Caregiver communication to the child as moderator and mediator of genes for language

Human language appears to be unique among natural communication systems, and such uniqueness impinges on both nature and nurture. Human babies are endowed with cognitive abilities that predispose them to learn language, and this process cannot operate in an impoverished environment. To be effectively complete the acquisition of human language in human children requires highly socialised forms of learning, scaffolded over years of prolonged and intense caretaker\u2013child interactions. How genes and environment operate in shaping language is unknown. These two components have traditionally been considered as independent, and often pitted against each other in terms of the nature versus nurture debate. This perspective article considers how innate abilities and experience might instead work together. In particular, it envisages potential scenarios for research, in which early caregiver verbal and non-verbal attachment practices may mediate or moderate the expression of human genetic systems for language

Social Network Limits Language Complexity

Natural languages vary widely in the degree to which they make use of nested compositional structure in their grammars. It has long been noted by linguists that the languages historically spoken in small communities develop much deeper levels of compositional embedding than those spoken by larger groups. Recently, this observation has been confirmed by a robust statistical analysis of the World Atlas of Language Structures. In order to examine this connection mechanistically, we propose an agent\u2010based model that accounts for key cultural evolutionary features of language transfer and language change. We identify transitivity as a physical parameter of social networks critical for the evolution of compositional structure and the hierarchical patterning of scale\u2010free distributions as inhibitory

Language development and disorders: Possible genes and environment interactions

Language development requires both basic cognitive mechanisms for learning language and a rich social context from which learning takes off. Disruptions in learning mechanisms, processing abilities, and/or social interactions increase the risks associated with social exclusion or developmental delays. Given the complexity of language processes, a multilevel approach is proposed where both cognitive mechanisms, genetic and environmental factors need to be probed together with their possible interactions. Here we review and discuss such interplay between environment and genetic predispositions in understanding language disorders, with a particular focus on a possible endophenotype, the ability for statistical sequential learning

Technology-Based Tools for English Literacy Intervention: Examining Intervention Grain Size and Individual Differences

Technology plays an increasingly important role in educational practice, including interventions for struggling learners (Torgesen et al., 2010; de Souza et al., 2018). This study focuses on the efficacy of tablet-based applications (see Word Reading, Grapholearn, and an experimental word-level program) for the purpose of supplementing early English literacy intervention with primary grades 1 and 2 children. The children were identified for learning support programs within Singaporean schools, which follow a bilingual policy, meaning children were learning reading in English plus an additional language. One hundred forty-seven children across seven schools participated (Mean age = 6.66). Within learning support classrooms, triplets of students matched on basic reading skills were randomly assigned to one of three groups: (1) phoneme-level, (2) rime-level, or (3) word-level focused interventions. All groups performed reading skills activities on iPads, across two phases over a 14-week period. Assessments for word reading accuracy and fluency, pseudoword decoding accuracy and fluency, and spelling were administered at four time points, pre- and post-intervention. Additional baseline measures were taken to assess individual differences in phonological awareness, orthographic awareness, general cognitive ability, statistical learning, and bilingual vocabulary knowledge. Mixed model analysis was conducted on the pre- to post-test measures across the two phases of the intervention (focused on accuracy then fluency). All groups made gains across the different literacy measures, while the phoneme-level intervention showed an advantage over the rime-level intervention, but not the word-level intervention, for decoding. There were also moderating effects of individual differences on outcomes. The general pattern of results showed an advantage of the word-level intervention for those with poorer phonological awareness for reading fluency; and a phoneme-level intervention advantage for those with poorer statistical learning ability. Children\u2019s bilingual group (English plus Mandarin, English plus Malay, or English plus Tamil) also showed differential effects of the type of intervention (e.g., phoneme- or word-level) on different outcome measures. These results, along with data collected from the tablets during the intervention, suggest the need to examine the interplay between different types of technology-based interventions and individual differences in learning profiles

Improved statistical learning abilities in adult bilinguals

Using multiple languages may confer distinct advantages in cognitive control, yet it is unclear whether bilingualism is associated with better implicit statistical learning, a core cognitive ability underlying language. We tested bilingual adults on a challenging task requiring simultaneous learning of two miniature grammars characterized by different statistics. We found that participants learned each grammar significantly better than chance and both grammars equally well. Crucially, a validated continuous measure of bilingual dominance predicted accuracy scores for both artificial grammars in a generalized linear model. The study thus demonstrates the first graded advantage in learning novel statistical relations in adult bilinguals

Is the mind inherently predicting? Exploring forward and backward looking in language processing

Prediction is one characteristic of the human mind. But what does it mean to say the mind is a ’prediction machine’ and inherently forward looking as is frequently claimed? In natural languages, many contexts are not easily predictable in a forward fashion. In English for example many frequent verbs do not carry unique meaning on their own, but instead rely on another word or words that follow them to become meaningful. Upon reading take a the processor often cannot easily predict walk as the next word. But the system can ‘look back’ and integrate walk more easily when it follows take a (e.g., as opposed to make|get|have a walk). In the present paper we provide further evidence for the importance of both forward and backward looking in language processing. In two self-paced reading tasks and an eye-tracking reading task, we found evidence that adult English native speakers’ sensitivity to word forward and backward conditional probability significantly explained variance in reading times over and above psycholinguistic predictors of reading latencies. We conclude that both forward and backward-looking (prediction and integration) appear to be important characteristics of language processing. Our results thus suggest that it makes just as much sense to call the mind an ’integration machine’ which is inherently backward looking

Early developing syntactic knowledge influences sequential statistical learning in infancy

Adults\u2019 linguistic background influences their sequential statistical learning of an artificial language characterized by conflicting forward-going and backward-going transitional probabilities. English-speaking adults favor backward-going transitional probabilities, consistent with the head-initial structure of English. Korean-speaking adults favor forward-going transitional probabilities, consistent with the head-final structure of Korean. These experiments assess when infants develop this directional bias. In the experiments, 7-month-old infants showed no bias for forward-going or backward-going regularities. By 13 \u202fmonths, however, English-learning infants favored backward-going transitional probabilities over forward-going transitional probabilities, consistent with English-speaking adults. This indicates that statistical learning rapidly adapts to the predominant syntactic structure of the native language. Such adaptation may facilitate subsequent learning by highlighting statistical structures that are likely to be informative in the native linguistic environment

Statistical learning bias predicts second-language reading efficiency

Statistical learning (SL) is increasingly invoked as a set of general-purpose mechanisms upon which language learning is built during infancy and childhood. Here we investigated the extent to which SL is related to adult language processing. In particular, we asked whether SL proclivities towards relations that are more informative of English are related to efficiency in reading English sentences by native speakers of Korean. We found that individuals with a stronger statistical learning sensitivity showed a larger effect of conditional word probability on word reading times, indicating that they more efficiently incorporated statistical regularities of the language during reading. In contrast, L2 English proficiency was related to overall reading speed but not to the use of statistical regularities

Is statistical learning trainable?

Statistical learning (SL) is the ability to implicitly extract regularities in the environment, and likely supports various higher-order behaviors, from language to music and vision. While specific patterns experience are likely to influence SL outcomes, this ability is tacitly conceptualized as a fixed construct, and few studies to date have investigated how experience may shape statistical learning. We report one experiment that directly tested whether SL can be modulated by previous experience. We used a prepost treatment design allowing us to pinpoint what specific aspects of \u201cprevious experience\u201d matter for SL. The results show that performance on an artificial grammar learning task at post-test depends on whether the grammar to be learned at post-test matches the underlying grammar structures learned during treatment. Our study is the first to adopt a pre-post test design to directly modulate the effects of learning on learning itself

The bottleneck may be the solution, not the problem

As a highly consequential biological trait, a memory \u201cbottleneck\u201d cannot escape selection pressures. It must therefore co-evolve with other cognitive mechanisms rather than act as an independent constraint. Recent theory and an implemented model of language acquisition suggest that a limit on working memory may evolve to help learning. Furthermore, it need not hamper the use of language for communication