# Morphological Complexity Modulates Morphological Processing: Evidence from Cross-Language Embedded Stem Priming in French-English Bilinguals

Hasibe Kahraman, Severine Casalis, Ludivine Javourey-Drevet, Amélie Menut, Bianca de Wit, Elisabeth Beyersmann

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Hasibe Kahraman, Severine Casalis, Ludivine Javourey-Drevet, Amélie Menut, Bianca de Wit, et al.. Morphological Complexity Modulates Morphological Processing: Evidence from Cross-Language Embedded Stem Priming in French-English Bilinguals. Scientific Studies of Reading, 2025, Scientific Studies of Reading, pp.1-19. 10.1080/10888438.2025.2462826. hal-04987435

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# Morphological Complexity Modulates Morphological Processing: Evidence from Cross-Language Embedded Stem Priming in French-English Bilinguals

Hasibe Kahraman, Séverine Casalis, Ludivine Javourey-Drevet, Amélie Menut, Bianca de Wit & Elisabeth Beyersmann

**To cite this article:** Hasibe Kahraman, Séverine Casalis, Ludivine Javourey-Drevet, Amélie Menut, Bianca de Wit & Elisabeth Beyersmann () Morphological Complexity Modulates Morphological Processing: Evidence from Cross-Language Embedded Stem Priming in French-English Bilinguals, Scientific Studies of Reading, ahead-of-print:ahead-of-print, 1-19, DOI: 10.1080/10888438.2025.2462826

**To link to this article:** https://doi.org/10.1080/10888438.2025.2462826

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# Morphological Complexity Modulates Morphological Processing: Evidence from Cross-Language Embedded Stem Priming in French-English Bilinguals

Hasibe Kahraman<sup> ID a </sup>, Séverine Casalis<sup> ID b </sup>, Ludivine Javourey-Drevet<sup> ID b </sup>, Amélie Menut<sup> ID b </sup>, Bianca de Wit<sup> ID a </sup>, and Elisabeth Beyersmann<sup> ID a </sup>

<sup>a</sup>School of Psychological Sciences, Macquarie University; <sup>b</sup> Univ. Lille, CNRS, UMR 9193 - SCALab - Sciences Cognitives et Sciences Affectives, Lille, France

## ABSTRACT

**Purpose:** The dynamic interaction between the two languages of a bilingual (L2 reader is a well-documented phenomenon in psycholinguistic literature on L2 processing). However, the effects of morphological complexity and orthographic transparency on cross-language transfer between similar-script languages remain unclear. The present study intended to address this question using a cross-language complex nonword priming paradigm in a lexical decision task.

**Methods:** In a lexical decision task, 101 late L1 French-L2 English bilinguals (42 females, age: M = 31.4, SD = 8.4, range = 18–50) responded to English stem targets (e.g. TREE) preceded by three types of L1 French stimuli: affixed nonwords (e.g. arbreur [treeness], non-affixed nonwords (e.g. arbrux [treew]), or unrelated nonwords (e.g. mondese [worldew]). Participants additionally took the English LexTale test and completed a short version of the Language Experience and Proficiency Questionnaire.

**Results:** The results revealed significant cross-language embedded stem priming relative to an unrelated condition. A comparison of the present data with that of the speakers of Turkish, a highly morphological complex agglutinative system, showed that the morphological complexity of bilinguals' L1 indeed modulates cross-language morphological priming, which was entirely absent in French, a less morphologically complex system.

**Conclusion:** This study confirms the presence of cross-language embedded word priming effects in similar-script bilinguals. It also provides the first evidence that cross-linguistic factors impact bilinguals' ability to process and identify affixes in reading, using cross-language stimuli.

Numerous studies have provided compelling evidence that morphemes, the smallest units of meaning, impact how morphologically complex words such as “departure” or “farmer” are organized and processed within the mental lexicon (e.g., Grainger & Beyersmann, 2017; Jared et al., 2017; Taft, 1979). These fundamental linguistic building blocks not only contribute to the existing lexical knowledge but also facilitate the acquisition of novel vocabulary (e.g., *Tiktoker*). A key piece of experimental evidence supporting this notion comes from masked primed lexical decision, showing that the visual recognition of stem targets (e.g., DEPART) is facilitated by the brief prior exposure to a morphologically complex word prime (e.g., *departure*). This finding has been reported in numerous masked priming studies in both first-

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language (L1) and second-language (L2) studies as well as cross-language studies (e.g., Dal Maso & Giraudo, 2014; Kim et al., 2015; Rastle & Davis, 2008). Critically, morphological priming effects have been shown to persist even when the morphological structure is superficial or violates linguistic rules (e.g., Beyersmann & Grainger, 2023; Rastle & Davis, 2008). That is, lexical decisions to the target “DEPART” are *equally* facilitated by a semantically transparent prime (e.g., “*departure*” where “*depart*” reflects the overall meaning), a semantically opaque prime (e.g., “*department*” where “*depart*” does not contribute to the overall meaning), relative to an unrelated prime (e.g., * creature ). The aim of the current study was to build on these prior findings by examining differences in cross-language morphological processing within French (L1) – English (L2) and Turkish (L1) – English (L2) bilinguals.

Cross-language morphological processing in bilinguals has been previously studied using cross-
language morphological priming, where a morphologically complex L1 prime is typically followed by
an L2 target (e.g., *coureur* [runner]<sup>1</sup> - RUN). For example, Ko and Wang (2015) found that briefly
presented constituent morpheme primes in L1 Korean significantly enhanced responses to their L2
English compound translations, for both semantically transparent and opaque targets . Similarly,
cross-language masked morphological priming effects have been reported in L1 Spanish – L2 English
bilinguals when English targets were preceded by either morphologically related or unrelated masked
Spanish primes (Duñabeitia et al., 2013), particularly with cognate prime-target pairs that shared
similar letters. In another study, L1 Chinese – L2 English bilinguals made lexical decisions on English
targets that were preceded by either morpho-semantically related or unrelated masked Chinese
compound primes, showing significant cross-language masked morphological priming, irrespective
of semantic relatedness (Wang et al., 2021). However, while these studies provide evidence for cross-
language morphological priming, questions remain regarding the modulating role of cross-linguistic
characteristics in cross-language L1 to L2 priming effects. The present study aimed to address this gap
by directly comparing two different groups of L2 English bilinguals, French-English and Turkish-
English bilinguals, who had an L1 that differed in two important dimensions: orthographic transpar-
ency and morphological complexity.

Morphological complexity and orthographic transparency across languages

Morphological complexity and orthographic transparency are two key concepts that relate to the
structure and internal representation of lexical knowledge. These concepts vary independently across
languages and influence the formation and recognition of lexical knowledge. Cross-linguistic differ-
ences in orthographic transparency and morphological complexity have been found to modulate
morphological processing in L1 (Beyersmann et al., 2020; De Simone et al., 2024; Mousikou et al.,
2020), but little is known regarding their role in cross-language transfer. In this section, we explain and
illustrate these concepts with examples.

Morphological complexity refers to the degree of variation in word formation from smaller units,
such as stems and affixes. Some languages have high morphological complexity, meaning that they use
many suffixes or prefixes to modify the meaning and function of words. Turkish, for example,
characterized by high morphological complexity, is an agglutinative language where complex words
are formed by adding multiple suffixes to a stem, each carrying a distinct grammatical or semantic
function. For instance, the word “evlerimizdeki” translates to “*in our houses*” and comprises the stem
“ev [house],” the plural suffix “_ler[_s],” the possessive suffix “_imiz [our],” the locative suffix “_de
[in],” and the determiner suffix “_ki [the].” The language has 199 suffixes, 142 of which are deriva-
tional and are used to create new words. Word structures are formed by applying productive affixes to
the stem words. This process yields words that are exceptionally long and sometimes equivalent to
entire sentences in English, such as “*Avustralyalılaştıramadıklarımızdan mısınız*,” which has
a definition of ‘*Are you one of those people whom we could not make to be Australian?*” This places
Turkish at the higher end of the morphological complexity spectrum.
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Other languages are characterized as less morphologically rich, meaning that they use fewer suffixes or prefixes to modify words. For example, English is considered less morphologically complex than French, and French less complex than Turkish. Within the context of derivational morphology, French, having a higher number of productive word forms and affixes than English, depends more heavily on morphological affixation for word formation. Sadeniemi et al. (2008) computed the average number of morphemes per word in various European languages to create a ranking based on morphological complexity. Their findings indicate that English has the least complex morphology, followed by French, with Finnish and Estonian exhibiting the richest morphology among all languages.² Further attempts at quantifying morphological complexity across languages used three morphological complexity methods of Linguistica (Bane, 2008), Juola (1998, 2008), and Type-token ratio (Kettunen, 2014). While all three methods agree on the ranking order of the English and French, Linguistica indicates that English has the least complex morphology (16.9%), followed by French (23.1%), which falls into the category of moderately complex languages (see Borleffs et al., 2017 for review of these methods in greater detail). As a result, within the scope of morphological complexity, French morphology is considered more complex compared to English, while Turkish is more complex than both languages.

Another dimension on which French and Turkish differ is orthographic transparency. Orthographic transparency refers to the consistency and simplicity of the mapping between written symbols (i.e., graphemes) and spoken sounds (i.e., phonemes). Turkish, a member of the Turkic family of Altaic languages, employs a phonemic spelling system where there is a one-to-one correspondence between letters and sounds. Due to its highly consistent and regular orthography, Turkish words can be easily pronounced from their written form, and vice versa, rendering Turkish highly orthographical transparency. In contrast, the orthography of the French language is less transparent. A single grapheme may represent different phonemes depending on the word or context, and conversely, a single phoneme can be represented by different graphemes. For instance, the letter “e” can be pronounced as /e/, /ɛ/, /θ/, or it may be silent, contingent on the word. Similarly, the sound /e/ can be spelled as “e,” “é,” “è,” “ê,” depending on the word and the letters that follow. English, on the other hand, is considered to have less orthographic transparency compared to French and Turkish due to its complex and irregular spelling system. English has numerous exceptions to phoneme-grapheme correspondence rules, making it less predictable. For example, the letter “a” in “cat”, “care”, and “cake” each represents a different sound.

What remains unclear is if and how cross-linguistic differences in morphological complexity and orthographic transparency modulate cross-language morphological processing. Prior research has shown a positive correlation between the morphological complexity of a language and the ability to segment morphologically complex words into morphemes in L1 (Beyersmann et al., 2020, 2021; Casalis et al., 2015). For example, Beyersmann et al. (2020), using an unprimed lexical decision task, found that German speakers, whose language has higher morphological complexity than French, showed larger morphological effects compared to French speakers in an unprimed lexical decision task. This suggests that morphological segmentation is more prominent in languages with more complex word structures. On the other hand, Mousikou et al. (2020) and De Simone et al. (2024) showed that the use of morphology depends on the orthographic transparency of a language. In their reading aloud study Mousikou et al. compared morphological processing across English, French, German, and Italian, and found that English readers, whose language has lower orthographic transparency, showed stronger morphological effects. Likewise, de Simone et al. used an unprimed lexical decision task to compare combinations of real and nonword stems and suffixes in English and Italian speakers. Their results indicated that morphological effects were more pronounced in English readers, replicating Mousikou et al.’s findings in a lexical decision task. Consistent with the Psychological Grain Size Theory (e.g., Goswami, 2008, 2010; Ziegler & Goswami, 2005), these results suggest that morphological units may help to resolve the ambiguity of the spelling-sound relations in deep orthographies such as English, hence yield stronger effects in languages with less consistent mapping between letters and sounds. Conducted within L1, these studies thus raise the question of whether cross-linguistic differences in morphological complexity or orthographic transparency within L1 may influence cross-language morphological processing in L2.
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## Theoretical models of morphological processing within- and across-languages

The largest part of theoretical groundwork of morphological processing in reading is based on evidence from first-language speakers. Given that bilingual models of morphological processing are built on the theoretical reasoning of frameworks from L1, we begin with a summary of models of within-language morphological processing. Two lines of theoretical reasoning exist that explain the similar priming observed with semantically transparent and opaque stimuli. According to the localized “decompositional” models, the initial stages of complex word recognition involve the identification of morphemic units, including stems and affixes, and their position specificity (e.g., Crepaldi et al., 2013; Spencer et al., 2023). Complex words are broken down into their constituent morphemes (e.g., *depart + ment*; *depart + ure*) before their meanings are retrieved from the mental lexicon. This process is hence pre-lexical and obligatory before any semantic information is available. In contrast, distributed models of morphological processing emphasize early influences of semantic information and suggest that priming from complex words results from the activation of their smaller units (e.g., letters or phonemes) that are tuned to the statistical regularities of the language. In this view, morphemes are not explicit lexical representations, but rather emergent properties of the morphological sensitivity, reflecting statistical patterns of the language (see Marelli et al., 2020 for a comprehensive review of alternative models on morphological processing). Despite the differences in their approach to morphemes, both models agree that morphological information is available and processed during the early, automatic stages of word recognition.

It is important to note that this distinction between localist and distributed models is separate from the role of statistical regularities in the formation of these representations (e.g., Stevens & Plaut, 2022). While distributed models inherently rely on statistical regularities, localist models also incorporate statistical information. For example, the SOLAR model of visual word recognition (Davis, 1999) demonstrates how localist representations can be influenced by statistical patterns during the learning and formation of morphemes (also see Verhoeven & Perfetti, 2022 for similar views). Additionally, decompositional models consider statistical regularities, acknowledging that several continuous variables, such as morphological family size, influence morphological processing. Conversely, there are distributed models that integrate decompositional aspects such as the FRACSS model (Marelli & Baroni, 2015).

Within the decompositional stream, the Word and Affix model (Beyersmann & Grainger, 2023) bridges theoretical understanding with empirical findings in the field of morphological processing. Again, while this model is grounded in L1 research, a recent extension to bilinguals has been developed (Kahraman & Beyersmann, 2023; Kahraman et al., 2023). This adaptation proposes the same three interconnected mechanisms for the recognition of a complex word that have been assumed within the original version of the Word and Affix model: morphological affix activation, non-morphological embedded word activation, and morpho-orthographic full decomposition. The first mechanism identifies and activates affixes within complex words (*_ment* in *department*; _ity_ in *farnity*; *re-* in *reuse*). The second mechanism extracts and activates stems embedded within words, regardless of their morphological status (e.g., *depart* in *department*, *departure*, or *departald*). Both information streams then proceed to the third mechanism of morpho-orthographic full decomposition which regulates the degree of inhibition between the active lexical representations, based on the combined length of active stems and affixes and their lexicality (i.e., facilitation for “*department*”, inhibition for “*departald*”).

Kahraman and Beyersmann (2023) reported that bilinguals gain simultaneous access to the lexical representations of both languages during word recognition, as hypothesized in the Bilingual Interactive Activation + model (BIA+ model; Dijkstra & van Heuven, 2002) and its implemented version, the Multilink model (Dijkstra et al., 2019). By building a bilingual version of the Word and Affix model, Kahraman and Beyersmann (2023) suggest that there is an automatic and unconscious activation of embedded words and affixes along with their translation equivalents in the other language.³ This model informs the present study's aim to examine the unique influences of
morphological complexity and orthographic transparency on cross-language morphological priming. Specifically, it provides a framework for understanding how bilinguals process affixed and non-affixed nonwords, highlighting the automatic activation of both morphological components and their translation equivalents. The model predicts that an affixed French nonword (e.g., *amouresse* [lovedom]) will activate the lexical representation of the embedded stem and its English translation equivalent (e.g., *amour* and *love*) as well as the embedded affix and its translation equivalent (e.g., _esse_ and _dom), while a non-affixed nonword (e.g., *amourugne* [lovedel]) will only activate its embedded word and translation equivalent (e.g., *amour* and *love*) in a lexical decision task. The model also predicts the greater cross-language priming with cognate prime-target pairs (e.g., *crystalline-cristalino* [crystaline]) compared to non-cognate pairs (*milky-leitoso* [milky]). This is based on an earlier finding by Comesaña et al. (2018) who conducted a masked priming lexical decision task that manipulated the cognateness of the L2 word, as well as the cognateness of its stems and affixes, in European Portuguese-English bilinguals. The findings revealed that priming increased with increasing cross-language overlap (cognateness), including for cognate suffixes compared to the L1 control group (see Chung et al., 2019 for electrophysiological evidence of cross-language morphological priming; also see Voga & Grainger, 2007; Wen & van Heuven, 2018 for behavioral evidence). This suggests that morphological priming is sensitive to cross-language similarities, but it is still unclear what the role of morphological complexity and orthographic transparency is in this process.

## Present study

To address this gap, the present lexical decision study was designed to investigate cross-language morphological priming in French-English bilinguals. Previous studies have shown that morphological priming, the facilitation of word recognition by a morphologically related word (or nonword) form, occurs across languages in bilingual speakers (e.g., Voga & Grainger, 2007; Wen & van Heuven, 2018). However, most of these studies have used complex words as primes, which may potentially introduce lexical competition effects (McClelland & Rumelhart, 1981). To overcome this limitation, the current study examined to which extent participants' lexical decisions to L2 English target words (e.g., TREE) are influenced by the prior presentation of French L1 morphologically complex nonword primes (e.g., *arbreur* [treeness]). We used the same paradigm as Kahraman et al. (Experiment 1; 2023), with English L2 target words (e.g., TREE) preceded by L1 French nonword primes that were either affixed (e.g., *arbreur* [treeness], non-affixed (e.g., *arbrux* [treew]), or unrelated (e.g., *mondese* [worldew]). If morphological information transfers across languages, significant embedded stem priming effects would be expected in the affixed and non-affixed nonword conditions compared to the unrelated control condition. Furthermore, if the presence of an affix enhances cross-language priming, we would expect significantly greater priming in the affixed nonword condition than in the non-affixed condition. Conversely, if the affix does not facilitate cross-language priming, no differences would be expected between the affixed and non-affixed conditions.

A second goal of the present study was to examine if cross-language morphological activation is influenced by morphological complexity or orthographic transparency of the L1 during reading, by directly comparing the current sample of French-English bilinguals against Kahraman et al.'s (2023) sample of Turkish-English bilinguals. To this end, the current data from French-English bilinguals were combined with Kahraman et al.'s (2023) data from Turkish-English bilinguals. Three alternative hypotheses explored the role of cross-linguistic differences in morphological complexity and orthographic transparency on cross-language morphological priming. First, if cross-language morphological transfer is modulated by L1 differences in morphological complexity, we would expect larger morphological priming effects in Turkish-English compared to French-English bilinguals. Second, if cross-language morphological transfer is modulated by L1 differences in orthographic transparency, we would expect larger morphological priming effects in French-English compared to Turkish-English bilinguals, because French-English bilinguals may be more reliant on the identification of sub-lexical morphemes. Third, if cross-language morphological transfer is robust and independent of L1
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differences in morphological complexity or orthographic transparency, the size of cross-language morphological priming should be comparable across the two speaker groups. Finally, an explanatory analysis of the role of individual differences in cross-language morphological priming was conducted. The hypotheses, along with the study's data analyses plan, were pre-registered (https://aspredicted.org/CDJ_JGQ).

# Methodology

## Participants

The required sample size was simulated for linear mixed effects models with slightly over 80% power to estimate study design power within the R environment (Version 2021.09.2, R Development Core Team, 2016) using the "simr" package. The "lme4" package was used for modeling, and the model was fitted using a competitive model testing approach and stored as the model for power analysis on inverse RTs. In line with the pre-registration, 101 French-English bilinguals (42 females, age: M = 31.4, SD = 8.4, range = 18–50) with advanced⁴ English proficiency participated in the Gorilla experiment (Anwyl-Irvine et al., 2019). Participants were recruited through the online platform Prolific (https://www.prolific.com) and received monetary reimbursement for their participation.

Based on participant responses to the language background questionnaire, the mean age at which English was acquired was 18.74, with first exposure to English at the age of 9.78. Their level of English proficiency in speaking, understanding, and reading on a 10-point scale was high (7.27, 8.20, 8.42, respectively). One participant who acquired English early in life were excluded prior to the analyses. The remaining bilingual participants were all late learners of English with a high level of English proficiency (see Table 1 for participant demographics).

## Materials

For the lexical decision task, 60 L2 English stems (e.g., FARM) were used as targets.⁵ The list of targets is presented in Appendix A. Targets were frequent (M = 4.8, SD = 0.6) and concrete (M = 4.5, SD = 0.5) non-cognate words with no pseudo-suffixes. Word concreteness were extracted from Brysbaert et al. (2014), and log10 frequencies (Zipf values) were calculated using the Lexique

<table><caption>Table 1. Participant demographics.</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2"></th><th rowspan="2">M</th><th colspan="2">FRENCH GROUP</th><th rowspan="2">M</th><th colspan="2">TURKISH GROUP</th></tr><tr><th>Range</th><th>max</th><th>Range</th><th>max</th></tr><tr><th colspan="2"></th><th colspan="2">(N = 101)</th><th colspan="2">(N = 72)</th><th colspan="2"></th></tr></thead><tbody><tr><td>Age</td><td></td><td><strong>31.4</strong></td><td>18–50</td><td></td><td><strong>30.7</strong></td><td>21–43</td><td></td><td></td></tr><tr><td>Number of females</td><td></td><td><strong>42</strong></td><td></td><td></td><td><strong>50</strong></td><td></td><td></td><td></td></tr><tr><td>Years<sup>13</sup></td><td>of education</td><td><strong>15.3</strong></td><td>1–30</td><td></td><td><strong>18.41</strong></td><td>5–24</td><td></td><td></td></tr><tr><td></td><td>Spent in an Eng.-speaking country</td><td><strong>2.59</strong></td><td>0–27</td><td></td><td><strong>3.25</strong></td><td>0–23</td><td></td><td></td></tr><tr><td rowspan="3">Age of</td><td>First contact with Eng.<sup>14</sup></td><td><strong>9.78</strong></td><td>4–15</td><td></td><td><strong>10.18</strong></td><td>3–22</td><td></td><td></td></tr><tr><td>First reading Eng.<sup>15</sup></td><td><strong>11.54</strong></td><td>4–20</td><td></td><td><strong>12.35</strong></td><td>6–25</td><td></td><td></td></tr><tr><td>Fluent reading Eng.<sup>16</sup></td><td><strong>16.98</strong></td><td>7–35</td><td></td><td><strong>16.32</strong></td><td>7–27</td><td></td><td></td></tr><tr><td rowspan="3">Level of Proficiency in<sup>17</sup></td><td>Speaking Eng.</td><td><strong>7.27</strong></td><td>1–10</td><td>10</td><td><strong>8.37</strong></td><td>5–10</td><td>10</td><td></td></tr><tr><td>Understanding spoken Eng.</td><td><strong>8.20</strong></td><td>4–10</td><td>10</td><td><strong>8.8</strong></td><td>5–10</td><td>10</td><td></td></tr><tr><td>Reading in Eng.</td><td><strong>8.42</strong></td><td>3–10</td><td>10</td><td><strong>8.98</strong></td><td>5–10</td><td>10</td><td></td></tr><tr><td rowspan="6">Current exposure to Eng. in<sup>18</sup></td><td>Interacting with friends</td><td><strong>4.57</strong></td><td>0–10</td><td>10</td><td><strong>7.12</strong></td><td>0–10</td><td>10</td><td></td></tr><tr><td>Interacting with family</td><td><strong>2.16</strong></td><td>0–10</td><td>10</td><td><strong>2.73</strong></td><td>0–10</td><td>10</td><td></td></tr><tr><td>Watching TV</td><td><strong>6.46</strong></td><td>0–10</td><td>10</td><td><strong>7.97</strong></td><td>0–10</td><td>10</td><td></td></tr><tr><td>Listening to radio/music</td><td><strong>7.26</strong></td><td>1–10</td><td>10</td><td><strong>7.89</strong></td><td>0–10</td><td>10</td><td></td></tr><tr><td>Reading</td><td><strong>6.30</strong></td><td>0–10</td><td>10</td><td><strong>8.16</strong></td><td>3–10</td><td>10</td><td></td></tr><tr><td>Language/Lab instruction</td><td><strong>4.95</strong></td><td>0–10</td><td>10</td><td><strong>5.5</strong></td><td>0–10</td><td>10</td><td></td></tr><tr><td rowspan="3">Eng.</td><td>AoA<sup>19</sup></td><td><strong>18.74</strong></td><td>12–44</td><td></td><td><strong>18.16</strong></td><td>9–27</td><td></td><td></td></tr><tr><td>Accentedness<sup>20</sup></td><td><strong>6.21</strong></td><td>1–10</td><td>10</td><td><strong>5.46</strong></td><td>0–10</td><td>10</td><td></td></tr><tr><td>Nonnativeness<sup>21</sup></td><td><strong>7.52</strong></td><td>0–10</td><td>10</td><td><strong>6.41</strong></td><td>0–10</td><td>10</td><td></td></tr></tbody></table>

Note. Eng= English; AoA= Age of Acquisition.
<figure><img src="image_23.png" /></figure>

database (New et al., 2004). English targets were translated into French, and these French translation equivalents were used as embedded stems in affixed and non-affixed primes. English targets and their French translations were matched on log10 frequency ($M = 4.8, SD = 0.6$ for English targets; $M = 4.6, SD = 0.8$ for French translations, $p = .17$), length ($M = 3.9, SD = 0.7$ for English targets; $M = 4.1, SD = 0.8$ for French translations, $p = .29$), number of syllables ($M = 1.1, SD = 0.3$ for English targets; $M = 1.1, SD = 0.2$ for French translations, $p = .70$), and number of phonemes ($M = 3.2, SD = 0.6$ for English targets; $M = 3.0, SD = 0.6$ for French translations, $p = .05$, see Table 2).

Suffixes (e.g., _eur, _al) and non-morphological letter strings (e.g., se, uve) were matched on length and positional specific bigram frequency (see Table 3). These stems were then combined with affixes and non-affixes to create nonword prime stimuli, which were matched on positional specific bigram frequency using CLEARPOND (Marian et al., 2012). The targets and their corresponding primes are shown in Appendix A.

Stems in the related (e.g., arbre) and unrelated condition (e.g., monde) were matched on length, number of syllables, number of phonemes, log10 frequency (Zipf values), orthographic neighborhood, OLD20, bigram frequency, and concreteness. The experimental design used for related nonwords was mirrored by combining half of the unrelated stems with affixes and the other half with non-affixes.

For the purposes of the lexical decision task, 60 English nonword filler stimuli⁶ were created by changing one to two letters in real English words (e.g., form-folm). Real word and nonword targets were matched as closely as possible on positional specific bigram frequency ($M = 5.886, SD = 3.777$ for nonword targets, $M = 7.654, SD = 4.995$ for real word targets, $p = .04$) and length ($M = 3.83, SD = 0.46$ for nonword targets, $M = 3.9, SD = 0.7$ for real word targets, $p = .44$). Similar to the experimental design followed for the real word targets, half of the nonword targets were preceded by affixed nonwords (e.g., ploual) and half were preceded by non-affixed nonwords (e.g., vagnese) with nonword stems formed by changing one or two letters in real French stems (e.g., clou, bagne). Three experimental lists were used in a Latin square design, with each list containing only one of the prime-target pairs so that each participant responded to each target only once.

<table><caption>**Table 2.** Mean Item Characteristics (SDs).</caption><thead><tr><th></th><th>English Targets</th><th>Their French Translations</th></tr></thead><tbody><tr><td>No of letters</td><td>3.9 (0.7)</td><td>4.1 (0.8)</td></tr><tr><td>No of phonemes</td><td>3.2 (0.6)</td><td>3.0 (0.6)</td></tr><tr><td>No of syllables</td><td>1.1 (0.3)</td><td>1.1 (0.2)</td></tr><tr><td>log10 frequency<sup>22</sup></td><td>4.8 (0.6)</td><td>4.6 (0.8)</td></tr><tr><td></td><td><strong>Related stem (e.g., arbre [tree])</strong></td><td><strong>Unrelated stem (e.g., monde [world])</strong></td></tr><tr><td>Length</td><td>4.1 (0.8)</td><td>4.1 (0.8)</td></tr><tr><td>log10 frequency</td><td>4.6 (0.8)</td><td>4.32 (0.8)</td></tr><tr><td>Bigram freq.</td><td>8,718 (5,065)</td><td>7,567 (5,187)</td></tr><tr><td>No of phonemes</td><td>3.02 (0.6)</td><td>2.88 (0.7)</td></tr><tr><td>No of syllables</td><td>1.05 (0.2)</td><td>1.10 (0.3)</td></tr><tr><td>Orth. N.</td><td>7.9 (4.9)</td><td>6.8 (5.4)</td></tr><tr><td>old20</td><td>1.4 (0.2)</td><td>1.48 (0.3)</td></tr><tr><td>Concreteness</td><td>4.5 (0.5)</td><td>4.59 (0.5)</td></tr><tr><td></td><td><strong>Affixes (e.g., _eur)</strong></td><td><strong>Nonaffixes (e.g., se)</strong></td></tr><tr><td>No of letters</td><td>2.5 (0.6)</td><td>2.5 (0.6)</td></tr><tr><td>Positional specific bigram frequency<sup>23</sup></td><td>387 (181)</td><td>515 (315)</td></tr></tbody></table>

<table><caption>**Table 3.** French suffixes, their English translation equivalents and meanings.</caption><thead><tr><th>French</th><th>English Translation</th><th>Examples</th><th>Meanings</th></tr></thead><tbody><tr><td>_eur</td><td>_er/ness</td><td>chant-chanteur</td><td>sing-singer</td></tr><tr><td>_eux</td><td>_ous</td><td>fame-fameux</td><td>fame-famous</td></tr><tr><td>_al</td><td>_al</td><td>tropic-tropical</td><td>tropic-tropical</td></tr><tr><td>_té</td><td>_ty/ness</td><td>fier-fierté</td><td>proud-pride</td></tr></tbody></table>
<figure><img src="image_24.png" /></figure>

## Procedure

This experiment was conducted online using the Gorilla platform (https://gorilla.sc). Each trial started with the presentation of a 500-ms fixation mark, 500-ms forward mask of hash keys, then a 200-ms prime in lowercase, then the target in uppercase. The target remained on screen until the response or until 3 second elapsed. Participants were told to respond as quickly and accurately as possible. After the lexical decision task, participants additionally took the English LexTALE test (Lemhofer & Broersma, 2012) and responded to a short version of the Language Experience and Proficiency Questionnaire (LEAP-Q; Marian et al., 2007) to ensure that participants were proficient in their L2 and that they were unbalanced French-English bilinguals.

## Results

Two sets of analyses are reported. The first set addressed our first goal, investigating cross-language morphological priming in French-English bilinguals. The second set of analyses addressed our second goal, examining the influence of cross-linguistic differences in morphological complexity and orthographic transparency on cross-language priming, by directly comparing the current sample of French-English bilinguals against Kahraman et al.'s (2023) sample of Turkish-English bilinguals.

## Analysis of French-English bilingual data

The data and analysis script were made available on https://osf.io/269qb/. Correct trials were used to conduct reaction time (RT) analysis. RT distributions and error rates for items and subjects were visually examined to remove extreme outliers. Five participants were excluded because their real word or nonword accuracies were below 50%. Data points below 250 ms or above 2000 ms were removed (0.1% of the total data). Data points with standardized residuals greater than 2.5 SD (Baayen, 2008) were also removed (2.9%). Table 4 displays the mean RTs and error rates (ERs) for French-English participants across conditions. The results focussed solely on RTs due to low error rates.

In line with a boxcox power transformation analysis, RTs were inverse-transformed to normalize RT distributions and reduce the effect of outliers. The final model included a fixed effect factor of prime type (affixed nonword, non-affixed nonword, unrelated) and two random effects factors (random intercepts and random slopes for subjects and items). Standardized trial order was included as a covariate in the models to statistically control for longitudinal task effects of fatigue or habituation and was kept in the analyses if it significantly improved the model, as measured by using ANOVA Type III (Version 3.1–2, Fox & Weisberg, 2019). Post-hoc comparisons between experimental and control conditions were carried out using the “emmeans” package (Version 1.8.8, Lenth, 2023), which were automatically adjusted for multiple comparisons using the “holm” method.

Twelve possible random effects structures were fitted using “bobyqa” optimizer (with the argument control = lmerControl(optimizer = “bobyqa”)), including a maximal random effect structure (Barr et al., 2013). The final model included the model with the smallest value of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) (Matuschek et al., 2017). The maximal random effects structure that included random slopes for the fixed effect “prime type” and

<table><caption>**Table 4.** Mean RTs and ERs across conditions in ms (SDs).</caption><thead><tr><th>L1 French – L2 English Group</th><th>Affixed Nonword</th><th>Non-affixed Nonword</th><th>Unrelated Nonword</th></tr></thead><tbody><tr><td>Example</td><td>arbreur-TREE</td><td>arbrux-TREE</td><td>mondese-TREE</td></tr><tr><td>RT</td><td>643 (136)</td><td>644 (135)</td><td>654 (133)</td></tr><tr><td><strong>Priming effect</strong></td><td><strong>11*</strong></td><td><strong>10*</strong></td><td></td></tr><tr><td>ER</td><td>0.04 (0.2)</td><td>0.04 (0.2)</td><td>0.05 (0.2)</td></tr></tbody></table>

Note. An asterisk (*) indicates statistical significance.
<figure><img src="image_25.png" /><figcaption>Table 5. Pairwise contrasts between prime types in the French group.</figcaption></figure>

<table><thead><tr><td>contrast</td><td>estimate</td><td>SE</td><td>z ratio</td><td>p value</td></tr></thead><tbody><tr><td>affixed-unrelated</td><td>-0.03</td><td>0.008</td><td>-3.89</td><td>&lt;0.0001</td></tr><tr><td>nonaffixed-unrelated</td><td>-0.02</td><td>0.008</td><td>-3.25</td><td>0.001</td></tr><tr><td>affixed-nonaffixed</td><td>-0.004</td><td>0.007</td><td>-0.60</td><td>0.54</td></tr></tbody></table>

Note. SE = Standard Error.

standardized covariate of trial order within the by-subject and the fixed effect “prime type” within the by-item random intercept led to a significant increase in model fit and was used in the analysis.

There was a robust main effect of prime type ($\chi^2(2) = 15.97, p < .0001$). Trial order was a significant covariate ($\chi^2(1) = 24.07, p < .0001$). The pairwise comparisons of factor prime type are shown in Table 5. Lexical decisions to words preceded by affixed and non-affixed related nonword primes (e.g., *arbreur-TREE;arbrux-TREE*) were significantly faster than the unrelated control condition ($z = -3.89, p < .0001$ for the affixed; $z = -3.25, p < .001$ for the non-affixed nonwords), showing cross-language embedded stem priming effects. However, there was no statistical difference between affixed and non-affixed nonword conditions ($z = -0.60, p = .54$).

We ran a set of exploratory (but pre-registered) analyses into the effects of language proficiency and language background where individual differences measures were standardized and added to the model⁷ to calculate the interaction with factor prime type. Following Freeman et al. (2022),⁸ individual differences in language proficiency were measured using LexTale and summed LEAP-Q scores for each participant. The two-way interactions between prime type and LexTale scores ($\chi^2(2) = 0.23, p = .88$) and prime type and summed LEAP-Q scores ($\chi^2(2) = 1.47, p = .47$) were not significant.

The findings revealed L1 French affixed and non-affixed primes significantly facilitated L2 (English) processing. Specifically, French-English bilinguals were able to map the lexical representations of stems embedded in L1 affixed and non-affixed nonwords onto the corresponding semantic representations of L2 translations. Similar magnitudes of affixed and non-affixed priming shows that there was no additional facilitation from the presence of an affix and that cross-language priming effects in French are purely due to the lexical activation of translated embedded stems in nonword stimuli. These findings provide clear evidence of the important role embedded stem activation plays in bilingual processing.

## Combined French and Turkish data analysis

A second set of analyses were conducted to directly assess the interaction between morphological priming and language group, by merging the French and Turkish datasets from Kahraman et al. (2023).⁹ The same model-fitting principles as in the first set of analyses were applied. The final model included two fixed effect factors of group (French, Turkish) and prime type (affixed nonword, non-affixed nonword, unrelated), their interactions, and two random effects factors (random intercepts and random slopes for subjects and items). Standardized trial order was included as a covariate in the models to statistically control for longitudinal task effects of fatigue or habituation and was kept in the analyses if it significantly improved the model, as measured by the “anova” function.

The Turkish group exhibited overall slower RTs ($t(11061) = -12.23, p < .0001$) and higher accuracy ($\chi^2(1) = 26.15, p < .0001$) compared to the French group,¹⁰ and also showcased higher English language proficiency ($t(9706.7) = -36.80, p < .0001$), as measured by chi-square and t-tests. To address these differences between groups, an inverse transformation was applied to the RTs, followed by a z-transformation within each group, and English language proficiency was included as a factor in the cross-linguistic group analysis.¹¹ Post-hoc comparisons between experimental and control conditions were carried out using the “emmeans” package (Version 1.8.8, Lenth, 2023), which automatically adjusted for multiple comparisons using the “holm” method. Twenty-five possible random effects structures were fitted using “bobyqa” optimizer
<figure><img src="image_26.png" /></figure>

(with the argument control = lmerControl(optimizer = "bobyqa")), including a maximal random effect structure (Barr et al., 2013). The final model included the model with the smallest value of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) (Matuschek et al., 2017). The random effects structure that included random slopes for the factor prime type and group within the by-item random intercept led to a significant increase in model fit and was therefore used in the analyses.

The main effect of prime type was highly significant ($\chi^2(2) = 61.34, p < .0001$). The main effect of group was not significant ($\chi^2(1) = 1.00, p = .31$). English language proficiency ($\chi^2(1) = 5.54, p = .01$) and trial order ($\chi^2(1) = 246.73 p < .0001$) yielded significant effects. The interaction of group (French, Turkish) and prime type (affixed nonword, non-affixed nonword, unrelated) was significant ($\chi^2(2) = 6.08, p = .04$; see Figure 1 for model-based estimates of response times per language group). Follow-up analyses focussed on whether cross-language embedded stem priming and cross-language morphological priming effects were modulated by group (see Table 6).¹² The difference between the two groups in embedded stem priming was significant ($p = .01$), suggesting that affixed priming in the Turkish group was greater in magnitude compared to the priming observed in the French group.

In the following analyses, post-hoc contrasts were conducted to explore the significant interaction between prime type, group, and English proficiency. A series of contrasts were defined to compare the interaction of each priming effect (e.g., the affixed priming effect was calculated by subtracting RTs in the affixed condition from the RTs in the unrelated condition) within each language group and across different proficiency levels. To get a sense for priming effects at the high and low ends of the proficiency spectrum, English proficiency values at the 5th (i.e., the lowest) and 95th (the highest) percentiles were identified. Next, estimated marginal means were calculated for the interaction of each priming type with group at these

<figure><img src="image_27.png" /><figcaption>Figure 1. Model-based estimates of response times in the Turkish (left) and French (right) groups.</figcaption></figure>

<table><caption>Table 6. Pairwise comparisons by prime type and group.</caption><thead><tr><th>group</th><th>contrast</th><th>estimate</th><th>SE</th><th>z ratio</th><th>p value</th></tr></thead><tbody><tr><td rowspan="3">Turkish vs French</td><td>Affixed vs unrelated</td><td>-0.09</td><td>0.03</td><td>-2.36</td><td>0.01</td></tr><tr><td>Nonaffixed vs unrelated</td><td>-0.02</td><td>0.03</td><td>-0.67</td><td>0.50</td></tr><tr><td>Affixed vs nonaffixed</td><td>-0.06</td><td>0.05</td><td>-1.71</td><td>0.08</td></tr></tbody></table>

Note. SE = Standard Error.
<figure><img src="image_28.png" /><figcaption>**Figure 2.** Modulation of cross-language affixed nonword priming by L2 English proficiency in French and Turkish readers. *Note.* The left side of the panels represents lower proficiency scores, which increase toward the right of the spectrum. English proficiency was found to affect affixed priming differently between Turkish and French speakers (see green trendlines in the left vs. right panels), with cross-language affixed nonword effects being more pronounced in low proficiency Turkish-English readers.</figcaption></figure>

specific proficiency levels using “emmeans.” Statistically significant contrasts included the interaction of cross-language affixed nonword priming with group ($z = 0.36, p = .008$), indicating a varying effect of language proficiency for different reader groups. This modulation is shown in Figure 2 for readers of French (Panel A of Figure 2) and of Turkish (Panel B of Figure 2).

## Discussion

Languages differ in morphological complexity and orthographic consistency, and these differences have provided the basis for making predictions of cross-linguistic interactions in this cross-language investigation. This study addressed two questions – whether cross-language morphological priming indeed occurs in similar script bilinguals, and whether word recognition skills in L2 vary as a function of morphological complexity and orthographic transparency of the L1 of the speaker. Participants made lexical decisions to English L2 target words (e.g., TREE) after briefly seeing primes for 200 ms that were either L1 French affixed nonwords, non-affixed nonwords, or unrelated nonwords.

The first question was addressed by investigating cross-language morphological priming effects within our sample of French-English bilinguals who showed comparable magnitude of facilitation in both affixed and non-affixed nonword priming conditions relative to the unrelated condition. This finding points to the embedded stem priming using the cross-language priming paradigm in similar-script languages. However, the lack of significant difference between the affixed and non-affixed conditions suggests that the presence of an affix did not enhance cross-language priming in French-English bilinguals.

These results are consistent with Kahraman et al. (2023) that introduced a preliminary model for the recognition of complex words in bilinguals. This model integrates the fundamental principles of the Word and Affix model (Beyersmann & Grainger, 2023) and Bilingual Interactive Activation + models (BIA + model; Dijkstra & van Heuven, 1998, 2002). Within this framework, orthographic representations are interconnected with their semantic equivalents through bidirectional links, facilitating the transfer of information in both bottom-up and top-down directions. The model proposes two robust processes: the non-morphological activation of embedded words and the morphological activation of affixes. The former process triggers the activation of whole words and their embedded constituents, while the latter process initiates the activation of affixes. Critically, these processes extract information from both languages that are simultaneously active in the bilingual mental lexicon. The authors emphasize the existence of an
<figure><img src="image_29.png" /></figure>

additional mechanism – L1 parsing strategy transfer – that elevates the activation levels in the affixed stimuli. As evidenced by the lack of difference between affixed and non-affixed stimuli, this transfer is sensitive to the morphological complexity of L1. French is characterized as moderately inflected language with opaque orthography, stands in contrast to Turkish, which is a highly inflected and agglutinative language, with transparent orthography. Consequently, this parsing strategy is not employed in the French group, while Kahraman et al. (2023) observed it in the Turkish group. This observation underscores the influence of the morphological structure of L1 on the processing of complex words in bilingual individuals.

The second question was addressed by directly comparing the current data from French-English bilinguals with Kahraman et al.'s (2023) data from Turkish-English bilinguals. This comparison is particularly important given the significant differences in morphological structures between Turkish and English, as opposed to the relatively closer structures of French and English. We tested whether the difference in the linguistic feature patterns between languages may reflect differences in the processing of morphology. In the Turkish dataset, the affixed primes showed a greater level of facilitation compared to the non-affixed primes. However, this difference was not observed in the French dataset, resulting in comparable levels of facilitation for both affixed and non-affixed priming. The analyses further revealed more pronounced morphological segmentation effects in Turkish-English bilinguals, that is in speakers of a language characterized by higher degrees of morphological complexity, compared to French-English bilinguals. Thus, our hypothesis – that morphological complexity moderates morphological priming – finds experimental support. This finding is in line with the effects reported in Beyersmann et al. (2020) and Casalis et al. (2015). For example, Beyersmann et al. (2020) found larger stem effects in German, a rich compounding language, compared to French. Casalis et al. (2015) reported a greater level of morphological processing in French children, speakers of a language with higher morphological complexity, in comparison to English children.

The results reported here are the first to clarify the relationship between cross-linguistic factors and morphological processing using cross-language stimuli. Additionally, these findings move forward our understanding of cross-linguistic influences on morphological processing in several ways. While previous studies utilized an unprimed lexical decision task in participants' L1, the transfer effects related to morphological structure shown in this study suggest that morphological complexity information may transfer between the two languages of a bilingual speaker.

Further, morphological complexity may affect morphological priming by influencing the manner how morphologically complex words are processed and represented. During word recognition, the segmentation of these complex words results in reduced reaction times as the activation of the embedded stem speeds up of lexical recognition. Moreover, the complexity and regularity of the morphological rules in the languages spoken by the bilingual individual may shape their ability to process and identify affixes in print. Given affixes are abstract units reading, readers of morphologically complex languages, such as Turkish, may require a considerable degree of reading experience and linguistic expertise to segment and process complex words into their stems and affixes. Thus, adult readers of languages with more complex and regular morphological structures, like Turkish or Finnish, may demonstrate a higher level of reading expertise, facilitating more efficient morphological processing.

In a series of pre-registered exploratory analyses, we also examined how individual differences in language proficiency influence cross-language priming effects. These analyses were exploratory due to inconsistent findings in previous research on morphological processing (e.g., Andrews & Lo, 2013; Brysbaert, 2024). Furthermore, various measures have been used to quantify individual differences in language proficiency, ranging from spelling dictation (e.g., Andrews & Lo, 2012) and spelling recognition (e.g., Kahraman & Kırkıcı, 2021) to vocabulary (e.g., Viviani & Crepaldi, 2022), reading speed (e.g., Medeiros & Duñabeitia, 2016), and reading comprehension (e.g., Beyersmann, Grainger, et al., 2015). These varied measures challenge the comparability of findings across studies. This complexity is further compounded in the present investigation because participants were second
language speakers of English, and their proficiency scores were based on self-reported measures from the LEAP-Q. Consequently, the exploratory nature of these additional analyses must be considered.

We quantified these differences by summing the English proficiency and exposure scores from the LEAP-Q (Marian et al., 2007) for each participant within the current French group and the Turkish group from Kahraman et al. (2023). These summed scores were then analyzed to explore the interactions between prime type, proficiency, and group. The results suggested that second language proficiency modulates cross-language embedded stem processing mechanisms differently across French-English and Turkish-English bilingual readers. As demonstrated in Panel B of Figure 2, Turkish readers with lower English proficiency showed greater cross-language priming from affixed nonwords, while no such difference was observed in French readers. This finding suggests that Turkish readers, with less robust English knowledge, may rely more heavily on the presence of affixes to aid in stem activation, leading to higher affixed nonword priming. This finding is consistent with a growing body of experimental evidence that suggests that morphological processing impacts participants’ responses in a particular language. For example, using language proficiency scores as an individual differences measure, Beyersmann, Casalis, et al. (2015) found that skilled English readers with lower scores exhibited greater affixed nonword priming effects. In addition, the influence of reading proficiency on embedded stem priming was reported to begin as early as Grade 2 (Beyersmann et al., 2019). Similarly, Beyersmann et al. (2016) found French readers with higher reading proficiency could more effectively extract embedded stems from non-affixed nonword primes, resulting in greater priming effects compared to readers with lower proficiency. Also, English readers demonstrated varying patterns of facilitation from transparent or opaque prime-target manipulation when their vocabulary and spelling skills were assessed in the context of their lexical decision-making processes (Andrews & Lo, 2013).

The present study provides additional evidence supporting the growing consensus regarding the impact of individual variability on morphological processing in L1 and L2. Critically, our findings highlight that this variability also extends to cross-language processing, contributing to the broader understanding of the complex interplay between morphological processing, language proficiency, and individual differences. However, we would like to emphasize that the tools and methods typically used to measure priming at the group-level, such as reaction time tasks, may not be sensitive enough to capture subtle individual differences. This can lead to variability in the data that is not necessarily related to the priming effect itself, making it hard to isolate the impact of the individual’s characteristics on their response. Hence, we highlight the inherent challenges in measuring priming at the individual level and the need for cautious interpretation of such finding.

## Concluding remarks and future directions

The present study focussed on the processing of cross-language affixed and non-affixed nonwords using the masked priming paradigm within a lexical decision task. It replicated Experiment 1 of Kahraman et al. (2023) with a new group of similar-script French-English bilinguals. The comparable facilitation from L1 French affixed nonwords and non-affixed nonwords on L2 English stem targets showed that bilinguals were able to rapidly extract embedded stems within primes and translate them in the other language, facilitating target decisions. This finding is novel and significant given that it confirms that cross-language embedded stem priming remains robust in languages with similar scripts.

This evidence was then compared with the prior evidence in Kahraman et al. (2023) who reported more affixed priming in Turkish-English bilinguals. The results revealed that the morphological complexity of the L1 modulates cross-language morphological priming, advancing our understanding of how cross-linguistic variables, particularly differences in L1 morphological complexity, may affect morphological processing in skilled adult bilinguals at the cross-language level.

It is important to note that Mousikou et al. (2020) and De Simone et al. (2024) found evidence for modulation by orthographic transparency in their cross-linguistic L1 studies, a finding not supported
<figure><img src="image_30.png" /></figure>

in the present study. One possible explanation is the inherent challenge in cross-linguistic research to quantify the extent to which languages differ in orthographic transparency and morphological complexity. It is not always straightforward to measure these differences across languages. Therefore, future research should extend this work to other languages to determine if the same pattern persists when examining languages with varying degrees of orthographic transparency and morphological complexity.

Another point is that in the current cross-language priming paradigm, the exposure to the L1 primes was relatively brief, and thus not directly comparable to paradigms like those used by Mousikou et al. and De Simone et al., which employed overt target presentation techniques. Additionally, Mousikou et al. and De Simone et al. compared Italian and French against English, which has a deep orthography, creating a strong contrast. Turkish and French do not differ as much in their orthographic transparency, so these smaller differences may be less likely to modulate cross-language priming. The significant difference in morphological complexity between Turkish and French, with Turkish being an agglutinative language and French not, might explain why we observed modulation in priming.

The combined results underscore the importance of cross-linguistic differences as a valuable and pivotal aspect of reading for bilinguals. This perspective may help address some of the inconsistencies found in word recognition models, particularly in their approach to morphological processing. However, to further investigate the impact of cross-linguistic differences, it will be necessary to collect more data from speakers of various languages. This could potentially lead to more comprehensive models of bilingual language processing and allow for a thorough understanding of the influence of language diversity on reading.

Finally, present findings suggest that there may be significant inter-individual variation in language processing among individuals in groups that are generally considered to be fairly uniform. By recognizing the individual variability in language processing mechanisms, such as embedded stem processing, reading models that specifically target these constructs may be developed. This approach not only enhances our understanding of the intricate mechanisms behind complex word reading but also paves the way for optimizing reading outcomes in bilinguals.

## Notes

1. The English translation of foreign words is presented in brackets.

2. Turkish is not represented in their study. However, Turkish and Finnish belong to the same language family (Sadeniemi et al., 2008), and hence Finnish and Turkish can be interchangeably used within the scope of morphological complexity.

3. It was suggested that the morpho-orthographic full decomposition might not be fully operational in unbalanced L2 cohorts (Kahraman & Beyersmann, 2024).

4. Advanced L2 proficiency refers to at least a B2 level in the Common European Framework for Languages (CEFR; Council of Europe, 2010), which is represented as a level 8 on a 10-point proficiency scale.

5. The present study aimed to use the same non-cognate targets as Kahraman et al. (2023). However, due to the high orthographic similarities in the items, only a subset of targets was selected for the present experiment. Targets used in the present experiment were strictly matched with those in Kahraman et al. on measures of concreteness, log10 frequency, length, number of syllables, number of morphemes, position specific bigram frequency, and OLD20. Additionally, to enhance the statistical power of our study and align the number of datapoints with that of the Turkish data, we increased the number of participants in the present research.

6. Nonword targets are taken from Kahraman and Beyersmann (2023).

7. The lmer syntax for computing the two-way interaction with, for instance, LexTale scores is: -1000/RT ~ prime. type * scale(LexTale) + scale(TrialN) + (prime.type ID) + (prime.type |Trial.Item), h1, control=lmerControl (optimizer="bobyqa," optCtrl=list (maxfun = 2e5), calc derivs=F)).

8. In line with prior research (e.g., Freeman et al., 2022), English language proficiency and exposure scores were summed to form a composite score of "English language proficiency/exposure." This approach contributes to a comprehensive understanding of bilingual participants' language abilities and personal experiences.

9. Note that 32 targets in French dataset were the same as in Turkish dataset.

10. This points to an accuracy/speed trade-off in the Turkish data.
11. The new results confirm the original findings and showcase a stronger interaction between prime type and group.

12. The mean RTs in the Turkish-English group of Kahraman and Beyersmann were as follows. Affixed nonword priming: 29 ms; non-affixed nonword priming: 19 ms.

13. This refers to the number of years of formal education and number of years spent in an English-speaking country.

14. Age when participants began acquiring English.

15. Age when participants began reading in English.

16. Age when participants became fluent in reading English.

17. Participants rated their proficiency on a 10-point rating scale from 0-10 on the domains of speaking, understanding spoken English, reading in English where 0 and 10 denoted to “none” and “perfect.”

18. Participants rated to which extent they were exposed to English on a 10-point rating scale from 0-10 in interacting with friends and family, watching TV and listening to radio/music, reading, and language-lab/self-instruction. 0 and 10 denoted to “none” and “always.”

19. Age at which participants became fluent in English. We believe that this definition is more realistic given that participants were not immersed in the foreign language at the time of the testing.

20. Based on how much of a foreign accent a participant has in English in their own perception.

21. Based on how frequently others identify participants as a non-native speaker based on their accent in English.

22. Log frequencies (Zipf values) were calculated from Lexique database of 31 million words (New et al., 2004).

23. Positional specific bigram frequencies were calculated from the CLEARPOND (Marian et al., 2012).

## Acknowledgments

We would like to thank Davide Crepaldi and the two anonymous reviewers for their insightful and valuable suggestions.

## Disclosure statement

No potential conflict of interest was reported by the author(s).

## Funding

This research was supported by a Discovery Early Career Researcher Award (DECRA) by the Australian Research Council (ARC) to Elisabeth Beyersmann [DE190100850] and International Macquarie University Research Excellence Scholarship (iMQRES) to Hasibe Kahraman. We also thank the French research unit SCALab (University of Lille and CNRS) for providing funding to Gorilla.

## ORCID

Hasibe Kahraman http://orcid.org/0000-0003-1274-9118

Séverine Casalis http://orcid.org/0000-0003-3850-1524

Ludivine Javourey-Drevet http://orcid.org/0000-0002-4183-5894

Amélie Menut http://orcid.org/0000-0001-5997-860X

Bianca de Wit http://orcid.org/0000-0002-3731-1887

Elisabeth Beyersmann http://orcid.org/0000-0001-9653-6106

## Ethics approval

Ethics approval was granted by the Macquarie University Human Research Ethics Committee (Approval Code: 52021649924861).

## Consent to participate

Participant consent was obtained prior to the experiments.
<figure><img src="image_31.png" /></figure>

# Open practices statement

The materials, the raw data, and analysis script are available at https://osf.io/269qb/

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<figure><img src="image_34.png" /></figure>

# APPENDIX A

## Experimental Stimuli

<table><thead><tr><td>TARGET</td><td>Affixed nonword</td><td>Non-affixed nonword</td><td>Unrelated word</td></tr></thead><tbody><tr><td>BONE</td><td>osté</td><td>osuve</td><td>dété</td></tr><tr><td>WORM</td><td>vereur</td><td>verche</td><td>baceux</td></tr><tr><td>BATH</td><td>baineur</td><td>bainche</td><td>côtux</td></tr><tr><td>FIRE</td><td>feuté</td><td>feuse</td><td>coueux</td></tr><tr><td>KEY</td><td>clété</td><td>cleche</td><td>cilche</td></tr><tr><td>TREE</td><td>arbreur</td><td>arbrux</td><td>mondese</td></tr><tr><td>WIRE</td><td>fileux</td><td>filche</td><td>ileche</td></tr><tr><td>WORD</td><td>motal</td><td>motuve</td><td>eauté</td></tr><tr><td>FRIEND</td><td>amieur</td><td>amise</td><td>tireux</td></tr><tr><td>SILK</td><td>soieté</td><td>soise</td><td>pérux</td></tr><tr><td>NECK</td><td>coleux</td><td>coluve</td><td>bléal</td></tr><tr><td>GOOSE</td><td>oieté</td><td>oiese</td><td>sudeux</td></tr><tr><td>FLEA</td><td>pucéte</td><td>pucese</td><td>oeufex</td></tr><tr><td>DRY</td><td>secur</td><td>secux</td><td>ânese</td></tr><tr><td>WALL</td><td>mureux</td><td>murche</td><td>ailuve</td></tr><tr><td>KING</td><td>roité</td><td>roiche</td><td>dixeur</td></tr><tr><td>SALT</td><td>seleur</td><td>selse</td><td>meceur</td></tr><tr><td>NEST</td><td>nidal</td><td>niduve</td><td>dosuve</td></tr><tr><td>HARD</td><td>dureur</td><td>durche</td><td>féeche</td></tr><tr><td>SEA</td><td>mereur</td><td>merse</td><td>jeuche</td></tr><tr><td>IRON</td><td>fereur</td><td>ferse</td><td>rueté</td></tr><tr><td>LANE</td><td>voieté</td><td>voiche</td><td>lireche</td></tr><tr><td>WOOD</td><td>boisal</td><td>boisux</td><td>pontuve</td></tr><tr><td>DEER</td><td>cerfeur</td><td>cerfuve</td><td>toital</td></tr><tr><td>KILL</td><td>tuereux</td><td>tueruve</td><td>cinquve</td></tr><tr><td>HEAD</td><td>têteux</td><td>teteche</td><td>aubux</td></tr><tr><td>JUMP</td><td>sauteux</td><td>sautux</td><td>cirux</td></tr><tr><td>FOOT</td><td>piedeur</td><td>pieduve</td><td>ivral</td></tr><tr><td>EMPTY</td><td>videux</td><td>vidux</td><td>jupeur</td></tr><tr><td>SKIN</td><td>peauté</td><td>peause</td><td>dameux</td></tr><tr><td>NIGHT</td><td>nuital</td><td>nuitux</td><td>ondal</td></tr><tr><td>SKY</td><td>cieleur</td><td>cieлуve</td><td>clouse</td></tr><tr><td>DAY</td><td>joureur</td><td>jourche</td><td>oeileur</td></tr><tr><td>ARM</td><td>braseux</td><td>brasux</td><td>drapal</td></tr><tr><td>WOLF</td><td>loupeux</td><td>loupux</td><td>thoneur</td></tr><tr><td>BLACK</td><td>noireux</td><td>noirche</td><td>pioneux</td></tr><tr><td>WING</td><td>aileur</td><td>alese</td><td>vertuve</td></tr><tr><td>BOOK</td><td>livral</td><td>livrux</td><td>sallese</td></tr><tr><td>CUT</td><td>coupeux</td><td>coupuve</td><td>hibouse</td></tr><tr><td>GLASS</td><td>verreté</td><td>verreche</td><td>écrouche</td></tr><tr><td>LIP</td><td>lèvreux</td><td>levrux</td><td>cadreté</td></tr><tr><td>YOUNG</td><td>jeuneux</td><td>jeunuve</td><td>vacheur</td></tr><tr><td>ROD</td><td>tigeur</td><td>tiguve</td><td>nageté</td></tr><tr><td>ROPE</td><td>cordal</td><td>cordux</td><td>lapinuve</td></tr><tr><td>SNOW</td><td>neigeal</td><td>neigese</td><td>genouté</td></tr><tr><td>DOG</td><td>chienal</td><td>chienche</td><td>mollese</td></tr><tr><td>PARTY</td><td>fêteux</td><td>fetux</td><td>gantuve</td></tr><tr><td>SWAN</td><td>cygneté</td><td>cygnuve</td><td>pluiche</td></tr><tr><td>ASH</td><td>frènal</td><td>frenux</td><td>évial</td></tr><tr><td>SAND</td><td>sablal</td><td>sablux</td><td>liègeur</td></tr><tr><td>SPOT</td><td>tachal</td><td>tachuve</td><td>pellal</td></tr><tr><td>SOAP</td><td>savonté</td><td>savonche</td><td>soclux</td></tr><tr><td>HOT</td><td>chaudal</td><td>chauduve</td><td>guêpux</td></tr><tr><td>COLD</td><td>froidal</td><td>froidux</td><td>plagese</td></tr><tr><td>SWEAT</td><td>sueurté</td><td>sueurse</td><td>douceté</td></tr><tr><td>AUNT</td><td>tanteux</td><td>tantuve</td><td>jambux</td></tr><tr><td>YEAR</td><td>anneur</td><td>annese</td><td>fouleche</td></tr><tr><td>WOOL</td><td>lainal</td><td>lainese</td><td>gorgese</td></tr><tr><td>FOG</td><td>brumeté</td><td>brumeche</td><td>couduve</td></tr><tr><td>HEART</td><td>coeural</td><td>coeurse</td><td>écranté</td></tr></tbody></table>