Experiment 1: different verbs

Method

Participants

The participants were 72 trilinguals (2 males and 70 females, with an average age of 20.75([18-26], SD = 1.77)) who were paid 60 RMB. They reported to have normal or corrected-to-normal vision. The study was approved by the ethics committee of the School of Psychology, South China Normal University. The participants speak Cantonese as their native language but use Mandarin as their primary language in daily life (all of them acquired Mandarin at an early age). They were university students who had either majored in English for more than two years, stayed in English-speaking countries for more than 1 year, or had IELTS scores of at least 6.5 or TOEFL scores of at least 90. We tested participants’ language proficiency with both a self-rating language questionnaire (for Cantonese, Mandarin, and English, see Hartsuiker et al., Reference Hartsuiker, Beerts, Loncke, Desmet and Bernolet2016; Huang et al., Reference Huang, Pickering, Chen, Cai, Wang and Branigan2019) and a Lextale test (for English only, see Lemhöfer & Broersma, Reference Lemhöfer and Broersma2012) (Table 1). Given that participants acquired Cantonese as their mother tongue, their AOA of Cantonese was earlier than Mandarin and English. They acquired Mandarin much earlier than English (p< .001, see Appendix A). Moreover, their rating of listening, reading, speaking fluency, and general proficiency for Mandarin was higher than both Cantonese and English (p < .001), and Cantonese was higher than English (p < .001), except that their rating of speaking pronunciation for Mandarin was similar to Cantonese and both of them were higher than English (p < .001)Footnote ². Participants’ average score on the Lextale test was 69.17, which was close to Dutch–English bilinguals with daily exposure of English (i.e., 75.5, see Lemhöfer & Broersma, Reference Lemhöfer and Broersma2012).

Table 1. Language background self-ratings and Lextale scores of English in Experiments 1 and 2

Note. Participants rated their proficiency in several modalities for each of the three languages on a 10-point scale (1 = very poor, 10 = very proficient) and rated their general proficiency for each language on a 5-point scale (1 = very poor, 5 = very proficient). The mean value is followed by standard deviation between round brackets and the range of scores between square brackets. I) Cantonese is their mother tongue from birth and therefore its AOA is 0; II) “[0-6]” indicates the range of AOA for Mandarin in the experiment; III) ”100” indicates the maximum score of the Lextale test. There was no significant difference for most of the language rating scores and the Lextale scores of participants between Experiments 1 and 2, except for the marginally significant difference for listening (p = .07) and speaking fluency (p = .08) of Cantonese.

Materials

In order to balance the structure bias of the prime verbs (i.e., verb bias) among the three languages, we selected six verbs with a similar structure bias in Mandarin, Cantonese, and English. We measured verb bias with a picture description task (Table 2). Verb bias was calculated as the log-odds for the DO responses following the verb divided by the PO responses (i.e., “log[(#DO + 1)/(#PO + 1)], see Bernolet & Hartsuiker, Reference Bernolet and Hartsuiker2010). First, we chose 11 English dative verbs that had the same structure preference in a British English corpus (i.e., the International Corpus of English, see Gries & Stefanowitsch, Reference Gries and Stefanowitsch2004) as their Mandarin translation equivalents in a set of Mandarin norms (N = 367, Chen et al., Reference Chen, Wang and Hartsuiker2022). Second, we performed a norming study of the Cantonese translation equivalents of these verbs (40 native speakers of Cantonese) and a further norming study with the English verbs for L2 English speakers (51 high-proficient Mandarin-English bilinguals), none of whom participated in Experiments 1 or 2Footnote ³. All the dative verbs showed a preference for PO in Cantonese, so we selected two relatively less PO-biased verbs. Their translation equivalents were DO-biased in both Mandarin and English. The other four prime verbs were PO-biased in all three languages. For the target verbs, we used the same four neutral-biased verbs (i.e., “递(pass)”, “赠(gift)”, “还(return)”, and “赔(compensate)”) in Mandarin as Chen et al. (Reference Chen, Wang and Hartsuiker2022).

Table 2. Structure bias of prime verb in Experiment 1

Note. Structure bias was calculated as the log-odds for the DO responses. Therefore, values larger than 0 indicate a DO-biased verb and values below 0 indicate a PO-biased verb. I) The verb ‘带(dai)’ was not included in the norming data of Mandarin by Chen et al. (Reference Chen, Wang and Hartsuiker2022), therefore we performed an online picture description experiment with 211 native Mandarin speakers to test its verb bias.

Similar to Chen et al. (Reference Chen, Wang and Hartsuiker2022), we constructed 48 sets of materials (Appendix S). Each set included six prime sentences involving dative structures (DO and PO) in three different languages (Cantonese, Mandarin, and English; Cantonese and Mandarin have the same orthographic system, Table 3), and two ditransitive target sentences (DO and PO) in Mandarin. Sometimes, the dative constructions in Chinese seem to differ from their counterparts in English. For instance, the preposition “给 (GEI, meaning “to”)” in a PO sentence (e.g., “Fumu liu yaoshi GEI baomu [Lit. parent left key to babysitter]”, see Table 3) can be also used as a verb marker in a DO sentence (e.g., “ Fumu liu-GEI baomu yiba yaoshi [Lit. parent left-GEI babysitter a key]”). Given that these verb markers in the DO sentences are argued to boost structural priming (Chen, Huang, Wang, Pickering, & Branigan, Reference Chen, Huang, Wang, Pickering and Branigan2016), we excluded them in the current study (also see Chen et al., Reference Chen, Wang and Hartsuiker2022). As in Chen et al. (Reference Chen, Wang and Hartsuiker2022), the target sentences contained a temporary ambiguity of the first syllable of the first noun phrase (NP1) (e.g., “Qiuyuan [football player]” and “Qiupai [racket]”, Figure 1). Therefore, the target sentences were ambiguous between a DO or a PO dative from the onset of the target verb to the onset of the second syllable of NP1. As a result, there were two ambiguous time windows: the target verb time window (1000 ms, from 200 ms after the onset of the target verb to 200 ms after the onset of the first syllable of NP1) and the first syllable of NP1 window (550 ms, from 200 ms after the onset of the first syllable of NP1 to 200 ms after the onset of the second syllable).

Table 3. Sample stimuli of prime sentences in Experiment 1

Note. “给(GEI, means “to”)” in Chinese can be either a preposition in a PO sentence (e.g., “gei[to]” in the PP “gei baomu [to the babysitter]”) or a verb marker in a DO sentence (e.g., “liu-GEI” in the VP “liu-GEI baomu yiba yaoshi [left-GEI Babysitter A Key]”). Following Chen et al. (Reference Chen, Wang and Hartsuiker2022), we excluded the verb markers in the DO sentences.

Fig. 1. Example target picture in Experiment 1.

Note. Participants saw the target picture when they heard the corresponding target sentence. The audio target sentence was a DO (e.g., “Yeye huan Qiuyuan yifu Qiupai [grandpa returns football player a racket]”, The grandpa returns the football player a racket) or PO (e.g., “Yeye huan Qiupai gei Qiuyuan [grandpa returns racket to football player]”, The grandpa returns a racket to the football player) sentence.

We divided the 48 sets of materials into 12 lists in a Latin Square design. Each list included three language blocks. Each block included eight prime sentences for each prime structure (DO or PO) with four prime sentences followed by DO target sentences and four prime sentences followed by PO target sentences. The experimental order of language blocks was balanced among lists. Moreover, in order to prevent any effects of the spatial collocation of the entities on the picture, each list had three versions so that each entity's position was balanced across versions. Additionally, there were 96 fillers, 32 per block (thus there were 32 fillers in each prime language). Half of the filler pairs had the same structure in the visual and audio sentences and half did not. Half of the filler audio sentences also involved homophonic syllables. Half of the filler audio sentences included one entity that mismatched the corresponding filler pictures. There were 48 trials for each language block, which were presented in a pseudo-random order. There were four practice trials for each block.

Procedure

We employed the visual-world structural priming paradigm (Arai et al., Reference Arai, van Gompel and Scheepers2007). Participants were first instructed to memorize all entities in the target pictures. Each trial in the experiment started with a fixation point on which participants needed to focus to trigger a drift-correction. Then, they read a prime sentence aloud in either Cantonese, Mandarin, or English and pressed the space bar to trigger a target picture. There was a 1500ms preview of the target picture. Next, they heard the Mandarin target sentence while looking at the picture at the same time. In 25% of the filler trials, the word ‘recall’ was presented after the filler picture. In such trials, participants were instructed to correctly describe the picture with one Mandarin sentence (i.e., they usually repeated the audio sentences or corrected one incongruent entity that mismatched the pictures). This recall task on a subset of the filler materials was implemented to mask the aim of the study and to keep participants’ attention. We used a SR-Research EyeLink-1000 (1000Hz sample rate) to record the movements of participants’ right eyes from the onset to 350ms post-offset of the audio sentences. The experiment took about 1 hour to complete.

Data analysis

We constructed three rectangular regions of interest for the entities on the target pictures (e.g., agent, recipient, theme). The temporarily ambiguous first post-verbal noun phrase (NP1) referred to either the animate recipient (e.g., “Qiuyuan [football player]”) or the inanimate theme (e.g., “Qiupai [racket]”). We calculated the proportion of looks to the recipient and theme in two critical ambiguous time windows: the verb window (1000ms) and the window of the first syllable of NP1 (550ms). After target verb onset, looks to the recipient suggested prediction of DO structure and looks to the theme suggested a PO structure. Therefore, the dependent variable was the different score of gaze probability between recipient and theme (Thothathiri & Snedeker, Reference Thothathiri and Snedeker2008a). The gaze probability data of these two entities was calculated with the empirical logit transformation (Barr, Reference Barr2008).

We analyzed the data with linear mixed models by using the “lme4” package in R (Bates & Maechler, Reference Bates and Maechler2009). We used mean-centered form for three predictors: prime structure (PO or DO), verb bias (as a continuous variable), and language block (Cantonese, Mandarin, and English). As for prime structure and verb bias, we had two fixed predictors in the model corresponding to their main effects. As for the language block, we had two variables representing two contrasts between the Mandarin block and the other two language blocks (i.e., Mandarin as a baseline: Mandarin-Cantonese contrast, and Mandarin-English contrast) (Scheepers, Raffray, & Myachykov, Reference Scheepers, Raffray and Myachykov2017). We analyzed the main effects and interactions of these predictors in LME models of the two critical time windows of the target verb and the first syllable of NP1. Because of model convergence issues (Barr, Levy, Scheepers, & Tily, Reference Barr, Levy, Scheepers and Tily2013), in the LME model of the time window of the target verb, we included a random intercept and a random slope of the Mandarin-English contrast for subjects, a random intercept and random slopes of prime structure and of the Mandarin-Cantonese contrast for items. In the LME model of the time window of the first syllable of NP1, we included a random intercept for both subjects and items, and a random slope of the Mandarin-Cantonese contrast for items. Next, we analyzed the predictors of prime structure and verb bias for the LME model of each language block separately, with the same random effect structure as the omnibus analysis (but of course without the random slopes of the contrasts between language blocks). If these models did not converge in the analysis of a specific language block, we then chose a secondary model with a simpler random effect structure (Barr et al., Reference Barr, Levy, Scheepers and Tily2013).

Furthermore, we implemented a cluster-based permutation analysis with the “permuco” package in R (Frossard & Renaud, Reference Frossard and Renaud2019) to analyze the time course of structure prediction in both ambiguous time windows. For the language block that showed a significant effect (i.e., main effect of prime structure or interaction effect of prime structure and verb bias), we tested the effect of the corresponding predictor (e.g., prime structure) with a full model of random effects for subjects (by-subject analysis) or items (by-item analysis) in each time bin (50ms) when controlling the family-wise error rate. In order to detect the beginning and end time point of structure prediction within these time windows, we employed F tests with a threshold of p < .05 (two sided), 5000 permutations, and the sum as a clustermass statistics (for data and scripts, see https://osf.io/dt5yw/ ; for the output of all LME models, see Appendix B). Compared to the bin-by-bin analysis, the permutation analysis can specify the time ranges that are significant for our predictors while controlling the family-wise error rate and avoiding the subjective choice of the bin size (Barr, Jackson, & Phillips, Reference Barr, Jackson and Phillips2014).

Results

Traditional Time-window Analysis

Figure 2 shows the time course of the proportion of looks to recipient and theme from the onset of the verb in the comprehension of audio target sentences as a function of prime condition per language block. The omnibus LME model showed a marginally significant main effect of prime structure in the time window of the first syllable of NP1 (β = .69, SE = .35, t = 1.95, p = .051), but not in the time window of the target verb (p >.1). Additionally, there was a marginally significant interaction between prime structure and the contrast between the English-to-Mandarin block and the Mandarin-to-Mandarin block in the time window of the target verb (β = -.93, SE = .54, t = -1.73, p = .08). There was no significant interaction between prime structure and verb bias or among prime structure, verb bias, and the contrasts of language blocks (p > .1).

Fig. 2. Difference in Proportion of Looks to Recipient and Theme for Each Time Bin (50ms) from Onset of Target Verb in Experiment 1.

Note. The verb time window is from 200ms to 1200ms and the time window of the first syllable of NP1 is from 1200ms to 1750ms. Plot A shows data from the Mandarin-Mandarin block. The plot indicates the difference in the proportions of looks to the recipient (predicting DO structure) and to the theme (predicting PO structure) during comprehension of Mandarin sentences after Mandarin DO vs. PO prime sentences; the grey rectangles within the time windows indicate the clusters (by-subject) where the main effect of structure was significant. Plot B (the Cantonese–Mandarin block) and Plot C (the English-Mandarin block) indicate the difference in the proportions of looks to recipient and theme during comprehension of Mandarin sentences after DO vs. PO prime sentences in Cantonese and English respectively. ^§p <.1, *p < .05, **p < .01, ***p < .001.

For the within-language block (Mandarin-to-Mandarin, see Figure 2A), there was a significant main effect of prime structure in both the time windows of the target verb (β = .82, SE = .38, t = 2.14, p < .05) and of the first syllable of NP1 (β = 1.03, SE = .44, t = 2.34, p < .05). Figure 2A illustrates the within-language structural priming effect: participants looked more at the recipient after DO primes than PO primes in both ambiguous time windows. However, no such priming occurred for either of the between-language blocks (i.e., Cantonese-to-Mandarin (p > .1, Figure 2B); English-to-Mandarin (p > .1, Figure 2C))Footnote ⁴. Additionally, in none of these language blocks was there an interaction between prime structure and verb bias in any time window (all p > .1).

Cluster-based Permutation Analysis for Eye-tracking Data

As for the main effect of prime structure in the Mandarin-to-Mandarin block, the permutation analysis showed one marginally significant cluster in the time window of the target verb: from 1050ms to 1200ms (cluster-mass = 18.23, p = .095, for a by-subject analysis); and one significant cluster in the time window of the first syllable of NP1: from 1200ms to 1400ms (cluster-mass = 29.93, p < .05, for a by-subject analysis) or from 1200ms to 1350ms (cluster-mass = 25.51, p < .05, for a by-item analysis).

Experiment 2: Related Verbs

Method

Participants

We recruited 72 further trilinguals (4 males and 68 females, with an average age of 21.39 ([18-30], SD = 2.42)) from the same population as Experiment 1. Again, participants acquired Cantonese as their mother tongue, which was earlier than Mandarin and English, and they acquired Mandarin much earlier than English (p < .001) (Table 1). Their rating of overall proficiency in Mandarin was higher than that in both Cantonese and English (p < .001), and ratings of Cantonese were higher than that of English (p < .001). The average Lextale score was 70.17.

Materials

The materials were the same as in Experiment 1, except that we created six triplets of translation-equivalent dative verbs in Cantonese, Mandarin, and English that were used as both prime and target verbs (i.e., give, hand, pass, bring, leave, rent).

Procedure

The procedure was the same as in Experiment 1. Additionally, we instructed participants to familiarize themselves with the verbs in all three languages and correctly translate them before they started the experiment.

Data analysis

Again, we used both the traditional time window analysis and cluster-based permutation analysis in both ambiguous time windows (target verb and the first syllable of NP1). Visual inspection of the data (Figure 3) suggested that there was a structure prediction effect in the Mandarin block that extended until the onset of the next phrase (the preposition “gei (to)” for PO sentences or the determiner “yige (a)” for DO sentences after NP1). We therefore performed an additional post-hoc analysis for the (unambiguous) time window of the second syllable of NP1 (650ms, from 200ms after the onset of the second syllable of NP1 to the onset of the preposition or determiner). We again treated prime structure, the two language block contrasts (Mandarin as the baseline), and their interactions as predictors. Due to model convergence issues (Barr et al., Reference Barr, Levy, Scheepers and Tily2013), in the LME models of the time window of the target verb, we included a random intercept and random slopes of prime structure, the Mandarin-English contrast, and the interaction between prime structure and the Mandarin-Cantonese contrast for subjects, and a random intercept and random slope of the interaction between prime structure and the Mandarin-English contrast for items. In the time window of the first syllable of NP1, we included a random intercept and random slopes of prime structure, the Mandarin-English contrast, and the interaction between prime structure and the Mandarin-English contrast for subjects, and a random intercept and random slopes of prime structure and the Mandarin-Cantonese contrast for items. In the LME model of the time window of the second syllable of NP1, we included a random intercept and random slope of prime structure for subjects, and a random intercept and random slope of the Mandarin-English contrast for items. Next, in the analysis for each language block, we used the same random effect structure (without the slopes of language contrasts). Again, if these models did not converge, we chose a secondary model with a simpler random effect structure (Barr et al., Reference Barr, Levy, Scheepers and Tily2013).

Fig. 3. Difference in Proportion of Looks to Recipient and Theme for Each Time Bin (50ms) from Onset of Target Verb in Experiment 2.

Note. The time window of the verb is from 200ms to 1200ms and the time window of the first syllable of NP1 is from 1200 ms to 1750 ms. The time window of the second syllable of NP1 is from 1750 ms to 2400 ms. Plot A (Mandarin-Mandarin), Plot B (Cantonese–Mandarin), and Plot C (English-Mandarin) indicate the difference in the proportions of looks to the recipient (predicting DO structure) and to the theme (predicting PO structure) during comprehension of Mandarin sentences after prime sentences in Mandarin, Cantonese, and English with a DO vs. PO structure. The grey rectangles within the time windows indicate the clusters (by-subject analysis) where the main effect of structure was significant. ^§p < .1, *p < .05, **p < .01, ***p < .001.

Combined analysis of Experiments 1 and 2

In order to investigate whether lexical overlap boosts structural priming, we combined the eye-movement data of Experiments 1 and 2, and compared the structural priming effects in the time windows of the target verb and the first syllable of NP1 for the Mandarin block and in the time window of the first syllable of NP1 for the Cantonese block. We treated prime structure (DO vs. PO), experiment (1 vs. 2), and their interaction as predictors in the linear mixed models. Because of model convergence issues (Barr et al., Reference Barr, Levy, Scheepers and Tily2013), we included random intercepts for both subjects and items in the LME models of the time window of the first syllable of NP1 for the Cantonese and Mandarin blocks; random slopes of prime structure and random intercepts for both subjects and items, and a random slope of experiment for items in the model of the time window of the target verb for the Mandarin block.

General Discussion

In two eye-tracking experiments, we investigated whether structural priming in comprehension is modulated by verb bias, prime language, and lexical overlap. Experiment 1 presented different verbs in prime and target. There was clear within-language structural priming (i.e., Mandarin-to-Mandarin). In particular, participants looked more at the recipient than the theme during auditory comprehension of target sentences after reading a DO prime sentence, and looked more at the theme than the recipient after a PO prime sentence. However, there was no abstract between-language structural priming (i.e., Cantonese-to-Mandarin or English-to-Mandarin). Additionally, neither within-language nor between-language structural priming was modulated by verb bias. Experiment 2 presented identical or translation-equivalent verbs between prime and target. We now not only found clear within-language structural priming (i.e., Mandarin-to-Mandarin, same verbs) but also between-language priming (i.e., Cantonese-to-Mandarin, cognate verbs). However, this cross-linguistic structural priming seemed to be limited to highly related languages with cognate verbs; it did not occur between English and Mandarin, which are unrelated and do not have cognate verbs. Moreover, the combined analysis showed a lexical boost and translation-equivalent cognate boost in comprehension: structural priming was stronger and longer lasting when the action was repeated between prime and target (Experiment 1 vs. 2) in both within-language (Mandarin-to-Mandarin) and between-language (Cantonese-to-Mandarin) processing. Below, we discuss the critical findings further.

In contrast to the predictions of implicit learning accounts, there was no inverse preference priming effect either within- or between-languages. This result does not support the predictions of implicit learning theories that assume non-lexicalist shared syntax for multilinguals (Chang et al., Reference Chang, Dell and Bock2006). This is because such accounts assume that structural priming is driven by error-based learning and therefore predict an inverse preference priming effect. Importantly, such an effect did not occur between languages (i.e., neither between closely related nor unrelated languages). There is therefore no support for the hypothesis that prediction errors in a specific language generalize to another language (see Muylle et al., Reference Muylle, Bernolet and Hartsuiker2021 for a similar finding in an artificial language learning experiment). Additionally, we did not find an inverse preference priming effect for L2 Mandarin speakers in within-language processing, whereas Chen et al. (Reference Chen, Wang and Hartsuiker2022) did show such an effect for L1 Mandarin speakers (note that this effect also occurred in L1 Dutch and L1 English speakers in production, see Bernolet & Hartsuiker, Reference Bernolet and Hartsuiker2010; Fine & Jaeger, Reference Fine and Jaeger2013; Jaeger & Snider, Reference Jaeger and Snider2013). This finding also does not support the predictions of implicit learning theory, which assumes a faster learning rate and stronger inverse preference learning in lower proficient languages (L2/L3). There is however another possible interpretation: the learning rate for L2 in our study (i.e., the language our participants were more proficient in) may be comparable to L1 in Chen et al. (Reference Chen, Wang and Hartsuiker2022), whereas the manipulation of verb bias (as a continuous variable) may not have been strong enough to evoke inverse preference priming.

We found structural priming during within-language comprehension (Mandarin-to-Mandarin) when the verb was different between prime and target, which replicated the findings of comprehension studies (Chen et al., Reference Chen, Wang and Hartsuiker2022; Thothathiri & Snedeker, Reference Thothathiri and Snedeker2008a). Furthermore, we found a lexical boost in online comprehension: structural priming was enhanced when the verb was repeated (Experiment 2 vs. Experiment 1). These findings in comprehension are consistent with production studies (Carminati, van Gompel, & Wakeford, Reference Carminati, van Gompel and Wakeford2019; Hartsuiker et al., Reference Hartsuiker, Bernolet, Schoonbaert, Speybroeck and Vanderelst2008; Huang et al., Reference Huang, Pickering, Yang, Wang and Branigan2016; Pickering & Branigan, Reference Pickering and Branigan1998; Rowland, Chang, Ambridge, Pine, & Lieven, Reference Rowland, Chang, Ambridge, Pine and Lieven2012), suggesting a shared mechanism between production and comprehension, at least when staying within one language.

Importantly, we did find cross-linguistic structural priming in online comprehension, but only in the Cantonese-to-Mandarin condition and only when there were translation-equivalent verbs between prime and target rather than unrelated verbs. The lack of abstract between-language priming is inconsistent with the findings in production (e.g., Cai et al., Reference Cai, Pickering, Yan and Branigan2011; Hartsuiker et al., Reference Hartsuiker, Pickering and Veltkamp2004; Huang et al., Reference Huang, Pickering, Chen, Cai, Wang and Branigan2019). The production studies showed clear abstract cross-linguistic priming of dative structure between Cantonese and Mandarin (Cai et al., Reference Cai, Pickering, Yan and Branigan2011; Huang et al., Reference Huang, Pickering, Chen, Cai, Wang and Branigan2019) or between English and Mandarin (Huang et al., Reference Huang, Pickering, Chen, Cai, Wang and Branigan2019). One possibility to explain the lack of English-to-Mandarin priming was that English only shares the DO representations with Mandarin DO-GEI sentences (e.g., “Chushi song-GEI mushi yige qiu [The chef gives-GEI the priest a ball]”, see Huang et al., Reference Huang, Pickering, Chen, Cai, Wang and Branigan2019), but not with DO-nomarker sentences (e.g., Table 3 in our study). We therefore reanalyzed the production data from Huang et al. and confirmed that Mandarin DO-nomarker responses can be primed by English or Cantonese DO-sentences. These findings suggested that dative constructions in these three languages do share fundamental aspects of their representation (Branigan & Pickering, Reference Branigan and Pickering2017). In particular, some production studies showed comparable structural priming within and between languages when the verb was different (Hartsuiker et al., Reference Hartsuiker, Beerts, Loncke, Desmet and Bernolet2016; Kantola & Van Gompel, Reference Kantola and Van Gompel2011), in line with the shared-syntax account.

One possible explanation for the differences between priming in production and comprehension is that priming effects in different modalities are related to different dependent variables. It may just be harder to find an effect on online measures like eye movements or reaction times than on syntactic choices. In a picture description task for production, speakers need to select a specific syntactic structure during sentence construction, where the syntactic choice directly taps into the syntactic level. However, in an online visual-world comprehension task, the priming effect is estimated by fixation, which involves the processing of not only the syntactic level, but also other levels like concepts, phonology, or cognitive control (e.g., attention). A more interesting explanation for the lack of abstract cross-linguistic priming in comprehension is that there is a closer association between the verb and structure in comprehension than production (see discussion in Arai et al., Reference Arai, van Gompel and Scheepers2007). In particular, comprehenders access the properties of a dative verb before they merge it with the structure of the whole target sentence. Therefore, sometimes the retrieval of the structure may depend on the specific verb (subcategorization structure) and abstract structural priming may be harder to detect (Arai et al., Reference Arai, van Gompel and Scheepers2007; Pickering & Traxler, Reference Pickering and Traxler2004). In contrast, when the structure retrieval does not depend on the prior access of a specific verb during offline comprehension (e.g., object relative clauses, “where is the princess that the child is pushing”), sometimes abstract cross-linguistic priming can be detected (see Kidd et al., Reference Kidd, Tennant and Nitschke2015). In short, our finding is compatible with the hypothesis that cross-linguistic structural priming in online comprehension is at least partly based on lexical information.

One important further element to consider in the difference between the two between-language conditions is that the verbs in the Cantonese-to-Mandarin condition were not only translation equivalents but also cognates, overlapping fully in orthography and partly in phonology (e.g., “留(lau)” to “留(liu); the English-to-Mandarin equivalent is “leave” to “留(liu)”). The cognate facilitation effects in our study are consistent with the findings in production (e.g., stronger cross-linguistic priming for the Cantonese–Mandarin cognate verbs (“lau-liu”) than English-Mandarin non-cognate verbs (“leave-liu”, Huang et al., Reference Huang, Pickering, Chen, Cai, Wang and Branigan2019; also see Bernolet et al., Reference Bernolet, Hartsuiker and Pickering2012)). These findings are compatible with a lexicalist, shared syntactic representations account (Hartsuiker & Bernolet, Reference Hartsuiker and Bernolet2017; Hartsuiker & Pickering, Reference Hartsuiker and Pickering2008; Schoonbaert et al., Reference Schoonbaert, Hartsuiker and Pickering2007). In this model, the activation of the shared representation would benefit from the activation flow from other linguistic levels in the condition with cognate verbs between languages (e.g., the conceptual level, the lemma level or word-form (phonology and orthography) level). We further discuss the locus of cognate facilitation effect for structural priming below.

How did cognate verbs facilitate structure prediction during comprehension in our study? The lemma hypothesis proposes that the lemmas of Cantonese and Mandarin cognate verbs are connected to each other and so will be co-activated in processing (Huang et al., Reference Huang, Pickering, Chen, Cai, Wang and Branigan2019). This lemma link may come about by Hebbian learning (Munakata & Pfaffly, Reference Munakata and Pfaffly2004) because, for instance, the Cantonese verb lemma “lau” co-activates the lemma of its Mandarin cognate verb “liu” via shared meaning, orthography, and phonology every time it is used, eventually leading to a lexical link. Therefore, in the processing of a Cantonese DO prime, the lemmas “lau” and “liu”, and the DO combinatorial node structure are all activated. Then, in the target sentence, the target Mandarin verb lemma “liu” is accessed and selected again (similar to the lexical boost). This repeated lemma activation contributes to cross-linguistic structural priming, leading to the cognate boost. Note that this boost might also be explained by activation feedback from the other levels (conceptual or word-form) to the syntactic level, without the formation of a lemma link. However, boosts from semantic and word-form levels between two different verbs are usually much weaker (and sometimes absent) than the boost effect of a repeated verb (lexical boost) within a language. For instance, Zhang, Bernolet, and Hartsuiker (Reference Zhang, Bernolet and Hartsuiker2021) found a phonological boost that was considerably weaker than the lexical boost for Mandarin-to-Mandarin priming. Moreover, Cantonese–Mandarin bilingual studies showed that cross-linguistic priming was not influenced by the phonological similarityFootnote ⁵ or the orthographic overlap (written vs. auditory modality) of cognate verbs (Cai et al., Reference Cai, Pickering, Yan and Branigan2011; Huang et al., Reference Huang, Pickering, Chen, Cai, Wang and Branigan2019).Footnote ⁶ However, one possible limitation of our study is that the Cantonese and Mandarin primes involved full orthographic overlap (differently from Huang et al. who used auditory Cantonese primes). The orthographic overlap may have resulted in a need to strongly suppress the Mandarin language when producing a prime sentence in Cantonese, followed by an effortful language switch when processing the target sentence in Mandarin. It is possible that the cognitive resources involved in language switching contributed to the lack of Cantonese-to-Mandarin priming.

Our findings cannot be explained by the shared-syntax accounts (including the implicit learning theory) or by separate-syntax accounts with connected representations accounts that we mentioned in the introduction. First, there was no error-based learning effect (i.e., inverse preference priming), whereas the implicit learning theory predicts such an effect (Chang et al., Reference Chang, Dell and Bock2006). Second, there was no cross-linguistic priming between English and Mandarin with either different verbs or translation-equivalent verbs, which does not support the residual activation model (Hartsuiker et al., Reference Hartsuiker, Pickering and Veltkamp2004; Schoonbaert et al., Reference Schoonbaert, Hartsuiker and Pickering2007). Third, there was no cross-linguistic priming for either related (Cantonese-to-Mandarin) or unrelated (English-to-Mandarin) languages with different verbs, which does not support any of these accounts.

In contrast to these accounts, we interpret the findings in terms of the development account of shared-syntax for multilinguals (Hartsuiker & Bernolet, Reference Hartsuiker and Bernolet2017, that was based on the residual activation account from Pickering & Branigan, Reference Pickering and Branigan1998). That is, cross-linguistic structural priming is driven by the residual activation of the combinatorial node of a specific structure (e.g., DO) that is shared between languages. In particular, such shared representations are constructed when speakers are highly proficient in two languages (e.g., Cantonese and Mandarin in our study). One interesting possibility is that lexically-based cross-linguistic priming might occur in the early stage of development for Cantonese–Mandarin bilinguals, because they might develop the lemma link between cognate verbs during the early item-specific learning. Thus, language proficiency might not influence Cantonese–Mandarin priming once the learners have passed that stage. In contrast, in the non-cognate translation-equivalent verbs condition (i.e., English-to-Mandarin), the process of sharing syntactic representations might only be completed at a very late stage of language development, when learners have reached high proficiency. Therefore, it is interesting for future studies to investigate the role of proficiency for these two types of cross-linguistic priming (e.g., Cantonese-to-Mandarin vs. English-to-Mandarin). However, the fully shared model of Cantonese and Mandarin cannot explain the lack of abstract cross-linguistic priming. One explanation is that within-language priming is always stronger than between-language priming, because the lemma node of the prime verb might co-activate the lemma nodes of other verbs in that language which facilitates structural priming in target sentences with the same language (Cai et al., Reference Cai, Pickering, Yan and Branigan2011). Moreover, abstract structural priming is possibly hard to detect with online measures in comprehension compared to production (e.g., Arai et al., Reference Arai, van Gompel and Scheepers2007; Branigan, Pickering, & McLean, Reference Branigan, Pickering and McLean2005; Ziegler & Snedeker, Reference Ziegler and Snedeker2019), so that the abstract Cantonese-to-Mandarin priming did not survive but Mandarin-to-Mandarin priming did.

In conclusion, our eye-tracking experiments showed abstract structural priming in language comprehension in Mandarin as an L2 and cross-linguistic priming between a related language (L1 Cantonese) and L2 Mandarin, but not between an unrelated language (L3 English) and L2 Mandarin. Moreover, we did not find an inverse preference priming in within- or between-language processing. These findings suggest a lexicalist shared-syntax mechanism for multilinguals in comprehension, in which: 1) the prediction errors in a specific language do not generalize to another language; 2) cross-linguistic structure prediction is based on the lexical cue of cognate verbs.