Do children learn from their prediction mistakes? A registered report evaluating error-based theories of language acquisition — TEI-tagged view

Judit FazekaspersName, Andrew JessoppersName, Julian PinepersName, Caroline RowlandpersName (2020date). “Do children learn from their prediction mistakes? A registered report evaluating error-based theories of language acquisition.” Royal Society Open Sciencetitle·j.

Object of study — whether surprising linguistic input produces more lasting syntactic learningterm·dom than predictable inputterm·dom

Research question — does less predictable inputterm·dom lead to more lasting change in syntactic representationsterm·key, in children and adults

Contribution — first evidence that surprising inputterm·dom leads to more learningterm·dom of the same structure than predictable inputterm·dom

Full text — tagged

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Do children learn from their predictionterm·key mistakes? A registered reportterm·key evaluating error-based theories of language acquisitionterm·key Abstract Error-based theories of language acquisitionterm·key suggest that children, like adults, continuously make and evaluate predictionsterm·key in order to reach an adult-like state of language use. However, while these theories have become extremely influential, their central claim—that unpredictable input leads to higher rates of lasting change in linguistic representations—has scarcely been tested. We designed a prime surprisal-based intervention study to assess this claim. As predicted, both 5- to 6-year-old children ( n = 72) and adults ( n = 72) showed a pre- to post-test shift towards producing the dativeterm·key syntactic structure they were exposed to in surprising sentences. The effect was significant in both age groups together, and in the child group separately when participants with ceiling performance in the pre-test were excluded. Secondary predictionsterm·key were not upheld: we found no verb-based learningterm·dom effects and there was only reliable evidence for immediate prime surprisalterm·key effects in the adult, but not in the child group. To our knowledge, this is the first published study demonstrating enhanced learningterm·dom rates for the same syntactic structure when it appeared in surprising as opposed to predictable contexts, thus providing crucial support for error-based theories of language acquisitionterm·key.

Introduction Predictionterm·key, the ability to anticipate other people's upcoming words or actions, plays a key role in a wide range of different human behaviours and activities, from making music [ 1 ] to playing volleyball [ 2 ]. Predictionterm·key plays such a central role in some theories of cognition that human brains have been described as ‘predictionterm·key machines' [ 3 , p. 81]. Predictionterm·key is particularly important in human communication. It has been suggested, for instance, that predictionterm·key can contribute to smooth turn-taking in conversation, not just because it enables us to anticipate when our partner will stop speaking and we can begin speaking ourselves, but also because, by successfully predicting upcoming words, we can give ourselves time to prepare an appropriate response [ 4 ]. Although some scholars question how central predictionterm·key's role in human communication really is [ 5 , 6 ], other theories go even further and claim that predictionterm·key is a key mechanism in language processing itself [ 7 , 8 ].

While the role of predictionterm·key in adult language use is well documented, there is also the further possibility that predictionterm·key is not just vital for using language, but also for acquiring it in childhood. This is the basis of error-based theories of language acquisitionterm·key. Error-based theories (which can explain learningterm·dom patterns outside of language as well [ 9 , 10 ]) suggest that children, like adults, continuously predict upcoming words in conversation, and use these predictionsterm·key to build up their competence in their first language by comparing what they predicted with the actual input received [ 11 , 12 ]. One such model, the frequency-based, connectionist Dual-path model [ 11 ], uses an error-based learningterm·key mechanism [ 13 ] to model the acquisition of syntax, the developmental phenomenon that is the focus of the current study. In this model, if there is a discrepancy between the predicted and actual syntactic structure, an error signal is generated, which is then used to adjust the weights that support syntactic knowledge. These weight changes accumulate over time and allow children's syntactic knowledge to gradually approximate the adult state (note, however, that this is not a stage-based theory; the process also results in representational change in adults, but less obvious change because adults' representations are less malleable).

There are several reasons why error-based theories of language acquisitionterm·key have gained wide support. First, they provide an interactive model that treats language acquisitionterm·key as the outcome of processing. According to error-based theories, children (and, in fact, adults) constantly predict words and evaluate predictionsterm·key while processing language. Every time they make an incorrect predictionterm·key, linguistic representations change, which, in children, moves them a step closer to the adult state. This means that error-based theories allow for the possibility that limitations in processing might influence acquisition. Second, rather than simply seeking to define children's state of knowledge at different developmental stages, these models explain how children move from one knowledge state to another. For instance, the Dual-path model [ 11 ] not only describes the error-based learningterm·key mechanism (that adjusts weights supporting linguistic knowledge in response to error signals), but also demonstrates how this mechanism leads to changes in performance over development (from being able to identify agent and patient roles in intermodal preferential looking experiments at an early age, to producing correct sentences with novel verbs later on). Error-based learningterm·key theories thus provide a specific learningterm·dom mechanism that can be tested experimentally. Third, models implementing error-based learningterm·key mechanisms are supported by experimental data and provide explanations for developmental phenomena that are challenging for earlier language acquisitionterm·key theories. For instance, an error-based noun-acquisition model proposed by Ramscar et al . [ 12 ] explains how overgeneralized forms (like ‘mouses') disappear from children's productions in the absence of explicit correction. When children predict the overgeneralized ‘mouses' form but hear ‘mice' instead, the associations between the plural of ‘mouse' and ‘mouses' weaken due to the error signal resulting from the incorrect predictionterm·key, while associations with ‘mice’ are strengthened. Over time, children start producing and predicting the more strongly associated ‘mice' form instead of ‘mouses'.

Despite widespread enthusiasm for theories that embrace the role of predictionterm·key as a learningterm·dom mechanism, there remains a major problem. There is to date only limited evidence that children actually do generate linguistic predictionsterm·key, and what evidence there is does not show that these predictionsterm·key are used for learningterm·dom. The most promising aspect of error-based theories—that they propose a viable and intuitive language learningterm·dom mechanism—has therefore yet to be systematically tested. The goal of the present study is to examine the role of predictionterm·key in language acquisitionterm·key by assessing whether less predictable (more surprising) input leads to more lasting change than more predictable inputterm·dom. Below, we review the current state of the literature, particularly previous developmental studies on predictionterm·key, before discussing the aims of the current study in more detail.

Language acquisitionterm·key plays a central role in developmental research on predictionterm·key, and several experimental studies assess the relationship between predictionterm·key and learningterm·dom. Some studies concentrate on the relationship between predictive abilities and certain aspects of language proficiency [ 14 – 17 ]. For example, Mani & Huettig [ 14 ] found that toddlers' predictionterm·key skills (measured using a version of the preferential looking paradigm) significantly correlated with their productive, but not their receptive vocabulary. Other studies have assessed the nature of children's linguistic predictionsterm·key in order to examine whether they could form the basis of learningterm·dom [ 18 – 21 ]. Gambi et al . [ 19 ] found that children can use semantic associations as a basis for their predictionsterm·key [ 18 ] and combine them with predictionsterm·key based on syntactic knowledge [ 22 ], showing that children's predictionsterm·key could be a viable basis for language acquisitionterm·key.

Studies targeting predictionterm·key in childhood typically use the visual word paradigm and have been successful in demonstrating that children use anticipatory eye gazes to visual scenes to predict upcoming words in sentences. However, they do not investigate whether this effect then leads to subsequent learningterm·dom. They only study whether or not children make predictionsterm·key; they do not examine whether the learningterm·dom mechanism compares these predictionsterm·key with actual input or whether the outcome of this comparison leads to subsequent language change. In other words, this paradigm does not address whether predictionsterm·key form part of an error-based learningterm·key mechanism.

There is also another, perhaps more fundamental, problem with using the visual world paradigm to study predictionterm·key. This is that so-called predictive looking could, in fact, be a result of a process of integration. In these studies, children listen to sentences where the final word is highly predictable, while their eye movements on an array of pictures are recorded [ 14 , 18 , 19 ]. Such studies have shown that children as young as 2 years old tend to look longer at pictures of objects that would be a more predictable ending for the sentence after hearing the verb, but before hearing the last word [ 14 ]. For example, they are more likely to look at a picture of a cake rather than a picture of a stone after hearing ‘The boy eats the big …', that is, before the sentence has been completed. These looks are referred to as anticipatory gazes and are regarded as evidence for predictionterm·key. However, according to Rabagliati et al . [ 23 ], it is possible that these effects are the result of integration and not predictionterm·key. If so, children would be looking at the picture of a cake after hearing eat because they chose cake as the most fitting sentence ending among the given picture alternatives, not because they predicted cake themselves. This means that instead of pre-activating upcoming words, children simply incorporate words based on the available visual input (see a similar discussion in the context of EEG research [ 24 ]). If so, these studies might not be providing an accurate measure of children's predictionsterm·key.

In summary, while some studies have shown a correlation between predictionterm·key and learningterm·dom, and others have shown the potential for predictionterm·key to act as a learningterm·dom mechanism, no studies, to our knowledge, have directly assessed whether predictionsterm·key lead to lasting changes in underlying linguistic representations—that is, whether they actually contribute to learningterm·dom in children (though see [ 25 , 26 ] for adult participants). In addition, doubts have been expressed in the literature about whether the visual word paradigm really measures predictionterm·key or integration.

Our study aims to directly investigate both of these issues. We tested whether predictionsterm·key lead to language learningterm·dom in childhood using a novel method—prime surprisalterm·key [ 26 , 27 ]—to assess whether less predictable linguistic input leads to more lasting language change than more predictable inputterm·dom. 1 This method not only provides us with information about the immediate and longer term outcome of correct and incorrect predictionsterm·key, but also overcomes the problems inherent in using the looking-while-listening paradigm, as it does not involve pictures of more or less predictable sentence endings, and so the responses cannot be guided by visual input. 2 Prime surprisalterm·key studies are based on the priming paradigm [ 28 , 29 ], which is often used to examine syntactic development [ 30 , 31 ]. In priming studies, participants are exposed to a prime sentence involving a particular syntactic structure (e.g. active or passive), and then asked to respond to a target stimulus (e.g. a video that they must describe). If participants re-use the previously processed structure, especially if prime and target sentence share no content, this shows that they have access to the shared (abstract) structural representation underlying the prime and target sentence. This methodology has been particularly useful in demonstrating at what age children develop knowledge of different, abstract syntactic structures. Prime surprisalterm·key takes this method a step further by contrasting priming effects in response to predictable and surprising stimuli.

Prime surprisalterm·key studies typically feature syntactic structures that can appear in different forms with similar meanings. Dativeterm·key structures, for instance, appear both as prepositional dativesterm·dom (PDterm·dom, e.g. ‘The student gave the report to the teacher') and double object dativesterm·dom (DODterm·dom, e.g. ‘The student gave the teacher the report'). While DODsterm·dom appear more often in adult language use overall, every verb has its own specific preferences: for instance, while the verb give occurs more often in a DODterm·dom structure than in a PDterm·dom structure, the verb bring prefers the PDterm·dom structure. Children need to acquire these links in order to produce well-formed sentences and avoid incorrect verb-structure pairings (such as ‘*the student spoke her teacher the answer').

Prime surprisalterm·key studies with both child and adult participants have found enhanced priming effects when a structure appeared with a mismatching as opposed to a matching verb [ 26 , 27 ]. According to the Dual-path model, these effects result from the error-based predictionterm·key mechanism: after hearing a verb, children predict the dativeterm·key structure that most often follows that particular verb. If they end up hearing a different structure to the one they predicted, the learningterm·dom mechanism produces an error signal, which they then use to adapt their syntactic knowledge accordingly. In a previous prime surprisalterm·key study [ 27 ], for instance, priming effects were larger when a DODterm·dom structure appeared with the verb bring (PD-biased) than when it appeared with the verb give (DOD-biased), without verb repetition between prime and target sentences. According to the Dual-path model, this occurs because, in the mismatching condition (e.g. DODterm·dom with bring ), participants are likely to make a predictionterm·key error. They are likely to predict that the PD-biased verb will be followed by the structure that appears more often with that verb (PDterm·dom). For example, after hearing ‘ the boy brings…' participants are more likely to predict ‘… the present to the girl ' (PDterm·dom) than ‘… the girl the present ' (DODterm·dom). Since this predictionterm·key will turn out to be incorrect, an error signal will be generated, which will, in turn, lead to a change in the weights supporting syntax and to a higher likelihood of the participant reproducing the structure that they have just heard. No such effect occurs in the matching condition: here, when a structure appears with a matching verb (e.g. DODterm·dom with give ), the participants are more likely to successfully predict the upcoming structure, which means that no error signal will be produced. In other words, according to the Dual-path model, the error signals and weight changes that lead to immediate prime surprisalterm·key effects are actually a consequence of the long-term learningterm·dom that will eventually result in adult-like syntactic preferences.

Although the verb-structure links leading to prime surprisalterm·key effects form a key part of syntactic knowledge, they are not fully adult-like at 5–6 years of age. According to error-based learningterm·key theories, children make predictionsterm·key from early on, but these early predictionsterm·key are based on limited linguistic input and therefore are more often incorrect. The older children are, the more adult-like their language becomes and the more correct predictionsterm·key they make. At the age of five, children have already accumulated enough knowledge to have verb-structure preferences similar to those of adults, but since these preferences are based on less linguistic input, they are weaker and more malleable. Children's weaker representations lead to stronger priming effects [ 27 ] and, according to error-based theories, more learningterm·dom as well. By contrast, the more developed adult system is less sensitive to the error signals produced by unexpected sentences, resulting in smaller priming and learningterm·dom effects.

Prime surprisalterm·key effects provide promising evidence for predictionterm·key in both children and adults, and suggest that incorrect predictionsterm·key influence subsequent behaviour in the short term. However, the key predictionterm·key of this account is that incorrect predictionsterm·key lead to learningterm·dom. To test this, we need to demonstrate that prime surprisalterm·key leads to lasting cumulative language change as well. To do this, we have developed a new design which combines the prime surprisalterm·key method with a paradigm designed to assess whether the original priming effects are cumulative and persistent (see Kaschak et al .'s work [ 32 ] for an adult study). Studies in this paradigm typically start with a baseline phase where participants' unbiased rates of the target construction are assessed (e.g. how many DODsterm·dom and PDsterm·dom they produce), followed by a test or bias phase where participants are biased towards the production of one of the structures (e.g. are only exposed to PDsterm·dom or DODsterm·dom). Finally, in a post-test phase, participants' rates of target construction are re-assessed to see whether they have shifted towards the structure they were biased towards in the previous phase.

Developmental studies using similar designs have shown that children's production frequencies can be shifted towards a less frequent structure by exposure in the bias phase [ 33 – 35 ]. These results are in line with the predictionsterm·key of the Dual-path model, but, due to the set-up of these experiments, they could have originated from sources other than error-based learningterm·key. For instance, some studies did not contrast the effects resulting from experience with less-expected structures with the effects resulting from experience with more-expected structures, in which case the post-test shift could be the result of cumulative facilitation from processing a structure multiple times rather than error-based learningterm·key [ 34 , 35 ]. Other studies included primes in the post-test phase (as well as the bias phase), meaning that the effects from the bias phase and those of immediate priming are measured on the same target items, making it difficult to tease apart long- and short-term effects [ 33 ]. The implication is that the strong predictionterm·key of the Dual-path model that less predictable (i.e. more surprising) linguistic input leads to more lasting language change still needs to be systematically tested.

We conducted a four-phase experiment with child and adult participants featuring both predictable and surprising structures in the bias phase, and only including target structures in the baseline and post-test phase. This way, we were able to directly contrast lasting language change resulting from more- or less-expected structures, and clearly differentiate between immediate and lasting effects of predictability. Furthermore, instead of simply contrasting effects of overall more- or less-expected structures (e.g. DODsterm·dom versus PDsterm·dom), we contrasted the effects of the same structure presented in a more or less predictable environment (by consistently presenting PDterm·dom and DODterm·dom structures with either matching or mismatching verbs in a within-participant design, table 1 ). Table 1. General study design showing different trials and verb biases in each phase—in the bias phase dark grey cells signal surprising prime sentences while light grey cells stand for predictable primes. When the structure is specified as ‘DODterm·dom' or ‘PDterm·dom', the experimenter produces a full (DODterm·dom or PDterm·dom) dativeterm·key, and when it's specified as ‘dativeterm·key' the participant completes a sentence stem with their choice of a dativeterm·key structure.

This allowed us to get clearer results from the child participants, whose overall dativeterm·key preferences are inconsistent and not yet adult-like [ 36 ], but who have already been shown to be sensitive to verb-bias effects [ 27 ]. Furthermore, by featuring the same number of PDterm·dom and DODterm·dom structures in both conditions, and only varying how likely it is that participants correctly predict them, we could ensure that the potential differences between results in each condition are due to differences in predictability.

In sum, error-based models that posit predictionterm·key as a learningterm·dom mechanism provide a very promising avenue for understanding the language acquisitionterm·key process. However, there is limited evidence for the existence of linguistic predictionterm·key in childhood, and its contribution to learningterm·dom has not been systematically examined. To our knowledge, this is the first study that directly targets the role of predictionterm·key in language development by assessing whether unpredictable input leads to more lasting language change than predictable inputterm·dom.

Methods The goal of this study was to examine the role of error-based learningterm·key in acquisition by assessing whether less predictable (more surprising) linguistic input leads to more lasting language change than more predictable inputterm·dom. To achieve this, we used the prime surprisalterm·key paradigm in a four-phase experiment, designed to induce error-based learningterm·key via prime surprisalterm·key. The prime surprisalterm·key paradigm capitalizes on the fact that some verbs are substantially more likely to appear in one dativeterm·key sentence structure than another in English and are thus surprising, despite being grammatical, in the alternative structure. Error-based learningterm·key predicts a bigger change in children's syntactic representationsterm·key (i.e. learningterm·dom) after surprising (e.g. PD-biased verb in a DODterm·dom structure) than unsurprising (DOD-biased verb in a DODterm·dom structure) primes.

Learningterm·dom is defined as a change in the underlying syntactic representationsterm·key and is operationalized as a change in performanceterm·mth from pre- to post-intervention in a production task. More specifically, learningterm·dom was deemed to have occurred if the children were significantly more likely to use the primed dativeterm·key structure post-intervention than pre-intervention (i.e. there was a change in the strength of the children's underlying syntactic representationsterm·key induced by the priming).

In the first, baseline phase of the study, we assessed participants' baseline rates of dativeterm·key production (i.e. how many DODsterm·dom and PDsterm·dom they produced). Participants described target video animations depicting transitive actions with dativeterm·key sentences, but were free to choose either PDterm·dom or DODterm·dom structures, and the experimenter described filler videos depicting non-causal actions that could be described with intransitive sentences.

The second, priming (or bias) phase was designed to elicit immediate prime surprisalterm·key effects [ 27 ] and biased the participants towards one of the dativeterm·key structures. Here, participants described target video animations depicting transitive actions in a similar way to the baseline phase, but the experimenter preceded these participant descriptions by describing prime animations using either DODterm·dom or PDterm·dom structure. Both structures were consistently paired with either matching or mismatching verbs in the prime sentences (e.g. PDsterm·dom only appeared with matching verbs, while DODsterm·dom only appeared with mismatching verbs for group A and vice versa for group B). This way, participants in group A were always subjected to PDsterm·dom in predictable sentences and DODsterm·dom in surprising sentences.

The third, post-test phase was similar to the baseline phase, but the goal was to reassess participants' rates of dativeterm·key production. If less predictable inputterm·dom leads to more lasting language change than more predictable inputterm·dom (as suggested by error-based learningterm·key theories), we expected participants' production in this phase to shift towards the structure they were exposed to with a mismatching verb in the bias phase (i.e. DODsterm·dom for participants in group A) compared with the baseline phase. In order to eliminate the influence of lexically based long-term priming effects, we used different verbs in the bias and test phases.

While the main focus of this study was abstract error-based learningterm·key, the second post-test aimed to assess potential verb-specific learningterm·dom effects. This phase was similar to the pre- and post-test phases, but the target sentences uttered by the participants re-used the PD- or DOD-biased verbs that were featured as primes in the bias phase. This way, we were able to detect a possible change in participants' verb-specific syntactic representationsterm·key without interfering with the abstract priming effects in the previous phases. If there is verb-specific error-based learningterm·key, we expect an enhanced shift towards the dativeterm·key structure the verb previously appeared with when the structure did not match the verb's bias. For instance, for the PD-biased verb bring, we expected a bigger shift towards the structure for participants for whom it consistently appeared with the mismatching DODterm·dom structure than for participants for whom it appeared with the matching PDterm·dom structure.

Unpublished results from Fisher & Lin [ 37 ] show that training with less-expected sentences can lead to larger shifts in dativeterm·key production than training with more predictable sentences if the verb is shared between training and test. Replicating these results in our study would serve as a good basis for comparison with our main focus, abstract error-based learningterm·key. The current study was pre-registered on the Open Science Framework (OSF); the accepted Stage 1 registration can be viewed at ( https://osf.io/khym8/ ).

Participants Seventy-two 5- to 6-year-old children (47 female, mean age 76.15 months, s.d. = 9.59 months) and 72 adults (62 female), all monolingual English-speakers, participated in the study. The child participants were recruited from schools in the area and the departmental database, while the adult participants were recruited from the university's student participation pool.

Ten child and two adult participants who produced ‘other' responses for more than half of the target trials in the test, post-test or second post-test phases were excluded. These participants were replaced in order to obtain 72 sets of data in each age group. Exclusion criteria for the target sentences will be discussed in the §2.7 Coding.

These age groups have shown sensitivity to verb-bias manipulations both in the target verb and in the prime verb (prime surprisalterm·key) conditions in a priming study involving dativeterm·key structures [ 27 ]. Children of this age consistently produce both PDterm·dom and DODterm·dom structures (with an average DODterm·dom production of approximately 30%) in corpus-based studies [ 38 ], and similar frequencies were observed in priming studies using a similar paradigm to our own [ 27 , 31 ]; therefore no floor or ceiling effects were expected to occur in this study.

We determined our sample sizes based on power calculations carried out to allow both of our key comparisons of interest and our manipulation check to be powered adequately. We carried out two sets of power calculations across 1000 iterations on simulated binomial data using mixed effects models, based on those that were used to carry out analyses on our observed data (see §3 Statistics and data analyses). Maximal models were fitted to the simulated data. If the model failed to converge on 20% of the simulations, it was rejected and simplified before the power analysis was repeated. As our main point of interest in this study was the performance of the child participants, our calculations were based on the effect sizes expected in this group.

Our first power calculation was carried out on our key comparison of interest assessing whether less predictable (more surprising) input leads to more lasting syntactic representationterm·key change than predictable inputterm·dom (see power calculation: https://osf.io/9ecjh/ and details of the analyses this calculation is targeting in §3.1). As there are currently no data available for our main comparison in the literature, we estimated our simulated effect sizes based on studies targeting contrasts that are in some respects similar to ours, such as 4-year-olds' post-intervention performance in a study involving the passive structure [ 34 ], an adult intervention study featuring the dativeterm·key structure [ 32 ] and a developmental study involving 5- to 6-year-olds looking at immediate prime surprisalterm·key effects featuring the dativeterm·key structure [ 27 ]. The effect sizes most relevant to our comparison in the following studies were 11% post-test shift in a passive intervention study with 4-year-olds [ 34 ], an average 7% post-intervention shift in a dativeterm·key study featuring adults [ 32 ] and 16% higher priming after mismatching primes than matching ones in an immediate prime surprisalterm·key study in 5- to 6-year-olds [ 27 ]. Based on the above results, we expect at least a 10% shift in both bias groups towards the structure participants were biased towards in the bias phase. In order to ensure that the study was adequately powered even if there were smaller than expected effect sizes, we estimated an average 5% shift in both bias groups (showing that participants' production in the post-test phase shifts towards the structure they were exposed to with a mismatching verb in the bias phase). Based on corpus-based studies [ 38 ] and priming studies using similar materials to our own [ 27 , 31 ], we estimated an average 30% baseline DODterm·dom production in the pre-test phase in both bias groups. Our power calculation showed that our key comparison of interest (post-test differences based on bias group captured by the prime-bias variable) had 93% power when featuring 66 participants. We planned to include 72 participants in each age group in order to have equal numbers of participants in the eight counterbalance groups and to account for 10% potential data loss.

We also carried out a separate power calculation to ensure that our manipulation check (immediate prime surprisalterm·key effect in the test or bias phase, see power calculation: https://osf.io/x2ykf/ and details of the analyses this calculation is targeting in §3.2) was adequately powered. As this phase aimed to replicate the effects in Peter et al .'s study [ 27 ], we simulated data based on the response frequencies in the 5- to 6-year-old group. We estimated an average DODterm·dom production of 24% and 35% in the matching PDterm·dom and DODterm·dom prime conditions and 19% and 41% in the mismatching PDterm·dom and DODterm·dom prime conditions. Our power analysis targeted the interaction of prime structure and verb bias. Based on these estimates, the power analysis returned 81.3% power when including 66 participants. With the inclusion of an extra six participants (to account for 10% potential data loss), this phase of the study was therefore also sufficiently powered.

Design The between-subject variables were age (adults versus children) and bias group (DOD-bias and PD-bias), and the within-subject variables were verb-bias match (match or mismatch), prime type (DODterm·dom and PDterm·dom) and phase (pre-test, bias phase, post-test and second post-test). The dependent variable was the choice of dativeterm·key structure in the target trials.

Predictionsterm·key We had four main predictionsterm·key, which are discussed in more detail in §3 (Statistics and data analyses). 1. Immediate prime surprisalterm·key: we expected to replicate the effects found in Peter et al .’s study [ 27 ] and find increased priming if the verb bias and the prime structure did not match in the prime sentence. 2. Learningterm·dom about abstract structures: we expected that less predictable (more surprising) input would lead to more learningterm·dom than predictable inputterm·dom. Therefore, we expected that participants' production in the post-test would shift towards the structure they were exposed to with mismatching verbs in the bias phase. 3. Verb-based learningterm·dom: due to the larger learningterm·dom effects resulting from unpredicted input, we expected that participants would be more likely to re-use the structure the target verb previously appeared in if that structure did not match the verb's bias. 4. Stronger effects in the child than in the adult group: due to the weaker and more malleable verb-biases in children compared with adults, we expected that the three above effects (immediate prime surprisalterm·key, learningterm·dom about abstract structures and verb-based learningterm·dom) would be larger for children than adults.

Visual stimuli The study featured video animations created in Moho 12, which were presented in E-prime 2.0 software [ 39 ]. Each participant saw 120 videos: 60 videos depicting transitive actions that can be described with prepositional or double object dativesterm·dom for the prime and target sentences and 60 videos depicting non-causal actions for the filler sentences.

The cartoons included 10 pairs of donor and recipient characters. Half of them were cartoon characters that are familiar to British children with proper noun names: Tigger and Piglet , Dora (the Explorer) and Boots , Marge and Homer , Lisa and Bart and Bob (the Builder) and Wendy . The other characters were referred to with determiner and noun NPs: the prince and the princess , the king and the queen , the student and the teacher , the doctor and the nurse and the boy and the girl . Particular donor and recipient characters were always featured together. A further 10 items acted as objects and were referred to with indefinite determiner and noun NPs: a ball , a toy , an orange , a cake , a peach , a sandwich, a pencil , a book , a napkin and an apple . The objects were consistently paired with one pair of characters (e.g. the ball was always featured with Bob and Wendy ).

In the bias phase, prime videos were always paired with a target video that included different characters from those in the prime. In order to control for the possibility that direction of transfer might influence structure choice, the animations depicted the direction of motion of transfer actions equally often from right-to-left and from left-to-right.

Sentence stimuli The study contained 120 sentences (including 60 verb stems) per participant: 16 prime and 16 target sentences plus 32 fillers in the bias phase, 10 target and 10 filler sentences in the pre- and post-test phases and 8 target and 8 filler sentences in the second post-test. The prime sentences appeared half the time as DODterm·dom sentences and half the time as PDterm·dom sentences. Both structures were consistently paired with either matching or mismatching verbs in the prime sentences (e.g. PDsterm·dom only appeared with matching verbs while DODsterm·dom only appeared with mismatching verbs for participant A and vice versa for participant B). The target sentences were produced by the participant (as either DODterm·dom or PDterm·dom sentences) based on the video stimuli.

For instance, a prime-target trial in the bias phase included a prime sentence such as ‘The king brought the queen a cat.' (DODterm·dom) or ‘The king brought the cat to the queen.' (PDterm·dom) and participants completed a sentence stem such as ‘Lisa dropped…' as a target sentence.

In order to avoid lexically based long-term priming effects, we used a different set of verbs in the bias phase and in the pre- and post-test phases. The study involved the following two sets of verbs, featured here with their DODterm·dom frequencies in the Manchester corpus [ 40 ] in brackets (for the computation of the dativeterm·key frequencies see [ 41 ]). The first set of verbs was used in the pre- and post-test phases. This set contained three equi-balanced verbs: feed (52%), slide (56%) and throw (49%), and one PD- and one DOD-biased verb: bring (27%) and give (89%). The second set of verbs was featured in the test phase and repeated in the second post-test. This set contained four PD-biased verbs: leave (32%), sell (24%), send (44%) and take (15%) and four DOD-biased verbs: award (83%), hand (63%), offer (77%) and show (93%).

We selected the above verbs based on the frequency of their dativeterm·key occurrences in the Manchester corpus [ 40 ]. These verbs have yielded immediate prime surprisalterm·key effects in other studies featuring similar age groups to ours [ 27 ] as well as in our pilot study featuring 5- to 6-year-old children.

We aimed to select verbs that had strong verb biases for the bias phase (as prime surprisalterm·key is defined as the negative logarithm of the verb bias [ 25 ]), but our choices were constrained by the limited number of verbs that appeared often in dativeterm·key structures in the Manchester corpus [ 40 ].

To control for sentence-specific preferences, we created eight counterbalance groups to ensure that (i) if the DODterm·dom structure consistently appeared with matching verbs in one counterbalance group, it appeared with mismatching verbs in the other (and vice versa for the PDterm·dom structure), (ii) if a verb appeared with a DODterm·dom in a counterbalance group, it appeared with a PDterm·dom in the other, and (iii) if a target sentence appeared in the pre-test in one counterbalance group, it appeared in the post-test in the other.

Semi-randomized 3 stimulus lists were created in which the prime and target sentences always followed each other in the bias phase and the same verb did not appear twice in immediate succession. In the test or bias phase, there was always a pair of filler sentences after every target sentence. In the other phases, filler and target phrases alternated with each other.

Procedure The study used the bingo game paradigm [ 27 , 31 ]. It took the form of a bingo game in which experimenter and child took turns to describe cartoon animations or pictures on a laptop computer.

The experimenter introduced the characters involved in the tasks by showing the participants cards featuring the characters. The experimenter and the participant sat in front of the computer side by side. 4 The experimenter described the first cartoon and asked the participant to repeat the sentence. The participant was then asked to produce a target sentence by describing a cartoon animation on the other side of the screen. To ensure that participants' responses contain the target verb, a stem-completion technique was used (e.g. the boy gave… ) [ 27 ]. 5 Each target sentence was immediately followed by an intransitive filler sentence.

After every two or three sentences, a smiley or frowny face appeared to signal whether a bingo card was available. If it was, the child or the experimenter got the card and could add it to their bingo grid. The first person to fill the bingo grid with bingo cards was the winner of the game, and the experiment was designed so that the participant always won.

Before beginning the study, there was a practice session to ensure that the participants understood the task. The practice session included intransitive sentences featuring three characters each (e.g. ‘The king and the queen were playing with the cat.'). In order to encourage the production of full dativesterm·key in the main study, we asked participants to mention all three characters in their descriptions during the practice session. To further encourage the production of full dativesterm·key in the study, the first verb featured as a target in both the pre- and post-test phase was a verb that cannot be used as an intransitive.

The bingo paradigm paired with the stem-completion technique has been successfully used to elicit dativeterm·key sentences in similar age groups and has resulted in low exclusion rates [ 27 , 31 ]. Furthermore, both the child and adult participants enjoyed participating in our pilot study featuring this paradigm and all participants completed the session.

After completing the bingo game, we measured children's baseline language abilities following a Stage 1 reviewer's request. As we aimed to capture individual differences in children's morphosyntactic abilities, we initially planned to use the Sentence Imitation Task from the Early Repetition Battery [ 42 ] (SIT). However, as members of our research group have found ceiling effects with a similar population to the one included in our study using SIT, we proposed using the Test for Reception of Grammar 2 [ 43 ] (TROG) instead. After discussion with the editor, we administered both tests, but as a ceiling effect—defined as over 70% of the children providing a correct answer for at least 25 out of the 27 items—occurred in the SIT, we included only the children's TROG scores in our analyses. The study lasted approximately 45 min, including a break, and participants received a sticker after the practice session.

Coding The experiment was audiotaped, allowing the transcription and coding of the utterances off-line. The first author transcribed the utterances. Then, two coders who were both blind to the experimental condition coded them. The first coder coded all utterances and a second coder coded 10% of the utterances in order to compute the Cohen's kappa inter-rater reliability [ 44 ]. Inter-rater reliability was high at 99.8% agreement, Cohen's kappa = 0.99. Coders resolved potential discrepancies by revisiting the sentences in question and the mutually decided code was included in the dataset.

A target response was considered a DODterm·dom if it contained the correct target verb followed by two noun phrases, and a PDterm·dom if it contained the correct target verb followed by a noun phrase and a prepositional phrase headed by ‘to'. Responses were coded as ‘other' if (i) the participant failed to repeat the prime correctly (even after help), (ii) if the participant produced a non-target verb, or (iii) if the target sentence could not be classified as a DODterm·dom or PDterm·dom response based on the above criteria (e.g. target responses containing a preposition other than ‘to’ or incomplete dativesterm·key such as ‘ the king gave the ball ').

Statistics and data analyses The data were analysed in R version 3.6.3 [ 45 ], through a series of logistic mixed effects models [ 46 , 47 ] fit using the lme4 1.1–23 package with the nloptwrap optimizer. These models were initially specified with subject and item as random grouping factors, each including all of the relevant within-subject and within-item fixed effects as random slopes with their associated correlation parameters. Where necessary, these models were simplified until there were no issues with convergence or singular variance-covariance matrices. The models were then assessed for overparametrization using a principal components analysis on the random effects structure, with further simplifications being performed if required [ 48 , 49 ]. In all models, the dependent measure was the binomially coded production of DODterm·dom structures (DODterm·dom = 1, PDterm·dom = 0). The factors were coded with effect/sum contrasts [ 50 , 51 ], while age (in months) was entered as a continuous variable and was centred to reduce multi-collinearity [ 52 ]. Confidence intervals (Est. [CI]) for the model estimates were obtained using parametric bootstrapping ( r = 1000). The confirmatory tests of the hypotheses and their p -values were obtained by sequentially removing individual contrasts from the fixed effect structure and running log-likelihood-ratio tests ( χ 2 ). However, we did not remove any fixed effects for the purpose of model selection or criticism; all fixed parameters were retained, even when they did not improve the model's goodness of fit.

At the request of a Stage 1 reviewer, we also performed a parallel series of Bayesian mixed effects models to match the frequentist analyses. These were implemented using the rstanarm 2.19.3 package, which provides front-end functions for using Stan [ 53 ] in an R environment. As we did not describe the Bayesian analyses in detail in the registered reportterm·key, these are regarded as exploratory analyses. Consistent with the frequentist analyses, we first attempted to include all relevant within-subject and within-item fixed effects as random slopes. The models were then simplified to address any issues with convergence or an excessive number of divergent transitions when the target average acceptance probability was set at 0.99. In some cases, it was necessary to remove control covariates (e.g. TROG score) from the fixed effect structure to reach a model specification supported by the data. However, we only considered removing fixed effect parameters when their variance estimates were close to zero and the random effects structure could not be simplified any further. None of the parameters that directly address our core hypotheses were removed. In accordance with the recommendations for binomial outcome measures [ 54 ], we used weakly informative priors on a Student's t distribution for the model intercept and predictors. The algorithm took 10 000 posterior estimates of each parameter (5000 samples across four Markov chains, with a warm-up of 2500 samples). We report the mean posterior Beta estimate, 95% credibility intervals (mean [95% CrI]), and the posterior probability of the parameter estimate being larger than (for positive estimates) or smaller than (for negative estimates) zero ( P ).

A key advantage of Bayesian analyses, compared with the frequentist approach, is that the interpretation of the Bayesian probability estimates ( P ) and credible intervals (Crl) is more intuitive than that of the frequentist p -value and confidence intervals [ 55 – 57 ]. Bayesian posterior probability allows us to determine the probability of the true effect being different than zero, given our data (without any reference to a null hypothesis), while credible intervals identify the upper and lower bounds of where the true mean lies with 95% certainty (for a 95% CI). Thus, Bayesian analyses allow us to make statements about the likelihood of an effect given the data, in a way that is not possible based on frequentist estimates. It is worth noting that while Bayesian and frequentist approaches allow us to quantify our effects in different ways, they tend to lead to similar conclusions when used with weak and uninformative priors (such as the ones used in our analyses).

We carried out three sets of analyses on different subsets of the response data to (i) explore whether less predictable (more surprising) linguistic input leads to more persistent language change than more predictable inputterm·dom with no repetition of verbs (our main hypothesis), (ii) assess whether we replicated the prime surprisalterm·key effects found in Peter et al . [ 27 ], and (iii) explore whether less predictable (more surprising) linguistic input leads to more lasting language change than more predictable inputterm·dom for repeated verbs. We assessed our fourth hypothesis (iv) that stronger effects would be observed in the child than in the adult group in each section separately to determine which learningterm·dom or priming effects are different in the two age groups. In the following sections, we describe all analyses involving data from both age groups together, but in order to explore the group-specific patterns in more detail, we also carried out analyses on the data from the two age groups separately. The main effects of age in months and TROG score (centred and rescaled) were added to the models examining data from the child group separately. All analysis scripts and relevant datasets can be accessed on https://osf.io/r8exu/ , and an overview of the analyses can be found in figure 1 . Figure 1. Analysis flowchart detailing pre-registered and exploratory analyses in the different stages of the study. All analyses were carried out using both frequentist and Bayesian mixed effects models.

Key comparison of interest—abstract learningterm·dom effects Hypothesis 1—H2a—participants’ production in the post-test shifts towards the structure they were exposed to with mismatching verbs in the bias phase and H2b—the shift described in H2a is stronger in the child than in the adult group This analysis tested the central predictionterm·key of error-based learningterm·key theories: that less predictable (more surprising) input leads to higher rates of lasting syntactic representational change than predictable inputterm·dom by testing whether the post-test scores differ in the two bias groups, while controlling for the pre-test performance. (Note that this is the second predictionterm·key presented in §2.3. Above, but we present it first here as it was our key analysis.) It was carried out on the target items from the post-test phase and the full model included (i) bias group (depending on whether participants were biased towards DODterm·dom or PDterm·dom structures in the bias phase), (ii) pre-test score (how many DODsterm·dom per dativesterm·key a participant produced in the pre-test phase), and (iii) age group (children or adults, in the combined model), as fixed effects, by-subject random intercept with no random slopes and by-item random intercept with random slopes for bias phase, pre-test score and age group, in the combined analyses. If participants are influenced by input predictability, we expected to find a main effect of bias group showing that participants' dativeterm·key production in the post-test phase is different in the two bias conditions and that they shifted towards the structure they heard with a mismatching verb in the bias phase (H2a). We also expected a stronger pre- to post-test shift in the child than in the adult group, demonstrated by an interaction between bias group and age group (H2b).

We carried out two sets of analyses here, our pre-registered analysis and an exploratory analysis, where we excluded participants who showed ceiling performance in the baseline phase. In the pre-registered analysis, we found that both age groups showed a pre- to post-test shift towards the dativeterm·key structure they were exposed to in surprising (as opposed to predictable) sentences in the bias phase. This result is in line with the central claim of error-based theories: that unpredictable input leads to higher learningterm·dom rates than predictable inputterm·dom. In the full dataset, this difference was significant in the frequentist analysis and the Bayesian posterior probability was high (96.88%), supporting Hypothesis 2a. However, the effect did not reach significance in the either of the age groups separately, though the Bayesian posterior probability was high, especially in the child group (adult group = 82.77%; child group = 91.22%). In addition, despite the numerically larger shift in the child compared with the adult group, the interaction of bias group and age group did not reach significance. Thus, we found no reliable support for Hypotheses 2b in our pre-registered analysis. As per a reviewer's request, the pre-registered analysis is only discussed in detail in the supplementary materials.

We hypothesize that the lack of significant results in the separate age groups was due to interference from participants who showed a ceiling performance in the baseline phase and thus could only be shifted in one direction. We addressed this possibility in exploratory analyses discussed below.

Exploratory post-test phase analyses: excluding participants who showed a ceiling performance in the pre-test phase One potential reason for the lack of significant results in the separate age groups, particularly the child group (for which the study was powered), is interference from ceiling performance in the pre-test phase. For instance, if a participant already produces 100% DODsterm·dom in the pre-test phase, they can only shift towards higher PDterm·dom (and not higher DODterm·dom) production in the post-test phase, meaning that it becomes impossible to adequately measure the effect of our main manipulation (positive or negative pre- to post-test shift in DODterm·dom production depending on bias group). While we expected no ceiling performance, some participants (21 adults and 14 children) produced exclusively PDsterm·dom or exclusively DODsterm·dom in the pre-test. Thus, we conducted a set of exploratory analyses including only the participants who produced both PDsterm·dom and DODsterm·dom in the pre-test phase, replicating the analyses discussed above. These exploratory analyses included 109 participants, 51 adults and 58 children. While this analysis is a better test of our main question, the reduction in participant size led to decreased statistical power.

Both age groups The maximal frequentist model supported by the data included bias group, age group and pre-test score as fixed effects and subject and item as random intercepts with pre-test score as a random slope for item. The Bayesian model included the same fixed effects with subject and item as random intercepts with bias group as a random slope for item.

We observed the expected significant bias group difference in the combined group: children showed an average 8.12%, while adults an average 3.38% pre- to post-test shift ( p = 0.018, Bayesian posterior probability: 97.09%; see figure 2 ). In addition, pre-test score had a significant positive effect showing that participants with higher baseline DODterm·dom performance also produced more DODsterm·dom in the post-test (all p s < 0.001, Bayesian posterior probabilities > 99.93%). Adults produced overall more DODsterm·dom than children, but this effect did not reach significance ( p = 0.067, Bayesian posterior probability: 96.33%). Importantly, children showed a larger pre- to post-test shift than adults, but this effect did not reach significance either ( p = 0.52, Bayesian posterior probability: 74.08%; table 2 ). Table 2. Results of the exploratory frequentist and the Bayesian analyses in the post-test phase per age group, excluding the ceiling participants. Italicized indicates significant results according to the frequentist analyses. comparison frequentist Bayesian Est. [CI] χ 2 p mean [95% CrI] P ( β > 0) both age groups intercept −0.12 [−0.83, 0.58] NA NA −0.13 [−0.89, 0.64] 64.32% pre-test score 1.38 [0.85, 1.93] 28.94 <0.001 1.37 [0.86, 1.92] 100% bias group 0.75 [0.05, 1.46] 5.60 0.018 0.7 [−0.02, 1.44] 97.09% age_group 0.71 [−0.04, 1.52] 3.35 0.067 0.71 [−0.06, 1.49] 96.33% bias group:age_group −0.37 [−1.8, 0.97] 0.41 0.52 −0.46 [−1.86, 0.95] 74.08% adult group intercept 0.31 [−0.38, 1.04] NA NA 0.32 [−0.46, 1.09] 80.33% pre-test score 1 [0.46, 1.54] 12.01 <0.001 1.01 [0.41, 1.65] 99.94% bias group 0.4 [−0.43, 1.25] 0.83 0.363 0.38 [−0.59, 1.32] 78.85% child group intercept −0.4 [−1.44, 0.7] NA NA −0.37 [−1.57, 0.8] 26.79% pre-test score 2.08 [0.93, 3.2] 10.57 0.001 1.97 [0.83, 3.17] 99.98% TROG score −0.06 [−0.71, 0.6] 0.00 0.952 −0.08 [−0.79, 0.6] 59.17% age −0.1 [−0.79, 0.59] 1.03 0.311 −0.07 [−0.89, 0.68] 56.69% bias group 1.14 [−0.15, 2.39] 4.35 0.037 1 [−0.36, 2.43] 92.46% trog score:age −0.19 [−0.94, 0.51] 0.04 0.85 −0.22 [−0.97, 0.54] 72.3% TROG score:bias group 0.61 [−0.73, 1.94] 0.30 0.583 0.61 [−0.74, 2.02] 81.15% age:bias group −1.11 [−2.57, 0.3] 3.31 0.069 −1.15 [−2.68, 0.38] 93.3% TROG score:age:bias group −0.28 [−1.67, 1.16] 0.16 0.694 −0.21 [−1.71, 1.3] 61.33% Adult and child groups In the adult group, the maximal frequentist model supported by the data included bias group and pre-test score as fixed effects and by-subject and by-item random intercepts with no random slopes, while the Bayesian model included the maximal effect structure. In the child group, the maximal frequentist model supported by the data included bias group, pre-test score, age in months and TROG score as fixed effects and by-subject and by-item random intercepts with by-item random slopes for pre-test score. The Bayesian analysis included the same fixed effects in addition to a by-subject random intercept with no random slopes and a by-item random intercept with random slopes for bias group. Importantly, we observed the expected pre- to post-test shift in all analyses in this dataset. Children produced 8.94% more DODsterm·dom in the DODterm·dom and 7.31% more PDsterm·dom in the PDterm·dom bias group at post-test compared with pre-test, while adults showed a 1.96% pre- to post-test shift in the DODterm·dom and 4.81% shift in the PDterm·dom bias group. The effect of bias group was significant when we analysed data from the two age groups together ( p = 0.018, Bayesian posterior probability: 97.09%) and, this time, in the child group separately as well ( p = 0.037, Bayesian posterior probability: 92.46%). The bias group effect did not reach significance in the adult group separately ( p = 0.36, Bayesian posterior probability: 78.85%). In addition, pre-test score had a significant positive effect in each exploratory analysis (all p s < 0.001, Bayesian posterior probabilities > 99.93%).

Summary of the results in the post-test phase In summary, the full dataset and the smaller but more representative subset dataset (that included only participants with no ceiling performance in the pre-test) both showed the expected bias group-dependent pre- to post-test shifts. While in the pre-registered analyses, the bias group difference only reached significance in the combined dataset, in the exploratory analyses both the combined and the child group separately showed a significant bias group difference. These results provide crucial initial evidence for the central claim of error-based theories that unpredictable input leads to higher rates of lasting language change than predictable inputterm·dom.

Confirming expected effects—immediate prime surprisalterm·key Hypothesis 1 — H1a — immediate priming effects are increased if the prime structure appeared with a mismatching as opposed to a matching verb (immediate prime surprisalterm·key effect) and H1b — the immediate prime surprisalterm·key effects are larger in the child than in the adult group This analysis served as a manipulation check: to confirm the differences in predictability between the different bias conditions (that are designed to lead to long-term changes in the post-test phase), we assessed whether they replicated the immediate prime surprisalterm·key effects found by Peter et al . [ 27 ].

These analyses were carried out on the target sentences from the bias phase. The full model included as fixed effects: (i) prime structure (DODterm·dom or PDterm·dom), (ii) prime-bias match (depending on whether the prime verb's bias matches or mismatches the prime structure), and (iii) age group (children or adults, in the combined model), and by-subject and by-item random intercepts and fully crossed random slopes for prime type and prime-bias match (and by-item random intercepts for age group in the combined analysis). Immediate structural priming is demonstrated if there is a greater proportion of DODterm·dom responses after DODterm·dom than PDterm·dom primes, and an immediate prime surprisalterm·key effect is demonstrated if there is a significant interaction between prime-structure and prime-bias match, showing that priming effects were larger if the prime verb's bias did not match the prime structure (H1a).

In line with the predictionterm·key of error-based learningterm·key theories that error-based learningterm·key results in greater changes to children's linguistic representations than to adults', we also expected a three-way interaction between prime-structure, prime-bias match and age group, showing that the prime surprisalterm·key effect (difference between priming after matching and mismatching verbs) is larger for the children than the adults (H1b).

Both age groups The maximal frequentist model supported by the data included prime-structure, prime-bias match and age group as fully crossed deviation-coded fixed effects and random intercept for subject (without random slopes), plus the random intercept for item with verb match as a random slope. The Bayesian analysis featured the same fixed effects with random intercepts for subject and item and no random slopes.

The frequentist model showed a main effect of age group, indicating that, overall, adults produced more DODsterm·dom than children ( p < 0.001, Bayesian posterior probability: 100%). There was also a main effect of prime structure, suggesting that participants were more likely to produce DODsterm·dom after hearing DODterm·dom than PDterm·dom primes ( p = 0.015, Bayesian posterior probability: 97.29%). Prime structure and age group produced a significant interaction ( p = 0.028, Bayesian posterior probability: 97.29%), indicating a larger priming effect in the child than in the adult group, as predicted. In terms of prime surprisalterm·key, there was a numerically larger priming effect after mismatching (surprising) than matching (predictable) primes, but this interaction did not reach significance ( p = 0.412, Bayesian posterior probability: 77.26%). Figure 3 suggests that a prime surprisalterm·key effect may exist in the adult, but not the child group, although the three-way interaction of prime-bias match, prime structure and age group did not reach significance ( p = 0.337, Bayesian posterior probability: 80.84%). To explore the group-specific patterns in more detail, we carried out additional analyses on the data from the two age groups separately ( figure 3 ; table 3 ). Figure 2. Pre- to post-test difference per age group and bias group, only including participants who did not show a ceiling performance in the pre-test. The dashed line represents no pre- to post-test change while the solid lines show the average per age and bias group shifts. For each condition, the violin lines represent the probability density of the data, and the jittered points show the pre- to post-test shift of each individual subject. Figure 3. Proportion of DODterm·dom production in the bias phase by age group and condition. For each condition, the shaded bars show the mean DODterm·dom production, the violin lines represent the probability density of the data, and the jittered points show the mean DODterm·dom production levels of each individual subject averaged across all trials in the given condition. Table 3. Results of the pre-registered frequentist and the Bayesian analyses in the bias phase per age group. Italicized indicates significant results according to the frequentist analyses. NA signifies values that were not computed by the Bayesian model. comparison frequentist bayesian Est. [CI] χ 2 p mean [95% CrI] P ( β > 0) both age groups intercept −0.4 [−0.94, 0.19] NA NA −0.39 [−1.01, 0.22] 89.75% prime type 0.33 [0.08, 0.57] 5.86 0.015 0.34 [−0.01, 0.68] 97.29% verb match 0.03 [−0.26, 0.33] 0.14 0.704 0.06 [−0.2, 0.32] 32.7% age group 2.48 [1.92, 3.03] 69.38 <0.001 2.49 [1.94, 3.07] 100% prime type:verb match −0.41 [−1.48, 0.68] 0.67 0.412 −0.41 [−0.64, 1.51] 77.26% prime type:age group −0.54 [−1.03, −0.03] 4.84 0.028 −0.51 [−1.03, 0.01] 97.29% verb match:age group 0.22 [−0.27, 0.71] 0.7 0.402 0.1 [−0.44, 0.62] 63.64% prime type:verb match:age group −1.04 [−3.28, 1.15] 0.92 0.337 −0.97 [−3.2, 1.2] 80.84% adult group intercept 0.84 [0.23, 1.45] NA NA 0.86 [0.21, 1.51] 99.33% prime type 0.05 [−0.27, 0.38] 0.09 0.759 0.06 [−0.37, 0.48] 61.40% verb match 0.16 [−0.24, 0.55] 0.65 0.422 0.13 [−0.34, 0.6] 71.43% prime type:verb match −0.93 [−2.3, 0.42] 1.76 0.185 −0.89 [−2.31, 0.56] 89.03% child group intercept −2.09 [−2.79, −1.25] NA NA −1.79 [−2.57, −1.07] 100% prime type 0.97 [0.33, 1.53] 10.4 0.001 0.74 [0.21, 1.3] 99.63% verb match 0.26 [−0.43, 0.91] 0.19 0.667 −0.04 [−0.61, 0.53] 56% age −0.88 [−1.41, −0.25] 1.17 0.279 −0.41 [−0.86, 0.04] 96.34% TROG 0.63 [0.08, 1.12] 5.4 0.02 0.5 [0.04, 1] 98.37% prime type:verb match −1.9 [−3.86, 0.18] 0.01 0.904 −0.09 [−1.84, 1.69] 54.08% prime type:age 0.26 [−0.39, 0.89] 0.13 0.721 NA NA verb match:age 0.15 [−0.51, 0.79] 0.98 0.322 NA NA prime type:TROG −0.21 [−0.79, 0.37] 0.37 0.545 NA NA verb match:TROG −0.01 [−0.6, 0.56] 0.28 0.598 NA NA age:TROG 0.61 [−0.03, 1.15] 0.03 0.852 NA NA prime type:verb match:age −2.5 [−4.8, −0.08] 0.8 0.372 NA NA prime type:verb match:TROG 0.34 [−1.77, 2.51] 0.03 0.872 NA NA Prime type:age:TROG −0.43 [−1.02, 0.23] 0.07 0.786 NA NA Verb match:age:TROG −0.57 [−1.17, 0.07] 3.22 0.073 NA NA prime type:verb match:Age:TROG 3.57 [1.18, 5.67] 10.89 <0.001 NA NA Adult and child groups In the adult group, the maximal frequentist model supported by the data included prime-structure and prime-bias match as fixed effects and random intercepts for subject and item with by verb-bias match random slopes for item, while the Bayesian analysis included the maximal model. In the child group, we fitted the same model used in our power calculations (§2.1), with the addition of age and syntactic knowledge as predictors. In addition to these fixed effects, the maximal frequentist model supported by the data also included a by-subject random intercept and a by-item random intercept with random slopes for prime-bias match. The Bayesian model reported here includes prime-structure and prime-bias match as fully crossed fixed effects, with TROG score and age added as covariates and with random intercepts for subject and item and random slopes for prime type and verb-bias match for both intercepts.

While the frequentist model detected a significant priming effect in the child group ( p = 0.001, Bayesian posterior probability: 99.63%), there was no evidence for priming in the adult group ( p = 0.76, Bayesian posterior probability: 61.40%). The effect of prime surprisalterm·key (demonstrated by an interaction between prime-structure and prime-bias match) did not reach significance in either age group separately (adults: p = 0.18, children: p = 0.9). However, the pattern of responses was different in the two groups. While adults showed the expected numerically larger priming effects after surprising prime sentences, children did not show this pattern ( figure 3 ). Furthermore, while the Bayesian posterior probability of prime surprisalterm·key was relatively high in the adult group (89.03%), it was very low in the child group (54.08%). In addition, TROG score also had a significant main effect ( p = 0.020, Bayesian posterior probability: 98.37%) in the child group, as children with more advanced syntactic knowledge (measured by the TROG test) were more likely to produce DODsterm·dom. The frequentist model also showed a significant four-way interaction between prime-structure, prime-bias match, age and TROG score ( p < 0.001). However, the Bayesian analysis did not include this interaction as we had to simplify the model structure due to convergence issues. This four-way interaction suggests that children who are younger and have lower TROG scores are more likely to show a sensitivity to the prime surprisalterm·key manipulation (the interaction of prime-structure and prime-bias match). We are cautious in our interpretation of this finding since there were no other significant lower level interactions in the model and we could not compute Bayesian estimates for this interaction (though it should be noted that the models were checked for overparametrization, see §3 Statistics and data analyses).

Summary of the pre-registered analyses in the bias phase There was an immediate structural priming effect, in that participants were more likely to produce DODsterm·dom after hearing DODterm·dom rather than PDterm·dom primes, though this result reached significance only in the child group, not the adult group, in the age group-specific analyses. Contrary to our predictionterm·key, however, there was no immediate prime surprisalterm·key effect when both age groups were considered together, nor in the child and adult groups when considered separately (though there was a numerical prime surprisalterm·key effect in the adult group alone). Thus, neither Hypothesis 1a (replication of prime surprisalterm·key effects) nor Hypothesis 1b (larger prime surprisalterm·key effects in the child than in the adult group) were supported by the current dataset.

Exploratory bias phase analyses—including baseline DODterm·dom performance We carried out a number of exploratory analyses to investigate these results further; all focused on determining whether our design choices could be responsible for the lack of a prime surprisalterm·key effect. Two of these analyses are reported only in the supplementary materials as they suggested that the confound proposed (bias group assignment and increasing predictability of the verb-structure pairings during the bias phase) did not affect our results. Below, we report the results of a third exploratory analysis that examined whether participants' baseline performance affected the likelihood of them showing prime surprisalterm·key effects. We repeated the analyses described above with the addition of baseline DODterm·dom performance (as measured in the pre-test phase) in the adult and the child group separately ( table 4 ). Table 4. Results of the exploratory frequentist and the Bayesian analyses in the bias phase per age group. Italicized indicates significant results according to the frequentist analyses. NA signifies values that were not computed by the Bayesian model. comparison frequentist Bayesian Est. [CI] χ 2 p mean [95% CrI] P ( β > 0) adult group intercept 0.37 [−0.21, 0.97] NA NA 0.39 [−0.26, 1.05] 88.61% c_baseline 0.89 [0.59, 1.18] 29.56 <0.001 0.9 [0.55, 1.26] 100% prime type 0.05 [−0.29, 0.38] 0.1 0.757 0.06 [−0.29, 0.41] 63.43% verb match 0.16 [−0.25, 0.54] 0.66 0.416 0.14 [−0.33, 0.6] 71.82% prime type:verb match −1.1 [−2.18, −0.03] 3.89 0.049 −1.16 [−2.33, 0.02] 97.71% child group intercept −1.43 [−2.11, −0.67] NA NA −1.18 [−1.9, −0.47] 99.94% c_baseline 1.08 [0.56, 1.53] 19.18 <0.001 1.17 [0.65, 1.72] 100% prime type 0.97 [0.37, 1.51] 10.52 0.001 0.71 [0.21, 1.23] 99.78% verb match 0.27 [−0.45, 0.92] 0.21 0.649 −0.05 [−0.61, 0.49] 56.96% age −0.75 [−1.2, −0.22] 2.77 0.096 −0.42 [−0.8, −0.04] 98.36% TROG 0.43 [−0.03, 0.84] 3.38 0.066 0.37 [−0.02, 0.78] 96.93% prime type:verb match −1.99 [−3.68, −0.13] 0.84 0.359 −0.61 [−2.1, 0.87] 79.42% prime type:age 0.26 [−0.38, 0.87] 0.11 0.74 NA NA verb match:age 0.14 [−0.51, 0.75] 0.99 0.32 NA NA prime type:TROG −0.2 [−0.75, 0.35] 0.37 0.54 NA NA verb match:TROG 0 [−0.52, 0.52] 0.31 0.577 NA NA age:TROG 0.39 [−0.16, 0.85] 0.01 0.929 NA NA prime type:verb match:Age −1.32 [−3.3, 0.72] 0.06 0.805 NA NA prime type:verb match:TROG 0.43 [−1.31, 2.21] 0 1 NA NA Prime type:age:TROG −0.43 [−1.02, 0.24] 0.06 0.808 NA NA verb match:age:TROG −0.59 [−1.21, 0.1] 3.18 0.074 NA NA prime type:verb match:age:TROG 2.88 [0.81, 4.66] 9.73 0.002 NA NA The maximal models included the same predictors as described in the pre-registered analyses with the addition of baseline DODterm·dom performance as a covariate. In the adult group, the maximal frequentist model supported by the data also included the random intercept for subject, and the random intercept of item with a random slope for verb-bias match. The Bayesian model contained the same fixed effects, with by-subject random intercepts with fully crossed random slopes for prime type and prime-bias match and by-item random intercepts with random intercept for verb-bias match and pre-test score. In the child group, the maximal frequentist model supported by the data included the fully crossed fixed effects of prime type, verb-bias match, age group and TROG score, with pre-test score as a covariate and by-subject and by-item random slopes and by-item random intercepts for verb-bias match. The model supported by the data in the Bayesian analysis included prime-structure and prime-bias match as fully crossed fixed effects and age, TROG score and baseline DODterm·dom performance as covariates, with by-subject and by-item random slopes, prime type and verb-bias match random slopes for both item and subject and by-item random intercepts for pre-test score.

As in the pre-registered analyses, we found a significant priming effect in the child ( p = 0.001, Bayesian posterior probability: 99.78%), but not in the adult group ( p = 0.76, Bayesian posterior probability: 63.43%). As expected, pre-test performance had a significant positive main effect showing that participants with high DODterm·dom production in the pre-test were also more likely to produce more DODsterm·dom in the bias phase (both p s < 0.01, Bayesian posterior probabilities: 100%). The most important result of this analysis is that prime surprisalterm·key had a significant effect in the adult group when baseline DODterm·dom performance was included ( p = 0.049, Bayesian posterior probability: 97.71%). While children still did not show a significant prime surprisalterm·key effect ( p = 0.36), the posterior probability of this effect was higher in this analysis (79.42%) than in the analysis without baseline DODterm·dom performance (54.08%). As in the pre-registered child group analysis, the frequentist model also produced a significant four-way interaction between prime-structure, prime-bias match, age and TROG score ( p = 0.002), though again this interaction was not included in the Bayesian analysis as we had to simplify the model structure due to convergence issues. As before, we are cautious in our interpretation of this finding due to the lack of corresponding lower level interactions and the absence of the Bayesian estimates.

Summary of bias group results In summary, we found a structural priming effect (participants were more likely to produce DODsterm·dom after hearing DODterm·dom than PDterm·dom primes) in both the pre-registered and in the exploratory analyses that included baseline DODterm·dom performance. However, this effect only reached significance in the child but not in the adult group. While participants overall were more likely to repeat the previously heard dativeterm·key structure when it was surprising as opposed to predictable, this immediate prime surprisalterm·key effect did not reach significance in our pre-registered analyses and was only significant in the separate adult group in our exploratory analyses.

Additional analyses of potential interest—verb-based learningterm·dom Hypothesis 3 — H3a — verb-based long-term effects of input predictability and H3b — the shift described in H3a is stronger in the child than in the adult group The third set of analyses was carried out on the target sentences from the second post-test phase and the goal was to detect verb-specific long-term priming effects. Here we had to deviate from our pre-registered analyses. We mistakenly specified the inclusion of a binary verb-bias predictor (depending on whether the verb featured here as a target is overall PD- or DOD-biased) in our analyses. However, due to our between-participants design in the bias phase, verb bias and bias group are not independent predictors. Instead, to target potential verb-based learningterm·dom effects, we included verb-bias match in the bias phase (match or mismatch), prime structure in the bias phase (PDterm·dom or DODterm·dom) and a continuous predictor of verb bias (based on the counts from the Manchester corpus). If there is verb-based learningterm·dom, we expect an interaction between verb-bias match and prime structure. The maximal models included (i) prime-structure (PDterm·dom or DODterm·dom depending on which structure the verb appeared in during the bias phase), (ii) prime-bias match (depending on whether the prime verb's bias matches or mismatches the prime structure), (iii) verb-bias (based on the Manchester corpus) and (iv) age group (adults or children, in the combined analysis), and by-subject and by-item random intercepts and random slopes for prime-structure and prime-bias match in the full model.

Here, we expected to see a main effect of prime structure showing that participants are more likely to re-use the structure in which they previously heard the verb and a main effect of verb-bias, thereby replicating Peter et al . [ 27 ]. Crucially, a lasting verb-specific prime surprisalterm·key effect (H3a) would be demonstrated by an interaction between prime-structure and prime-bias match showing that participants are more likely to re-use the structure the target verb previously appeared in if that structure did not match the verb's bias. H3b, larger verb-based learningterm·dom effects in the child then in the adult group would be demonstrated by a three-way interaction between prime-structure, prime-bias match and age group.

Both age groups together The maximal frequentist model supported by the data included prime-structure, verb-bias match (depending on whether the verb featured here appeared in a matching or mismatching sentence), verb bias (proportion of DODsterm·dom per dativeterm·key occurrence in the Manchester corpus) and age group (adults or children) as fixed effects and subject as a random intercept with a random slope for verb-bias match, and a by-item random intercept with a random slope for age group. The Bayesian model included the maximal effect structure.

The frequentist model detected a significant effect of age group ( p < 0.001, Bayesian posterior probability: 100%) and verb bias ( p < 0.003, Bayesian posterior probability: 97.77%), suggesting that participants in the adult group were more likely to produce DODsterm·dom, and that participants produced more DODsterm·dom with verbs that had a higher DOD-bias in the Manchester corpus. Importantly, the main effect of prime type also reached significance ( p < 0.001, Bayesian posterior probability: 99.80%), indicating that participants overall were more likely to produce DODsterm·dom with verbs that were featured in DODterm·dom (as opposed to PDterm·dom) sentences in the bias phase. However, the interaction of prime-structure and prime-bias match did not reach significance ( p = 0.98; Bayesian posterior probability: 50.97%). Thus, we found no evidence for Hypothesis 3a—verb-based learningterm·dom effects—in this analysis. Furthermore, the interaction of prime-structure, prime-bias match and age group did not reach significance either ( p = 0.95, Bayesian posterior probability: 58.18%), providing no evidence for Hypothesis 3b, larger verb-based learningterm·dom effects in the child than in the adult group ( figure 4 ; table 5 ). Figure 4. Proportion of DODterm·dom production in the second post-test phase per age group and condition. For each condition, the shaded bars show the mean DODterm·dom production, the violin lines represent the probability density of the data, and the jittered points show the mean DODterm·dom production levels of each individual subject averaged across all trials in the given condition. Table 5. Results of the pre-registered frequentist and the Bayesian analyses in the second post-test phase per age group. Italicized indicates significant results according to the frequentist analyses. NA signifies values that were not computed by the Bayesian model. comparison frequentist Bayesian Est. [CI] χ 2 p mean [95% CrI] P ( β > 0) both age groups intercept −0.23 [−0.85, 0.4] NA NA −0.24 [−0.92, 0.44] 76.56% verb bias 0.96 [0, 1.86] 8.81 0.003 0.99 [0.03, 1.97] 97.77% age group 3.75 [2.13, 5.07] 34.34 <0.001 3.77 [2.55, 5.14] 100% verb match −0.41 [−0.84, 0.01] 1.94 0.164 −0.45 [−1.08, 0.18] 92.64% prime type 0.81 [0.36, 1.27] 13.94 <0.001 0.91 [0.3, 1.58] 99.8% age group:verb match 0.17 [−0.68, 1.03] 0.15 0.7 0.35 [−0.8, 1.55] 71.66% age group:prime type −0.04 [−0.88, 0.77] 0.01 0.932 0.03 [−1.14, 1.22] 52.22% verb match:prime type −0.07 [−4.19, 3.81] 0 0.984 −0.06 [−4.09, 3.97] 50.97% age group:verb match:prime type −0.15 [−4.97, 4.67] 0 0.952 −0.47 [−5.13, 4.24] 58.18% adult group intercept 1.87 [−0.07, 3.26] NA NA 0.87 [0.52, 1.28] 100% verb bias 1.29 [−0.21, 2.58] 7.07 0.008 0.49 [−0.34, 1.3] 89.19% prime type 0.83 [0.19, 1.45] 7.7 0.006 0.45 [−0.02, 0.93] 96.84% verb match −0.33 [−0.95, 0.3] 1.18 0.278 −0.11 [−0.59, 0.36] 68.71% prime type:verb match −0.56 [−7.4, 5.13] 0.03 0.852 −0.72 [−4.04, 2.45] 68.29% child group intercept −2.39 [−3.42, −0.84] NA NA −2.12 [−3.13, −1.26] 100% verb bias 0.84 [−0.24, 1.8] 7.32 0.007 0.86 [−0.17, 1.87] 95.26% prime type 0.68 [−0.51, 1.78] 4.97 0.026 0.89 [0.1, 1.72] 98.68% verb match −0.58 [−1.72, 0.69] 2.23 0.136 −0.66 [−1.54, 0.2] 93.17% ageTR −0.56 [−1.49, 0.59] 0.03 0.866 NA NA TROG 0.57 [−0.5, 1.41] 2.61 0.106 NA NA prime type:verb match −1.59 [−7.33, 4.18] 0.02 0.901 0.56 [−3.93, 5.12] 59.96% prime type:age −0.23 [−1.31, 0.84] 0.02 0.875 NA NA verb match:age −0.35 [−1.42, 0.78] 0.49 0.483 NA NA prime type:TROG 0.81 [−0.33, 1.8] 4.12 0.042 NA NA verb match:TROG −0.35 [−1.38, 0.74] 1.16 0.281 NA NA age:TROG 0.88 [−0.35, 1.85] 0.99 0.321 NA NA prime type:verb match:age −2.34 [−6.33, 2.01] 0.58 0.445 NA NA prime type:verb match:TROG −1.17 [−4.88, 3.01] 0.8 0.372 NA NA prime type:age:TROG −0.01 [−0.93, 0.92] 0.01 0.92 NA NA verb match:age:TROG 0.28 [−0.78, 1.31] 0.47 0.49 NA NA prime type:verb match:age:TROG 3.96 [−0.58, 7.7] 5.05 0.025 NA NA Adult and child group In the adult group, the maximal frequentist model supported by the data included prime-structure, verb-bias match and verb bias as fixed effects and by-subject and by-item random intercepts with verb-bias random slopes for subject. The Bayesian model included the same fixed effects and by-subject and by-item random intercepts and by-subject random slopes for prime type and by-item random slopes for verb-bias match. The maximal frequentist model supported by the data in the child group included prime-structure, verb-bias match, verb bias, age and TROG score as fixed effects and by-subject and by-item random intercepts without random slopes. The Bayesian model supported by the data included prime-structure, verb-bias match and verb bias as fixed effects and by-subject and by-item random intercepts and by-subject random intercepts for verb-bias match and by-item random slopes for prime type.

The pattern of results was similar in the two age groups. The main effects of prime structure (adults: p = 0.006, Bayesian posterior probability: 96.84%; children: p = 0.026, Bayesian posterior probability: 98.68%) and verb bias (adults: p = 0.008, Bayesian posterior probability: 89.19%; children: p = 0.007, Bayesian posterior probability: 95.26%) were significant, showing that participants were more likely to produce a DODterm·dom structure if they heard the target verb in a DODterm·dom structure in the bias phase and if the verb was more DOD-biased. In the child group, this structure repetition was significantly stronger in children with higher TROG scores ( p = 0.042, this interaction was not included in the Bayesian analyses). Importantly, the interaction of prime type and prime-bias match did not reach significance in the separate age groups either (both p s > 0.85). The Bayesian analysis suggested that the posterior probability of a verb-based learningterm·dom effect is 68.29% in the adult and 59.96% in the child group. Despite the lack of evidence for verb-based learningterm·dom effects, the frequentist model in the child group showed a significant four-way interaction between prime-structure, prime-bias match, age and TROG score ( p = 0.025; this interaction was not included in the Bayesian models as we had to simplify the model structure due to convergence issues). This four-way interaction suggests that younger children who have a lower TROG score are more likely to show sensitivity to our verb-dependent error-based learningterm·key measure (the interaction of prime-structure and verb-bias match in the bias phase). As with the four-way interactions discussed in the bias phase, we are cautious in our interpretation of this finding due to the lack of most corresponding lower level interactions and the absence of the Bayesian estimates.

Summary of the results of the second post-test phase While both age groups were more likely to re-use the dativeterm·key structure they heard the verbs with in the bias phase, this effect was not modulated by whether the structure was surprising or predictable. This study therefore provided no evidence for Hypothesis 3a, verb-specific error-based learningterm·key effects. As we found no significant interaction between prime type, verb-bias match and age group, this analysis did not support Hypothesis 3b (larger verb-based learningterm·dom effects in the child than in the adult group) either ( table 6 ). Table 6. Appearance of expected response patterns per study phase and age group. In the bias phase the table shows whether participants demonstrated immediate prime surprisalterm·key and structural priming effects, the latter in brackets. In the post-test phase, the table shows whether participants showed more learningterm·dom for abstract structures after surprising as opposed to predictable sentences. In the second post-test phase, the table shows whether verb-based learningterm·dom rates were higher in surprising sentences. In brackets we can see whether participants were likely to use the dativeterm·key structure in phase 4 that specific verbs appeared with in the bias phase. age group adults children both groups together bias phase—prime surprisalterm·key (priming) ✓ (✗) baseline DODterm·dom production included ✗ (✓) ✓ (✓) Prime surprisalterm·key n.s. post-test phase—abstract learningterm·dom ✓ n.s. ✓ no ceiling in pre-test ✓ second post-test phase—verb-based learningterm·dom (structure repetition) ✗ (✓) ✗ (✓) ✗ (✓) Discussion The goal of the current study was to evaluate the central predictionterm·key of error-based theories of language acquisitionterm·key: that surprising linguistic input leads to higher rates of learningterm·dom than predictable inputterm·dom. To achieve this, we embedded a prime surprisalterm·key study in a four-stage intervention study to assess both the short- and long-term effects of predictability.

The most important result of the study was that we found support for the above claim. In the first (pre-test) phase of the study, we assessed participants' baseline rates of dativeterm·key production. In the second (bias) phase, we presented participants with surprising and unsurprising sentences, designed to bias them towards one of the dativeterm·key structures. In the third (post-test) phase, we reassessed participants’ spontaneous rates of dativeterm·key production. As expected, we found that both adults and children showed an accelerated learningterm·dom rate for the same structure if it was previously presented in a surprising as opposed to predictable context. Both age groups were more likely to produce DODsterm·dom in the DOD- as opposed to the PD-bias group in the post-test phase. Furthermore, both adults' (average 4.25% shift) and children's (average 6.12% shift) pre- to post-test production shifted towards the dativeterm·key structure they were exposed to in surprising sentences in the previous phase. This effect (difference between DOD- and PD-bias group, with baseline DODterm·dom production taken into account) was significant in the pre-registered analyses that included both the adult and the child group. In addition, although the frequentist analysis did not reach significance in either the adult or child groups separately, the Bayesian analysis suggested that the posterior probability of these effects was high, especially in the child group (adult group: 82.77%, child group: 91.22%). In sum, even though participants in both groups heard the same number of DODterm·dom and PDterm·dom structures, their production changed based on which structure was predictable and which one was surprising in the previous phase. This is crucial evidence for a central predictionterm·key of the Dual-path model [ 11 ]: an increased learningterm·dom rate for the same structure when it appeared earlier in a surprising as opposed to a predictable sentence.

As the magnitude of the pre- to post-test shift in the child group was similar to what we estimated in our power calculations (average 5% shift in the power calculation and an average 6.12% shift in the child dataset), the lack of significant effects when we analysed the child group data alone was surprising. We surmised that this might have been due to ceiling performance: 14 children and 21 adults produced only DODsterm·dom or only PDsterm·dom in the pre-test phase. Thus, we carried out a set of exploratory analyses excluding participants with ceiling pre-test performance, which demonstrated significant learningterm·dom effects when we analysed data from the two age groups together and, this time, in the child group separately as well.

To our knowledge, this is the first study that has found such learningterm·dom effects, providing initial experimental evidence for a central claim of error-based learningterm·key theories: that surprising inputterm·dom leads to more learningterm·dom compared with predictable inputterm·dom. These results are also in line with previous studies demonstrating that children's production frequencies can be shifted towards a less frequent structure by exposure in the bias phase [ 30 , 34 ]. Our study contributes to this literature by specifically showing that these differences can be traced back to input predictability.

A secondary goal of the study was to determine if we could replicate the immediate prime surprisalterm·key effects found in previous studies [ 26 , 27 ]. We found larger priming effects after surprising as opposed to predictable primes in the adult group, but neither the priming nor the prime surprisalterm·key effect reached significance in the pre-registered analyses. The Bayesian analysis showed that the posterior probability of priming was 61.40% while the posterior probability of prime surprisalterm·key was 89.03%. Children showed a significant priming effect (Bayesian posterior probability: 99.63%), but there was no sign of prime surprisalterm·key in this group (Bayesian posterior probability: 54.08%).

This failure to replicate immediate structural priming and prime surprisalterm·key effects was unexpected, so we explored potential explanations in exploratory analyses. Our study had a between-subjects design in the bias phase, where participants either heard only DOD-biased verbs (paired with either DODterm·dom (predictable) or PDterm·dom (surprising) structures), or only PD-biased verbs (paired with either PDterm·dom (predictable) or DODterm·dom (surprising) structures), table 1 ). This is unlike previous prime surprisalterm·key studies in which all participants heard all four types of sentences. While this design was necessary to contrast learningterm·dom rates for predictable versus surprising sentences and thereby test our primary hypothesis, it may have interfered with any immediate prime surprisalterm·key effects. Two of these analyses (reported in the electronic supplementary material) did not change the pattern of results. The third analysis in which we included baseline DODterm·dom performance in the models, to control for the effect of participants' individual differences in baseline performance, was reported above. Here, adults showed significant prime surprisalterm·key effects when their baseline DODterm·dom production rate was taken into account (Bayesian posterior probability: 97.71%), although there was no significant prime surprisalterm·key effect in the child group (Bayesian posterior probability: 79.42%). The Bayesian posterior probability of a prime surprisalterm·key effect was higher in the child group when baseline dativeterm·key production was taken into account (79.42% as opposed to 54.08%). It is possible that, due to the large variability in children's DODterm·dom production, prime surprisalterm·key in childhood is particularly sensitive to the difference between within and between-participant designs. (Note though that these exploratory analyses may have been underpowered as our power calculation did not include the additional baseline predictor.) While we cannot be certain of the source of the discrepancy between long-term learningterm·dom effects and immediate prime surprisalterm·key in the child group, it is worth noting that the pattern we observed is not compatible with the Dual-path model. This model suggests that immediate priming effects are the product of the same learningterm·dom mechanism that leads to long-lasting changes in syntactic knowledge. It would therefore predict similar effects with respect to immediate prime surprisalterm·key and learningterm·dom. The disconnect between these effects raises questions about whether learningterm·dom and priming are always induced by the same mechanism. However, as the main goal of our study was not to assess the relationship between immediate prime surprisalterm·key and long-term learningterm·dom, the results of this comparison must be interpreted with caution and followed up in further studies.

The last phase of the study, the second post-test, targeted verb-dependent error-based learningterm·key effects. In this phase, we expected participants to be more likely to use the same dativeterm·key structure that specific verbs appeared with in the bias phase. We also expected that the likelihood of structure repetition would be higher if the structure was unexpected in the bias phase. While we found that participants in both age groups were significantly more likely to repeat the structures the verb appeared with previously, this effect was not modulated by how surprising the structure was (it did not reach significance in the frequentist analyses and the Bayesian analyses suggested that the posterior probability of this effect is between is 50.97% and 68.29% depending on age group). This study therefore does not provide evidence for verb-dependent error-based learningterm·key effects. At first glance, these results seem to be in conflict with unpublished results from Fisher & Lin [ 37 ] who detected stronger verb-based learningterm·dom effect after unpredictable verb-structure pairings. However, it is not possible to draw strong conclusions based on the absence of these effects in the current study, since the final phase of the study was exploratory and provided a less sensitive test of learningterm·dom than the main test of abstract learningterm·dom. The partially between-participant design led to both uneven target verb-bias rates and uneven baseline DODterm·dom rates in the different conditions that may have masked any learningterm·dom effects. Furthermore, both participants' abstract learningterm·dom effects and their previous dativeterm·key production with the same verbs may have interfered with the results in this phase.

Finally, we assessed whether the priming and learningterm·dom effects we found were sensitive to age and syntactic knowledge. The Dual-path model predicts that learningterm·dom effects should decrease as the learner accumulates more linguistic knowledge and develops stronger linguistic representations. In our study, this predictionterm·key would be supported if children consistently showed both larger learningterm·dom and prime surprisalterm·key effects than adults (H1b, H2b and H3b) and if, within the child group, these effects were larger in children who were younger or had less advanced syntactic knowledge. The current study did not find any conclusive evidence for any such effects. While both the abstract (post-test phase) and verb-based learningterm·dom effects (second post-test phase) were numerically larger in the child than in the adult group, the interaction of learningterm·dom effect and age group did not reach significance in any of our analyses. In addition, there was no significant effect of immediate prime surprisalterm·key in the child group during the bias phase.

The contribution of age and syntactic knowledge (measured by the TROG test) also did not lead to a clear conclusion. As suggested by the Dual-path model, younger children and those with lower TROG scores showed larger learningterm·dom and prime surprisalterm·key effects in most analyses, except in the post-test phase, where TROG score had a positive effect. However, none of these effects reached significance. Despite the lack of lower level interactions, the frequentist models detected a significant interaction of immediate prime surprisalterm·key age and TROG score and verb-based learningterm·dom, age and TROG score, indicating that younger children with lower TROG scores demonstrate larger prime surprisalterm·key and verb-based learningterm·dom effects. While these results are in line with the predictionsterm·key of the Dual-path model, in the absence of lower level interactions and Bayesian estimates, we cannot be certain that these effects are reliable. Our study therefore does not provide conclusive evidence regarding the contribution of age and syntactic knowledge to the learningterm·dom mechanism in question, and this question needs to be addressed in future studies which are designed (and adequately powered) for exploring these comparisons.

The current study had three main limitations. First, as our main interest was surprisal-dependent abstract learningterm·dom, we had to induce different levels of predictability for the different dativeterm·key structures, which led to compromises when designing phases targeting immediate prime surprisalterm·key and verb-based learningterm·dom. We have discussed these modifications and their potential consequences in the previous sections. Second, as we targeted a previously untested question, we had to base our power calculations on effects corresponding to similar, but not identical research questions. As a result, we were unable to account for all the factors that emerged. Thus, it is crucial for future studies to replicate our results using power calculations that are updated based on the current data. The final limitation of our study lies in the nature of our method, the prime surprisalterm·key paradigm. While it can directly address potential changes in language production depending on the predictability of the input, it does not give us any information about online processing differences between predictable and surprising sentences. Future work should therefore combine this method with online measures such as EEG or eye-tracking in order to explore how these learningterm·dom effects unfold over time.

Conclusion Our study embedded the prime surprisalterm·key paradigm in a four-stage intervention study to address a strong, but as yet not directly tested, claim of error-based learningterm·key theories that surprising inputterm·dom leads to more learningterm·dom than predictable inputterm·dom. Although we did not replicate all the results from the previous literature (in particular, the lack of immediate prime surprisalterm·key in our child group was unexpected), we confirmed our primary hypothesis: that less predictable (more surprising) input leads to higher rates of lasting syntactic representational change compared with predictable inputterm·dom. Both adults' and children's dativeterm·key production shifted towards the (surprising) structure they were biased towards in the previous phase. To our knowledge, this is the first demonstration that exposure to the same syntactic structure leads to an increased learningterm·dom rate if this structure was presented in a context that made it surprising rather than predictable. The present work also contributes by establishing an experimental paradigm that can be used to target further aspects of error-based learningterm·key theories of language acquisitionterm·key in the future.

Supplementary Material