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This study examined the impact of language dominance on variation in American Sign Language (ASL) production among 100 proficient deaf and hard of hearing signers who acquired ASL before age eight. While ASL variation has traditionally been attributed to factors like age of acquisition, proficiency, and sociolinguistic influences, this study introduced language dominance, a known factor modulating the presence of linguistic elements from one language within another among bilingual speakers. Findings revealed that ASL-English language dominance moderately predicted the use of English mouthings (operationalized here as mouthings) and ASL classifiers (operationalized as classifiers): ASL-dominant signers produced fewer mouthings and more classifiers, while English-dominant signers displayed the opposite pattern. Notably, this influence was consistent in both native and early nonnative signers, suggesting that the integration of English elements is not solely due to proficiency limitations but also reflects bilingual language dynamics. These results indicate that sign-spoken bilinguals may often operate in a bilingual mode, accessing both ASL and English syntactic structures during ASL discourse. Implications extend to ASL documentation and proficiency tests, as traditional monolingual frameworks may not capture the fluid syntactic variation in signing ecologies. This finding also suggests that aspects of English grammar may be amodal for ASL signers, with potential applications for bilingual processing models.
Abstract
This study examined the impact of language dominance on variation in American Sign Language (ASL) production among 100 proficient deaf and hard of hearing signers who acquired ASL before age eight. While ASL variation has traditionally been attributed to factors like age of acquisition, proficiency, and sociolinguistic influences, this study introduced language dominance, a known factor modulating the presence of linguistic elements from one language within another among bilingual speakers. Findings revealed that ASL-English language dominance moderately predicted the use of English mouthings (operationalized here as mouthings) and ASL classifiers (operationalized as classifiers): ASL-dominant signers produced fewer mouthings and more classifiers, while English-dominant signers displayed the opposite pattern. Notably, this influence was consistent in both native and early nonnative signers, suggesting that the integration of English elements is not solely due to proficiency limitations but also reflects bilingual language dynamics. These results indicate that sign-spoken bilinguals may often operate in a bilingual mode, accessing both ASL and English syntactic structures during ASL discourse. Implications extend to ASL documentation and proficiency tests, as traditional monolingual frameworks may not capture the fluid syntactic variation in signing ecologies. This finding also suggests that aspects of English grammar may be amodal for ASL signers, with potential applications for bilingual processing models.
This article asks whether the dominance of English and American Sign Language (ASL) in highly proficient deaf signers predicts the incorporation of English into ASL. Language dominance is here understood as a gradient construct shaped by a combination of various contextual factors, including years of use, current use, attitudes, and proficiency levels in both languages. This construct reflects the relative strength of two languages in a bilingual individual. A positive finding would suggest that styles of ASL characterized by the incorporation of English are not only products of limited ASL proficiency, accommodation to second-language users, or sociolinguistic factors. Instead, it would imply that signers, like spoken language bilinguals, engage in codeswitching and experience crosslinguistic influence. Any incorporation of English among proficient signers, based on the relative strength of ASL and English, could have significant implications for sign language documentation, proficiency assessments, and language processing models. This study utilized empirical data from native and early signers, collected as part of a broader investigation into factors influencing styles of ASL (Lindeberg 2022a).
Signing styles in proficient signers in the United States vary along a continuum from "ASL-like" to "English-like" (Woodward 1973). On this continuum, English-like styles tend to display more items that resemble items in the English linguistic system. These items include lexical signs or fingerspelled (FS) words that share English semantics. For example, ASL signers may use the sign TO as a preposition or an infinitive marker in a manner that resembles the English word to. English-like styles also tend to include syntactic structures known in English (signing in English word order) and English mouthing (voiceless lip movements resembling English words, often paired with a sign that has similar semantic content as the mouthing). Hereafter, mouthings will refer specifically to English mouthing. In contrast, ASL-like styles make greater use of signing space, often employing depicting strategies such as entity classifier constructions. Here, entity classifiers comprise signs where the actions of an object-other than a person's hand-are productively encoded with the hands. For example, a signer can use an entity classifier to depict a car driving uphill by moving their hand, which represents the car, in an upward motion. For the remainder of this article, classifiers will refer specifically to entity classifier constructions, which are a common type of classifier construction in ASL. Facial markers (nonmanual markers) are also more evident in ASL-like styles.
The examples in figures 1 and 2, which express the same proposition-"A person is under the roof"-demonstrate differences between English-like and ASL-like signing. Figure 1 presents a relatively English-like utterance, with a word order more familiar to English speakers than the sentence in figure 2. Figure 1 contains both lexicalized signs (one, person, and under) and a lexicalized FS representation (FS:r-o-o-f), illustrating English-like signing. The sign under also has iconic features, like many other ASL signs, visually representing something beneath an object. Still, its meaning largely aligns with the English preposition under.
n contrast to figure 1, the utterance in figure 2 is a relatively ASL-like expression of the same proposition but utilizes the signing space with a classifier (CL), CL:person_under_roof, with a syntactic order uncommon in English. If a person had been introduced earlier in the discourse, the expression FS:r-o-o-f CL:person_under_ roof could function as an independent, complete clause. In that case, the initial signs, one person, would be redundant, as the classifier already incorporates the concept of the entity.
Another difference between the utterances in figures 1 and 2 is the expected number of mouthings. Spoken language mouthings are more likely to appear with lexicalized signs than classifier signs (e.g., Johnston, Roekel and Schembri 2016). The relatively English-like utterance in figure 1 has four lexicalized signs, while the ASL-like counterpart in figure 2 has three, potentially resulting in more mouthings in the utterance in figure 1.
Several factors have been suggested to influence a signer's position on the ASL-to-English continuum, including incomplete acquisition of ASL or English (Herbert and Pires 2017), attitudes toward English (Lucas and Valli 1992), codeswitching behavior (Grosjean 2010; Hoffmeister and Moores 1987), the formality of the situation (Woodward 1973), and adaption to interlocutors (Lucas and Valli 1992). Other factors may include random variation ( Johnston et al. 2016) or a combination of external factors (Lucas and Valli 1992).
Less is known about variation in the production of ASL-like and English-like signing styles within early signers. There appear to be some tendencies among early signers when considering proficiency levels. Several studies suggest that deaf individuals exposed to ASL at birth (native) or early childhood show more consistency across a range of linguistic tasks. These tasks include grammatical judgment (e.g., Boudreault and Mayberry 2006; Hauser et al. 2016; Mayberry et al. 2011; Novogrodsky et al. 2017), handshape detection (Morford and Carlson 2011), and accurate production of classifier signs or "verb directions" (Newport 1990). Still, it remains unclear whether deaf native and early signers cluster consistently at the same point on the ASL-like-to-English-like continuum, as this has not been empirically examined using statistical models.
Codeswitching, Language Contact, and Crosslinguistic Influence in Sign Languages
Research on bilingual behavior suggests that variation in the occurrence of English in ASL among native and early signers may be expected, possibly resulting in differing styles along the ASL-to-English continuum. Spoken and signed languages share similarities in that elements from two distinct languages can appear in conversations between bilingual interlocutors. This behavior is commonly attributed to codeswitching in spoken languages and may manifest as an accent at the phonological level or as lexemes, semantics, or syntactic constructions from one language appearing in an utterance otherwise in another language.
Codeswitching can be categorized into three types: alternations (switching occurs between sentences but not within sentences), insertions (a single word from one language appears within an utterance in another language), and congruent lexicalization (a shared language structure is realized with morphemes from both languages) (Muysken 2000). According to Treffers-Daller (2009), these three categories may represent segments along a continuum of language separation, ranging from maximum separation (alternations) to minimal separation (congruent lexicalization), with insertions occupying an intermediate position. Treffers-Daller points out that from a psycholinguistic perspective, the distinction between unintentional (and habitual) congruent lexicalization on the one hand and involuntary cross-linguistic influence on the other hand may be particularly blurry.
Researchers have suggested that items characterizing English-like signing may be understood as not only similar to English but also as English-based (e.g., Grosjean 2010; Herbert and Piers 2017; Hill 2012; Lucas and Valli 1992). The English-like phenomenon could be explained by congruent lexicalization, or dense codeswitching (e.g., Green and Wei 2014), in which signers produce constructions in ASL that are also familiar in English, like in figure 1. In this scenario, ASL signing styles may reflect signers' conscious or unconscious dense codeswitching habits.
Tools to consistently determine whether signers engage in codeswitching (i.e., tools that distinguish between spontaneously incorporating spoken items and using English-like constructions that are entrenched into ASL) are still lacking ( Johnston et al. 2007). For instance, it may be unclear if a signer is opportunistically switching to English or utilizes English-like constructions that have been conventionalized in ASL. However, at least three quantitative studies have used grammatical indicators to argue that spoken language influences signed utterances through codeswitching (Hoffmeister and Moores 1987), language contact (Lucas and Valli, 1992) and crosslinguistic influence (Manhardt, Brouwer, and Özyürek 2021). These studies suggest that a decrease in the use of sign language-specific elements, such as classifiers and spatial signing, correlates with an increase in elements that resemble structures in the ambient spoken language, including grammatical markers and speech-like mouthings.
In the first of these studies, Hoffmeister and Moores (1987) found that two native signers used more English-like signing in formal settings compared to two other nonnative signers, attributing this difference to the natives' greater proficiency in English. They argued that native signers could codeswitch to English features due to their bilingual competence, while nonnative signers lacked this ability. Similarly, Lucas and Valli (1992) observed that deaf signers adapted their signing styles to match their interlocutors, with signers adopting a more English-like style if their partner used it as well. They suggested that this variation resulted from ASL-English contact, forming a third linguistic system that combines elements of both languages, and hypothesized that external factors might predict when this system is used. In a more recent study, Manhardt et al. (2021) suggested that Dutch speech influences Dutch Sign Language, leading to a greater use of Dutch-like prepositions rather than classifiers. Manhardt et al. argued that speech knowledge in hearing signers may constrain the use of classifiers. Although their focus was on hearing signers, their data showed considerable overlap between hearing and deaf signers.
On a theoretical level, several studies have argued that deaf signers spontaneously incorporate elements traceable to the ambient spoken language into their signing (e.g., De Meulder et al. 2019; Grosjean 2010; Stokoe 1960). Lillo-Martin et al. (2016) proposed the synthesis language model within a formal minimalist framework, explaining how signers may select syntactic elements from both ASL and spoken English during conversations. Although their work primarily focuses on hearing signers, the model does not depend on hearing levels and accounts for ASL-English codeswitching, crosslinguistic influence,
Role of Language Dominance in English Influence on ASL | 605 and calquing. The model reflects observations that the distinctions between these phenomena are often blurred (Treffers-Daller 2009).
These studies point toward a scenario where intentional or unintentional dense codeswitching is prevalent among proficient deaf signers, although none provide statistical evidence. If codeswitching impacts ASL styles, we would expect to observe a statistical association between ASL styles and language dominance, a predictor of codeswitching, in proficient deaf signers.
Language Dominance in the Signed Modality
In spoken languages, language dominance may influence codeswitching, including the magnitude, directions, and patterns of switching (e.g., Valdés-Fallis 1978; Olson 2024). Language dominance also predicts the presence of crosslinguistic influence (e.g., Amengual and Simonet 2020; Schmid and Yılmaz 2018). In those studies, language dominance is understood as a multifaceted construct, encompassing self-reports of quantitative language use in distinct domains, often including attitudes and self-assessed proficiency levels, too.
The understanding of language dominance as a multifaceted construct affecting a set of linguistic skills has evolved through the years, where greater speed and automaticity are often associated with the more dominant language (Birdsong 2016). In an early study, Bahrick et al. (1994) showed that recent language use and linguistic environment correlated better with performance on tasks requiring speedy retrieval of words than a lexical decision task (which is a measure of static knowledge). This is in line with Gertken, Amengual, and Birdsong (2014), who suggested that tasks that correlate with a multifaceted language dominance construct (e.g., speeded retrieval of words) reflect processing skills. Skills that tap into competence (e.g., selfpaced grammatical judgment tests) may not correlate well with a multifaceted construct of language dominance. Additional studies have confirmed that language dominance constructs are associated with processing skills by pairing objective measures with self-reports of language dominance. Objective measures include letter fluency (Shishkin and Ecke 2018), lexical translation tasks (Dunn and Fox Tree 2009), translating sentences (Flege, MacKay, and Piske 2002), and reaction times in which participants must identify patient and agent roles in sentences (Gertken, Birdsong, and Amengual 2012). Notably, language dominance can shift throughout an individual's lifespan (Birdsong 2016) and is more closely associated with ease of language processing, referred to in this study as fluency, than with static linguistic knowledge, referred to as proficiency.
The association between language dominance and automaticity could explain links between language dominance and codeswitching behavior. Switching to a dominant language seems more frequent than switching to a less dominant language, presumably because the more dominant language is more accessible (e.g., Heredia and Altarriba 2001; Olson 2024; Poulisse and Bongaerts 1994; Rodriguez-Fornells et al. 2012).
Signers have also been included in theoretical discussions about the effects of multifaceted language dominance constructs. Grosjean (2010) has discussed the fact that deaf signers may not necessarily be dominant in a sign language but could be dominant in a spoken language. For example, a deaf signer may be dominant in a spoken language overall by slightly preferring speech over sign language and being highly comfortable using written language.
Effects of language dominance on sign languages have been reported in at least two empirical studies. In the education context, Stewart (1985) observed that ASL-dominant students tended to retell stories in ASL, even if the stories had originally been told in Signed English. He argued that dominance effects resulted in students translating the stories into their dominant language, ASL. Dominance levels were based on teachers' ratings of students' skills in both Signed English and ASL. Another study by Lindeberg (2022b) showed that sign-sign bilinguals are subject to language dominance in their two sign languages. Self-reported language dominance correlated with phonological fluency tasks in their two sign languages, suggesting that signers vary in the extent they are dominant in their sign languages, with better lexical access in their dominant language.
Those two studies suggest that language dominance could predict the occurrence of spoken (or written) items in signed language. For example, if a signer is more dominant in English than ASL, English items may be more accessible, resulting in an English-like style of ASL.
Co-activation of ASL and English
Research shows that both bilingual speakers and deaf signers may activate both of their languages, even in monolingual contexts. This further supports the hypothesis that signers may dynamically incorporate spoken language elements into their signing. For instance, bilingual speakers have been shown to activate both languages in monolingual situations (e.g., Thierry and Wu 2007). Similarly, research has demonstrated that deaf signers activate signs when reading words in monolingual contexts (e.g., Kubus et al. 2015; Morford et al. 2011). More recent studies indicate that deaf signers also activate written or spoken words when responding to or producing signs (e.g., Hosemann et al. 2020; Hänel-Faulhaber et al. 2022; Gimeno-Martínez, Mädebach, and Baus 2021; Lee et al. 2019), confirming that this activation is bidirectional. Given this bidirectional activation, signers may communicate in a "bilingual language mode," where interlocutors share the same two languages (Grosjean 2010) and both languages are fully accessible for production (e.g., Bialystok 2024; Green and Wei 2014). Consequently, these findings support the hypothesis that signers' spoken languages are accessible while producing signs, potentially influencing their signed utterances.
The Bilingual Ecology of ASL
Generalizations about the effects of language dominance on bilingual behavior in various populations must be approached with caution, particularly when the data are biased toward spoken language ecologies. A key distinction in the linguistic ecology of deaf ASL signers is that ASL and English have interacted closely in deaf schools and families for over two centuries. American deaf and hard of hearing signers live in predominantly spoken language societies and are often at least somewhat bilingual in ASL and English. They almost exclusively use ASL with interlocutors who are also familiar with English.
In linguistic ecologies where a minority language is used almost exclusively by speakers who are also fluent in a majority language, a complete shift to the majority language is common (e.g., Haugen 1938; Wiltshire, Bird, and Hardwick 2022). Such language shifts can introduce variables like individual language attrition and weakened linguistic conventions. Identifying linguistic patterns in bilingual populations using a minority language can be challenging, as individuals may be at different stages of a shift toward monolingualism in the majority language (de Bruin, 2019).
In contrast, limited access to spoken language generally prevents deaf signers from fully shifting to a majority spoken language. This may contribute to the stable coexistence of ASL and English across generations. This stability allows for the linguistic examination of a minority language used exclusively by bilinguals while controlling for language shift variables. The close yet stable relationship between ASL and English may lead to a noticeable influence of English on ASL. Although this influence could be expected to correlate with language dominance, its prevalence might dilute or obscure any specific effects of language dominance. Supporting this idea, Johnston et al. 2016) suggest that spoken elements in signed discourse can appear random and unrelated to specific background factors.
Implications for Research
The question of whether language dominance hypothetically affects ASL styles has implications for sign language research in at least three areas.
First, when collecting grammatical data from sign languages, researchers may face challenges identifying the varying influence of spoken language on signing. If language dominance predicts the occurrence of specific elements in sign language, it could be included as an additional control variable, adding depth to grammatical analyses.
Second, accounting for any role language dominance plays in sign language production could improve the accuracy of mapping participants' static linguistic knowledge. A signer's grammatical knowledge might not be fully captured if their tendency to switch to English in ASL is not controlled.
Third, evidence that language dominance affects the occurrence of English-like items in ASL would support a scenario where signers spontaneously include English-based items in the production process of a signed utterance. When sampling data to test bilingual language production models (e.g., Bialystok 2024; Emmorey et al. 2008; Green and Wei 2014), one may not solely depend on modality,
Role of Language Dominance in English Influence on ASL | 609 equating signing with the production of ASL, but may need to consider whether a signer includes English-based items in their signing.
Research Question
The research question specifically examined whether there is a statistical association between language dominance and ASL style among 100 proficient deaf adult signers. Language dominance was measured using an adaptation of the Bilingual Language Profile (Birdsong, Gertken, and Amengual 2012), widely used in previous research (e.g., Barking, Backus, and Mos 2022; Onnis, Chun, and Lou-Magnuson 2018; Stocker and Berthele 2020). Signing styles were quantified based on the use of mouthings and classifiers. An increase in mouthings and a decrease in classifiers would indicate a more English-like signing style, whereas a decrease in mouthings and an increase in classifiers would suggest a more ASL-like signing style.
A positive result would suggest that the inclusion of English-like items in ASL is influenced by the degree of access to both languages. In other words, signers may choose the most accessible linguistic elements, a hallmark of codeswitching and crosslinguistic influence.
A negative answer could indicate that codeswitching or crosslinguistic influence is a minor or nonsignificant factor in the variation observed among deaf ASL signers. Other factors previously highlighted in the literature may better predict styles of ASL, including individual grammar (Herbert and Piers 2017), attitudes (Lucas and Valli 1992), and random variation ( Johnston et al. 2016). A negative answer could also suggest that the measures used in this study, mouthings and classifiers, do not serve as indicators of codeswitching and crosslinguistic influence.
In this article, deaf refers to individuals who are deaf or hard of hearing, and native describes those who grew up with at least one deaf caretaker.
Method
Participants
One hundred adult deaf signers (seventy-four females; M age = 37.18, SD = 8.94) participated between June and October 2021. All were exposed to ASL before age eight (M = 1.56, SD = 2.38). The majority (n = 59) had attended both mainstream and deaf schools, while thirteen participants had exclusively attended mainstream programs, and twenty-eight had only attended deaf schools. The number of participants with at least one deaf parent (n = 51) was nearly equal to those with only hearing parents (n = 49). Data on race/ethnicity were not collected, as this was not part of the inclusion or exclusion criteria, limiting the generalizability of the results.
Acquisition of ASL before age eight was an inclusion criterion to control for variation from late ASL acquisition, either as a first or second language. This criterion was used as a proxy for native-like competence in ASL. Also, a well-validated proficiency test capable of distinguishing between proficiency and fluency levels-and sensitive enough to capture variation among early signers without taking a disproportionate share of the overall session-was not available. The assumption that native and early signers tend to be highly proficient is supported by literature, particularly in studies comparing deaf native and early signers with late signers (e.g., Boudreault and Mayberry 2006; Cormier et al. 2012; Hauser et al. 2016; Rubio-Fernandez et al. 2022). However, it is acknowledged that early acquisition does not always guarantee proficiency (Cheng et al. 2021), as language loss can occur after early exposure (Schmid 2022).
The rationale for still using the age of acquisition as a proxy of proficiency in this study is that adult deaf and hard of hearing signers in the United States typically continue to use ASL after initial exposure because ASL is the only language fully accessible to them. Additionally, the target sample size of 100 participants was intended to mitigate the influence of less proficient signers (potential outliers) on statistical models. According to self-reports, the sample was highly proficient in ASL, with a mean proficiency score of 10.32 (SD = 1.51, range = 1-11), where eleven represents the maximum level of proficiency.
Materials and Measures
Participants completed a language dominance questionnaire during individual videoconferences and retold four video-based stories. They also took fluency and English proficiency tests as part of a larger study
(Lindeberg 2022a). Below are details on the questionnaire and storyretelling tasks.
The Language Dominance Questionnaire. The language dominance questionnaire, adapted from the Bilingual Language Profile (Birdsong et al. 2012), surveyed four communication modes: ASL, written English, spoken English, and English signing. Spoken English included listening, speech, and lipreading, while English-signing encompassed SimCom, Signed Exact English, and other English-like forms, as well as spontaneous English-like signing used to accommodate novice signers. The formally labeled forms of English-signing are familiar to members of the Deaf community and represent artificial attempts to visualize spoken language through conventional signs (Scott and Henner 2021).
The questionnaire covered six areas: language use, history, proficiency, attitudes, written English experience, and hearing experience. Adjustments were made to align with deaf community terminology (e.g., using "native" to reflect parental hearing status instead of childhood language). The questions were pilot-tested with native and nonnative signers.
ASL and English dominance were calculated separately, with English-related questions (including experience with written English and hearing) combined to reflect overall English dominance. The language dominance index was derived by subtracting the ASL score from the English score, with equal weighting across the six areas.
In the original Bilingual Language Profile (Birdsong et al. 2012), a score of zero indicates balanced bilingualism. However, in this study, the inclusion of literacy and hearing-related questions for English, but not ASL, resulted in a bias toward English dominance when participants achieved maximum scores in both languages. Adjusting the scoring to produce a zero for perfectly balanced bilinguals would require subjective definitions of balance.
The full questionnaire and scoring system are detailed in Lindeberg (2022a).
Retelling Stories in ASL. The ASL story retellings were based on four short video clips, presented to participants in the same order. These clips featured characters engaged in physical actions designed to elicit classifiers. Each signed response was annotated using ELAN (2022), a video annotation software.
1. Pat and Mat in a Movie (Marek 2016)-1:40-minute excerpt
2. Johnny English Reborn (Parker 2011)-2:02-minute excerpt
3. The Pink Panther Strikes Again (Edwards 1976)-1:23-minute excerpt
4. Johnny English (Howitt 2003)-2:14-minute excerpt
Mouthings were defined as any lip movements resembling spoken English. No judgments were made regarding whether these mouthings had become lexicalized morphemes in ASL. Nonmanual adverbials not derived from English were excluded. I annotated all mouthings myself. Although I am not a native English speaker, I could easily identify mouthings accompanying manual signs due to their clear resemblance to spoken English words. Given this clarity, additional verification was deemed unnecessary.
Any productive sign that included a handshape representing an object was annotated as a classifier. However, the distinction between lexical signs and classifiers can be unclear (Ferrara and Halvorsen 2017). For instance, the ASL sign for picture may resemble a classifier depicting a flash. Context and the manner of use often determine whether a sign is lexical or an entity classifier. When the signer appeared to depict a flash rather than merely the sign for picture, the sign was annotated as a classifier.
To ensure consistent identification of classifiers, the number of classifiers produced by twenty randomly selected participants retelling the fourth clip (Howitt 2003) was coded by a hearing undergraduate ASL assistant, who also was a native signer, at the University of Texas at Austin and compared to my annotations. Before annotating, the student reviewed Cormier, Smith, and Sevcikova-Sehyr (2015), which outlines practical issues in annotating classifiers. We discussed ambiguous examples to achieve a common understanding of what constituted a classifier. The undergraduate student then annotated independently, without access to my annotations. The interrater reliability (Spearman's correlation) between our annotations was rs = .90, p < .001, indicating robust classifier identification.
Two variables were created from these annotations: the proportion of mouthings and the number of classifiers per minute.
The proportion of signs accompanied by mouthings served as an indicator of the overall presence of mouthings in an utterance (e.g., Herbert and Pires 2017; Johnston et al. 2016; Nadolske and Rosenstock 2007). It was calculated by dividing each participant's number of signs with mouthings by their total number of signs produced across retellings of all four video clips.
For classifiers, however, the number of classifiers per minute was used instead of the proportion of classifiers (total number of classifiers divided by total number of signs), as the former captures important information about fluency. Two signers with similar classifier proportions may differ in how many classifiers they are able to process within the same timeframe. To more accurately reflect fluency, the study used classifiers per minute rather than the proportion of classifiers. This measure was obtained by dividing each participant's total number of classifier signs produced across retellings of all four video clips by the total duration (in minutes) of those retellings.
Procedure
Participants were recruited through snowball sampling. Prospective participants were first screened using informal questions to ensure they met the inclusion criteria. The final sample was asked for consent in both ASL and written English. Most participants completed the language dominance questionnaire in written English through a website before the storytelling task. While video-based ASL questions were considered, they can take considerably longer to watch, and participants may still require clarifications. Instead, I, the test administrator, signed the questions in ASL for those who preferred it. The questionnaire was delivered using the LimeSurvey software package (LimeSurvey GmbH [n.d.]). Participants were informed that "spoken English" referred to producing audible sentences in English, distinguishing it from written forms. "English-signing" was defined as adapting to less proficient signers by signing with English syntax or using a sign system (e.g., SimCom or Signed Exact English). Most participants completed the questionnaire during a videoconference (via Zoom, FaceTime, or video phone), while some completed it independently prior to the session.
The storytelling task was conducted during the same videoconference with me. I was the sole administrator for all participants to control for potential variations introduced by accommodating to interlocutors (Lucas and Valli 1992). My ASL proficiency, according to the ASLPI (a common test in the United States), is 4+ (proficient), reflecting acquisition of ASL in early adolescence after acquiring another sign language at birth. I use ASL daily, including with my wife and children, who are native ASL signers.
Before retelling the video-based stories, participants were asked to answer the following question in English print within one minute in ASL: "What is the difference between a sheep and a dog?" This exercise was meant to "turn on" both ASL and English and prepare participants for the retelling sessions.
For each of the four clips, participants were asked to retell the story in under two minutes immediately after viewing. Still images from the clips were displayed during the retelling to aid in recalling content. The clips varied in complexity and detail, with their order designed to gradually increase the task's difficulty, starting with Pat and Mat in a Movie to avoid overwhelming at the outset. To prevent a ceiling effect in highly proficient signers, the final clip, Johnny English, included relatively more details and actions. The retelling task was screen-recorded for later analysis in ELAN.
Statistical Methods
This study employed path analysis, a type of structural equation modeling (SEM) that focuses exclusively on direct paths among observed variables. No latent variables were included, allowing for a direct examination of the relationships between the language dominance indices, native status, and the outcomes proportion of mouthings and average classifiers per minute.
Statistical differences in linguistic skills between native and nonnative signers have been well-documented (e.g., Boudreault and Mayberry 2006; Hauser et al. 2016). A binary variable distinguishing native from nonnative signers was included to control for native status.
The covariance between the response variables was added to test whether the relationship between mouthings and classifiers reported in the literature (e.g., Herbert and Pires 2017; Hill,2012; Johnston et al. 2016; Lucas and Valli 1992) could be replicated in this study. Additionally, covariance between language dominance and native status was included to assess differences between native and nonnative signers.
Before data collection, a power analysis was conducted for a more extensive study (Lindeberg 2022a). In this article, an SEM was used to specifically examine language dominance and native status. Post hoc power analyses for the SEM indicated that the sample size provided high power (approaching 1.00) for detecting the reported effects.
Results
All participants' total duration of signing was eight hours and twentythree minutes. The participants produced 46,181 signs, which included 10,099 classifier constructions and 24,178 mouthings.
The proportion of mouthings was similar across stories, as indicated by an analysis of variance from a mixed-effects model with mouthings as the dependent variable, language dominance index, story, and native status as fixed effects, and participants as a random effect, F(3, 297) = 2.53, p = 0.06. In contrast, the average number of classifiers per minute varied across stories, F(3, 297) = 33.73, p < .001, based on an identical model with classifiers per minute as the dependent variable.
The path analysis, conducted using the lavaan package in R (Rosseel 2012; R Core Team 2022), consisted of two regression models and covariances between the response variables (proportion of mouthings and classifiers per minute) and between the independent variables (language dominance index and native status). The language dominance index and classifiers-per-minute variables showed much greater variation compared to the proportion of mouthings and native
Model estimation method: Maximum Likelihood. Optimization method: nonlinear minimization subject to box constraints. status. To address this, the language dominance index and classifiersper-minute variables were scaled to have a mean of 0 and a standard deviation of 1. The results from the path analysis model are displayed in table 2.
The path analysis yielded a perfect fit, as the number of unique pieces and estimated parameters were equal. To examine the fit, the impact of native status on the outcome variables was set to equal each other in a second separate test model to reduce the number of estimated variables by one. The degree of freedom was increased to one, and chi-square test, Comparative Fit Index, and Standardized Root Mean Residual showed good fit. Root Mean Square Error of Approximation (RMSEA) values had a wide confidence and a p-value above 0.05; however, the RMSEA values were not considered given the low degree of freedom (Kenny, Kaniskan, and McCoach 2015). The statistical associations in both models were also comparable to separate linear regression models and t-tests.
As shown in table 2, language dominance negatively predicted the proportion of mouthings, suggesting that higher ASL dominance is associated with a lower proportion of mouthings. Conversely, ASL dominance was positively related to using classifiers per minute, indicating that higher ASL dominance indices increase the number of classifiers per minute. The effect of native status was weak in both regression models and only approached significance in the model with classifiers per minute as the response variable. Classifiers per minute (R2 = .45) may more robustly mirror language dominance levels than the utilization of mouthings (R2 = .33).
The covariances showed a negative relationship between classifiers per minute and the proportion of mouthings, indicating that as classifier use increased, mouthings, as defined earlier for this study, decreased. Additionally, a positive covariance between language dominance and native status suggested that native signers in this cohort tended to have greater ASL dominance than nonnatives.
Figure 3 illustrates the statistical relationships in the model.
Discussion
Results confirmed that language dominance significantly predicted the use of ASL classifiers and English mouthings among early and native signers. This suggests that language dominance indices, which account for various factors, may serve as a better indirect measurement of fluency than native status alone. The moderate R2 values indicate that while language dominance explains some variation, other factors, including adaptation to interlocutors (Lucas and Valli 1992), sociolinguistic factors (Hill 2012), proficiency (Herbert and Piers 2017), and random variation ( Johnston et al., 2016) likely contribute to signing style, too.
Additionally, the response variables (mouthings and classifiers) showed significant covariance, aligning with previous studies reporting a negative relationship between these variables (e.g., Herbert and Pires 2017; Lucas and Valli 1992). Native signers were more likely to be dominant in ASL, as suggested by the positive covariance between language dominance and native status, even though native status itself did not significantly predict variation in signing styles when controlling for language dominance.
The statistical association between language dominance and ASL styles in a proficient sample is consistent with studies of language dominance in spoken languages, where dominance correlates closely with ease of processing (e.g., Bahrick et al. 1994; Birdsong 2016; Gertken et al. 2014). The construct of language dominance may be independent of modality and apply to deaf signers who are bilingual in the ambient majority language. ASL-dominant signers likely access classifiers more fluently, while English-dominant signers may more easily produce English-like constructions, including mouthings. In other words, an ASL-dominant signer and an English-dominant signer may be familiar with the same types of constructions in ASL and English but differ in their ease of access to ASL-like and English-like syntactic and lexical constructions. The effect of language dominance on ASL style in this study confirms the findings of Lindeberg (2022b), where language dominance affected sign language fluency in bilinguals of two sign languages.
The bilingual behavior observed in this study suggests that codeswitching and crosslinguistic influence may be prevalent in the signed modality. Given the association between relative dominance in ASL and English and styles of ASL in a proficient sample, signers appear to dynamically incorporate English items, modulated by their dominance in ASL and English. All participants used mouthings and classifiers at varying degrees, suggesting an omnipresence of both ASL and English (see also Johnston et al. 2016 for similar observations). In spoken language studies, the proportion of switching or influence is considered very high when it occurs in around 30 percent of utterances (Beatty-Martínez et al. 2020b).
Despite the fact that ASL and English have been in a close relationship for two centuries, language dominance predicted ASL styles. This finding suggests that ASL and English have not blended into a stable mixed language with a fixed grammar in the bilingual mind. Rather, signers appear familiar with distinct conventions in both languages, enabling them to adopt positions along a continuum of signing styles that reflect their current language dominance level.
This study did not examine individual switching between ASL-like and English-like signing (e.g., Lucas and Valli 1992). Variations in the type and amount of action in the four video clips likely also played a role in the uneven distribution of classifiers per minute across the retelling tasks. Tools to differentiate between shifts in ASL styles and influences from the stimuli were not available for this study.
Instead of focusing on switching between styles of ASL, this study examined signers' inclination to incorporate English-like items into their ASL based on language dominance. The profile of this study's sample resembles that of habitual dense codeswitchers in spoken languages, where interlocutors who are bilingual in the same two languages frequently engage in codeswitching. Beatty-Martínez, Navarro-Torres, and Dussias (2020a) suggested that the relevance of language membership is minimized in habitual codeswitchers. If habitual codeswitching speakers stop paying attention to language boundaries, we might expect signers to do the same. As the influence of spoken language appears prevalent in signed production, the boundary between aspects of ASL and English, such as specific lexemes or syntactic constructions, may naturally become blurred.
Translanguaging, a framework where the boundaries between languages are often considered social constructs (e.g., Baker and Wright 2001; Otheguy, García, and Reid 2015), may offer a useful lens to describe the apparent abundance of English influence in ASL (e.g., De Meulder et al. 2019). According to the translanguaging framework presented by Otheguy et al. (2015), signers might draw from a unitary linguistic resource with no clear distinction between ASL and English.
This study did not provide evidence for unitary linguistic systems in bilinguals, and the term "translanguaging" could, in practice, be similar to descriptions of dense codeswitching (Treffers-Daller 2024), where distinctions between languages may become blurred. Theoretically, some signers, particularly those who display more English-like signing, may perceive their linguistic system as more unitary compared to ASL-dominant signers, who may be less accustomed to incorporating English items. This aspect was not examined in this study, but future research could explore signers' perceptions of linguistic boundaries.
When considering formal theoretical models aimed at bilinguals, the data in this study aligns with the synthesis language model (Lillo-Martin et al. 2016), which predicts the mixing of items from a spoken and signed language. For example, classifiers, which can be seen as syntactic choices when viewed as morphologically complex constructions of bound morphemes (Sandler and Lillo-Martin 2006, 91), may be generated instead of structures that follow English conventions during syntactic derivation.
While this study focused on the ability to produce linguistic items, several measures in the language dominance questionnaire are arguably sociolinguistic in nature. For example, questions about current use, years of use, and language attitudes reflect social patterns. ASL-like and English-like styles can thus be viewed as sociolinguistic parameters as well (e.g., Lucas and Valli 1992; Hill 2012). For example, in the early twentieth century, deaf white students in the United States were more likely exposed to oral education than deaf Black students (Settles 1940), which may have resulted in a higher proportion of white signers becoming dominant in English. Similarly, deaf children living in rural areas may have limited access to schools with ASL instruction, highlighting how social factors hypothetically can influence language dominance.
Application of Language Dominance to Language Documentation and Testing
The observed effect of language dominance in this study offers insights into language documentation, proficiency testing, and the development of bilingual production models.
Language documentation. When documenting sign languages, distinguishing between spoken language influence and features particular to a sign language can be challenging ( Johnston et al. 2007). The language dominance variable may help differentiate between elements that reflect spoken language influence and those that are stable and invariant across signers in data from elicited tasks or conversational corpora. This study found that language dominance affects the influence of spoken language when controlling for factors like topic, second language effects (by including only native and early signers), and interlocutors. Using language dominance as a controlling factor could potentially reduce inconsistency and improve replicability in crosslinguistic studies. However, whether the effects of language dominance diminish when these factors are not controlled for remains to be explored.
Proficiency testing. Combining several objective measures of ASL with language dominance questionnaires, as done in research on spoken languages (e.g., Bahrick et al. 1994; Gertken et al. 2014), could further inform us which aspects of a test optimally measure proficiency versus those more related to fluency. Pairing multiple objective behavioral measures might also help identify which parts of the ASL grammar are more sensitive to fluency levels. For instance, Amengual and Simonet (2020) found that linguistic skills that are harder to maintain may correlate less with language dominance. Specifically, they showed that phonemes occurring relatively infrequently with unpredictable distribution were less affected by language dominance.
Bilingual production models. The interaction between language dominance and signing style may also have implications for tests aiming to develop bilingual production models. The positive statistical association between English-like signing and dominance in English implies that certain linguistic items in ASL production are more closely associated with English than others, challenging the notion that modality alone can determine the linguistic affiliation of items in a signed utterance.
Bilingual production models (e.g., Bialystok 2024; Emmorey et al. 2008; Green and Wei 2014) outline how two languages co-occur in an utterance. However, challenges may arise when the language affiliation of items is unclear, as in sign languages (e.g., Johnston et al. 2007). While this study did not provide a method for distinguishing ASL from English items, it showed that signing styles align with language dominance in ASL and English, suggesting that some aspects of grammar may be amodal; that is, aspects of English grammar may appear both in speech and in manual signs.
For some linguistic items, the language affiliation is clear. For example, English-voiced speech is rarely mixed into ASL conversations (e.g., Emmorey et al. 2008), and ASL classifiers typically do not occur in spoken English. But other aspects, such as English word order and morphology that contribute to English-like signing, may be less tied to a specific modality. Distinguishing between amodal items (which appear in both spoken and signed languages) and modality-specific items (which appear only in one modality) could be crucial when designing tests to explore how signers manage their sign language and the ambient majority language (e.g., Emmorey et al. 2008; Manhardt et al. 2021; Ormel et al. 2012).
The results of behavioral tasks and electrophysiological (ERP) measures could depend on whether the stimuli used, such as syntactic constructions or the semantics of lexemes, appear in both ASL and English or in only one of them. If a signer, for example, assigns the same semantics to an infinitive verb and uses it in similar syntactic environments in both ASL and English, they may largely draw on the same cognitive resources when producing the verb rather than selecting an item specifically from ASL or English. Conversely, a signer who rarely uses this infinitive verb construction in ASL might produce different behavioral or ERP responses for the same item compared to a signer who uses it more frequently in ASL. These scenarios are tentative, serving as hypotheses that suggest avenues for further investigation.
Role of Language Dominance in English Influence on ASL | 623
Limitations
This study has several limitations that should be considered when interpreting the findings. One significant limitation is the lack of direct measures of overall ASL proficiency, which may vary among deaf native and early signers. This study primarily relied on self-reported proficiency, which lacks precision in capturing actual proficiency levels (e.g., Tomoschuk, Ferreira, and Gollan 2019). For example, Herbert and Pires (2017) argued that signing variation could be explained by idiosyncratic grammars, suggesting that proficiency levels are significant predictors. In this study, only two proxy measures of syntactic choices, classifiers and mouthings, were used. A more comprehensive examination of proficiency levels could provide a clearer understanding of variation within this population.
Another limitation involves the language dominance construct used in the current study, which was tailored for a general population of native and early ASL-English bilinguals. The participants were recruited by snowball sampling and likely did not represent the true population of adult deaf native and early signers in the United States. Although only 5-10 percent of deaf ASL signers may be considered native (Mitchell and Karchmer 2004), 51 percent of participants in this study were native signers.
The language dominance construct used here may not apply universally across different populations, leading to potential replication issues. For example, questions related to educational settings or the use of cochlear implants and hearing aids might not be applicable in other countries or to subpopulations within the United States. Future studies should investigate how well the construct in this study applies to other populations of sign-spoken bilinguals.
The influence of the test administrator on signing styles presents another potential issue for replicability, especially when different test administrators are used across studies. One solution to mitigate this issue is to have at least two test administrators with differing styles or proficiency levels and use them as independent variables to control for interlocutor adaptations.
Finally, this study did not test the research question in naturalistic conversations. It remains unclear whether the effects of language dominance observed here would emerge in less controlled environments.
624 | Sign Language Studies
Conclusion
This study found a significant relationship between language dominance and signing styles, suggesting an impact of language dominance on ASL production. Notably, this influence was observed in both native and early nonnative signers, representing a relatively proficient population. These findings indicate that the presence of English elements in ASL is not solely due to limited ASL proficiency or sociolinguistic factors, but is also shaped by the relative strength of ASL and English. Signers more dominant in English seem to have better access to English-like constructions, while those dominant in ASL have greater access to ASL-specific structures, thereby influencing their overall signing style.
The results suggest that codeswitching behavior and crosslinguistic influence may be significant factors in ASL production. These findings have implications for sign language documentation, the development of proficiency tests, and the refinement of bilingual processing models.
Data Availability Statement
The data and scripts supporting the findings of this study are available in Open Science Framework at https://osf.io/jn7su/?view_only =12999ccd5a1f442cbea4ec2899cfa857.
Competing Interests Declaration
Competing interests: The author(s) declare none.
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Author's Note
Correspondence regarding this article can be addressed to Dag Johan Lindeberg, [email protected].
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