The N400 ERP component is a negative-going waveform that typically peaks around 400 ms. First reported by Kutas and Hillyard (1980), the N400 has been widely interpreted as a neural marker of semantic processing and integration. For example, incongruent words in semantically constraining sentences (e.g., “He spread his toast with socks”) elicit larger N400 amplitudes than congruent words (e.g., “He spread his toast with butter”). The N400 is not limited to linguistic input and has been observed across different modalities (Delogu et al., 2019). For instance, semantically incongruent facial expressions (e.g., Debruille et al., 1996) evoke similar N400 effects as incongruent words, indicating that the component is not limited to linguistic processing. This finding supports the view that the N400 is a domain-general marker of semantic processing (for a review, see Kutas and Federmeier, 2011). The N400 has also been linked to predictive processing, as the brain uses context to anticipate upcoming information (Brothers et al., 2017; Nieuwland, 2019). The N400, therefore, serves as an index of semantic processing, sensitive to both context and prediction across modalities.

In bilinguals, the N400 tends to be smaller in amplitude and delayed in the second language (L2) compared to the first language (L1) (e.g., Hahne and Friederici, 2001; Midgley et al., 2009; Newman et al., 2012), indicating that processing semantic information in the L2 is generally less automatic and requires greater cognitive effort. The N400 is also sensitive to crosslinguistic activation, with reduced amplitudes for target words overlapping phonologically or semantically with their translation equivalents in the non-target language (Chen et al., 2017; Hoshino and Thierry, 2012; Thierry and Wu, 2007). Furthermore, bilinguals perceive unrelated concepts as more semantically related than monolinguals do, as shown in a smaller N400 effect (i.e., reduced N400 amplitudes for related compared to unrelated pictures) (e.g., Friesen et al., 2016; Ning et al., 2020). N400 effects are also modulated by individual differences in language experience such as proficiency, age of acquisition, and daily exposure (e.g., Kotz and Elston-Güttler, 2004; Moreno and Kutas, 2005). In sum, the N400 reveals how semantic information is processed across languages and different bilingual experiences.

The P600, first reported by Osterhout and Holcomb (1992), is a positive-going waveform that typically peaks around 500 ms post-stimulus onset and can persist for several hundred milliseconds. This positivity appears in response to ungrammatical sentences and other anomalies (Kaan et al., 2000). The P600 is elicited across both written and spoken modalities by a variety of syntactic violations, such as those found in garden-path sentences (Osterhout et al., 1994), and has also been linked to thematic role anomalies, discourse integration, and pragmatic processing (Osterhout et al., 1994; Burkhardt, 2007; Delogu et al., 2018).

In studies on bilingual language processing, the P600 has been shown to be smaller in amplitude or delayed in the L2 compared to the L1 (Midgley et al., 2009; Hoeks et al., 2004). Greater P600 amplitudes have also been observed in response to less frequent but grammatical L2 structures (Zheng and Lemhöfer, 2019). In both cases, researchers have suggested that these patterns reflect increased processing effort and less engagement of control mechanisms during syntactic reanalysis in L2. These P600 effects vary by age of acquisition and proficiency, with early and highly proficient bilinguals showing neural responses that closely resemble those of monolingual native speakers (Sabourin and Stowe, 2008; Rossi et al., 2006). Similarly, P600 effects have been found during code-switching (Weber-Fox and Neville, 1996), reflecting the engagement of cognitive control processes, as bilinguals must manage competition between two active languages. Together, these findings suggest that P600 modulations can index either syntactic processing difficulty (e.g., when processing in the L2) or the engagement of cognitive control processes (e.g., during code-switching).

The N2 is a negative-going waveform peaking around 200–350 ms post-stimulus onset and reflects early engagement of cognitive control mechanisms (Moreno et al., 2002). For instance, in Go/No-Go paradigms participants respond to go trials and withhold their response to no-go trials. Compared to go trials, infrequent no-go trials elicit a larger N2, reflecting the recruitment of inhibitory control and conflict monitoring (Heidlmayr et al., 2020; Donkers and Van Boxtel, 2004). Therefore, the N2 serves as a neural marker of cognitive control, which is relevant for studying how bilinguals manage competition between simultaneously activated languages (Jonkman, 2006).

Research on bilingual language processing has shown that the N2 is sensitive to language-switching demands. For example, Jackson et al. (Jackson et al., 2001) observed larger N2 amplitudes during language switches, suggesting the involvement of control processes in language selection. Subsequent studies confirmed this switch effect, linking the N2 to greater demands in conflict monitoring (Verhoef et al., 2010). Training in language switching has been shown to enhance conflict monitoring efficiency. This is evidenced by shorter N2 latencies (Verhoef et al., 2010; Kang et al., 2018, 2023) and increased N2 amplitudes for non-trained executive control tasks (Zhang et al., 2015), suggesting transfer effects of language training to broader cognitive control functions.

N2 modulations in bilinguals have also been observed in non-linguistic tasks (Grundy et al., 2017b). Compared to monolinguals, bilinguals often show larger N2 amplitudes and earlier latencies in Go/No-Go and Flanker tasks, suggesting enhanced domain-general conflict monitoring abilities (Barac et al., 2016; Morales et al., 2015). However, language group differences are not always observed. A meta-analysis by Kousaie and Phillips (2012) revealed that differences in N2 amplitudes between monolinguals and bilinguals are often task-dependent and tend to emerge most clearly under conditions of high conflict (see Comishen and Bialystok, 2021 for similar effects in the P300 component).

Finally, factors such as L2 proficiency and age of second language acquisition modulate N2 responses. Higher proficiency has been linked to larger N2 amplitudes in auditory no-go trials (Fernandez et al., 2013, 2014), while both early L2 learners (Sullivan et al., 2014) and bilingual children (Barac et al., 2016) show earlier N2 onset latencies than monolinguals, suggesting more efficient conflict detection. Overall, the N2 emerges as a reliable marker of early control processes in bilinguals, reflecting adaptation to the demands of managing two active languages.

Studies exploring the N2 component frequently report effects on the P300 component, which is a positive deflection emerging between 300 and 600 ms after stimulus onset (Antoniou, 2023). The P300 is commonly divided into P3a and P3b (Polich, 2007). The P3a, with a frontal distribution and shorter latency, is generally associated with attentional capture and response inhibition (Folstein and Van Petten, 2008; Pires et al., 2014). The P3b, with a centro-parietal distribution, appears later and is closely linked to updating and working memory processes (Polich, 2012).

Studies using nonverbal executive control tasks have consistently shown that bilinguals exhibit larger P300 amplitudes and shorter latencies than monolinguals, particularly in high-conflict conditions (e.g., Go/No-Go or Flanker tasks; Moreno et al., 2014; Fernandez et al., 2013; López-Rojas et al., 2022). These findings have been interpreted as more efficient stimulus categorization and faster engagement of attentional control in bilinguals. Moreover, bilinguals immersed in dual-language contexts show increased P3b amplitudes during memory tasks (López-Rojas et al., 2022), suggesting greater engagement of context monitoring and updating mechanisms under highly demanding conditions. However, tasks involving linguistic conflict (e.g., Stroop) sometimes show greater P300 amplitudes for monolinguals, possibly reflecting differences in neural recruitment strategies (Comishen and Bialystok, 2021). Together, these findings highlight the P300 as a valuable neural marker for tracking how bilinguals allocate control resources when managing two language systems and adapting to varying task demands (Kousaie and Phillips, 2012).

EEG recordings not only capture ERPs, but they also allow the analysis of neural oscillations, offering complementary information about brain activity (Buzsáki and Draguhn, 2004). Neural oscillations are rhythmic patterns of neural activity that support communication between different brain regions and cognitive systems. Through time-frequency or synchronization analyses, neural oscillations are typically characterized in terms of frequency bands (e.g., theta, beta, gamma) and their associated power (i.e., the magnitude of oscillatory activity within a given frequency band). Importantly, neuroscience research has shown that distinct patterns of brain oscillations are linked to different cognitive processes (Ward, 2003). For example, theta oscillations have been associated with memory processes, whereas alpha oscillations are commonly linked to attentional allocation and control mechanisms.

In terms of language processing, theta-band activity has been consistently linked to word learning, lexical access, and memory consolidation (Lisman, 2010; Lisman and Jensen, 2013). Previous studies have shown that newly learned words elicit increased theta power, suggesting a role of theta oscillations in memory encoding (Bakker-Marshall et al., 2018; Grabner et al., 2007). Similar theta modulations have been observed in bilingual contexts, such as during word translation, in which theta oscillations are associated with the activation of semantic representations (Grabner et al., 2007). Beyond word-level processing, neural oscillations also capture higher-level processing. While decreases in alpha and beta power are associated with increased syntactic and semantic demands, increases in theta and delta activity are associated with the coordination of working memory processes during semantic processing (Bastiaansen and Hagoort, 2006). Importantly, bilinguals often show reduced alpha and beta modulations compared to monolinguals despite similar behavioral performance, which has been interpreted as more efficient neural processing during sentence comprehension (Kielar et al., 2014).

Neural oscillations are also sensitive to individual differences and task demands during bilingual language processing (e.g., Morucci et al., 2025). For example, Blanco-Elorrieta et al. (2019) found that during sentence processing, delta activity was modulated by language proficiency, with more proficient L2 speakers being better able to track sentences under more demanding noise conditions. Other studies have highlighted how oscillatory dynamics capture language-related differences in broader cognitive domains, such as creativity. For instance, Jończyk et al. (2024) found lower alpha power when bilinguals completed a creative task in L1 compared to L2, along with greater beta desynchronization in L2 than in L1, suggesting that operating in an L2 is associated with reduced interference from competing mental representations. Another commonly used methodological approach to analyze neural oscillations involves resting-state recordings (Custo et al., 2017). In psycholinguistics research, resting-state recordings have already been examined in a growing number of studies exploring the impact of prior bilingual experience on neural oscillations (e.g., Bice et al., 2020; Calvo et al., 2023). For instance, Amoruso et al. (2024) found that delta and beta power can distinguish bilinguals from monolinguals, pointing to experience-related changes in brain organization. Complementing these findings, resting-state EEG work has shown that age of acquisition and patterns of language use are associated not only with changes in oscillatory power (beta, gamma), but also with connectivity measures across theta, alpha, and gamma bands (Soares et al., 2021). Overall, these studies provide evidence that bilingualism reorganizes neural networks.

Taken together, EEG research provides converging evidence that bilingual language processing engages a set of language-relevant and domain-general neural mechanisms that unfold over time and are shaped by language experience. ERP components capture distinct stages of semantic processing, syntactic reanalysis, conflict monitoring, and context updating, while oscillatory analyses reveal complementary frequency-based dynamics related to memory, attention, and control. These studies highlight the value of EEG for creating theoretically grounded predictions that can be further tested using EEG and eye-tracking co-registration. Note that in this paper, we will focus on ERP measures when formulating predictions for open questions in the field of bilingualism (refer to Sections 4.1 and 4.2), as ERPs have been more widely used in previous co-registration work in psycholinguistic research. The potential contribution of neural oscillations is discussed in the section on future directions (refer to Section 5). Next, we move from EEG to eye tracking and briefly review a sample of eye-tracking research that has advanced our understanding of bilingual language processing.

The Visual World Paradigm (VWP; Tanenhaus et al., 1995) has become a valuable method for studying spoken language comprehension. In a typical VWP experiment, participants listen to auditory stimuli while viewing a display containing a set of objects that vary in their relation to the speech input. For example, in studies examining spoken word recognition, the display may include: (1) a target item (e.g., beaker), whose label appears in the speech signal; (2) a competitor item (e.g., beetle), whose label overlaps with the target's word form or meaning; and (3) control items (e.g., carriage), whose labels are unrelated to the speech input and serve as baselines (Allopenna et al., 1998). The objects are often presented as line drawings, photographs, or realistic visual scenes on a computer screen (see Huettig et al., 2011 for a review).

Crucially, adding eye-tracking to the VWP has allowed researchers to address different questions in language processing (e.g., Altmann and Kamide, 1999; Chambers et al., 2004; Huettig and McQueen, 2008). By manipulating the relationship between what participants see and hear, this approach can be used to examine how eye movements are influenced by the timing and location of visual input relative to speech (Tanenhaus, 2007). As speech unfolds, listeners may shift their gaze from one object to another (e.g., from the competitor to the target), revealing the dynamics of real-time language processing. This interaction between gaze and the unfolding speech signal enables users of the VWP to investigate the time course of language processing, including mechanisms such as lexical activation, competition, and prediction. In the VWP, common early measures include fixation proportions, total fixation time, and the number of fixations and saccades time-locked to key points in the auditory input (refer to Conklin et al., 2018 as well as Godfroid, 2020 for reviews).

Applied to bilingualism, the VWP has been particularly useful for uncovering parallel activation and competition across languages. (Marian and Spivey 2003b) discovered that when Russian-English bilinguals heard an English word like marker, they showed a higher proportion of eye movements to a visual competitor whose Russian label (marka, “stamp”) was phonologically related, even though the task was conducted entirely in English. This finding, replicated across modalities and in a variety of language pairs, provides compelling evidence that bilinguals activate lexical candidates in both of their languages (e.g., Canseco-Gonzalez et al., 2010; Cutler et al., 2006; Ju and Luce, 2004; Shook and Marian, 2012). Subsequent visual world studies have shown that crosslinguistic activation varies with individual differences in proficiency, language dominance, and cognitive control (e.g., Blumenfeld and Marian, 2007; Botezatu et al., 2022). For instance, Blumenfeld and Marian (2013) found that bilinguals' fixation patterns to between-language competitors (e.g., pool—pulgar, “thumb” in Spanish) differed systematically by language proficiency. Bilinguals with higher proficiency in a second language directed more fixations to crosslinguistic competitors, indicating greater parallel activation across languages. Similarly, Sarrett et al. (2022) showed that target fixation latencies were delayed when crosslinguistic competitors were present, highlighting the cost of resolving lexical competition for bilinguals. These findings underscore how the VWP's temporal precision allows researchers to trace not only when crosslinguistic competition emerges but also how individual differences influence its resolution.

The VWP has also been used to study other cognitive processes in bilinguals including memory. Fernandez-Duque et al. (2023) showed that Spanish-English bilinguals with high Spanish proficiency made more fixations to Spanish phonological competitors and subsequently remembered them better than control items, suggesting that heightened crosslinguistic competition can enhance both visual attention and memory for competing items. These findings highlight how eye movements can reveal not only the dynamics of crosslinguistic competition, but also the long-term effects of dual-language processing in shaping broader cognitive function.

Eye-tracking has also played a central role in uncovering the cognitive mechanisms underlying reading in bilinguals (Rayner, 2009). During reading, fixations are essential for recognizing words and integrating them into the overall meaning of the text (Clifton et al., 2007). Consequently, the temporal precision of eye-tracking has become instrumental in studies on reading for differentiating (early) word identification from (late) integration processes.

The distinction between early word identification vs. late integration processes has been particularly useful for addressing a central question in bilingual reading research: whether bilinguals activate only one language when reading (selective access) or whether both languages are activated in parallel (non-selective access). Evidence strongly supports the non-selective view, indicating that bilinguals' two languages interact during visual word recognition. For example, interlingual homographs (e.g., pie in English vs. pie “foot” in Spanish) elicit longer first-pass reading times than control words, reflecting between-language competition (e.g., Libben and Titone, 2009; Whitford and Titone, 2015). In contrast, cognates (e.g., piano in English and Spanish) yield shorter first fixation durations, indicating facilitation due to shared form and meaning (e.g., Duyck et al., 2007; Van Assche et al., 2009). Early fixation measures are also sensitive to lexical frequency, age of acquisition, and language proficiency, revealing how both individual and linguistic factors shape visual word processing (Cop et al., 2015; Hessel et al., 2021; Liversedge et al., 1998; Whitford and Titone, 2019). Late measures, on the other hand, capture the cognitive demands involved in reading, including the detection of processing difficulty and the resolution of linguistic and contextual ambiguity (for a review see Keating and Jegerski, 2014). For instance, regression-path duration and total reading times increase when bilinguals encounter ambiguous syntactic structures, reflecting the costs of integration (Cop et al., 2017; Whitford and Titone, 2019). These measures are thus highly informative for understanding morphosyntactic processing in bilinguals (e.g., Dussias and Sagarra, 2007; Dussias et al., 2013). Examining both early and late measures provides detailed information about the different stages of bilingual language processing. Whereas early measures reveal how bilinguals manage crosslinguistic activation and competition during word recognition and lexical access, late measures capture the integration of syntactic and contextual information.

Across a variety of paradigms, eye-movement measures index both early stages of word recognition and later stages of semantic and syntactic processing, revealing robust evidence for parallel crosslinguistic activation in bilingual individuals (Shook and Marian, 2013). By testing theoretical predictions with real-time measures of visual attention during spoken and written language comprehension, eye-tracking provides a methodological bridge between model-based assumptions and empirical observations. Importantly, the temporal precision of eye-tracking closely matches that of EEG, making it possible to directly link eye-tracking measures to ERP components during naturalistic tasks. In the next section, we review studies that have applied co-registration of eye movements and ERPs to study language processing and discuss the methodological strength of co-registration for bilingualism research.

One promising application of co-registration lies in its potential to extend findings of parallel crosslinguistic activation derived from studies using the VWP (e.g., Sarrett et al., 2022; Andras et al., 2022; Spivey and Marian, 1999). Because eye-tracking provides a precise index of visual attention and perception during language processing, integrating eye-tracking with EEG in the VWP makes it possible to time-lock shifts in attention with corresponding neural responses.

A clear benefit of combining the two techniques in the VWP is that they provide complementary information on how task stimuli are processed. In studies of crosslinguistic co-activation, for example, a bilingual Spanish-English speaker listening to the auditory target word cow (/ka℧/) may look not only at the target picture of a cow, but also to a crosslinguistic phonological competitor (e.g., helmet, in Spanish “casco”,/'kasko/). These gaze patterns reveal attentional engagement, as well as lexical access and competition through gaze behavior. The addition of EEG allows researchers to determine whether the phonological competitor was processed semantically (e.g., via an N400), inhibited (e.g., via a P300), or reanalyzed (e.g., via a P600; see illustration in Figure 2). The combined approach can also be valuable because the activation of the non-target language may not always impact gaze behavior or lead to significant differences between fixations on target and competitor items. Instead, co-registration may allow researchers to capture the brain activity elicited by both types of items, revealing neural responses to language co-activation not evident in eye movement metrics. This constitutes a clear case where neither EEG nor eye-tracking alone can determine whether co-activation engaged automatic or controlled processes.

Similarly, as the field moves toward understanding how individual differences in bilingual language experience shape cognition (de Bruin, 2019), co-registration offers the opportunity to disentangle overlapping individual-difference effects in crosslinguistic co-activation. Specifically, bilinguals with different profiles (e.g., proficiency, age of acquisition, language use and exposure) may show similar gaze patterns while differing in their underlying neural responses (i.e., neural–gaze decoupling; see Grundy et al., 2017a for a similar pattern with behavioral outcomes), or conversely, comparable neural responses accompanied by distinct attentional strategies. When using co-registration in the VWP, there is the possibility that low-proficiency L2 speakers may show eye movements comparable to high-proficiency bilinguals when resolving crosslinguistic competition. However, the low-proficiency bilingual group may still exhibit delayed or enhanced ERP responses (e.g., larger N400 or P600 amplitudes) compared to the high-proficiency group, reflecting increased processing effort or reduced efficiency. In contrast, highly proficient bilinguals may show a tighter coupling between gaze behavior and neural indices of competition, reflecting more efficient coordination between visual strategies and neural processes.

Despite its potential, using co-registration with the VWP presents several methodological challenges. Unlike visual stimuli, an auditory stimulus unfolds over time, making it difficult to define a clear onset for time-locking ERPs. Additionally, due to the high number of ocular artifacts, spiked potentials associated with saccades could overlap with ERP components of interest and contaminate the data. To address these concerns, researchers can extract FRPs at multiple key timepoints (Dimigen et al., 2011), which helps identify different brain responses elicited as the word is gradually presented. For example, first fixations to a phonological or semantic competitor may reflect early stages of lexical access and semantic processing. In the time window anchored to this fixation, researchers could expect modulations in the N400, which is sensitive to semantic processing and the ease of connecting auditory input with visual information (Dimigen et al., 2022). In contrast, longer fixation durations preceding a behavioral response (e.g., mouse click) may indicate later processes, such as conflict resolution or reanalysis, which are likely going to be associated with components such as the P600. By comparing EEG activity across different gaze events, co-registration can help clarify when and how bilinguals undergo shifts in neurocognitive processing.

Another key challenge that researchers face when combining EEG and eye-tracking in the VWP is the large number of eye movements it elicits. As in other visual search tasks (e.g., Chabal and Marian, 2015), participants explore the visual display freely, often directing multiple fixations to different areas in the display before and after hearing the target word. Eye movements introduce muscular ocular artifacts into the EEG signal, compromising the quality of the signal and making it difficult to isolate ERPs (Degno et al., 2021). Several strategies can help mitigate this issue. One common approach is to apply independent component analysis (ICA) to identify and remove artifacts related to eye movements (Chaumon et al., 2015). This process is often guided by signals from electrooculogram (EOG) electrodes, which are placed above and below the eye (for vertical movements) and at the outer corners of the eyes (for horizontal movements). Collecting data from the EOG channels improves the accuracy of artifact removal and helps preserve the underlying neural signal. Recent developments have optimized ICA for free viewing by adjusting preprocessing parameters and using eye-tracking data to guide component rejection (Dimigen, 2020; OPTICAT code available). There are also useful tutorials and resources online showing how eye tracking data can guide ocular artifact detection and correction (Degno et al., 2021). As noted previously in Section 4, another effective solution is to analyze FRPs, which allows researchers to isolate time windows of brain activity that have been previously cleaned from blinks and saccadic eye movements (i.e., during periods of gaze stability; Dimigen and Ehinger, 2021). Furthermore, researchers can simplify the visual complexity of the task. For instance, presenting two items instead of four reduces the number of eye movements needed to inspect the display, while still eliciting meaningful competition between referents. Arranging stimuli closer to the screen center can also minimize peripheral scanning.

Beyond advancing our understanding of how bilinguals manage the parallel activation of both languages, co-registration holds promise for elucidating complex cognitive processes such as prediction and updating during comprehension.

Research on high-level comprehension processes in bilinguals have employed reading comprehension tasks based on situation model theories (Bower and Morrow, 1990; Zwaan, 2001) to investigate how bilinguals manage inferential processing across their two languages. In the Situation Model Revision task (Pérez et al., 2019, 2024), participants build an initial mental representation of a narrative and subsequently update it when new, conflicting information arises. The Situation Model Revision task captures bilinguals' ability to monitor and update mental representations in both their L1 and L2. By aligning eye movements with brain activity elicited during the task, we can gain a better understanding of the monitoring and updating processes involved in bilingual comprehension during inference revision. For instance, previous studies have found that when readers encounter a word that contradicts their initial inference (e.g., “violin” after having inferred “guitar”), they often show longer fixation durations and regressions to earlier parts of the text (Pérez et al., 2016). These eye-movement patterns reflect processing difficulty and reanalysis. With co-registration, researchers can examine whether such fixations and regressions reflect successful semantic processing. For example, a larger N400 time-locked to the first fixation on the unexpected word would indicate a semantic mismatch with the current mental model (Kuperberg et al., 2011; Pérez et al., 2015). However, if that word is subsequently fixated after rereading the sentence, and the N400 is significantly reduced, this may suggest successful integration of the updated inference. Therefore, co-registration can help distinguish between automatic inference updating and controlled processing strategies during comprehension, beyond what can be inferred from behavioral or neural measures alone.

Regarding the impact of different bilingual experiences on comprehension, co-registration can also provide insightful findings. Bilinguals with higher proficiency or earlier age of acquisition may show a better temporal correspondence between eye-movement indices of reanalysis (e.g., fewer regressions and shorter fixation durations on the critical word) and neural markers of inference updating (e.g., reduced N400 amplitudes), reflecting a more automatized revision process (Luk et al., 2011; Prior et al., 2014). In contrast, less proficient bilinguals may exhibit longer or more frequent regressions while still showing typical N400 or P600 responses, suggesting the engagement of compensatory mechanisms to achieve successful comprehension (Pérez et al., 2019). By capturing both convergent and divergent neural–gaze patterns, co-registration allows psycholinguistics to disentangle how prior bilingual experience modulates the engagement of automatic and controlled processes during inference revision and comprehension.

Additionally, given the role of the P3b in updating processes (Polich, 2007, 2003), examining whether and when it emerges after an unexpected word is encountered can shed light on how bilinguals update their mental representations. Correlating P3b amplitude with regression patterns and fixation durations on the updated sentence could help disentangle the cognitive cost of revising information during reading across both languages. Similarly, P600 effects associated with reanalysis may appear in sentences with longer total reading times and more regressions, indicating effortful suppression of the initial inference and the construction of new situation models (Burkhardt, 2007).

To implement co-registration effectively in an inference revision task, several methodological challenges need to be addressed. As with the VWP (see section 4.2), ocular artifacts and neural signals need to be aligned with precise task events. However, a more specific concern about this task is its similarity to naturalistic reading paradigms, where participants read continuous text at their own pace (Hasson and Egidi, 2015). The self-paced reading procedure introduces variability in fixation times and regression patterns, complicating the alignment of eye-movement measures with ERP components elicited by specific task events. This issue is especially problematic when multiple regressions or fixations occur around critical regions of interest. Recent co-registration studies in psycholinguistics using natural reading tasks offer some solutions. For instance, FRPs time-locked to fixations on target words can be used to observe the brain activity evoked when the item is specifically fixated, as well as when processing begins. Moreover, strategies such as correlating regression patterns with specific ERP components allow researchers to distinguish different processing phases during reading (Himmelstoss et al., 2020; Dimigen and Ehinger, 2021; Metzner et al., 2016).

While the natural flow of reading introduces complexity, the growing literature on co-registration in reading provides tools and frameworks that can be adapted to bilingual comprehension paradigms. There are also specific strengths that make inference-revision tasks suitable for co-registration. For example, texts in inference revision tasks are often presented sentence-by-sentence or word-by-word, which makes them more appropriate for EEG and eye-tracking than free-reading paradigms while still maintaining some degree of ecological validity. Researchers can take advantage of this presentation format by introducing triggers that mark sentence or word onset to improve the time-locking of brain activity and its synchronization with gaze data. In addition, synchronization can be further improved by using shared event markers between EEG and eye-tracking recordings. Thus, co-registration can enhance the interpretation of results regarding the revision of the mental model and how the revision process is modulated when reading in L2.