Syntactic Adaptation: A Robust Phenomena with Open Questions

1. Introduction

Syntactic adaptation is the phenomenon whereby exposure to particular syntactic structures influences subsequent language processing and production. When individuals hear or read a specific structure, they become more likely to use this structure in their own speech or writing, and they comprehend sentences containing this structure faster. This adaptive process reflects the dynamic nature of language processing, allowing speakers to adjust to the syntactic distribution in the speech or writing of their conversational partners or texts they read. It may even be one of the mechanisms behind language learning (Babineau et al., 2023; Rabagliati et al., 2016). The studies collected in this Special Issue comprehensively examine syntactic adaptation across different participant ages, languages, and experimental paradigms, both offering confirmatory evidence for established phenomena and challenging other suggested phenomena and underlying mechanisms.

The theoretical landscape of syntactic adaptation has been shaped by three main classes of accounts that differ fundamentally in their proposed underlying mechanisms. Error-based and implicit-learning accounts share substantial common ground, both proposing that adaptation reflects genuine learning driven by statistical regularities in the input. The seminal error-based learning model of Chang et al. (2006) proposes that speakers continuously make predictions about upcoming words during comprehension, and when these predictions fail (prediction error), the system updates connection weights between meaning representations and syntactic structures. The model accounts for both immediate and cumulative priming effects through a single learning mechanism that operates throughout the lifespan. Jaeger and Snider (2013) developed a related implicit-learning account that focuses on expectation adaptation, proposing that speakers rationally update their expectations about syntactic structure probabilities based on recent experience, with larger prediction errors leading to greater adaptation.

Activation-based accounts propose different mechanisms centered on transient memory processes rather than learning. Pickering and Branigan (1998) proposed that syntactic priming results from the residual activation of abstract syntactic representations in the mental lexicon. When a particular syntactic structure is processed, the corresponding combinatorial nodes become activated and remain so temporarily, making the same structure more likely to be selected in subsequent production. Crucially, this account also explains the lexical boost effect—the robust finding that priming is significantly enhanced when prime and target sentences share the same verb (Pickering & Branigan, 1998). This phenomenon is theoretically important because it reveals that syntactic adaptation cannot be explained solely by abstract structural representations—if priming were purely abstract, the specific verb used should not matter, yet the lexical boost shows that repeated verbs enhance priming effects. According to Pickering and Branigan’s account, verb lemmas are linked to combinatorial nodes representing syntactic structures, so when the same verb is repeated, both the abstract structural representation and the verb–structure connection receive residual activation, resulting in enhanced priming. The account predicts that priming effects should decay rapidly as activation dissipates, and initially struggled to explain cumulative effects or long-term adaptation.

The lexical boost presents a particular challenge for purely learning-based accounts, leading to proposals for dual-mechanism theories. Subsequent work has suggested that while abstract priming may reflect implicit learning, the lexical boost effect may have a distinct origin in explicit memory for the prime sentence, with the repeated verb acting as a retrieval cue (Chang et al., 2006; see also Hartsuiker et al., 2008; Reitter et al., 2011), rather than arising from the same implicit learning mechanism that drives abstract priming.

Hybrid models attempt to reconcile these competing perspectives by proposing that multiple mechanisms operate simultaneously. Reitter et al. (2011) developed a hybrid model combining frequency-driven activation boosts from transient processing with contextual associations that drive predictions. Their model incorporates both the rapid decay predicted by activation accounts and the learning mechanisms proposed by error-based accounts, suggesting that different aspects of syntactic adaptation may be governed by distinct cognitive mechanisms with different temporal dynamics.

Despite decades of research, some fundamental questions about the mechanisms underlying syntactic adaptation remain debated or lack unequivocal experimental support. The studies featured in this Special Issue provide a comprehensive examination of these theoretical debates, offering confirmatory evidence for established phenomena and challenging previous findings or assumptions about the underlying mechanisms. The empirical findings converge on six key themes: the robustness of cumulative priming effects, the mixed evidence for prime surprisal and error-driven learning, developmental differences that may reveal underlying mechanisms, the role of individual variation, questions about domain generality versus language specificity, and task and context specificity. Together, these contributions provide a synthesis of current knowledge and a roadmap for resolving ongoing theoretical debates in syntactic adaptation research.

2. Major Themes and Findings

2.1. Cumulative Priming: A Robust Phenomenon

Perhaps the most consistently supported finding across the studies in this issue is the existence of cumulative priming effects. Multiple studies demonstrate that structural priming effects can accumulate over time, with repeated exposure to target structures leading to progressively stronger adaptation effects. This pattern emerges clearly in Schimke and Pappert (2024)’s study of German possessive constructions, which observed increasing production rates of the preferred structure during priming sessions. Similarly, Kholodova et al.’s developmental study of German dative constructions found immediate as well as cumulative structural priming effects occurring across all age groups, from 3-to-4-year-olds to adults. Kaan’s ERP study of the English and-coordination ambiguity also documented that readers change their processing as a function of recent exposure, and adaptation was not immediate but developed over the course of the experiment. Karkaletsou et al. (2024)’s study of Canadian French bilinguals showed adaptation effects in response times across trials in an acceptability rating task, even when examining structural innovations that deviate from standard grammar (though acceptability ratings themselves did not show adaptation). Atkinson and Omaki demonstrate that comprehenders adapt gap predictions when processing filler-gap dependencies following exposure to less frequent gap locations. Kuz et al. (2024)’s study, which found null results for cross-domain conflict adaptation, nevertheless replicated cumulative syntactic adaptation within the language domain using temporarily ambiguous garden-path sentences.

The robustness of cumulative priming provides strong support for models that incorporate learning mechanisms beyond simple transient activation. These findings suggest that syntactic adaptation involves more than momentary changes in processing preferences—it reflects genuine learning that builds over time. The consistency of this effect across studies using different methodologies, populations, languages, and linguistic phenomena underscores its fundamental importance in understanding how linguistic experience shapes language processing.

2.2. The Inverse Preference Effect and Error-Riven Learning: Mixed Evidence

A central prediction of error-based learning theories is that more surprising or unexpected structures should produce larger priming effects, as they generate stronger prediction-error signals. This prediction has been tested through two related but distinct independent measures. Prime surprisal refers to the general phenomenon where structures that violate expectations (based on contextual probability or verb biases) should yield stronger priming effects. Inverse preference effects represent a specific instantiation of this principle, testing whether less frequent structures produce stronger priming than more frequent alternatives, under the assumption that less frequent structures are inherently more surprising (see Fine et al., 2013; cf. Harrington Stack et al., 2018).

The evidence for these effects in the current collection is notably mixed. Schimke and Pappert (2024)’s study of German possessive constructions specifically tested inverse preference effects but found no evidence that the less frequent structure led to larger priming effects than the more frequent structure. In the field of surprisal, Gambi and Messenger (2023)’s study of 4-year-olds learning English direct object datives found that children exposed to sentences that encouraged them to make incorrect predictions about upcoming words (which then generated prediction errors) showed a tendency towards greater improvements in their post-test comprehension scores compared to those exposed to sentences that did not encourage such predictions. While the authors caution that these preliminary findings need confirmation with larger samples, their results provide tentative empirical support for the role of prediction error in linguistic structure acquisition.

In contrast, Fazekas et al. (2024)’s systematic review of prime surprisal studies included in this issue raises important methodological concerns about current approaches to testing error-driven learning theories. The limited scope of existing studies, combined with inconsistencies in statistical power and surprisal measurement, suggests that prime surprisal may not yet represent a general tool for assessing error-based learning as researchers had hoped. This does not necessarily invalidate error-driven learning theories, but it highlights the need for more refined experimental approaches and clearer theoretical predictions about what constitutes “surprising” input.

2.3. Developmental Perspectives: Age as a Window into Mechanisms

The developmental studies in this issue provide insights into the mechanisms underlying syntactic adaptation. Error-driven learning theories make specific predictions about how prior experience should modulate adaptation effects. According to these theories, learners with weaker or less stable prior knowledge should show stronger adaptation when encountering unexpected input, as they generate larger prediction errors that drive larger changes in predictions. Children, with their developing and less entrenched linguistic representations, thus provide a good testing ground for error-driven accounts by offering a window into adaptation mechanisms when priors are naturally weaker (see Rowland et al., 2012; cf., Fazekas et al., 2020). Kholodova et al. (2023)’s research on German dative constructions demonstrates that structural priming effects are strongest in the youngest children (3–4 years) and gradually decrease with age, suggesting that younger children’s less stable syntactic representations make them more susceptible to adaptation, thus supporting a strong prediction of the error-driven learning account (see below for similar evidence from bilinguals).

As predicted by the dual-path account (Chang et al., 2006), the lexical boost effect, the finding that repeating the same word in the prime and target creates larger priming effects, shows the opposite developmental pattern. Kholodova et al. found that lexical boost effects were absent in 3-to-4-year-olds, but gradually emerged with increasing age, possibly due to developmental changes in working memory capacity or explicit memory systems. This supports the idea that abstract structural priming and the lexical boost depend on different paths.

As mentioned above, Gambi and Messenger (2023)’s study of 4-year-olds found that children showed a tendency towards greater improvements in comprehension scores when exposed prediction errors. Though the authors did not compare children with adults, their tentative findings are at least compatible with the idea that error-driven learning mechanisms may be more detectable in developing systems where priors are naturally weaker.

This developmental evidence helps adjudicate between competing theoretical accounts. The fact that young children show stronger adaptation effects may explain why some adult studies fail to detect expected patterns, particularly when statistical power is limited. If adaptation effects naturally weaken as linguistic representations become more stable and entrenched, this has important implications for how we interpret null results in mature language systems. However, these are only a few datapoints, with many different studies finding no change as a function of age (see Messenger et al., 2022, for a discussion), and some even finding stronger effects in adults or older children (see Havron et al., 2019, 2021).

2.4. Individual Variation: Strong Support

Several studies in this issue highlight substantial individual differences in adaptation effects. Kaan (2023)’s research on and-coordination processing reveals considerable variation in both the type and magnitude of adaptation across participants, with substantial differences in how individual readers changed their processing over the course of the experiment. Similarly, Karkaletsou et al. (2024)’s work on bilingual adaptation to structural innovations shows that individual language experience factors, including dominance, proficiency, and exposure patterns, significantly influence both acceptability ratings and response times for structural innovations. Their findings demonstrate that participants with lower contact with French (weaker priors) were more accepting of innovative sentences compared to participants with higher French contact (stronger priors), suggesting that adaptation is modulated by the strength of existing linguistic representations. This pattern aligns with error-driven learning predictions: speakers with weaker priors about French grammatical constraints should be more willing to adapt to structural innovations that violate standard grammar.

This individual variation presents both challenges and opportunities for the field. On the one hand, it may obscure group-level effects and contribute to replication difficulties. In her ERP study, Kaan notes that the lack of strong plausibility effects at the group level could reflect individual participants showing different patterns, with some displaying negativity and others positivity in response to anomalies, which cancel each other out in group analyses. On the other hand, systematic investigation of individual differences may provide insights into the factors that modulate adaptation, potentially revealing distinct adaptation mechanisms or developmental trajectories. Karkaletsou et al. (2024)’s findings suggest that the degree of language contact and dominance patterns systematically influence how bilinguals adapt to structural innovations, pointing toward principled sources of individual variation rather than random noise in the data.

2.5. Domain Generality: Limited Support

The question of whether syntactic adaptation reflects domain-general cognitive control mechanisms or language-specific processes was also addressed in one study. According to domain-general theories, the same cognitive control mechanisms that help resolve conflicts in non-linguistic tasks (such as overriding the automatic tendency to read color words in a Stroop task) should also facilitate the resolution of parsing conflicts in garden-path sentences, where readers must revise their initial syntactic interpretation. If this were true, experiencing conflict in a Stroop task should prime the cognitive control system and improve subsequent garden-path processing, and this is indeed what a number of studies reported (e.g., Hsu & Novick, 2016). However, Kuz et al. (2024)’s carefully controlled experiments that eliminated potential confounds failed to find evidence that non-linguistic conflict (the Stroop task) facilitates subsequent garden-path sentence processing. Across four experiments with more than 500 participants, they observed robust Stroop and ambiguity effects, but no conflict adaptation in any experiment, even when using a sentence-reading task that eliminated the visual object confound present in previous studies that had found cross-domain effects (such as Hsu & Novick, 2016).

These null results do not rule out domain-general mechanisms (though see, e.g., Zhu et al., 2025, for evidence that adaptive control may be task-specific), but they suggest that such effects may be more limited than previously thought. Kuz et al. (2024) propose that language-specific cognitive control mechanisms might operate in both trial-level and cumulative syntactic adaptation. The authors conclude that while the data largely fail to support a domain-general cognitive control mechanism, adaptation effects within language processing remain strong and consistent.

2.6. Task and Context Specificity

Several studies reveal important limitations in the generalizability of adaptation effects. Atkinson and Omaki (2023)’s research on filler-gap dependency processing shows that adaptation effects may not transfer across tasks, even when exposure occurs in quasi-naturalistic contexts. The results of their study’s first experiment demonstrated that comprehenders dampened their direct object gap predictions following exposure to prepositional object gaps. However, Experiments 2A and 2B revealed that these adaptation effects did not transfer when the quasi-naturalistic exposure phase was presented as a separate task, or when participants needed to generalize from a syntactic to a semantic measure of gap predictions. Similarly, Karkaletsou et al. (2024)’s work on bilingual adaptation demonstrates that structural innovations show different adaptation patterns depending on the specific construction type and individual language experience, with adaptation reflected only in response times and not in acceptability ratings at the group level.

These findings highlight the importance of experimental context in studying adaptation effects and may have implications for natural language use and conversation dynamics. Atkinson and Omaki (2023) conclude that filler-gap adaptation effects may be specific to a singular experimental task environment, while Karkaletsou et al. (2024) show that the type of dependent measure (online versus offline) can determine whether adaptation effects are observable. Rather than representing a limitation, this context specificity could reflect an adaptive feature of the language system, allowing speakers to rapidly adapt to the specific linguistic patterns of individual interlocutors or conversational contexts without overgeneralizing these adaptations to other situations. An important avenue for future research would be to understand when people generalize across contexts and when learning remains constrained to specific environments. Based on the theoretical mechanisms discussed throughout this review, we might predict that generalization occurs when learners have weaker priors about the relevant linguistic structures (as seen in developmental and individual difference effects), when the adaptation is driven by implicit learning mechanisms rather than explicit memory retrieval, and when the adapted structures are frequent and reliable enough to warrant updating broader linguistic expectations rather than context-specific adjustments. Eski and Onnis (2024)’s finding that priming strength does not differ between single-speaker and multi-speaker contexts suggests that some aspects of adaptation may generalize across social contexts, even when other types of transfer (such as across tasks or dependent measures) remain limited.

2.7. Theoretical Implications

The collective findings from this Special Issue have several important theoretical implications that advance our understanding of syntactic adaptation mechanisms. First, the consistent evidence for cumulative priming across diverse populations, constructions, and languages provides robust support for models that incorporate genuine learning mechanisms rather than purely transient activation effects. The fact that this phenomenon emerges in studies ranging from Schimke and Pappert (2024)’s German possessive constructions to Karkaletsou et al. (2024)’s bilingual structural innovations suggests that cumulative adaptation represents a fundamental characteristic of human language processing.

However, the mixed evidence for prime surprisal and inverse preference effects suggests that simple error-driven learning accounts may be insufficient to explain the full range of adaptation phenomena. Fazekas et al. (2024)’s systematic review reveals significant methodological challenges in testing these core predictions of error-driven theories, while Schimke and Pappert (2024)’s null findings for inverse preference effects indicate that the relationship between structure frequency and adaptation strength might be more complex than initially theorized. This does not invalidate error-driven learning theories but highlights the need for more research and unifying methodology and definitions.

The developmental findings provide particularly interesting insights into underlying mechanisms. The age-related decrease in abstract structural priming effects, combined with the opposite developmental trajectory for lexical boost effects documented by Kholodova et al. (2023), suggests that multiple mechanisms with different developmental timelines contribute to syntactic adaptation. This dissociation strongly supports dual-mechanism theories and helps explain why adaptation effects may be difficult to detect in adult populations with mature, stable linguistic representations. The finding that children, with their weaker priors, show stronger adaptation effects aligns with error-driven learning predictions and provides a principled explanation for developmental changes in adaptation strength.

The evidence for substantial individual variation, demonstrated across multiple studies, highlights the need for theories that can account for systematic differences in adaptation capacity between people or strength between personal contexts. Karkaletsou et al. (2024)’s findings that language contact patterns predict adaptation to structural innovations, and Kaan (2023)’s documentation of individual differences in ERP responses, suggest that factors such as linguistic experience, working memory capacity, and representation strength systematically modulate adaptation. Rather than representing noise in the data, these individual differences may reveal the underlying cognitive mechanisms that govern when and how adaptation occurs.

The limited support for domain-general mechanisms, as evidenced by Kuz et al. (2024)’s null findings for cross-domain conflict adaptation, suggests that syntactic adaptation may be more language-specific than previously assumed. This has important implications for theories that propose shared cognitive control mechanisms across linguistic and non-linguistic domains, suggesting instead that adaptation mechanisms may be specialized for language processing. While only one contribution to this Special Issue addressed this question, it was a large-scale study. Moreover, these findings converge with other, recent evidence (e.g., Zhu et al., 2025) suggesting that cognitive control mechanisms may not only be specific to language but to particular tasks.

Finally, the evidence for task and context specificity revealed by Atkinson and Omaki (2023), and the differential patterns across online versus offline measures documented by Karkaletsou et al. (2024), suggest that adaptation effects are more constrained than previously thought. This specificity may actually reflect an adaptive feature of the language system, allowing rapid adjustment to local contexts without inappropriate overgeneralization. The theoretical challenge is to understand when generalization occurs versus when learning remains context-bound—a question that may be answered by considering the strength of learners’ priors (not just in children, but also in adult language learners; see, e.g., Kaan et al., 2019), the reliability of the adapted structures, and the underlying learning mechanisms involved.

Together, these findings point toward a more nuanced theoretical landscape where multiple mechanisms operate under different conditions, with their relative contributions determined by factors such as age, individual differences, linguistic experience, and contextual constraints.

These laboratory findings may also have important implications for real-world language use. The robustness of cumulative priming suggests that our syntactic preferences are constantly being shaped by recent conversational experience, potentially allowing speakers to align with their interlocutors’ linguistic patterns. The developmental findings indicate that children’s heightened adaptation may facilitate language learning by making them more responsive to input patterns, while adults’ more constrained adaptation may reflect the stability needed for effective communication. The individual differences findings suggest that factors like bilingualism, language experience, and cognitive capacity may systematically influence how readily people adapt to new linguistic environments.

2.8. Methodological Considerations and Future Directions

Based on the theoretical and empirical patterns revealed in this collection, we recognize four major research priorities. First, developmental trajectories require systematic investigation: The finding that abstract priming decreases with age while lexical boost effects increase suggests these mechanisms have different developmental timelines. Future studies should test whether this dissociation holds across different construction types and languages, and whether individual differences in working memory or explicit memory development predict the emergence of lexical boost effects. Longitudinal designs tracking the same children over time could reveal whether adaptation strength predicts later language outcomes (see Reuter et al., 2018). This is particularly important given that the evidence on whether abstract priming effects are larger in children than adults is mixed, but with a prevalence of studies finding no change as a function of age (see Messenger et al., 2022, for discussion).

Second, rather than treating individual variation as noise, future work should develop formal models that predict who will show strong adaptation effects under what conditions. Specific hypotheses should test whether adaptation strength correlates with measures of representation stability (e.g., vocabulary size, grammatical knowledge), working memory capacity, or exposure to linguistic variation. Computational models could simulate how different levels of prior knowledge lead to different adaptation patterns.

Third, the finding that adaptation effects do not necessarily transfer across tasks or contexts demands investigation of when generalization occurs in natural language use. For example, studies could test whether social factors (speaker status, relationship) modulate adaptation strength. While there is some work on whether syntactic adaptation effects are modulated by beliefs about one’s partner (e.g., human or computer; Cowan et al., 2015), much still remains to be explored regarding social influences on syntactic adaptation (cf. lexical and phonetic adaptation).

Last, prime surprisal methodology needs standardization. The mixed evidence for error-driven learning partly reflects inconsistent approaches to measuring surprisal. The field needs agreed-upon methods for quantifying structural surprisal that work across languages and constructions, larger sample sizes to detect potentially small effects, and pre-registered studies that can definitively test key predictions of error-based theories.

3. Conclusions

The studies in this Special Issue provide a comprehensive view of current research on syntactic adaptation, revealing both the robustness of certain phenomena and the limitations of current theoretical and methodological approaches. While cumulative priming emerges as a highly reliable effect across diverse populations and constructions, other predicted phenomena, such as prime surprisal effects and cross-domain adaptation, receive less consistent support.

These findings point toward several key priorities for future research: developing more reliable methods for testing error-driven learning theories, investigating the developmental trajectories of different adaptation mechanisms, and understanding the sources and significance of individual variation.

Perhaps most importantly, this collection demonstrates the value of systematic replication and the inclusion of null results in building a comprehensive understanding of syntactic adaptation. Null results are often underrepresented in the literature, but the failure to find robust prime surprisal effects, the lack of transfer across tasks, and the absence of immediate adaptation in some studies could suggest constraints on theories of syntactic adaptation. These null results should not thus be viewed merely as failures to replicate or find expected effects, but as important data points that could help define the boundaries of adaptation phenomena. The systematic review of prime surprisal studies exemplifies this approach, providing a critical evaluation of the field’s ability to test key theoretical predictions.

The adaptive nature of human language processing revealed by these studies speaks to the flexibility of the cognitive systems underlying language use. Understanding the mechanisms, constraints, and individual differences in this adaptability remains a central challenge for theories of language processing and acquisition. The work presented in this Special Issue provides both a groundwork for this understanding and a roadmap for future investigations.