Comparing generativist and constructivist accounts of the use of the past tense form in early child Japanese

AbstractThe present study investigated children's early use of verb inflection in Japanese by comparing a generativist account, which predicts that the past tense will have a special default-like status for the child during the early stages, with a constructivist input-driven account, which assumes that children's acquisition and use of inflectional forms reflects verb-specific distributional patterns in their input. Analysis of naturalistic data from four Japanese children aged 1;5 to 2;10 showed that there was substantial by-verb variation in the use of inflectional forms from the earliest stages of verb use, and no general preference for past tense forms. Correlational and partial correlational analyses showed that it was possible to predict the proportional frequency with which the child produced verbs in past tense versus other inflectional forms on the basis of differences in the proportional frequency with which the verb occurred in past tense form in the child's input, even after controlling for differences in the rate at which verbs occurred in past tense form in input averaged across the caregivers of the other children in the sample. When taken together, these results count against the idea that the past tense has a special default-like status in early child Japanese, and in favour of a constructivist input-driven account of children's early use of verb inflection.</jats:p

Can Automated Gesture Recognition Support the Study of Child Language Development?

Children's prelinguistic gestures play a central role in their communicative development. Early gesture use has been shown to be predictive of both concurrent and later language ability, making the identification of gestures in video data at scale a potentially valuable tool for both theoretical and clinical purposes. We describe a new dataset consisting of videos of 72 infants interacting with their caregivers at 11&12 months, annotated for the appearance of 12 different gesture types. We propose a model based on deep convolutional neural networks to classify these. The model achieves 48.32% classification accuracy overall, but with significant variation between gesture types. Critically, we found strong (0.7 or above) rank order correlations between by-child gesture counts from human and machine coding for 7 of the 12 gestures (including the critical gestures of declarative pointing, hold outs and gives). Given the challenging nature of the data - recordings of many different dyads in different environments engaged in diverse activities - we consider these results a very encouraging first attempt at the task, and evidence that automatic or machine-assisted gesture identification could make a valuable contribution to the study of cognitive development

Building a unified model of the Optional Infinitive Stage: simulating the cross-linguistic pattern of verb-marking error in typically developing children and children with Developmental Language Disorder

Verb-marking errors are a characteristic feature of the speech of typically-developing (TD) children and are particularly prevalent in the speech of children with Developmental Language Disorder (DLD). However, both the pattern of verb-marking error in TD children and the pattern of verb-marking deficit in DLD vary across languages and interact with the semantic and syntactic properties of the language being learned. In this paper, we review work using a computational model called MOSAIC. We show how this work allows us to understand several features of the cross-linguistic data that are otherwise difficult to explain. We also show how discrepancies between the developmental data and the quantitative predictions generated by MOSAIC can be used to identify weaknesses in our current understanding and lead to further theory development; and how the resulting model (MOSAIC+) helps us understand differences in the cross-linguistic patterning of verb-marking errors in TD children and children with DLD

Modelling Children's Sentence Recall using an Encoder-Decoder Network

Elicited imitation is a widely used method for testing a child's knowledge of a language for scientific or clinical purposes. A child hears an utterance and is asked to repeat what they have heard. While it is assumed that their fluency or speed in doing so is contingent on their linguistic competence, little is known about the cognitive mechanisms and/or representations involved. To explore this, we train an encoder-decoder model, consisting of recurrent neural networks, to encode and reproduce a corpus of child-directed speech and then test its performance on the experimental task of Bannard and Matthews (2008). In that study pre-school children were asked to repeat high- and low-frequency four-word sequences in which the first three words were identical (e.g., sit in your chair and sit in your truck) and the final words and bigrams were closely matched for frequency. We find that like those children our model makes more errors on the initial three words when they are part of a low-frequency than a high-frequency sequence, despite the fact that the words being repeated are identical. We explore why this might be and pinpoint some possible similarities between the model and child language processing

Exposition et appropriation dans l’acquisition de la langue maternelle

Quand on sort du cadre des couches socio-professionnelles moyennes en Europe, on comprend vite que pour l’acquisition de la langue maternelle, la situation dyadique (modèle mère-enfant) est loin d’être la règle, ce qui affecte également la conception de l’étayage. Une théorie générale de l’acquisition doit d’abord différencier les données linguistiques (input) dans l’environnement social de l’enfant et les données qu’il est capable de traiter seul ou avec l’aide d’autres personnes (intake) ; elle doit ensuite proposer une solution méthodologique qui permette d’établir des corrélations précises entre environnement linguistico-social et développement acquisitionnel de l’enfant. Avec cette conception, les recherches dans le cadre d’une telle théorie s’opposent à la tradition chomskyenne.As soon as one leaves the nest of the European professional middle classes, one realises that the dyadic (mother-child) context is far from representing the rule in first-language acquisition and one is forced to revise one’s understanding of scaffolding. A general theory of acquisition has first to make the Corderian distinction between the linguistic material of the child’s environment (input) and within this, those data she is capable of processing alone or with the help of others (intake) and then to put forward a methodological procedure which allows precise correlations between the child’s socio-linguistic environment and her linguistic development. A theory and methodology such as this go against the Chomskyan framework

Prime Surprisal as a Tool for Assessing Error-Based Learning Theories: A Systematic Review

Error-based learning theories of language acquisition are highly influential in language development research, yet the predictive learning mechanism they propose has proven difficult to test experimentally. Prime surprisal—the observation that structural priming is stronger following more surprising primes—has emerged as a promising methodology for resolving this issue as it tests a key prediction of error-based learning theories: surprising input leads to increased structure repetition as well as learning. However, as prime surprisal is a relatively new paradigm, it is worth evaluating how far this promise has been fulfilled. We have conducted a systemic review of PS studies to assess the strengths and limitations of existing approaches, with 13 contributions selected out of 66 search results. We found that alongside inconsistency in statistical power and how the level of surprisal is measured, the limited scope of current results cast doubt on whether PS can be used as a general tool to assess error-based learning. We suggest two key directions for future research: firstly, targeting the scope of the prime surprisal effect itself with reliable statistical power and appropriate surprisal measurements across a greater variety of languages and grammatical structures; and secondly, using the prime surprisal method as a tool to assess the scope of an error-based learning mechanism utilising conditions in which prime surprisal has been reliably established.</jats:p

Exploring the acquisition of verb inflections in Japanese: A probabilistic analysis of seven adult-child corpora

The acquisition of verb morphology is often studied using categorical criteria for determining the productivity of a morpheme. Applying this approach to Japanese, an agglutinative language, this study finds no consistent order for morpheme acquisition and that productivity could be explained by sampling effects. To examine morpheme acquisition using more graded measures of productivity, the authors compared various regression models for predicting the age of acquisition of 311 verb forms across a large combined corpus of seven Japanese-speaking children (aged 1;1–5;1). Complex forms were learned earlier than frequency-matched simple forms, and morpheme ending identity explained substantial variation. Both of these findings suggest that children have some segmented morphemes and have learned some of their semantic/pragmatic characteristics. Sampling would predict that verb form acquisition would be sensitive to lemma and ending frequency, but acquisition was also sensitive to aspects of input frequency that were independent of these factors, and this suggests that children are encoding whole verb forms in addition to creating forms with compositional morphological rules