For example, Brysbaert et al. (1999) showed that L1 homophonic words (e.g. dier; Dutch for ‘animal’) prime L2 words (e.g. DIRE; French for ‘to say’; Brysbaert et al., 1999). Cross-script homophones make it possible to avoid cross-language orthographic overlap (Dimitropoulou et al., 2011; Kim & Davis, 2003; Voga & Grainger, 2007). The obtained phonological priming effects have been considered sub-lexical (e.g. Dimitropoulou et al., 2011). However, when interlingual homophones are used (e.g. Kim & Davis, 2003) the same phonological representation refers to different meanings across languages. This in turn might suggest not only sub-lexical but also lexical activation. For instance, Duyck (2005; Exp. 5) found that L2 (English) targets (e.g. CORNER) were recognized faster when preceded by an associative L1 (Dutch) homophone (e.g. hook – related to Dutch word ‘hoek’ meaning ‘corner’). This suggests that both meanings of a homophone can become active, in turn, suggesting lexical processing. Possibly the lexical activation sends feedback to the sub-lexical phonological level, thus exerting a top-down influence on the supposedly sub-lexical bottom-up process. There are multiple computational models that modulate the sub-lexical and lexical processes of visual word recognition (and reading aloud) in a single language. For example, the dual-route cascaded (DRC) model (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Mousikou et al., 2010), the connectionist dual process model (CDP+(+); Perry, Ziegler, & Zorzi, 2007, 2010; Zorzi, Houghton, & Butterworth, 1998) and the triangle model (Plaut, McClelland, Seidenberg & Patterson, 1996; Harm & Seidenberg, 1999, 2004). All models assume an early locus of GPC, which is reflected by the MOPE. These models accommodate GPC rules in a single language only as their focus has not been on multiple languages. However, the BIA model is specifically adapted for bilinguals (Dijkstra & Van Heuven, 2002) from the monolingual Interactive Activation Model (IA) (McClelland & Rumelhart, 1981). The BIA model assumes a single lexicon in which words are interconnected and can mutually inhibit each other. The BIA+ model extends the assumption of non-selectivity to orthographic, phonological and semantic representations. In addition, language nodes for each language collect activation from one lexicon and if the language node receives enough activation, it inhibits all words from the opposite lexicon. All above-mentioned models assume within-language phonological priming in the L1 (Mousikou et al., 2010; Schiller, 2007; Timmer & Schiller, 2012; Timmer et al., 2012). As long as the GPC of the L2 does not contradict the

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L1 GPC, even within-language phonological priming in the L2 can be expected. Future improvements/endeavors of DRC, CDP++, and triangle model could try to accommodate for differing GPC in two or more languages if the present study can establish that L2 phonology can prime L1 phonology. During our investigation of sub-lexical cross-linguistic (L2 to L1) masked priming, we make sure to look at purely bottom-up sub-lexical phonological processing without top-down feedback from the lexicon. Hence, we manipulated only the onset of the prime-target pairs. In this way, no lexical phonological activation will facilitate or interfere, as might be true for cognates and homophones. Further, we will use a reading aloud task instead of a task involving only visual word recognition to avoid focusing on lexical processing and promote sub-lexical phonological processing. In a reading aloud task, sub-lexical orthography needs to be converted into phonology for correct execution of the task, though this is debatable for visual word recognition tasks. And most important, in contrast to previous cross-language research we recorded EEG to track the time course of GPC to show whether the cross-language sub-lexical phonological activation is similar to within-language sub-lexical activation in single word reading aloud (Timmer & Schiller, 2012; Timmer et al., 2012). To this aim, bilingual Dutch (L1)–English (L2) participants read aloud L1 target words primed with L2 words. The onset of the primes was manipulated to disentangle the contributions of orthographic and phonological activation during reading aloud. Four conditions were created: (1) grapheme and phoneme match (O+P+; e.g. kite – KUNST, ‘art’), (2) phoneme mismatch (O+P; e.g. knee – KUNST; the onset hki in knee is mute), (3) grapheme mismatch (OP+; e.g. crime – KUNST; the onset hci in crime is pronounced /k/), (4) grapheme and phoneme mismatch (OP; e.g. mine – KUNST). It is expected that orthographically related L2 primes will not lead to faster response times on L1 target words. Instead, phonologically related primes will decrease response times on L1 targets, as demonstrated in the previous L1 literature on onset effects (Mousikou et al., 2010; Schiller, 2007; Timmer & Schiller, 2012; Timmer et al., 2012) and L2 literature (Timmer & Schiller, 2012). If GPC is automatic not only for the L1 but also for the L2 rules, ERPs are expected to reveal orthographic and phonological priming effects that reflect the GPC process. The critical condition is the phonological comparison O+P+ (e.g. kite – KUNST, ‘art’) vs. O+P (e.g. knee – KUNST). If the language of the prime is not recognized as L2 then primes will be processed as L1 nonwords since the phonology rules of L2 will not be activated. It has been previously shown that non-word primes do not have a lexical representation, but automatically activate the phonology of the L1 if the target words are presented in L1 (e.g. Carreiras, Perea, Vergara, & Pollatsek, 2009). Therefore, in our experiments, if L2 phonology is not activated both prime-target pairs (e.g. O+P+ (e.g. kite – KUNST, ‘art’) and O+P (e.g. knee – KUNST)) activate the onset hki as /k/, which match the L1 target KUNST. Note, that in Dutch (L1) the hki is always pronounced as a /k/ at the onset of a word, no matter whether it is followed by a vowel or a consonant. Thus, if this is the case there should not be significant differences between match and mismatch conditions. However, if L2 primes are read according to L2 phonology, the hki in the onset hkni in knife is mute and thus faster RTs are expected for kite – KUNST compared to knee – KUNST. If the phonology of both languages is rapidly activated then we expect to show phonological priming as has been found in previous studies (e.g. Mousikou et al., 2010; Schiller, 2007; Timmer & Schiller, 2012; Timmer et al., 2012). Furthermore, the time course of both orthographic and phonological activation is expected to start as soon as 120 ms after target onset, as has been previously shown by within-language MOPE research (Timmer & Schiller, 2012; Timmer et al., 2012).

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2. Method

2.3. Design and procedure

2.1. Participants

Participants were individually seated in a dimly lit room at approximately 80 cm from the computer screen and asked to fill in a self-rating proficiency questionnaire for the English language. Afterwards, the experimental task was started. Participants were instructed to read aloud Dutch target words as quickly and accurately as possible. Response latencies were measured with a voice-key. During the whole task, the EEG was recorded. The experiment started with a practice block to familiarize the participants with the task. Four experimental blocks, each consisting of forty experimental and thirty-two filler items, followed the practice block. Four blocks of prime-target pairs were created such that none of the blocks contained the same prime or target twice. The order of blocks was randomized for each participant as well as the order of the target words within blocks. There was a short break in-between blocks. For both practice and experimental trials, each trial consisted of the following sequence: a fixation mark (‘+’; between 400 and 700 ms in duration), a forward mask of seven hash marks (‘#’; 500 ms), the English prime word in lower-case letters (48 ms), a backward mask of seven hash marks (‘#’; 17 ms), and the target word in upper-case letters. The target word stayed on the screen until a response was given or a maximum of 2 s elapsed. Between each trial a blank screen was presented for 1 s. To match the prime in length to the target, percentage signs (‘%’) were added preceding and following the prime word (e.g. knee% – KUNST) as has been done in previous masked priming studies (Ferrand, Segui, & Humphreys, 1997; Grainger & Jacobs, 1993; Horemans & Schiller, 2004; Schiller, 2004, 2008; Timmer et al., 2012). All items were presented in Courier New with a font size of 18 and in black-onwhite background in the middle of the screen. The average length of the words presented on the screen was 4 cm with 2.3° of visual angle. After completion of the experiment, participants were asked to perform a lexical decision task (Meara, 2005). Also, participants received the list of English primes used in the experiment and were asked to mark the words of which they did not know the pronunciation and/or the meaning. Afterwards, they also read the list of words aloud to the experimenter to judge whether they truly knew the correct pronunciation of the word. One session lasted approximately 1 h and 20 min, including the electrode application and removal.

Twenty-five native Dutch speakers (seven males; average age = 22.4; SD = 4.36), with English as a second language, participated in the experiment. All had normal or corrected-to-normal vision and no history of neurological impairment or language disorders. Participants signed an informed consent form before the start of the experiment in accordance with institutional guidelines and regulations of the local ethics committee from the Faculty of Social and Behavioral Sciences at Leiden University. Data of three participants were rejected due to technical problems (n = 2) or extremely slow responses (above 2 SDs of the group mean; n = 1), yielding a total of 22 subjects (six males; average age = 22.3; SD = 4.46) used in the analyses. Participants first started learning English at an average age of eight. All participants completed a self-rating proficiency questionnaire (see Table 1 for an overview). In addition, they completed the lexical decision task of Meara (2005) to match participant’s level of English. The Meara task gives an indication of their English proficiency with a maximum score of 5000, which equals a native speaker. The Meara test always presents a subset of the vocabulary of 5000 words to each participant. They had to indicate whether or not the letter string was an English word. The non-words included in the Meara task are very similar to real words to increase the difficulty of the task. 2.2. Materials Forty Dutch experimental target words were selected and matched with four types of primes. The target and prime words were matched for syllable structure (one or two), length (four to eight; average of five letters), and frequency (respectively: a mean frequency of occurrence of 18.1 per million (SD = 22.55) and 17.8 (SD = 21.20) according to CELEX; Baayen, Piepenbrock, & Gulikers, 1995). The target words started with a hkni, hki, or hsi onset. To create the four prime words, the onset of the target was manipulated: (1) matching onset in both orthography and phonology (O+P+; e.g. kite – KUNST (‘art’); grapheme- and phoneme-match condition), (2) matching orthography only (O+P; e.g. knee – KUNST; phonememismatch condition), (3) matching phonology only (OP+; e.g. crime – KUNST; grapheme-mismatch condition), and (4) mismatching onset in both orthography and phonology (OP; e.g. mine – KUNST; grapheme- and phoneme-mismatch condition). The experimental target and prime words can be found in Appendix A. In addition, thirty-two filler target words were selected and also matched with four prime types. Onsets of filler targets were different from experimental targets, i.e. hci, hfi, or hji. None of the filler target and prime words appeared in experimental trials.

Table 1 Mean answers (and standard deviations) to the self-rating proficiency questionnaire (range: 0–10% or 100%) and the proficiency test (range: 0–5000) of Meara (2005). Mean (SD) Age starting to learn English Active skills Passive skills High-school grade English % Speaking English during a day % Reading English during a day % Listening English during a day LDT score (0–5000; Meara, 2005)

8.1 (2.82) 7.6 (0.95) 8.0 (1.61) 7.6 (0.94) 13.9 (18.65) 50.3 (27.42) 31.4 (22.05) 3705 (896.94)

2.4. Apparatus and data acquisition For EEG registration the 10/20 system with thirty-two electrosites on the scalp was used. The electrodes were of the Ag/AgCl type. In addition, four flat type electrodes were used to record the electro-oculogram (EOG): one above and one below the left eye to monitor eye-blinks, and one to the external canthi of each eye to monitor horizontal eye-movement. Further, two electrodes were placed at the mastoid for off-line re-referencing. BioSemi ActiView software was used to register the EEG signal, sampled at a rate of 512 Hz. 2.5. Data analysis For the behavioral data a mixed-effects model analysis was used. This analysis allows for the inclusion of multiple random factors in one analysis (Brysbaert, 2007; Quené & Van den Bergh, 2008). To remove a possible non-normality and the positively skewed distribution in the data, a logarithmic transformation (i.e. log 10) was carried out on the RTs (Keene, 1995; Limpert, Stahel, & Abbt, 2001; Quené & Van den Bergh, 2008) and centered at the sample mean (Raudenbush & Bryk, 2002). In the present study,

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random factors were participants and items with crossed fixed factors being Orthography (grapheme-mismatch vs. graphemematch), and Phonology (phoneme-mismatch vs. phoneme-match). The behavioral data were analyzed with R using mixed effect models (lmer) (Baayen, Davidson, & Bates, 2008). A maximal random structure approach was used by including all the random effects when possible (i.e. intercepts and slopes for participants and items and the slopes for the fixed effects). The correlation term between a random intercept and a slope were removed when the LME maximal structure failed to converge (Barr, Levy, Scheepers, & Tily, 2013). We looked at main effects by removing main fixed effects from the full model. The log-likelihood ratio test (v2) was used for significance testing. To a subject and item random model we included the factors Orthography and Phonology and their random slopes and the interaction between them in a forward direction (Barr et al., 2013). In addition, t-values below 2 or above 2 will be taken as significant at the 95% confidence level (Gelman & Hill, 2007). For the EEG analysis, epochs of 500 ms with a 200 ms pre-stimulus baseline were created. The EEG signal was filtered with a high-pass filter of 0.01 Hz/24 dB and a low-pass filter of 30 Hz/ 24 dB. Ocular artifacts were corrected using the Gratton, Coles, and Donchin (1983) algorithm. Non-ocular artifacts were removed based on the following criteria: trials with amplitudes below 200 lV, above +200 lV, or made a voltage step of 50 lV within 200 ms. The ERP grand averages were time-locked to the onset of the target word and calculated separately for each of the four conditions over participants. For the EEG data, a mixed-effects model analysis could not be applied, since ERP software does not allow for separate item coding. Therefore, mean amplitude values per condition and per participant were submitted to a repeated measures ANOVAs. The fixed factors Orthography (grapheme-mismatch vs. graphemematch), Phonology (phoneme-mismatch vs. phoneme-match) and Localization (frontal: AF3, AF4, F3, F4, F7, F8, Fz vs. central: C3, C4, Cz, FC1, FC2, CP1, CP2 vs. posterior: P3, P4, P7, P8, PO3, PO4, Pz). A similar ANOVA was run with Lateralization (left: AF3, F3, FC5, C3, CP5, P3, PO3 vs. mid: Fz, FC1, FC2, Cz, CP1, CP2, Pz vs. right: AF4, F4, FC6, C4, CP6, P4, PO4), Orthography (grapheme-mismatch vs. grapheme-match), and Phonology (phoneme-mismatch vs. phoneme-match). The spatial factors Localization and Lateralization were analyzed in separate analyses in order to include as many electrodes as possible and to keep a symmetrical arrangement (e.g. Christoffels, Firk, & Schiller, 2007; Ganushchak & Schiller, 2010; Federmeier & Kutas, 1999). All analyses were conducted for three time windows: 125–175 ms, 175–250 ms, and 250–350 ms post target presentation. The windows were determined by analyzing 25 ms time windows for significance from target onset up to 500 ms, The onset and offset of the time windows was determined by having at least 2 consecutive 25 ms bins reach significant effects and no more than one bin that did not reach significance in between. To avoid possible ocular or speech artifacts we did not analyze the ERPs beyond 350 ms. 3. Results 3.1. Behavioral data Two items, i.e. KEIZER (‘emperor’) and KNOBBEL (‘knobble’), and their corresponding priming conditions generated a large proportion of deviating pronunciations and were excluded from all analyses. Further, naming latencies shorter than 200 ms and longer than 1000 ms (0.84% of the data), voice-key errors (0.95% of the data), and incorrect responses (0.48% of the data) were discarded from the analysis, leaving a total of 98% of the trials in the analysis. Model comparison showed that removing Orthography significantly improved the goodness of model fit (v2(4) = 22.74,

p < .001). Response latencies between the grapheme-match (O+P and O+P+; 573 ms) and the grapheme-mismatch (OP and OP+; 574 ms) conditions did not differ from each other, yielding no priming effect. However, removing Phonology did not improve the goodness of model fit (v2(4) = 2.44, p < .65). Showing that response latencies for the phoneme-match conditions (OP+ and O+P+; 568 ms) were 11 ms faster than for the phoneme-mismatch conditions (OP and O+P; 579 ms). For an overview of the RTs per condition see Table 2 and the results from the LME analyses is presented in Table 3. 3.2. Electrophysiological data Fig. 1 provides an overview of the stimulus-locked waveforms for Orthography and Phonology. Figs. 2–5 provide an overview of the mean amplitude values per location for Orthography and Phonology. Fig. 6 provides topographical maps for the effect of Orthography and Phonology in all three time windows (125–175, 175– 250, and 250–350 ms). 3.2.1. Time window 125–175 ms The main effects of Orthography and Phonology were not significant (F(1, 21) = 2.72, MSe = 40.37, ns; F < 1, respectively). The interaction between Orthography and Phonology were significant (F(1, 21) = 5.24, MSe = 54.57, p < .05). Localization and Lateralization interacted with Orthography (F(2, 42) = 5.34, MSe = 5.09, p < .05; F(2, 42) = 4.43, MSe = 1.739, p < .05, respectively) but not Phonology (Localization: F(2, 42) = 2.68, MSe = 10.25, ns; Lateralization: F < 1). The three-way interaction was also significant (Localization: F(2, 42) = 3.26, MSe = 8.47, p < .08; Lateralization: F(2, 42) = 3.41, MSe = 1.55, p < .05). To explore the significant interactions, separate ANOVAs were run for the orthographic and phonological effects, which will be described in detail below. 3.2.1.1. Orthography. The grapheme- and phoneme-mismatch condition (OP; e.g. mine – KUNST) differed significantly from the grapheme-match condition (O+P; e.g. knee – KUNST; F(1, 21) = 6.89, MSe = 54.46, p < .05). The interaction with Localization showed a fronto-central distribution (F(2, 42) = 7.24, MSe = 7.52, p < .05). In the frontal and central regions, the OP condition had more negative amplitudes than the O+P condition (frontal: F(1, 21) = 16.16, MSe = 13.27, p < .005; central: F(1, 21) = 9.08, MSe = 24.03, p < .01). This difference was not significant in the posterior region (F < 1). The interaction with Lateralization showed a mid-right distribution (F(2, 42) = 6.79, MSe = 1.69, p < .005). The main effect of Orthography was marginally significant at the left hemisphere (F(1, 21) = 3.65, MSe = 19.91, p = .07). However, at midline and the right hemisphere the Orthography effects was significant (F(1, 21) = 8.79, MSe = 24.91, p < .01; F(1, 21) = 9.29, MSe = 19.64, p < .01, respectively). Thus, Orthography effect appears to have frontal–central distribution with focus in the right hemisphere. Further, the grapheme-mismatch condition (OP+; e.g. crime – KUNST) did not differ significantly from the grapheme- and phoneme-match condition (O+P+; kite – KUNST; F < 1). For this condition, there were no significant interactions with spatial factors (all Fs < 1).

Table 2 Mean response latencies in ms (and standard error) per condition over all participants.

Mean RT in ms (SD)

O+P+

O+P

OP+

OP

568 (11.67)

577 (11.66)

567 (11.65)

581 (11.66)

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Table 3 Estimated coefficients, standard error (SE), and t-value from mixed effects model for fixed and random effects. Predictor

Coefficient

SE

t-Value

Fixed effects Intercept Phonology Orthography Phonology  Orthography

2.75331 0.0084 0.0013 0.00514

0.0089 0.00185 0.00185 0.0037

309.25* 4.57* 0.72 1.39

Random effects Subjects Items Residual

0.00000000000001023 0.0002392 0.002793

* Bold values are significant at the 95% confidence level with t-values below -2 or above 2 (Gelman & Hill, 2007).

3.2.1.2. Phonology. The grapheme- and phoneme-mismatch condition (OP; e.g. mine – KUNST) did not differ from the phonemematch condition (OP+; e.g. crime – KUNST; F(1, 21) = 3.09, MSe = 31.25, ns). However, there was a significant interaction between Phonology and Localization (F(2, 42) = 4.12, MSe = 14.12, p < .05). The Phonology effect, with more negative deflections in the OP condition than the OP+ condition, was significant at frontal sites (F(1, 21) = 5.99, MSe = 15.36, p < .05). At central sites, the effect was marginally significant (F(1, 21) = 4.26, MSe = 18.39, p = 0.51; OP: 3.42 lV, SE = 0.78; OP+: 4.43 lV, SE = 0.68). This difference was not significant in the posterior region (F < 1). There were no significant interactions with Lateralization (F(1, 21) = 2.19,

MSe = 2.11, ns) showing a marginally significant main effect of Phonology (F(1, 21) = 4.06, MSe = 34.44, p = .057) throughout the brain. The phoneme-mismatch condition (O+P; e.g. knee – KUNST) did not differ significantly from the grapheme- and phonemematch condition (O+P+; e.g. kite – KUNST; F(1, 21) = 3.12, MSe = 63.79, ns). Nor was the interaction with Localization or Lateralization significant (both Fs < 1). 3.2.2. Time window 175–250 ms Neither the main effect for Orthography (F(1, 21) = 2.24, MSe = 38.04, ns) nor for Phonology (F < 1) were significant. There was no significant interaction between Orthography and Localization (F < 1). However, the interaction between Phonology and Localization was significant (F(2, 42) = 4.12, MSe = 12.37, p < .05). There was also a significant interaction between Orthography and Phonology (F(1, 21) = 6.62, MSe = 62.23, p < .05). The three way interaction was not significant (F < 1). The interactions with Lateralization were as follows: Orthography by Lateralization: F(1, 21) = 4.47, MSe = 1.82, p < 0.5; Phonology by Lateralization: F(1, 21) = 2.41, MSe = 2.45, ns; Orthography by Phonology by Lateralization: F(2, 42) = 1.47, MSe = 1.78, ns. To explore the significant interactions, separate ANOVAs were run for the orthographic and phonological effects, which will be described in detail below. 3.2.2.1. Orthography. The grapheme- and phoneme-mismatch condition (OP; e.g. mine – KUNST) differed significantly from the grapheme-match condition (O+P; e.g. knee – KUNST;

Fig. 1. Averaged stimulus-locked ERP waveforms displaying orthographic and phonological priming with grapheme- and phoneme match (O+P+; solid black lines; e.g. kite – KUNST), grapheme match (O+P; dashed black lines; e.g. knee – KUNST), phoneme match (OP+; solid gray lines; e.g. crime – KUNST), and grapheme- and phoneme mismatch (OP; dashed gray lines; e.g. mine – KUNST).

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Fig. 2. Averaged lV amplitudes for the two orthographic priming comparisons (e.g. (1) OP: mine – KUNST vs. O+P: knee – KUNST; (2) O+P+: kite – KUNST vs. OP+: crime – KUNST) for the three time windows per brain region (frontal–central–posterior) with error bars included.

F(1, 21) = 7.06, MSe = 61.70, p < .05). This effect did not interact with spatial factors (Localization: F < 1; Lateralization: F(2, 42) = 3.13, MSe = 1.51, ns). In contrast, the grapheme-mismatch condition (OP+; e.g. crime – KUNST; 4.12 lV, SE = 0.65) did not differ significantly from the grapheme- and phoneme-match condition (O+P+; kite – KUNST; 3.61 lV, SE = 0.70; F(1, 21) = 1.59, MSe = 38.57, ns). Neither was there a significant interaction between Orthography and Localization (F < 1) or Lateralization (F(2, 42) = 3.02, MSe = 2.18, ns).

3.2.2.2. Phonology. The grapheme- and phoneme-mismatch condition (OP; e.g. mine – KUNST) was significantly different from the phoneme-match condition (OP+; e.g. crime – KUNST; F(1, 21) = 6.72, MSe = 37.16, p < .05). There was no significant interaction between Phonology and Localization (F(2, 42) = 1.72, MSe = 14.88, ns). However, the interaction between Phonology and Lateralization was significant (F(2, 42) = 3.65, MSe = 1.91, p < .05). The Phonology effect was stronger at the left hemisphere (F(1, 21) = 6.81, MSe = 14.82, p < .05) and at midline (F(1, 21) = 10.28, MSe = 16.89, p < .05) compared to the right region (F(1, 21) = 3.96, MSe = 15.92, p = 0.6). In contrast, the phoneme-mismatch (O+P; e.g. knee – KUNST) and the grapheme- and phoneme match condition (O+P+; e.g. kite – KUNST) did not significantly differ from each other (F(1, 21) = 1.89, MSe = 88.14, ns). However, Phonology did interact with Localization (F(2, 42) = 3.74, MSe = 8.14, p = .05). The Phonology effect had a posterior distribution with more positive amplitudes for the O+P than the O+P+ condition in the posterior region (frontal and central: both Fs < 1; posterior: F(1, 24) = 6.12,

MSe = 26.05, p < .05). The interaction with Lateralization was not significant (F < 1). 3.2.3. Time window 250–350 ms The main effect of Orthography was marginally significant (F(1, 21) = 3.83, MSe = 38.10, p = .064). The main effect of Phonology was not significant (F < 1). Further, Orthography interacted with Localization (F(2, 42) = 12.25, MSe = 4.53, p < .001) and Lateralization (F(2, 42) = 3.47, MSe = 1.38, p < .05). The remaining interactions with spatial factors did not reach significance (Phonology by Localization: F(2, 42) = 1.15, MSe = 21.89, ns.; all other Fs < 1). To explore the interaction between Orthography and Localization/Lateralization, separate ANOVAs were run for each brain region. The Orthography effect had a frontal–central distribution with more negative amplitudes for the grapheme-mismatch than the grapheme-match conditions (frontal: F(1, 21) = 9.92, MSe = 15.27, p < .005; central: F(1, 21) = 5.06, MSe = 14.63, p < .05; posterior: F < 1). In addition, the Orthography effects were more localized towards the right hemisphere than the left one (left: F(1, 21) = 1.36, MSe = 17.93, ns.; mid: F(1, 21) = 4.89, MSe = 15.07, p < .05; right: F(1, 21) = 6.78, MSe = 11.15, p < .05). 4. Discussion The current study investigated whether the second language (L2) phonology can be activated during first language (L1) word production. The behavioral results revealed that Dutch native (L1) speakers of English (L2) read aloud Dutch target words (e.g. KUNST) faster when they were preceded by English prime words in the phoneme-match conditions (O+P+: kite; OP+: crime) com-

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Fig. 3. Averaged lV amplitudes for the two phonological priming comparisons (e.g. (1) OP: mine – KUNST vs. OP+: crime – KUNST; (2) O+P+: kite – KUNST vs. O+P: knee – KUNST) for the three time windows per brain region (frontal–central–posterior) with error bars included.

pared to the phoneme-mismatch conditions (OP: mine; O+P: knee). In contrast, there was no difference in reading aloud latencies between the grapheme-match (O+P+: kite; O+P: knee) and the grapheme-mismatch condition (OP: mine; OP+: crime). These cross-language behavioral results are in agreement with the previous within-language behavioral research showing that the MOPE is a phonological and not an orthographic effect (Mousikou et al., 2010; Schiller, 2007; Timmer & Schiller, 2012; Timmer et al., 2012). The present results demonstrated that the English (L2) phonology is also activated in Dutch participants even when the participants are in their native monolingual environment. Note that all of our participants reported to be unaware of the primes and thought that the experiment involved only L1 words. The critical phonological comparison is O+P+ (kite – KUNST) vs. O+P (knee – KUNST) where GPC deviates for the grapheme hkni depending on the language. As the primes were presented subconsciously, one could argue that participants could process the English primes as Dutch non-words. For instance, if our participants did not know any English, all primes would be read as non-words. In this situation, three primes start with the same sound in Dutch (e.g. kettle, knife, curve) and the fourth differs (e.g. bush). Therefore, no differences between the three primes are expected. However, we see that both the O+P+ condition (e.g. kettle) and the OP+ condition (e.g. curve) showed faster RTs than the O+P condition (e.g. knife; respectively, F(1, 1565) = 4.93, p < .05; F(1, 1581) = 6.80, p < .01). This is in line with L2 phonological activation mismatching the phonology between prime and target in the O+P condition (e.g. knife – KUNST). Our results suggest rapid activation of the phonology of L2 primes regardless of the fact that the L1 GPC did not

always follow the L2 GPC. This suggests that both L1 and L2 GPC were active simultaneously. We run additional analyses to collaborate this statement, which we outline below. Among the fillers, there were 20 prime-target pairs starting with an hfi or hji. The pairs were formed similar to experimental conditions (see Appendix B). The OP+ condition (e.g. phone – FIETS) revealed faster RTs than the OP condition (e.g. pain – FIETS; F(1, 816) = 5.65, p < .05). The only way in which this facilitation effect can be explained is by activation of the L2 phonology for the prime (e.g. hphi in phone is pronounced as /f/) matching the target FIETS. Thus, in accordance with our main analysis this shows that a prime like knee can produce a MOPE for the naming of a Dutch word like KUNST. In contrast to the behavioral results, the electrophysiological data revealed a significant orthographic priming effect starting 125 ms and continuing up to 350 ms after target onset. This corresponds to the N250 which has been associated with the GPC process in visual word recognition (Grainger & Holcomb, 2009). Within-language masked priming studies show the timing of the N250 between 150 and 250 ms after word presentation in visual word recognition (e.g. Carreiras et al., 2009; Dufau, Grainger, & Holcomb, 2008; Grainger, Kiyonaga, & Holcomb, 2006; Grainger & Holcomb, 2009; Holcomb & Grainger, 2006; Midgley, Holcomb, & Grainger, 2009) and between 120 and 280 ms for reading aloud (Timmer & Schiller, 2012; Timmer et al., 2013). Phonological priming, in the present study, also started 125 ms after target onset and continued up to 250 ms (i.e. 125–175 ms, and 175–250 ms time windows). Previous lexical decision literature suggested a later time course for phonology (i.e. 350– 550 ms) when a similar masked priming paradigm as in the pres-

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Fig. 4. Averaged lV amplitudes for the two orthographic priming comparisons (e.g. (1) OP: mine – KUNST vs. O+P: knee – KUNST; (2) O+P+: kite – KUNST vs. OP+: crime – KUNST) for the three time windows per brain region (left–mid–right) with error bars included.

ent study was used (Carreiras et al., 2009). The later activation of phonology during a lexical decision task compared to a reading aloud task might be attributed to the importance of phonological activation for correct execution of the latter task. Reading aloud tasks in different languages (i.e. Persian: 80–160 ms; Timmer et al., 2012; English for native speakers of Dutch: 120–180 and 180–280 ms; Timmer & Schiller, 2012) indeed demonstrated an earlier time course of phonological activation similar to the effects shown in the present study. Further, as for the behavioral results, we run an additional analysis on filler trials. The phonological comparison in the fillers (e.g. OP+: phone – FIETS – OP: pain – FIETS) show that the L2 phonology /f/ from phone can influence the L1 phonology /f/ from FIETS. The 125–175 ms time window revealed no effect of Phonology (F(1, 21) = 3.31, MSe = 89.02, ns) but it revealed a significant interaction with Localization (F(2, 42) = 7.91, MSe = 12.11, p < .005). The frontal and central regions revealed a difference between the OP+ and the OP conditions (respectively, F(1, 21) = 6.59, MSe = 37.43, p < .05; F(1, 21) = 4.48, MSe = 46.22, p < .05) which was not present in the posterior region (F < 1). This phonological priming effect did not show in the 175–250 ms time window (Phonology: F(1, 21) = 1.50, MSe = 111.23, ns; Phonology * Localization: F < 1) but was significant again in the 250– 350 ms time window (F(1, 21) = 9.60, MSe = 179.47, p < .005) over the entire scalp (Phonology by Localization: F(2, 42) = 2.59, MSe = 18.20, ns). This phonological comparison gives additional evidence for sub-lexical activation of the L2 phonology. To summarize, the ERP results reveal that the time course of reading aloud in cross-language priming is very similar to that of within-language priming. These results are in agreement with rapid

sub-lexical orthographic and phonological activation of the L2 primes without a possible lexical feedback loop as could have been the case for cognate and interlingual (pseudo)homophone priming investigated in the L2 to L1 visual word recognition literature (Duyck, 2005; Van Wijnendaele & Brysbaert, 2002). We suggest that both the phonology of the L1 and L2 that belong to one grapheme (e.g. /n/ and /kn/ for hkni) are activated rapidly. The phonology that matches the language of the word (e.g. knife, /n/) will quickly dominate the phonology that does not match an existing word (e.g. /k/). To explain the present cross-linguistic phonological priming effect, models of reading (aloud) should be adapted to include L2 GPC simultaneously with the L1 GPC. The BIA+ model assumes that the sub-lexical orthographic and phonological representations become activated very rapidly for both the L1 and L2 GPC. The DRC, CDP++, and triangle model cannot explain our MOPE results in their current form. In fact, the DRC model has problems accounting for the MOPE results even in one language settings. For instance, DRC cannot account for the finding that unpronounceable strings of consonant do not facilitate reading aloud a target word matching in onset (Dimitropoulou, Duñabeitia, & Carreiras, 2010). Thus, current computational models should be adapted to achieve a better goodness of fit between the models and MOPE data. Such adjustments could possibly be achieved with a simple extension. The models mentioned above all deal differently with the GPC process. These will be discussed separately below. Within the DRC+ model, GPC takes place in the non-lexical route, as opposed to the lexical route. GPC is rule based, with regular graphemes being matched to their corresponding phonemes (e.g. hki is pronounced as /k/) and more complex rules for graphemes that are, for example, context-sensitive (e.g. hci is pro-

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Fig. 5. Averaged lV amplitudes for the two phonological priming comparisons (e.g. (1) OP: mine – KUNST vs. OP+: crime – KUNST; (2) O+P+: kite – KUNST vs. O+P: knee – KUNST) for the three time windows per brain region (left-mid-right) with error bars included.

Fig. 6. Topographic maps of the difference waves are shown for the three time-windows analyzed (125–175, 175–250, and 250–350).

nounced as an /s/ when the following letter is a front vowel such as /e, i, y/ and pronounced as a /k/ when the following letter is a back vowel such as /o/ or /a/) and graphemes that consist of multiple letters (i.e. at the onset of words, the grapheme hki followed by

an hni corresponds to a single phoneme /n/ instead of two phonemes). These rules help us to read words like CARPET and KNEE correctly and are seen as regular GPC (Coltheart et al., 2001; Mousikou et al., 2010). Thus, when the GPC of the L2 contradicts the GPC

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of L1 (e.g. hkni pronounced as /k/ in Dutch and /n/ in English) new rules should be added to accommodate multiple print-to-soundassociation for bilinguals. Models could learn to select the corresponding phonology based on the language context (e.g. the language of the word) gives hkni the pronunciation of /k/ in KNOOP (a Dutch word) and /n/ in KNEE (an English word) in a similar fashion as is true for multiple print-to-sound associations in the L1. In addition, words from both languages should be included (in the lexical route) as is the case for the bilingual lexicon of the BIA+ model (Dijkstra & Van Heuven, 1998; Van Wijnendaele & Brysbaert, 2002). The CDP++ and the triangle models train GPC however, the nature of training is varied. In the CDP++ model both the input (written word) and output (phonological word) are given during the training phase. The running phase, reflecting reading aloud, is used to adjust the input representation by placing the graphemes in the positions that best represents the phonology (e.g. longer graphemes are preferred over shorter: hkni of hki to read knife; context sensitivity for hci in carpet; Perry et al., 2007, 2010). In the triangle model, during a training period, orthographic input units are converted to phonological output units through hidden units. After the conversion of each word, the phonological output is compared to the phonological target word. Based on error for the output units, weights are updated to reduce the error (Harm & Seidenberg, 1999, 2004). To accommodate for L2 GPC deviating from L1 GPC, the CDP++ and triangle model could be trained on words from the second language in addition to the first language. In the CDP++ model, these grapheme–phoneme correspondences will be present through rule-like behavior. For the triangle model, this happens through the adjustment of weights and for the DRC model, L2 GPC should be added to the non-lexical route when they deviate from L1 GPC. The present results give way to future endeavors for computational specialists to investigate the detailed implications for their models. To conclude, we suggest that words with multiple print-tosound associations in two languages (e.g. hkni as /n/ in (L2) knife and /k/ in (L1) knoop ‘button’) will be handled in a similar manner as words with multiple print-to-sound associations in a single language (e.g. hci as /s/ in cement or /k/ in canvas). This is explained within the network architecture learning procedure for regular words and not irregular words. The MOPE is present for regular words but absent for irregular words (Forster & Davis, 1991; Kinoshita & Woollams, 2002; Mousikou et al., 2010). As the present data could function in a similar way to regular GPC, priming effects are not expected to diminish. An additional point of discussion is that we investigated orthographic and phonological priming by means of two comparisons for each effect (i.e. orthography: (1) OP: mine – KUNST vs. O+P: knee – KUNST; (2) O+P+: kite – KUNST vs. OP+: crime – KUNST; phonology: (1) OP: mine – KUNST vs. OP+: crime – KUNST; (2) O+P+: kite – KUNST vs. O+P: knee – KUNST). The RTs reveal the same effects for both comparisons within orthographic (no priming) and phonological (priming) priming as was true for other RT literature that used a similar design (Mousikou et al., 2010; Schiller, 2007). The ERPs revealed that the two comparisons within our priming effects had similar patterns, but the orthographic comparison in the phonological unrelated context (i.e. OP: mine – KUNST vs. O+P: knee – KUNST) reached significance in earlier time windows compared to the orthographic comparison in the phonological related context; (i.e. O+P+: kite – KUNST vs. OP+: crime – KUNST; see Figs. 2 and 4). The same holds for the phonological comparison: priming is reflected earlier in the orthographic unrelated context (i.e. OP: mine – KUNST vs. OP+: crime – KUNST) than the orthographic related context (i.e. O+P+: kite – KUNST vs. O+P: knee – KUNST; see Figs. 3 and 5). A post hoc explanation for the order of

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activation of the comparisons could have to do with the context in which one looks at orthographic or phonological priming. In other words, when we look at orthographic priming (i.e. O vs. O+), phonology is controlled (i.e. P or P+). The orthographic comparison in which the phonological context is incongruent (OP vs. O+P) takes place before the orthographic comparison in which the phonological context is congruent (O+P+ vs. OP+). Vice versa, phonological priming (i.e. P vs. P+) also demonstrates an earlier effect when the orthographic context is incongruent (OP vs. OP+) compared to congruent (O+P+ vs. O+P). Alternatively, the order of orthographic comparisons could be due to onset ambiguity. The first comparison contains unambiguous onset graphemes (e.g. O+P: knee hkni in English always corresponds to the phoneme /n/). The second comparison contains ambiguous onset graphemes (e.g. OP+: crime hci has multiple print-to-sound-associations, /k/ or /s/). This ambiguity may cause slower processing of the grapheme. Another English word reading study compared (un)ambiguous onsets in the target and not the prime words (Timmer & Schiller, 2012). They found orthographic priming in the frontal region, 180–280 ms time window, for target words with ambiguous onsets (e.g. GENIUS) but not unambiguous onsets (e.g. PHOBIC). Similarly, the order of phonological comparisons could be explained by multiple print-to-sound-associations. The first comparison has multiple print-to-sound-associations in English; however, the second comparison has multiple print-to-sound-associations between languages (e.g. O+P: knee hkni pronounced as /k/ in Dutch and /n/ in English). This may take additional processing time. English–Spanish cognates with high orthographic and low phonological similarity between the languages (e.g. base in English /beIs/ and in Spanish /base/) demonstrated slower RTs and higher error rates. This suggests that differential phonological representations belonging to similar orthography slow down feed-forward processing (Schwartz, Kroll, & Diaz, 2007). Thus, it is possible that multiple print-to-sound associations within a language take more processing time during orthographic priming, and that multiple print-to-sound associations between languages take more processing time during phonological priming. Though these data only give a first glance at the effect of context and multiple-print-to-sound associations (in L1 or/and L2) this could have potential influence for computational models. For example, the DRC model has a specific order of picking the rule that is applied is as follows: (1) context-sensitive rules (i.e. hci as /s/ or hki); (2) two-letter grapheme rules (i.e. hpi followed by hhi is /f/ and not /p/); (3) single-letter rules (i.e. hmi as /m/) (see pp. 216–217 of Coltheart et al., 2001 for a more detailed explanation). The present data might suggest that the order of rules is different as orthographic priming for hkni in knife took place before hci in carpet. For the CDP++ and triangle model it might also be necessary to incorporate a slight delay in timing (i.e. additional cycle) for graphemes with multiple print-to-sound associations in a single language (e.g. hci as /s/ or /k/) and a bit longer delay for multiple print-to-sound associations in a bilingual context (e.g. hkni as /n/ or /k/). Note that future research is necessary to investigate the congruence of the context into account when looking at the time course of grapheme-to-phoneme conversion to help models take context and phonological ambiguity into account. To conclude, the conversion of graphemes into phonemes in the present cross-language priming task takes place between 125 and 350 ms after target word presentation and can be assumed to be rapid, automatic, and sub-lexical for both L1 and L2 phonology. Acknowledgment The authors would like to thank Cari Bogulski for her help with the mixed-effects model analysis in R.

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Appendix A. Stimulus materials

Target

KNAAP (‘boy’) KNAL (‘bang’) KNIE (‘knee’) KNIK (‘knod’) KNIKKER (‘marble’) KNOBBEL (‘knack’) KNOL (‘tuber’) KNOOP (‘button’) KNOP (‘handle) KNUL (‘fellow’) KAAK (‘cheek’) KARPER (‘carp’) KEGEL (‘cone’) KEIZER (‘emperor’) KELK (‘glass’) KIEM (‘germ’) KOEK (‘cookie’) KOGEL (‘bullet’) KRUL (‘curl’) KUNST (‘art’) SABEL (‘sword’) SATIJN (‘satin’) SEIN (‘sign’) SERVET (‘napkin’) SFEER (‘atmosphere’) SIGAAR (‘cigar’) SINT (‘saint’) SIROOP (‘syrup’) SKELET (‘skeleton’) SLAG (‘hit’) SLIJK (‘mud’) SMID (‘blacksmith’) SNAAR (‘string’) SOEP (‘soup’) SPATEL (‘spatula’) STOK (‘stick’) STUNT (‘stunt’) STUUR (‘wheel’) SUIKER (‘sugar’) SUKKEL (‘simpleton’)

Stimulus materials for the additional analysis in the discussion (continued)

Primes O+P+

O+P

OP+

OP

kettle kite kelp kill kernel kitten kink king kiss kick king kernel kidney kitten kiss kelp kick kettle kiln kite senior socket sore spider sink

knife knob knot knave knobble knuckle knit knight knee knack knob knuckle knock knobble knot knife knight knave knit knee shovel shoulder shin shower shock

Curve Cult Code Card Cotton Cable Cage Calf coast cash corn corner carcass collar court cash camp cable cave crime cigar censor cyst circuit cite

bush dust wave Tart rebel mirror mill laugh lack wasp lock purple garlic marble doze barn rash devil track mine merit poker pork bundle boat

sorrel soap saddle sailor sieve saint soil speed sack suburb seal spoon sauce secret salad

shadow shaft shelter shuttle shell shark shred ship shame shrimp shirt shield sheep shortage shepherd

cypress cellar censure cipher cell cereal cedar circle circus cervix center cider cement ceiling cycle

motor fork mango banquet nose dump lark nerve list needle pile mite trace glory trolley

Appendix B. Stimulus materials for the additional analysis in the discussion

Target

FIETS (‘bike’) FOOI (‘tip’) FUIF (‘party’) FLUIT (‘flute’) FEEST (‘celebration’) FAZANT (‘pheasant’)

Primes OP+

OP

phone phase phrase phantom photo phobia

pain leak grail dove route termite

Target

FAKKEL (‘torch’) FIGUUR (‘figure’) FONTEIN (‘fountain’) FABRIEK (‘factory’) JUF (‘teacher’) JURK (‘dress’) JUDO (‘judo’) JONG (‘young’) JEUK (‘itch’) JICHT (‘gout’) JACHT (‘hunting’) JEUGD (‘youth’) JOCHIE (‘sonny boy’) JUWEEL (‘jewel’)

Primes OP+

OP

phoenix pheasant pharaoh physics yawn yarn yell yam yellow yolk youth yard yoyo yield

nuzzle partner buttock minor race bowl lime height pall dart mess tone gallon rival

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Second language phonology influences first language word naming.

The Masked Onset Priming Effect (MOPE) has been reported in speakers' first languages (L1). The aims of the present study are to investigate whether s...
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