Language history attenuates syntactic prediction in L1 processing

Abstract

An eye-tracking experiment in the Visual World Paradigm was conducted to examine the effects of language history on the predictive parsing of sentences containing relative clauses in the first-learned language of fluent bilingual adults. We compared heritage speakers of Spanish (HSs)—who had spent most of their lives immersed in an English-dominant society—to Spanish–English late bilinguals (LBs), who did not begin immersion in an English-dominant society until adulthood. Consistent with studies of monolinguals, the LBs demonstrated a subject/object relative clause processing asymmetry, i.e. a processing advantage during subject relative clauses and a processing disadvantage during object relative clauses. This suggests that the LBs actively predicted the syntactic structure of subject relative clauses, consistent with the active filler hypothesis. The HSs, on the other hand, did not exhibit this processing asymmetry, suggesting less active prediction. We conclude, therefore, that decreased exposure to the first-learned language causes less active prediction in first-language processing, which causes both disadvantages, and interestingly, advantages, in processing speed.

Introduction

Predictive processing in monolinguals

Since Cooper (1974) first demonstrated the closely time-locked relationship between spoken language processing and fixations of the eyes on elements of a visual scene, the Visual World Paradigm (VWP: Allopenna et al. 1998) has proven to be a powerful methodology for studying online language processing. Although some aspects of VWP studies vary depending on the phenomenon of interest (for a review, see Huettig et al. 2011), VWP studies involve tracking the real-time location of participants’ eye gaze on a visual display while they listen to spoken language and complete a task, such as clicking on an image. The VWP has been argued to provide ecologically valid measures of language processing because (1) the linguistic input is auditory, so confounds caused by variation in levels of literacy are avoided; (2) the input is associated with a behavioral goal, so it is relevant to the listener; (3) the input is associated with real-world referents; and (4) no metalinguistic judgments are required (Tanenhaus et al. 1995).

Most notably, research in the VWP has demonstrated that listeners actively predict upcoming linguistic information, before it is actually processed, using semantic and morphosyntactic cues from processed spoken language. For example, if a listener views a visual scene consisting of a cake and various distractor objects while listening to sentences such as the boy will move the cake or the boy will eat the cake, their gaze will fixate on the depiction of the cake before the onset of the spoken word cake if the sentence contains the verb eat (because the cake is the only edible object in the scene). While listening to the sentence with the verb move, on the other hand, fixations on the cake will not begin until the onset of the word cake. This demonstrates that listeners utilize selectional information carried by verbs to facilitate prediction of potential objects (Altmann and Kamide 1999).

A number of studies have attempted to identify factors which modulate the ability of listeners to predict a verb’s object from its semantic content, and have found that age (Borovsky et al. 2012), comprehension ability (Nation et al. 2003), and receptive vocabulary size (Mani and Huettig 2012) do not affect predictive ability, such that children and adults across a range of receptive ability show equally robust predictive processing. However, productive vocabulary size (Mani and Huettig 2012) has been shown to correlate positively with predictive ability. This relationship between productive and predictive ability suggests that predictive processing might be facilitated by a kind of emulation during processing (e.g., Chang et al. 2006; Pickering and Garrod 2007). In other words, predictive processing might depend on “covert imitation” by the production system (Pickering and Garrod 2007).

Kamide et al. (2003) demonstrated that the semantic information recruited by listeners to predict upcoming information does not come only from the verb, but rather, selectional information carried by the sentence’s agent also facilitates prediction. Moreover, listeners make semantic predictions about more than just the upcoming theme; they also predict the potential goal of the verb. Finally, Kamide et al. (2003) observed Japanese monolingual participants making predictions based on case marking morphemes, demonstrating that listeners utilize morphosyntactic cues, in addition to semantic cues, to make predictions about upcoming linguistic information. This suggests that predictive processing does not rely simply on lexical associations stored in memory (e.g., Bar 2007, 2009), but also relies on more abstract mechanisms of syntactic parsing.

A number of other morphosyntactic cues have been shown to be used by listeners to facilitate prediction. For example, temporal information encoded in grammatical tense is used by listeners, who are more likely to look at a full glass than an empty glass when they hear the phrase the man will drink…, and vice versa when they hear the phrase the man has drunk… (Altmann and Kamide 2007). Grammatical gender is perhaps the most widely studied morphosyntactic predictive cue (e.g., Brouwer et al. 2017; Dahan et al. 2000; Lew-Williams and Fernald 2007). The results of these studies demonstrate that both child and adult monolingual speakers of languages with grammatical gender systems consistently use gender information encoded on a determiner to predict the upcoming noun. Similarly to semantic prediction, morphosyntactic predictive ability has been shown to correlate with productive ability, but not age or receptive ability (Brouwer et al. 2017; Lew-Williams and Fernald 2007), suggesting an emulative basis of predictive language processing regardless of the type of cue.

Predictive processing in bilinguals

Given the apparent ubiquity of prediction in monolingual sentence processing, it becomes useful to ask if and how prediction manifests in bilingual and nonnative sentence processing. The majority of studies of bilingual and nonnative predictive processing have focused on the use of morphosyntactic cues, although Brouwer et al. (2017) studied semantic prediction in bilingual children along the lines of Altmann and Kamide (1999), and found that 4 and 5 year old bilingual children exhibited semantic prediction in their L2 to an equal degree as their monolingual peers. In fact, among the 4 year olds, bilingual participants exhibited more rapid prediction than monolingual participants, suggesting a possible bilingual advantage in semantic prediction, similar to that seen in non-linguistic anticipation tasks (e.g., Bonifacci et al. 2011).

To our knowledge, studies of bilingual and nonnative morphosyntactic prediction have focused mainly if not exclusively on cues from grammatical gender marking, perhaps due to the robustness of their predictive utility in monolinguals (e.g., Brouwer et al. 2017; Dahan et al. 2000; Lew-Williams and Fernald 2007), and the difficulty of their acquisition by adult L2 learners compared to child L1 learners (e.g., Karmiloff-Smith 1979; Scherag et al. 2004; Slobin 1985). The main questions investigated have been whether bilinguals and L2 learners use grammatical gender to facilitate prediction similarly to monolinguals, and if so, what factors modulate this predictive ability. Despite the narrow range of phenomena investigated, the results bearing on bilingual and nonnative morphosyntactic prediction have been quite mixed. In an auditory naming task, early bilinguals, but not fluent late bilinguals, behaved like monolinguals in utilizing gender marking to facilitate prediction in their L2 (Guillelmon and Grosjean 2001). Similarly, in an event-related potentials (ERP) study, L2 learners did not exhibit the N400 effect elicited in native speakers when encountering an unexpectedly gendered noun (Martin et al. 2013); however, another ERP study reported an equivalent N400 across monolinguals, late bilinguals, and early bilinguals when encountering an unexpected gender marking (Foucart et al. 2014).

Within the VWP, the results have been similarly mixed. Lew-Williams and Fernald (2010) found that L2 learners of Spanish did not exhibit prediction from gender marking, unlike monolingual Spanish speakers (Lew-Williams and Fernald 2007). However, the L2 learners in this study were only exposed to Spanish in the classroom, and all had similar levels of proficiency. Dussias et al. (2013) found, on the other hand, that highly proficient L2 Spanish listeners did make predictions based on grammatical gender, similarly to L1 listeners, while less proficient L2 listeners did not. This study also found that the presence (or absence) of grammatical gender in the L1 affected predictive processing in the L2, such that L1 Italian-L2 Spanish listeners made more efficient predictions in Spanish than their L1 English counterparts, because Italian has a grammatical gender system, and English does not (Dussias et al. 2013). This is consistent with the finding that syntactic similarity between the L1 and the L2 facilitates more efficient processing in the L2 (Tolentino and Tokowicz 2011). Another factor that has been found to be relevant to L2 predictive processing is productive accuracy in the L2. For example, among L1 English-L2 German listeners, only those L2 learners who were the most accurate in producing German gender marking used gender information to make predictions about the upcoming noun, similarly to German monolinguals (Hopp 2012). This constitutes further support for the emulative account of predictive processing in both the L1 and the L2. Finally, Grüter et al. (2012) found that adult L2 learners of Spanish exhibited predictive processing from gender marking only on novel nouns, and not on familiar nouns, suggesting that L2 learners might utilize different strategies in making predictions than monolingual listeners.

In sum, although L2 speakers of languages with grammatical gender systems clearly utilize these cues, to some degree, to make predictions in L2 processing, the literature has also discovered limitations on predictive ability in the L2, such as proficiency, L1 similarity/difference, and productive accuracy. Grüter et al. (2014) proposed a generalization from this set of findings that nonnative speakers of a language have a Reduced Ability to Generate Expectations (RAGE) while processing that language. In other words, nonnative speakers have attenuated predictive processing of their nonnative language compared to native speakers. The RAGE hypothesis is consistent with findings in other experimental paradigms, e.g., lexical naming tasks, that relative dominance in a language corresponds to the “speed with which speakers access its vocabulary and structure-building operations” (O’Grady et al. 2009), such that the structures of a less dominant language are accessed more slowly. In situations where the L2 is the less dominant language, then, we can see the RAGE hypothesis as adding expectations to the list of structures which are accessed more slowly.

Although the research to date on L2 predictive processing has been quite fruitful, research on the effects of bilingualism on L1 predictive processing has been, to our knowledge, so far nonexistent. This constitutes an important gap in the literature on bilingual prediction, given that a number of studies have demonstrated that exposure to a second language can impact first-language processing. For example, when tested in their L1 (Spanish), Spanish–English bilinguals who were dominant in their L2 (English) exhibited the attachment preferences of English monolinguals for ambiguous relative clauses, i.e. low attachment, rather than the attachment preferences of Spanish monolinguals, i.e. high attachment (Dussias and Sagarra 2007; Fernandez 2002). Other research has characterized the change in L1 processing caused by bilingualism as a kind of weakening (Gollan et al. 2008; O’Grady et al. 2009) or attrition (Polinsky 2011), especially in cases where the L1 is a societal minority language or heritage language, and the L2 the societal majority language. The unique value of studying L1 processing in bilinguals is that it allows one to tease apart the effects of proficiency, age of acquisition, order of acquisition, and other factors known to impact linguistic processing which often confound each other in L2 processing (e.g., Puig-Mayenco et al. 2018). The present study aims to be the first attempt at studying how language history in bilinguals affects predictive processing in the L1, using the well-attested relative clause processing asymmetry as the linguistic structure of interest.

Relative clause processing

A wealth of experimental studies have demonstrated that subject relative clauses (SRCs), such as (1) below, are easier, faster, or less costly to process than object relative clauses (ORCs), such as (2).

  1. 1.

    The cat, that bites the rabbit, kicks the dog.

  2. 2.

    The cat, that the rabbit bites, kicks the dog.

This processing asymmetry, or SRC preference, has been found in monolinguals of English (e.g., Traxler et al. 2002), Spanish (Betancort et al. 2009) and a host of other languages from a range of genetic families, including, for example, Dutch (Frazier 1987; Mak et al. 2002), Hungarian (MacWhinney and Pleh 1988), Turkish (Kahraman et al. 2010), Hebrew (Arnon 2005), Korean (Kwon et al. 2013; Miyamoto and Nakamura 2003), and Mandarin (Hu et al. 2016). The SRC preference has also been replicated across various experimental methodologies, including eye-tracking of reading (Betancort et al. 2009; Traxler et al. 2002), pupillometry (Just and Carpenter 1993; Piquado et al. 2010), event-related potentials (ERP: King and Kutas 1995), functional magnetic resonance imaging (fMRI: Caplan et al. 2002; Cooke et al. 2002; Just et al. 1996), and positron emission tomography (PET: Caplan et al. 2000; Stromswold et al. 1996).Footnote 1

The widespread SRC preference has been argued to reflect an active filler parsing strategy, whereby the parser actively tries to complete unbounded dependencies as soon as possible by filling potential gaps at the first available opportunity (e.g., Clifton and Frazier 1989; Frazier 1987; Stowe 1986). In both SRCs and ORCs, a potential gap occurs immediately after the relativizer (e.g., that or who in English; que or quien in Spanish), so the parser predicts that it will be filled with the relativized noun. In the case of an SRC, this parse is correct, and the sentence is processed smoothly. In the case of an ORC, on the other hand, this parse is incorrect, and the parser must reanalyze, costing time and cognitive resources. According to this account, the SRC/ORC processing asymmetry is the product of predictive syntactic processing, where prediction succeeds for SRCs, giving the listener a processing advantage, and fails for ORCs, causing the listener a processing disadvantage.

To our knowledge, no studies have yet examined the effects of bilingualism on the relative clause processing asymmetry. However, a number of studies have investigated the effects of bilingualism on relative clause attachment preferences (Dussias and Sagarra 2007; Fernandez 2002; White et al. 2013), as well as accuracy in the production (Ezeizabarrena et al. 2017) and comprehension (Chan et al. 2017; Polinsky 2011) of relative clauses. In general, these studies have found that decreased dominance in one language of a bilingual causes greater transfer from the other language, decreased accuracy in production, and decreased accuracy in comprehension of relative clauses in that language. Moreover, ORCs are more susceptible to a decrease in accuracy than SRCs, such that, for example, English-dominant Russian–English bilinguals demonstrate decreased comprehension accuracy of ORCs compared to SRCs in Russian, while Russian monolingual adults and children, and more balanced Russian–English bilingual children, perform at ceiling in comprehending both types of relative clauses (Polinsky 2011).

Moreover, although the VWP has been successfully used to study other aspects of relative clause processing in a number of languages (Nakamura et al. 2012 in Japanese; Wu et al. 2014 in Chinese), no studies have yet utilized the VWP to investigate the relative clause processing asymmetry in any language, in monolinguals or bilinguals. For this reason, the present study constitutes a novel contribution to the literatures on the VWP, relative clause processing, and bilingual processing.

The present study

In this study, participants’ gaze fixations were recorded while they listened to auditorily presented Spanish sentences with either an SRC or ORC and decided which of three images (one target and two distractors) was best described by the sentence. We measured predictive processing by measuring fixations on the target image prior to the actual moment of processing the linguistic information required to select the target image. We tested two groups of fluent Spanish–English bilingual adults: heritage speakers of Spanish (HSs) and Spanish–English late bilinguals (LBs). Bilinguals of these two languages were chosen because the syntax of relative clauses in Spanish and English is highly similar, reducing the possibilities of language transfer (Montrul 2010; Scontras et al. 2015) or increased processing difficulty caused by dissimilarities between L1 and L2 syntax (Tolentino and Tokowicz 2011). Although the relativizer is obligatory in Spanish relative clauses but optional in English ORCs, and English has fixed word order in relative clauses while Spanish allows for post-verbal subjects, the Spanish relative clause stimuli used in our study all had a word order identical to that in English, which is also grammatical in all varieties of Spanish.

All of our participants learned Spanish as a first language, and were fluent in both Spanish and English. However, the HSs were immersed in an English-dominant society early in life, while the LBs were not immersed in an English-dominant society until adulthood. Our main question was whether this difference in the history of relative exposure to the L1 and the L2 would affect the predictive processing of L1 relative clauses. Since Spanish relative clauses are highly similar to English relative clauses, and all of the lexical items we used are highly frequent, any observed differences in predictive processing between groups should not be due to cross-linguistic influence or decreased speed of lexical access, but rather due to general differences in patterns of predictive processing shaped by the language history of each group.

Given that both groups of subjects were fluent in Spanish, we hypothesized that both groups would exhibit prediction in their processing of Spanish relative clauses, indicated by increased fixations to the target image before the completion of the spoken stimulus. According to the active filler hypothesis, we also predicted that both groups would exhibit an SRC/ORC processing asymmetry, such that SRCs would be processed more quickly than ORCs. However, given that the LBs had a longer history of exposure to only Spanish than the HSs, we predicted that they would exhibit more active prediction in Spanish than the HSs, such that they would process both SRCs and ORCs more quickly than the HSs. In other words, we predicted that the HSs would exhibit weakening (Gollan et al. 2008; O’Grady et al. 2009), attrition (Polinsky 2011) or a reduced ability to generate expectations (Grüter et al. 2014) in their predictive processing of L1 relative clauses, compared to LBs, due to their longer history of immersion in an English-dominant society. Note that we did not aim to differentiate between these various theoretical characterizations of our hypothesized reduction in predictive processing in the HSs; our primary goal was to test whether a longer history of immersion in an L2 environment would attenuate L1 predictive processing, independently of any particular description of bilingual processing. We reviewed these previous descriptions only as background to our current study and its findings.

Method

Participants

Forty-one Spanish–English bilingual adults residing in New York City were given financial compensation to participate in this study. All participants were fluent in both Spanish and English, had normal (or corrected-to-normal) vision and hearing, and did not take antihistamines on the day of the experiment. Participants were screened for pre-determined inclusion criteria, and gave written consent to participate in this study.

Participants were classified as either heritage speakers (HSs) or late bilinguals (LBs) based on criteria commonly used in heritage speaker studies (Benmamoun et al. 2013). This information was collected with a language background questionnaire developed in our lab, which included commonly collected items pertaining to language history (Li et al. 2006), and additional items pertaining to demographics, language ability, and language exposure. Additionally, language background information was collected with the Bilingual Language Profile (BLP: Birdsong et al. 2012) which generates a quantitative measure of relative dominance in one language or the other based on responses to questions pertaining to history, use, proficiency, and attitudes. The value of the dominance score generated by the BLP is positive if the participant is English-dominant, and negative if the participant is Spanish-dominant. Mean participant characteristics by group for six variables of interest, along with the results of independent samples t tests testing the significance of group differences, are summarized in Table 1.

Table 1 Participant characteristics by group

The HS group (n = 21) consisted of individuals who were either born in the anglophone US (n = 11) or moved to the anglophone US at the age of 8 or younger (n = 10, M 5.10, SD 2.00). The HSs were 19–48 years old (M 26.67, SD 8.24), raised by caregivers from Spanish-speaking regions, and spoke primarily Spanish with their caregivers until at least age 10. LB participants (n = 20) were 19–55 years old (M 30.75, SD 9.17), born and raised in a Spanish-speaking region, and moved to the anglophone US at the age of 17 or older (M 25.20, SD 7.18). Eleven Latin American countries/regions of origin were represented in this group. All participants, both HSs and LBs, self-rated their proficiency in both Spanish and English as 3 or higher on a 5-point scale. Although there was a significant group difference in Spanish proficiency, both group means were well above 4, confirming that both groups were highly proficient in Spanish. Potential effects of proficiency on processing these particular relative clause stimuli were further explored by analyzing offline comprehension accuracy (see Sect. 5.2).Footnote 2

Stimuli

The stimuli consisted of 40 grammatical complex Spanish sentences, with 10 items per condition and 20 fillers. There were two experimental conditions based on relative clause type: subject relative clauses (SRCs) as in (3), and object relative clauses (ORCs) as in (4). The relative and matrix verbs were always transitive. During the same session, participants also completed a similar experiment with matrix intransitive verbs. However, only the results of the transitive experiment are reported in this article. All items consisted of anthropomorphic animals with masculine gender in Spanish, and all relative clause items were subject-embedded. Filler sentences contained subject/object control constructions.

figurea

Stimuli were recorded in a sound-proof booth by a female L1 Spanish speaker from Uruguay (who was a Spanish–English late bilingual) using SoundForge as natural running speech with neutral prosody and sampled at 44.1 kHz. All stimuli were normalized, amplified to an average loudness of − 26.00 dB, and the noise filtered out using Audacity® 2.0.3 (Audacity Team 2014), then exported as WAV files.

Along with the auditorily presented sentences, each stimulus also included an array of three images which consisted of a target image and two distractors. One distractor was always consistent with the linguistic stimulus until the matrix verb. The other distractor always corresponded to an interpretation of the other type of relative clause until the matrix verb; e.g., if the stimulus was an SRC, then this distractor would depict a scenario where the matrix subject was the object of the relative clause. An example set of images is presented in Fig. 1, which is also the visual display corresponding to example (3) above. The relative positions of the images on the screen were randomized by trial. In the example below, the target image is on the right, the ‘consistent’ distractor is in the middle, and the ‘other RC’ distractor is on the left.

Fig. 1
figure1

Sample visual display during experimental trial

Procedure

The experiment involved a picture-selection task using auditory Spanish stimuli, thereby reducing confounds caused by variation in levels of L1 literacy. Stimuli were presented over external speakers at a comfortable volume, with a concurrent visual display presented on a computer screen using E-Prime 2.0 (Schneider et al. 2002). Each trial began with a black cross fixation marker appearing on the screen, that the participant then clicked to see the images. After familiarizing themselves with the images, the participant clicked again to hear the auditory stimulus. The participant then selected the image that best matched the aurally presented sentence with a mouse click. Gaze fixations were recorded throughout each trial at 60 Hz using a Tobii TX300 eye-tracker. The presentation order of stimuli was pseudorandomized. The entire session, including the questionnaires and the other experiment with intransitive matrix sentences, lasted about 40 min.

Analysis

Division of gaze data into temporal regions

Following previous studies of the relative clause processing asymmetry which have utilized eye-tracking of reading (e.g., Traxler et al. 2002), we divided the gaze data into four temporal regions (as depicted in Fig. 2).

Fig. 2
figure2

Division of auditory stimuli into temporal regions

Region 1 extended from the onset of the spoken sentence until the onset of the first word after the relativizer que. Region 1 was equivalent in the SRC and ORC conditions. During Region 1, the participant received no information that would allow them to eliminate any of the distractor images as possible correct answers. Therefore, across participants and across conditions, we expected fixation proportions on the target image to be approximately at chance, or approximately 33%, because there were 3 image choices. (Predictions by region are summarized in Table 2).

Table 2 Predictions for proportion of fixations on the target image during each temporal region

Region 2, or the Relative Clause Region (cf. Traxler et al. 2002), extended from the onset of the first word after the relativizer que until the onset of the matrix verb. In the SRC condition, the word following que was the subordinate verb. In the ORC condition, the word following que was the determiner el. Comprehension of the Relative Clause Region would allow participants to eliminate one distractor image as a possible correct answer, leaving two possible correct answers. Therefore, we posited that complete comprehension in the Relative Clause Region, along with active prediction of the correct image, would be indicated by a target fixation proportion of approximately 50% because two possible correct answers would remain. Since we expected target fixation proportions to be at approximately 33% at the beginning of the region and approximately 50% following full comprehension, we expected that, across participants and conditions, the mean target fixation proportion during the Relative Clause Region would be between 33 and 50%. Traxler et al. (2002) found that participants took longer to read the Relative Clause Region in the ORC condition than the SRC condition, suggesting that this region is processed more slowly in ORCs than in SRCs. For this reason, we expected that, across participants, target fixation proportions would be significantly lower in this region in the ORC condition than the SRC condition. Moreover, we expected that, across conditions, the HS group would demonstrate slower processing than the LB group indicated by a lower target fixation proportion in this region.

Region 3, or the Matrix Clause Region, extended from the onset of the matrix verb to the offset of the last word in the sentence.Footnote 3 The Matrix Clause Region was equivalent in the SRC and ORC conditions. Comprehension of the Matrix Clause Region, along with active prediction, would allow participants to eliminate the other distractor image as a possible correct answer, leaving only the target image as a possible correct answer. Therefore, we posited that complete comprehension in the Matrix Clause Region would be indicated by a target fixation proportion of close to 100%. Since we expected target fixation proportions to be at approximately 50% at the beginning of the region and close to 100% following full comprehension, we expected that, across participants and conditions, the mean target fixation proportion during the Matrix Clause Region would be between 50 and 100%. Traxler et al. (2002) found that participants continued to take longer to read the region following the Relative Clause Region in the ORC condition than the SRC condition, suggesting that ORCs continue to be processed more slowly than SRCs in this region. For this reason, we expected that, across participants, target fixation proportions would be significantly lower in this region in the ORC condition than the SRC condition. We also expected that, across conditions, the HS group would continue to demonstrate slower processing than the LB group indicated by a lower target fixation proportion in this region.

Region 4 extended from the offset of the spoken sentence to the moment the participant clicked a response, or the median by-participant response time (whichever was shorter). Participants received no new information during this region that would be expected to cause changes in fixation proportions. If both distractor images were already eliminated during comprehension of the Matrix Clause Region, then we expected target fixation proportions to be at close to 100% from beginning to end of Region 4. Therefore, we expected that, across participants and conditions, the mean target fixation proportion would be at close to 100% in this region.

Analysis methods for gaze data

Observations for which the eye tracker was at less than its highest validity level were removed. As a result, 8.9% of the eyetracking data was removed. Then, only gaze data from trials where the participant responded correctly was analyzed. The proportions of fixations on the target image within each region were modeled with zero- and one-inflated beta mixed-effects models with relative clause type, group, and the interaction as fixed effects and by-participant and by-item random intercepts in R using the gamlss::gamlss() function and the BEZINF() family (R Core Team 2017; Rigby and Stasinopoulos, 2005). Zero- and one-inflated beta mixed-effects models have been shown to be particularly well-suited for proportion data between 0 and 1 that also includes non-negligible amounts of 0 s and 1 s (e.g., Ospina and Ferrari 2012). The zero- and one-inflated beta mixed-effects models estimate 4 parameters for the distribution of target fixation proportions: μ (mu), the estimate of the mean for target fixation proportions between 0 and 100% non-inclusive; σ (sigma), the estimate of precision, dispersion, or variance for target fixation proportions between 0 and 100% non-inclusive; ν (nu) the estimate of 0% fixations on the target compared to other responses; and τ (tau) the estimate of 100% fixations on the target compared to other responses.

Analysis methods for behavioral data

In addition to the gaze data, two behavioral measures were also analyzed: comprehension accuracy and reaction time. Participant-average accuracy was calculated by relative clause type by participant. Comprehension accuracy was operationalized as dichotomous accuracy (0,1) and modeled with a logistic mixed-effects model with relative clause type, group, and the interaction as fixed effects and by-participant and by-item random intercepts in R using the lme4::glmer() function (Bates et al. 2015). Reaction time was log-transformed to address the non-gaussian distribution. Log-transformed reaction time was modeled with a linear mixed-effects model with relative clause type, group, and the interaction as fixed effects and by-participant and by-item random intercepts in R using the lme4::lmer() function (Bates et al. 2015). The significance of predictor variables was determined using the z-score, in the logistic model, and t values with degrees of freedom estimate with the Satterthwaite approximation using the lmerTest::summary() function (Kuznetsova et al. 2017).

Results

Eye-tracking results

As a first visual aid for understanding trends in the fixation patterns of our participants, proportions of fixations on each image are plotted in Fig. 3 by a normalized time measure by group by condition. Vertical dotted lines represent the delineation of the temporal regions, and the horizontal dotted line represents chance.

Fig. 3
figure3

Proportion of fixations on each image by normalized time by group by condition

A number of trends can be seen in Fig. 3. First, fixations to the target image tended to increase throughout the stimulus, suggesting basic predictive processing in both groups for both RC types. Next, we see that patterns of distractor fixations were different for each RC type. During ORCs in Region 2, participants tended to look at the ‘other RC’ distractor, i.e. the distractor that corresponded to an SRC, more than the other distractor. During SRCs in Region 2, on the other hand, participants tended to look more at the ‘consistent’ distractor. This suggests that participants tended to predict a subject-relative structure, regardless of the type of RC they were actually hearing. By Region 3 in ORCs, however, patterns of fixations to the distractors generally switched, such that participants started to fixate more on the ‘consistent’ distractor as they processed more linguistic information. Finally, LBs showed a very early increase in fixations to the target during SRCs, suggesting that they had a particularly strong preference for SRCs.

Next, proportions of fixations on the target image were analyzed by group by condition within each region. Figures 4, 5, 6 and 7 display mean target fixation proportions with 95% confidence interval error bars for each participant group and each relative clause type in each of the four regions. In Region 1, as seen in Fig. 4, across group and across condition, the proportion of target fixations was approximately at chance, as expected. Results of statistical tests are summarized in Table 3. For the majority of trials participants either did not fixate at all on the target item (44.7%, n = 330) or fixated 0–100% non-inclusive on the target (49.7%, n = 367). Relative clause type was a significant predictor of trials where there were 0 fixations on the target, such that participants were more likely to not fixate at all on the target for ORCs than SRCs [B = 0.54, SE(B) = 0.25, t = 2.15, p < 0.05]. However, this effect is difficult to interpret, given that the linguistic information in Region 1 is identical between ORCs and SRCs. Group and the interaction between relative clause type and group were significant predictors of target fixations between 0 and 100. HSs were significantly more likely to fixate on the target than LBs [B = 0.26, SE(B) = 0.11, t = 2.49, p < 0.05] although this effect was reversed for ORCs as indicated by the significant interaction with condition [B = − 0.53, SE(B) = 0.18, t = − 2.90, p < 0.01]. Again, it is difficult to interpret this effect in Region 1, as participants have not yet received enough information to differentiate between relative clause types. No variables significantly predicted dispersion for fixations between 0 and 100%. Given that there were only n = 42 (5.7%) trials where participants fixated 100% of the time on the target, the model predicting 100% fixation is underpowered and therefore uninterpretable.

Fig. 4
figure4

Target fixation proportions by group by condition in Region 1

Fig. 5
figure5

Target fixation proportions by group by condition in Region 2

Fig. 6
figure6

Target fixation proportions by group by condition in Region 3

Fig. 7
figure7

Target fixation proportion by group by condition in Region 4

Table 3 Zero- and one-inflated beta regression model fit to proportion of target fixation during Region 1

As seen in Fig. 5, in Region 2, target fixation proportions rose to between 33 and 50%, indicating that both groups were actively predicting the correct image by eliminating distractor images. Statistical tests of differences by group and condition are summarized in Table 4. Similar to Region 1, for the majority of trials participants either did not fixate at all on the target item (31.0%, n = 229) or fixated 0–100% non-inclusive on the target (55.0%, n = 406). HSs were significantly more likely to not fixate at all on the target in Region 2 than LBs [B = 0.99, SE(B) = 0.26, t = 3.75, p < 0.001], indicating slower processing for HSs than LBs, as expected. However, this group difference was limited to SRCs, as indicated by the significant interaction [B = − 1.22, SE(B) = 0.37, t = − 3.28, p = 0.001]. Moreover, although participants were significantly more likely to not fixate at all on the target for ORCs than SRCs [B = 1.20, SE(B) = 0.27, t = 4.40, p < 0.001], this effect was fully mitigated in HSs as indicated by the significant interaction [B = − 1.22, SE(B) = 0.37, t = − 3.28, p = 0.001]. In other words, LBs were slower on ORCs than SRCs, as expected, while HSs did not show an asymmetry between the conditions. A possible explanation for this is presented in Sect. 6. HSs were more variable in target fixation than LBs [B = 0.40, SE(B) = 0.12, t = 3.27, p = 0.001], although this effect was mitigated in ORCs for HSs [B = − 0.58, SE(B) = 0.18, t = − 3.14, p < 0.01]. No variables significantly predicted 100% target fixation in Region 2.

Table 4 Zero- and one-inflated beta regression model fit to proportion of target fixation during Region 2

As seen in Fig. 6, target fixation proportions were mostly above 50% in Region 3, suggesting continued predictive processing in both groups, as expected. Table 5 summarizes the results of statistical tests of effects of group and condition. The proportions of target fixations were most varied in Region 3. While the proportions of target fixations for most trials were between 0 and 100% (47.8%, n = 351), a roughly equal, sizable amount of fixations were either totally to the target (28.7%, n = 211) or completely avoided the target (23.4%, n = 172). Similar to Region 2, although only approaching significance, HSs were more likely than LBs to not fixate at all on the target [B = 0.52, SE(B) = 0.27, t = 1.90, p = 0.06], suggesting continued slower processing for HSs compared to LBs in Region 3. Participants were significantly more likely to not fixate at all on a target ORC than a target SRC [B = 0.97, SE(B) = 0.28, t = 3.51, p < 0.001], although this effect was reversed in HSs as indicated by the significant interaction [B = − 1.26, SE(B) = 0.40, t = − 3.18, p < 0.01]. This suggests both that LBs were slower at processing ORCs than SRCs, while HSs showed the reverse pattern, and that HSs were actually faster than LBs at processing ORCs in Region 3. A possible explanation for this puzzling result will be given in Sect. 6. No variables significantly predicted the mean of target fixation proportions between 0 and 100%. Group approached significance as a predictor of non-extreme target-fixation dispersion, such that HSs were more variable than LBs. No variables significantly modulated 100% target fixation in Region 3. If a participant looked at the target image for the whole of Region 3, it was similarly likely that it was a HS or LB for either relative clause trial type.

Table 5 Zero- and one-inflated beta regression model fit to proportion of target fixation during Region 3

As seen in Fig. 7, target fixation proportions were still well below 100% in Region 4, which is surprising, given that this gaze data only includes correct-response trials. However, target fixation proportions were still well above chance. Statistical tests of differences between groups and conditions are summarized in Table 6. In Region 4, the majority of target fixation proportions were between 0 and 100% (68.7%, n = 434) and over 1 in 4 trials were 100% target fixations (26.9%, n = 170). Given that there were only n = 28 (4.4%) trials where participants fixated 0% of the time on the target, the model predicting 0% fixation is underpowered and therefore uninterpretable. No variables significantly predicted the mean or the dispersion of target fixation proportions between 0 and 100%, and no variables significantly modulated 100% target fixation in Region 4. In Region 4 (after the end of the sentence), neither group nor relative clause type significantly predicted fixations on the target image.

Table 6 Zero- and one-inflated beta regression model fit to proportion of target fixation during Region 4

In summary, LBs showed a higher proportion of target fixations in Regions 2 and 3 for SRCs compared to ORCs, indicating active prediction of the structure of SRCs. HSs, on the other hand, did not exhibit this SRC preference in any region.

Behavioral results

Participant-average response accuracy was at ceiling for both groups and relative clause types (all mean scores above 0.87), as can be seen in Fig. 8 and Table 7. However, across group, participants were less accurate on ORCs (M 0.88, SD 0.13) than SRCs (M 0.96, SD 0.08). This was further explored by modeling the data with a logistic mixed-effects model, the results of which are summarized in Table 8. The maximal model was significant when compared to the null, intercept-only model [χ2(3) = 12.64, p < 0.01]. Neither group nor the interaction between group and relative clause type were significant predictors of comprehension accuracy. Therefore, it is unlikely that group differences in proficiency played a role in explaining the group differences in processing. Relative clause type, on the other hand, was a significant predictor of accuracy [B = − 1.67, SE(B) = 0.51, z = − 3.6, p < 0.01] indicating that both HSs and LBs were significantly less accurate on ORCs than SRCs. However, as noted above, ceiling effects make this difference difficult to interpret.

Fig. 8
figure8

Participant-average accuracy by group by relative clause type

Table 7 Participant-average accuracy by group by relative clause type
Table 8 Logistic mixed-effects model of accuracy

Log-transformed reaction time on correct responses was comparable across group and relative clause type, as can be seen in Fig. 9 and Table 9, although some trends were observed. Across group, participants were slower on ORCs (M 7.83, SD 0.86) than SRCs (M 7.58, SD 0.85). Across relative clause type, HSs (M 7.56, SD 0.94) were faster to respond than LBs (M 7.86, SD 0.74). This was further explored by modeling the data with a linear mixed-effect model, the results of which are summarized in Table 10. The maximal model was not significant when compared to the null, intercept-only model [χ2(3) = 4.87, p = 0.18]. No variable significantly predicted log-transformed reaction time although relative clause type approached being a significant predictor of reaction time [B = 0.24, SE(B) = 0.14, t(34.01) = 1.73, p = 0.09] indicating that both HSs and LBs were slower on ORCs than SRCs.

Fig. 9
figure9

Average log-transformed reaction time by group by relative clause type

Table 9 Average log-transformed reaction time by group by relative clause type
Table 10 Linear mixed-effects model of log-transformed reaction time

Across both behavioral measures, there were no significant group-level differences. HSs and LBs performed comparably. A significant SRC preference was observed for accuracy and a non-significant SRC preference was observed for reaction time.

Discussion

To summarize the results of the eye-tracking experiment, Spanish–English late bilinguals (LBs) demonstrated the relative clause processing asymmetry in their L1, indicated by increased fixations to the target image during subject relative clauses (SRCs) compared to object relative clauses (ORCs) in Region 2 (the Relative Clause Region) and Region 3 (the Matrix Clause Region). Consistent with the active filler hypothesis, this suggests that LBs actively predicted the syntactic structure of SRCs in their L1, similarly to monolinguals. To our knowledge, this is the first time that the SRC/ORC processing asymmetry has been observed using the VWP, constituting further evidence for the robustness of the SRC/ORC processing asymmetry, and for the utility of the VWP in measuring relative clause processing.

HSs, on the other hand, did not demonstrate the expected SRC/ORC processing asymmetry, as they had equivalent target fixation proportions during SRCs and ORCs in Region 2, and they actually showed a slight preference for ORCs in Region 3. Although HSs clearly exhibited predictive processing to some degree, such that their fixations to the target image increased to above chance before Region 4, they did not appear to utilize syntactic prediction to a degree equivalent to the LBs, because they did not exhibit the expected SRC/ORC processing asymmetry. Decreased levels of prediction in the HSs compared to the LBs was further evidenced by the fact that the HSs showed decreased target fixations compared to the LBs during SRCs in Region 2. Taken together, this suggests that HSs did not utilize the subject advantage conferred by the predictive active filler parsing strategy.

Interestingly, however, evidence for decreased prediction in HSs was also found in a HS advantage over LBs during ORCs in Region 3. Recall that a listener utilizing the active filler parsing strategy predicts the structure of SRCs in all cases, and therefore when they are confronted with an ORC, they must re-analyze and suffer a processing penalty. A listener who is utilizing a less active predictive strategy, then, would be expected to be less negatively affected by an “unexpected” ORC. Federmeier et al. (2002) and Federmeier (2007) note that predictive processing entails a risk of failure, which tends to be avoided by those populations for whom failure would be most costly (such as older listeners). The perhaps counterintuitive finding, then, is that a reduced ability to generate expectations can cause processing advantages, as well as disadvantages. We posit, therefore, that the decreased syntactic prediction we found in HS processing compared to LBs is the result of a conservative processing strategy aimed at reducing the risk of failure (Federmeier, 2007; Federmeier et al. 2002).

It is possible that processing failure is more costly for HSs than LBs in their L1 because they have relatively decreased dominance in their L1, as evidenced by the significant group difference in the dominance scores output by the Bilingual Language Profile (see Sect. 3.1). Previous literature suggests that greater dominance in a language causes more ready accessibility of structure-building operations in that language (e.g., O’Grady et al. 2009). Therefore, syntactic reanalysis (a form of structure-building) would be more time-consuming and cognitively costly in a less dominant language. It is possible, therefore, that the group difference we observed in this study is actually attributable to differences in language dominance between the two groups, such that greater dominance in the first-learned language causes more active prediction in first-language syntactic processing. A future study (Stover et al. in progress) will test a third group of participants who bridge the gap on the continuum of dominance between HSs and LBs, and analyze the effects of dominance, operationalized as a continuous variable, on prediction in syntactic parsing in the first-learned language. We predict that dominance will have a continuous effect on the predictive parsing of relative clauses, such that greater dominance in the L1 will correlate positively with target fixations in Region 2 during SRCs, and negatively with target fixations in Region 3 during ORCs.

Finally, it is important to note that none of the group differences observed in the eye-tracking data were apparent in the behavioral measures of comprehension accuracy or reaction time. That is, both groups performed at ceiling in comprehension accuracy, and did not differ significantly in reaction time. This is not particularly surprising, given that both groups were highly proficient in Spanish. However, the fact that interesting group differences were found in the eye-tracking results provide support for the unique utility of online measures, such as visual world eye-tracking, in studying the effects of bilingualism on language processing.

Conclusion

This article reported a study that utilized eye-tracking in the VWP to examine the effects of language history on predictive parsing in bilinguals’ first-learned language. We found that fluent bilinguals across groups exhibited predictive processing in their L1. However, HSs, who had spent most of their lives immersed in an L2-dominant society, exhibited less active prediction in their parsing of L1 relative clauses compared to LBs, who were not immersed in the L2-dominant society until adulthood. This was evidenced by the fact that LBs demonstrated an SRC/ORC processing asymmetry, while HSs did not. In other words, the LBs demonstrated a processing advantage during SRCs, and a processing disadvantage during ORCs, neither of which were exhibited by the HSs. We conclude, therefore, that decreased exposure to the first-learned language causes less active prediction in first-language processing. A future study (Stover et al. in progress) will examine whether the construct of language dominance, composed of a number of relevant variables, including exposure, has a continuous effect on predictive processing in the first-learned language.

This study constitutes (a) the first use of the VWP to measure the relative clause processing asymmetry; (b) the first study of the relative clause processing asymmetry in bilinguals; and (c) the first study of the effects of bilingualism on predictive processing in the first-learned language. Additional future studies will examine the time-course of prediction within each temporal region in the relative clause stimuli in order to identify the contributions of particular events (e.g., the onsets of particular words) to predictive processing. Moreover, future studies will examine the relationship between predictive ability and productive ability in bilinguals’ L1, in order to explore the possible emulative basis of L1 predictive processing in bilinguals.

Notes

  1. 1.

    Despite the typological and methodological robustness of the SRC preference, it is likely not a complete universal (e.g. Basque: Carreiras et al. 2010). Even in Basque, however, an asymmetry exists, such that ORCs are easier to process than SRCs.

  2. 2.

    A reviewer suggested that we include proficiency as its own predictor variable, along with group, in our regression models. However, we decided against this in the present study because proficiency was strongly correlated with group (r = 0.65, p < 0.001), which would have reduced the reliability of the models’ parameters (Hutcheson and Sofroniou 1999). A follow-up study (Stover et al. in progress) will investigate the role of continuous measures of language dominance in explaining the group patterns observed here.

  3. 3.

    Note that Traxler et al. (2002) only analyzed fixations on the matrix verb itself. However, since comprehension of the object of the matrix verb is crucial for our participants to select the correct image, we chose to include both the matrix verb and its object in this region. Future analyses will examine whether particular events within each region contributed significantly to changes in fixation proportions.

References

  1. Allopenna, P. D., Magnuson, J. S., & Tanenhaus, M. K. (1998). Tracking the time course of spoken word recognition using eye movements: Evidence for continuous mapping models. Journal of Memory and Language,38(4), 419–439.

    Google Scholar 

  2. Altmann, G. T. M., & Kamide, Y. (1999). Incremental interpretation at verbs: Restricting the domain of subsequent reference. Cognition,73, 247–264.

    Google Scholar 

  3. Altmann, G. T. M., & Kamide, Y. (2007). The real-time mediation of visual attention by language and world knowledge: Linking anticipatory (and other) eye movements to linguistic processing. Journal of Memory and Language,57, 502–518.

    Google Scholar 

  4. Arnon, I. (2005). Relative clause acquisition in Hebrew: Toward a processing-oriented account. In A. Brugos, M. R. Clark-Cotton, & S. Ha (Eds.), Proceedings of the 29th Boston University Conference on Language Development (pp. 37–48). Somerville: Cascadilla Press.

    Google Scholar 

  5. Bar, M. (2007). The pro-active brain: Using analogies and associations to generate predictions. Trends in Cognitive Sciences,11, 280–289.

    PubMed  Google Scholar 

  6. Bar, M. (2009). The pro-active brain: Memory for predictions. Philosophical Transactions of the Royal Society B,364, 1235–1243.

    Google Scholar 

  7. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software,67(1), 1–48.

    Google Scholar 

  8. Benmamoun, E., Montrul, S., & Polinsky, M. (2013). Heritage languages and their speakers: Opportunities and challenges for linguistics. Theoretical Linguistics,39(3–4), 129–181.

    Google Scholar 

  9. Betancort, M., Carreiras, M., & Sturt, P. (2009). The processing of subject and object relative clauses in Spanish: An eye-tracking study. The Quarterly Journal of Experimental Psychology,62(10), 1915–1929.

    PubMed  Google Scholar 

  10. Birdsong, D., Gertken, L. M., & Amengual, M. (2012). Bilingual language profile: An easy-to-use instrument to assess bilingualism. COERLL: University of Texas at Austin.

    Google Scholar 

  11. Bonifacci, P., Giombini, L., Bellocchi, S., & Contento, S. (2011). Speed of processing, anticipation, inhibition and working memory in bilinguals. Developmental Science,14(2), 256–269.

    PubMed  Google Scholar 

  12. Borovsky, A., Elman, J., & Fernald, A. (2012). Knowing a lot for one’s age: Vocabulary skill and not age is associated with anticipatory incremental sentence interpretation in children and adults. Journal of Experimental Child Psychology,112(4), 417–436.

    PubMed  PubMed Central  Google Scholar 

  13. Brouwer, S., Özkan, D., & Küntay, A. C. (2017a). Semantic prediction in monolingual and bilingual children. In E. Blom, L. Cornips, & J. Schaeffer (Eds.), Cross-linguistic influence in bilingualism. In honor of Aafke Hulk (pp. 49–73). Amsterdam: John Benjamins Publishing Company.

    Google Scholar 

  14. Brouwer, S., Sprenger, S., & Unsworth, S. (2017b). Processing grammatical gender in Dutch: Evidence from eye movements. Journal of Experimental Child Psychology,159, 50–65.

    PubMed  Google Scholar 

  15. Caplan, D., Alpert, N., Waters, G., & Olivieri, A. (2000). Activation of Broca’s area by syntactic processing under conditions of concurrent articulation. Human Brain Mapping,9, 65–71.

    PubMed  Google Scholar 

  16. Caplan, D., Vijayan, S., Kuperberg, G., West, C., Waters, G., Greve, D., & Dale, A. M. (2002). Vascular responses to syntactic processing: Event-related fMRI study of relative clauses. Human Brain Mapping,15(1), 26–38.

    PubMed  Google Scholar 

  17. Carreiras, M., Duñabeitia, J. A., Vergara, M., de la Cruz-Pavía, I., & Laka, I. (2010). Subject relative clauses are not universally easier to process: Evidence from Basque. Cognition,115, 79–92.

    PubMed  Google Scholar 

  18. Chan, A., Chen, S., Matthews, S., & Yip, V. (2017). Comprehension of subject and object relative clauses in a trilingual acquisition context. Frontiers in Psychology,2017, 8.

    Google Scholar 

  19. Chang, F., Dell, G. S., & Bock, K. (2006). Becoming syntactic. Psychological Review,113(2), 234–272.

    PubMed  Google Scholar 

  20. Clifton, C., & Frazier, L. (1989). Comprehending sentences with long-distance dependencies. In G. N. Carlson & M. K. Tanenhaus (Eds.), Linguistic structure in language processing. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  21. Cooke, A., Zurif, E. B., DeVita, C., Alsop, D., Koenig, P., Detre, J., et al. (2002). Neural basis for sentence comprehension: Grammatical and short-term memory components. Human Brain Mapping,15, 80–94.

    PubMed  Google Scholar 

  22. Cooper, R. M. (1974). The control of eye fixation by the meaning of spoken language: A new methodology for the real-time investigation of speech perception, memory, and language processing. Cognitive Psychology,6, 84–107.

    Google Scholar 

  23. Dahan, D., Swingly, D., Tanenhaus, M. K., & Magnuson, J. S. (2000). Linguistic gender and spoken-word recognition in French. Journal of Memory and Language,42, 465–480.

    Google Scholar 

  24. Dussias, P. E., Kroff, J. R. V., Tamargo, R. E. G., & Gerfen, C. (2013). When gender and looking go hand in hand: Grammatical gender processing in L2 Spanish. Studies in Second Language Acquisition,35, 353–387.

    Google Scholar 

  25. Dussias, P. E., & Sagarra, N. (2007). The effect of exposure on syntactic parsing in Spanish–English bilinguals. Bilingualism Language and Cognition,10(1), 101–116.

    Google Scholar 

  26. Ezeizabarrena, M. J., Munarriz, A., & Loidi, U. (2017). Bilingual production of relative clauses in languages with opposite head-complement directionality. In K. Bellamy, M. W. Child, P. González, A. Muntendam, & M. C. P. Cuoto (Eds.), Multidisciplinary approaches to bilingualism in the Hispanic and Lusophone world (pp. 283–309). Amsterdam: John Benjamins Publishing Company.

    Google Scholar 

  27. Federmeier, K. D. (2007). Thinking ahead: The role and roots of prediction in language comprehension. Psychophysiology,44(4), 491–505.

    PubMed  PubMed Central  Google Scholar 

  28. Federmeier, K. D., McLennan, D. B., De Ochoa, E., & Kutas, M. (2002). The impact of semantic memory organization and sentence context information on spoken language processing by younger and older adults: An ERP study. Psychophysiology,39, 133–146.

    PubMed  Google Scholar 

  29. Fernandez, E. M. (2002). Relative clause attachment in bilinguals and monolinguals. In R. R. Heredia & J. Altarriba (Eds.), Bilingual sentence processing (pp. 187–215). Amsterdam: Elsevier.

    Google Scholar 

  30. Foucart, A., Martin, C. D., Moreno, E. M., & Costa, A. (2014). Can bilinguals see it coming? Word anticipation in L2 sentence reading. Journal of Experimental Psychology Learning Memory, and Cognition.

  31. Frazier, L. (1987). Syntactic processing: Evidence from Dutch. Natural Language and Linguistic Theory,5, 519–559.

    Google Scholar 

  32. Gollan, T. H., Montoya, R. I., Cera, C., & Sandoval, T. C. (2008). More use almost always means a smaller frequency effect: Aging, bilingualism, and the weaker links hypothesis. Journal of Memory and Language,58(3), 787–814.

    PubMed  PubMed Central  Google Scholar 

  33. Grüter, T., Lew-Williams, C., & Fernald, A. (2012). Grammatical gender in L2: A production or real-time processing problem? Second Language Research,28(2), 191–215.

    PubMed  PubMed Central  Google Scholar 

  34. Grüter, T., Rohde, G., & Schafer, & A. J. (2014). The role of discourse-level expectations in non-native speakers’ referential choices. Proceedings of the annual Boston University Conference on Language Development.

  35. Guillelmon, D., & Grosjean, F. (2001). The gender marking effect in spoken word recognition: The case of bilinguals. Memory and Cognition,29(3), 503–511.

    PubMed  Google Scholar 

  36. Hopp, H. (2012). Grammatical gender in adult L2 acquisition: Relations between lexical and syntactic variability. Second Language Research,29(1), 33–56.

    Google Scholar 

  37. Hu, S., Gavarró, A., Vernice, M., & Guasti, M. T. (2016). The acquisition of Chinese relative clauses: Contrastic two theoretical approahces. Journal of Child Language,43(1), 1–21.

    PubMed  Google Scholar 

  38. Huettig, F., Rommer, J., & Meyer, A. J. (2011). Using the visual world paradigm to study language processing: A review and critical evaluation. Acta Psychologica.

  39. Hutcheson, G., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. London: Sage Publication.

    Google Scholar 

  40. Just, M. A., & Carpenter, P. A. (1993). The intensity dimension of thought: Pupillometric indices of sentence processing. Canadian Journal of Experimental Psychology,47(2), 310–399.

    PubMed  Google Scholar 

  41. Just, M., Carpenter, P., & Keller, T. (1996). Brain activation modulated by sentence comprehension. Science,274, 114–116.

    PubMed  Google Scholar 

  42. Kahraman, B., Sato, A., Ono, H., & Sakai, H. (2010). Relative clauses processing before the head noun: Evidence for strong forward prediction in Turkish. In H. Maezawa & A. Yokogoshi (Eds.), Proceedings of the 6th Workshop on Altaic Formal Linguistics (WAFL6). MIT Working Papers in Linguistics 61 (pp. 155–170). Cambridge: MIT Press.

    Google Scholar 

  43. Kamide, Y., Altmann, G. T. M., & Haywood, S. L. (2003). The time-course of prediction in incremental sentence processing: Evidence from anticipatory eye movements. Journal of Memory and Language,49, 122–156.

    Google Scholar 

  44. Karmiloff-Smith, A. (1979). A functional approach to child language: A study of determiners and reference. Cambridge: Cambridge University Press.

    Google Scholar 

  45. King, J., & Kutas, M. (1995). Who did what and when? Using word- and clause-level ERPs to monitor working memory usage in reading. Journal of Cognitive Neuroscience,7, 376–395.

    PubMed  PubMed Central  Google Scholar 

  46. Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software,82(13), 1–26.

    Google Scholar 

  47. Kwon, N., Kluender, R., Kutas, M., & Polinsky, M. (2013). Subject/object processing asymmetries in Korean relative clauses: Evidence from ERP data. Language (Baltim),89(3), 537–585.

    PubMed Central  Google Scholar 

  48. Lew-Williams, C., & Fernald, A. (2007). Young children learning Spanish make rapid use of grammatical gender in spoken word recognition. Psychological Science,18(3), 193–198.

    PubMed  PubMed Central  Google Scholar 

  49. Lew-Williams, C., & Fernald, A. (2010). Real-time processing of gender-marked articles by native and non-native Spanish speakers. Journal of Memory and Language,63(4), 447–464.

    PubMed  PubMed Central  Google Scholar 

  50. Li, P., Sepanski, S., & Zhao, X. (2006). Language history questionnaire: A Web-based interface for bilingual research. Behavior Research Methods,38(2), 202–210.

    PubMed  Google Scholar 

  51. MacWhinney, B., & Pleh, C. (1988). The processing of restrictive relative clauses in Hungarian. Cognition,29, 95–141.

    PubMed  Google Scholar 

  52. Mak, W. M., Vonk, W., & Schriefers, H. (2002). The influence of animacy on relative clause processing. Journal of Memory and Language,47(1), 50–68.

    Google Scholar 

  53. Mani, N., & Huettig, F. (2012). Prediction during language processing is a piece of cake—but only for skilled producers. Journal of Experimental Psychology,38(4), 843–847.

    PubMed  Google Scholar 

  54. Martin, C. D., Thierry, G., Kuipers, J., Boutonnet, B., Foucart, A., & Costa, A. (2013). Bilinguals reading in their second language do not predict upcoming words as native readers do. Journal of Memory and Language,69, 574–588.

    Google Scholar 

  55. Miyamoto, E., & Nakamura, M. (2003). Subject/object asymmetries in the processing of relative clauses in Japanese. In G. Garding & M. Tsujimura (Eds.), Proceedings of the west coast conference on formal linguistics 22 (pp. 342–355). Somerville: Cascadilla Press.

    Google Scholar 

  56. Montrul, S. (2010). Dominant language transfer in adult second language learners and heritage speakers. Second Language Research,26(3), 293–327.

    Google Scholar 

  57. Nakamura, C., Arai, M., & Mazuka, R. (2012). Immediate use of prosody and context in predicting a syntactic structure. Cognition,125, 317–323.

    PubMed  Google Scholar 

  58. Nation, K., Marshall, C. M., & Altmann, G. T. M. (2003). Investigating individual differences in children’s real-time sentence comprehension using language-mediated eye movements. Journal of Experimental Child Psychology,86, 314–329.

    PubMed  PubMed Central  Google Scholar 

  59. O’Grady, W., Schafer, A. J., Perla, J., Lee, O., & Wieting, J. (2009). A psycholinguistic tool for the assessment of language loss: The HALA project. Language Documentation and Conservation,3(1), 100–112.

    Google Scholar 

  60. Ospina, R., & Ferrari, S. L. P. (2012). A general class of zero-or-one inflated beta regression models. Computational Statistics and Data Analysis,56(6), 1609–1623.

    Google Scholar 

  61. Pickering, M. J., & Garrod, S. (2007). Do people use language production to make predictions during comprehension? Trends in Cognitive Sciences,11, 105–110.

    PubMed  Google Scholar 

  62. Piquado, T., Isaacowitz, D., & Wingfield, A. (2010). Pupillometry as a measure of cognitive effort in younger and older adults. Psychophysiology,47(3), 560–569.

    PubMed  PubMed Central  Google Scholar 

  63. Polinsky, M. (2011). Reanalysis in adult heritage language: New evidence in support of attrition. Studies in Second Language Acquisition,33, 305–328.

    Google Scholar 

  64. Puig-Mayenco, E., Cunnings, I., Bayram, F., Miller, D., Tubau, S., & Rothman, J. (2018). Language dominance affects bilingual performance and processing outcomes in adulthood. Frontiers in Psychology,2019, 9.

    Google Scholar 

  65. R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

  66. Rigby, R. A., & Stasinopoulos, D. M. (2005). Generalized additive models for location, scale and shape, (with discussion). Appl. Statist.,54(3), 507–554.

    Google Scholar 

  67. Scherag, A., Demuth, L., Rösler, F., Neville, H. J., & Röder, B. (2004). The effects of late acquisition of L2 and the consequences of immigration on L1 for semantic and morpho-syntactic language aspects. Cognition,93(3), B97–B108.

    PubMed  Google Scholar 

  68. Schneider, W., Eschman, A., & Zuccolotto, A. (2002). E-Prime (Version 2.0). [Computer software and manual]. Pittsburgh: Psychology Software Tools Inc.

    Google Scholar 

  69. Scontras, G., Fuchs, Z., & Polinsky, M. (2015). Heritage language and linguistic theory. Frontiers in Psychology,6, 1545.

    PubMed  PubMed Central  Google Scholar 

  70. Slobin, D. (1985). The crosslinguistic study of language acquisition, Vol. I and II. Hillsdale: Erlbaum.

    Google Scholar 

  71. Stover, L. M., Stern, M. C., Lowry, C., Martohardjono, G., & Madsen II, C. N. Effects of language dominance on L1 relative clause processing(Manuscript in preparation).

  72. Stowe, L. A. (1986). Parsing WH-constructions: Evidence for on-line gap location. Language and Cognitive Processes,1, 227–245.

    Google Scholar 

  73. Stromswold, K., Caplan, D., Alpert, N., & Rauch, S. (1996). Localization of syntactic comprehension by positron emission tomography. Brain and Language,52, 452–473.

    PubMed  Google Scholar 

  74. Tanenhaus, M. K., Spivey-Knowlton, M. J., Eberhard, K. M., & Sedivy, J. C. (1995). Integration of visual and linguistic information in spoken language comprehension. Science,268(5217), 1632–1634.

    Google Scholar 

  75. Tolentino, L. C., & Tokowicz, C. (2011). Across language, space, and time: A review of the role of cross-language similarity in L2 (morpho)syntactic processing as revealed by fMRI and ERP methods. Studies in Second Language Acquisition,33(1), 91–125.

    Google Scholar 

  76. Traxler, M. J., Morris, R. K., & Seely, R. E. (2002). Processing subject and object relative clauses: Evidence from eye movements. Journal of Memory and Language,47, 69–90.

    Google Scholar 

  77. White, L., Goad, G., Goodhue, D., Hwang, G., & Lieberman, M. (2013). Syntactic ambiguity resolution in L2 parsing: Effects of prosodic boundaries and constituent length. In Proceedings of the annual Boston University Conference on Language Development.

  78. Wu, F., Luo, Y., & Zhou, X. (2014). Building Chinese relative clause structures with lexical and syntactic cues: Evidence from visual world eye-tracking and reading times. Language Cognition and Neuroscience,29(10), 1205–1226.

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank our study participants, Richard G. Schwartz, and Second Language Acquisition Lab research assistants Daniela Castillo, Omar Ortiz, Christina Dadurian, Andrea Monge, Matthew Stuck, and Armando Tapia.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Michael C. Stern.

Ethics declarations

Conflict of interest

On behalf of all authors, Michael C. Stern states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Stern, M.C., Madsen, C.N., Stover, L.M. et al. Language history attenuates syntactic prediction in L1 processing. J Cult Cogn Sci 3, 235–255 (2019). https://doi.org/10.1007/s41809-019-00048-y

Download citation

Keywords

  • Bilingual processing
  • Relative clause processing
  • Active filler
  • Prediction
  • Visual world paradigm
  • Language dominance