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How parents talk to young children matters to language and cognitive development. In the early years the quantity, quality, and diversity inherent in language from parents in the home predict differences in vocabulary knowledge, school readiness, and later academic achievement. However, most of what is known about child-directed speech (CDS) comes from studies of monolingual parents, and little is known about features of speech from bilingual parents. Here, we asked whether degree of bilingualism assessed within a single parent might be positively associated with CDS features that are known to facilitate children’s lexical and grammatical structures across languages – parental partial repetitions. During unscripted narrations (n = 91) of a picture book to their toddlers in English, mothers who reported being more bilingually balanced used a higher proportion of self-repetitions (both single words and 2-word combinations) within a brief time-frame. At the same time, more bilingual mothers preserved the same degree of lexical diversity as more monolingual mothers. The results obtained even accounting for differences in socio-economic status. These findings are discussed in terms of adaptive strategies that bilingual parents may consciously or unconsciously adopt in bilingual language development

Bilinguals are widely reported to have certain kinds of cognitive advantages, including language learning advantages. One possible pathway is a language-specific transfer effect, whereby sensitivity to structural regularities in known languages can be brought to to-be-acquired languages that share particular features. Here we tested for transfer of a specific linguistic property, sensitivity to retroflexion as contrastive phonemic feature. We designed a task for bilinguals with homogeneous language exposure (i.e., bilingual in the same languages) and heterogeneous feature representation (i.e., differing levels of proficiency). Hindi and Mandarin Chinese both have retroflexion in phoneme contrasts (Hindi: stop consonants, Mandarin: sibilants). In a preregistered study, we conducted a statistical learning task for the Hindi dental-retroflex stop contrast with a group of early parallel English-Mandarin bilinguals, who varied in their Mandarin understanding levels. We based the target sample size on power analysis of a pilot study with a Bayesian stop-rule after minimum threshold. Contrary to the pilot study (N = 15), the main study (N = 50) did not find evidence for a learning effect, nor language-experience variance within the group. This finding suggests that statistical learning effects for the feature in question may be more fragile than commonly assumed, and may be evident in only a small subsample of the general population (as in our pilot). These stimuli have previously shown learning effects in children, so an additional possibility is that neural commitment to adults’ languages prevents learning of the fine-stimulus contrast in question for this adult population.

Early vocabularies typically contain more nouns than verbs. Yet, the strength of this noun-bias varies across languages and cultures. Two main theories have aimed at explaining such variations; either that the relative importance of nouns vs. verbs is specific to the language itself, or that extra-linguistic factors shape early vocabulary structures. To address this debate, the present study compares the relative distribution of verbs and nouns within the same language—English—between Malay–English and Mandarin–English bilingual infants and toddlers. The English receptive lexicons of Mandarin–English bilingual children contained more verbs than those of Malay–English bilinguals, suggesting that the noun-bias is modulated by factors external to English. We discuss the potential role of socio-cultural differences on the composition of children early vocabularies.

Parents often use partial self-repetitions with variation in successive utterances (e.g., Want to get your ball? Get your ball? Do you want to get your ball?). Such ‘variation sets’ contain latent distributional information about the building blocks of language and are predictive of children's lexical and grammatical structures. Because these properties in parents of atypically developing children are virtually unknown, we compared for the first time variation sets in parental speech directed to toddlers with Autistic Spectrum Disorders (ASD), Down Syndrome (DS), and a baseline group of Typically Developing toddlers (TD). In Study 1, we analyzed transcripts of mothers' child-directed utterances during naturalistic dyadic play interactions. While children's mean developmental age was the same across the three groups, we found that measures of partial repetitions in child-directed speech were larger in the ASD than in the DS and typical groups. In Study 2 we also found that these larger measures in the ASD group were mainly driven by the mother, as opposed to the father. Because partial repetitions decrease with chronological age of the child in typical groups, and the atypical children were older than the TD group, our findings suggest compensating modes of communication in parental speech to atypical populations, especially the ASD group. The study validates the extension of structural/statistical analyses of language to compare parental communication to typical and atypical populations. Accepted version This work was supported by the NAP Start-up Grant M4081597 (G.E.) from Nanyang Technological University Singapore. The founder agencies had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.

Adults’ 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 months, 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. Ministry of Education (MOE) We are grateful to Sook Whan Cho and Hongoak Yun for facilitating data collection at Asan Hospital in Seoul, South Korea, and Jae-un Kim and Young-In Lee for running Experiment 2. Support came from a Start Up Grant and a Singapore Ministry of Education Tier 1 grant (RG81/14) to L.O.

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–child 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.

A generally held assumption about human statistical learning is that learners keep track of the global statistics of the elements of interest across the entire set of stimuli they are exposed to. In naturalistic settings, this assumption is problematic because it requires that the cognitive system keep track of an exponentially growing number of relations while determining which of those relations are relevant and which are not. We investigated a more plausible assumption, namely, that statistical learning proceeds incrementally, using small windows of opportunity in which the relevant relations are assumed to hold over temporally contiguous objects or events. This local statistical learning hypothesis was tested on two learning tasks, one involving non-adjacent structures and the other -- novel word-to-world mappings. Our results suggest that human subjects make use of simple general-purpose cognitive heuristics that exploit temporal contiguity and contrast, leading to superior learning in tasks that belong on two different levels of language acquisition.

Simplicity: A Cure for Overgeneralizations in Language Acquisition? Luca Onnis (l.onnis@ warwick.ac.uk) Department of Psychology, University of Warwick CV4 7AL Coventry, UK Matthew Roberts (m.roberts.2@ warwick.ac.uk) Department of Psychology, University of Warwick CV4 7AL Coventry, UK Nick Chater (nick.chater@warwick.ac.uk) Department of Psychology and Institute for Applied Cognitive Science, University of Warwick CV4 7AL Coventry, UK Abstract A formal model of learning as induction, the simplicity principle (e.g. Chater & Vitanyi , 2001) states that the cognitive system seeks the hypothesis that provides the briefest representation of the available data− here the linguistic input to the child. Data gathered from the CHILDES database were used as an approximation of positive input the child receives from adults. We considered linguistic structures that would yield overgeneralization, according to Baker’s paradox (Baker, 1979). A simplicity based simulation was run incorporating two different hypotheses about the grammar: (1) The child assumes that there are no exceptions to the grammar. This hypothesis leads to overgeneralization. (2) The child assumes that some constructions are not allowed. For small corpora of data, the first hypothesis produced a simpler representation. However, for larger corpora, the second hypothesis was preferred as it lead to a shorter input description and eliminated overgeneralization. Introduction Overgeneralizations are a common feature of language development. In learning the English past tense, children typically overgeneralize the ‘-ed’ rule, producing constructions such as we holded the baby rabbits (Pinker, 1995). Language learners recover from these errors, in spite of the lack of negative evidence and the infinity of allowable constructions that remain unheard; it has been argued that this favours the existence of a specific language-learning device (e.g. Chomsky, 1980; Pinker, 1989). This is an aspect of the ‘Poverty of the Stimulus’ argument. We report on a statistical model of language acquisition, which suggests that recovery from overgeneralizations may proceed from positive evidence alone. Specifically, we show that adult linguistic competence in quasi-regular structures may stem from an interaction between a general cognitive principle, simplicity (Chater, 1996) and statistical properties of the input. According to Baker’s Paradox (Baker, 1979) children are exposed to linguistic structures that they subsequently overgeneralize, demonstrating that they capture some general structure of the language. However, some generalizations are grammatically incorrect and children do not receive direct negative evidence from caretakers (e.g. corrections labeling such overgeneralizations as disallowed). The paradox is that non-occurrence is not in itself evidence for the incorrectness of a construction because an infinite number of unheard sentences are still correct. The irregularities that Baker referred to can be broadly labeled alternations (Levin, 1993; see also Culicover, 2000). For instance, the dative alternation in English allows a class of verbs to take both the double-object construction (He gave Mark the book) and the prepositional construction (He gave the book to Mark). Hence the verb give alternates between two constructions. However, certain verbs seem to be constrained to one possible construction only (He donated the book to Mark is allowed, whereas *He donated Mark the book is not). Such verbs are non- alternating. From empirical studies we know that children do make overgeneralization errors that involve alternations, such as *I said her no (by analogy to I told her no, Bowerman, 1996; Lord 1979). In this paper we present alternation phenomena from the CHILDES database (MacWhinney, 2000) of child-directed speech which will be used in the computer model. Secondly, we introduce the simplicity principle (Chater, 1996), based on the mathematical theory of Kolmogorov Complexity (Kolmogorov, 1965). Thirdly, we present an artificial language designed to model the CHILDES data, and describe simplicity-based models of language processing and the simulations of recovery from overgeneralizations. Lastly we discuss the limitations of this specific model and some implications for research on language acquisition.

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. MOE (Min. of Education, S’pore) Accepted version

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. MOE (Min. of Education, S’pore)

As a highly consequential biological trait, a memory “bottleneck” 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.

We used event-related potentials (ERPs) to investigate the time course and distribution of brain activity while adults performed (a) a sequential learning task involving complex structured sequences, and (b) a language processing task. The same positive ERP deflection, the P600 effect, typically linked to difficult or ungrammatical syntactic processing, was found for structural incongruencies in both sequential learning as well as natural language, and with similar topographical distributions. Additionally, a left anterior negativity (LAN) was observed for language but not for sequential learning. These results are interpreted as an indication that the P600 provides an index of violations and the cost of integration of expectations for upcoming material when processing complex sequential structure. We conclude that the same neural mechanisms may be recruited for both syntactic processing of linguistic stimuli and sequential learning of structured sequence patterns more generally.

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning.

Language acquisition may be one of the most difficult tasks that children face during development. They have to segment words from fluent speech, figure out the meanings of these words, and discover the syntactic constraints for joining them together into meaningful sentences. Over the past couple of decades, computational modeling has emerged as a new paradigm for gaining insights into the mechanisms by which children may accomplish these feats. Unfortunately, many of these models assume a computational complexity and linguistic knowledge likely to be beyond the abilities of developing young children. This article shows that, using simple statistical procedures, significant correlations exist between the beginnings and endings of a word and its lexical category in English, Dutch, French, and Japanese. Therefore, phonetic information can contribute to individuating higher level structural properties of these languages. This article also presents a simple 2-layer connectionist model that, once trained with an initial small sample of words labeled for lexical category, can infer the lexical category of a large proportion of novel words using only word-edge phonological information, namely the first and last phoneme of a word. The results suggest that simple procedures combined with phonetic information perceptually available to children provide solid scaffolding for emerging lexical categories in language development.

How are hierarchically structured sequences of objects, events or actions learned from experience and represented in the brain? When several streams of regularities present themselves, which will be learned and which ignored? Can statistical regularities take effect on their own, or are additional factors such as behavioral outcomes expected to influence statistical learning? Answers to these questions are starting to emerge through a convergence of findings from naturalistic observations, behavioral experiments, neurobiological studies, and computational analyses and simulations. We propose that a small set of principles are at work in every situation that involves learning of structure from patterns of experience and outline a general framework that accounts for such learning.

When learning language, young children are faced with many seemingly formidable challenges, including discovering words embedded in a continuous stream of sounds and determining what role these words play in syntactic constructions. We suggest that knowledge of phoneme distributions may play a crucial part in helping children segment words and determine their lexical category, and we propose an integrated model of how children might go from unsegmented speech to lexical categories. We corroborated this theoretical model using a two-stage computational analysis of a large corpus of English child-directed speech. First, we used transition probabilities between phonemes to find words in unsegmented speech. Second, we used distributional information about word edges – the beginning and ending phonemes of words – to predict whether the segmented words from the first stage were nouns, verbs, or something else. The results indicate that discovering lexical units and their associated syntactic category in child-directed speech is possible by attending to the statistics of single phoneme transitions and word-initial and final phonemes. Thus, we suggest that a core computational principle in language acquisition is that the same source of information is used to learn about different aspects of linguistic structure.

Connectionist psycholinguistics is an emerging approach to modeling empirical data on human language processing using connectionist computational architectures. This article reviews progress made in the area of connectionist syntactic processing. Constituency, structure dependency, and recursion are notions central to traditional generative theories of language and are seen as the hallmark of symbolic processing. Because connectionist models are subsymbolic and their knowledge emerges from large distributed activation of neuronlike units, their ability to learn and process aspects of syntax represents an important alternative paradigm in cognitive science. They also invite one to rethink traditional notions such as the competence–performance distinction and boundless recursion.

Variation set structure – partial overlap of successive utterances in child-directed speech – has been shown to correlate with progress in children’s acquisition of syntax. We demonstrate the benefits of variation set structure directly: in miniature artificial languages, arranging a certain proportion of utterances in a training corpus in variation sets facilitated word and phrase constituent learning in adults. Our findings have implications for understanding the mechanisms of L1 acquisition by children, and for the development of more efficient algorithms for automatic language acquisition, as well as better methods for L2 instruction.

We introduce Incremental Semantic Analysis, a fully incremental word space model, and we test it on longitudinal child-directed speech data. On this task, ISA outperforms the related Random Indexing algorithm, as well as a SVD-based technique. In addition, the model has interesting properties that might also be characteristic of the semantic space of children.

Connectionist psycholinguistics is an emerging approach to modeling empirical data on human language processing using connectionist computational architectures. Here we review progress made in the area of connectionist syntactic processing. Constituency, structure dependency, and recursion are notions central to traditional generative theories of language and are seen as the hallmark of symbolic processing. Because connectionist models are subsymbolic and their knowledge emerges from large distributed activation of neuron-like units, their ability to learn and process aspects of syntax represents an important alternative paradigm in cognitive science. They also invite a rethink of traditional notions such as the competence/performance distinction and boundless recursion.

A long standing question concerning natural human language is, why are some languages more complex than others? More specifically, why do some languages utilize intricate morphological structure – in some entire sentences often consist of a single complex word – while in others, all words are void of internal structure, and rely only on ordering and contextual clues to signal grammatical dependencies. There is no intrinsic reason for one strategy over the other, and attested human language spans the spectrum of possibilities. On the one hand, linguists have proposed a number of theories for why this diversity exists, most centering on the intimate social structure of the speakers as the explanatory factor for increases in morphological composition, and the addition of second-language speakers as the explanatory factor for decreases in morphological composition. However, this work is based on inductive observation of extant language, and at present descriptive and untestable. Furthermore, the time span over which such morphological developments would take place is at least an order of magnitude greater than that reachable with current data and reconstruction methods. On the other hand, much work in the hard sciences (particularly cosmology and astrophysics) has overcome similar time gaps using computational simulations. Indeed, sound mathematical models of social networks, and complex networks in general, have emerged and been applied to linguistic issues such as the diffusion of lexical innovation. However, the level of representation in such models is at a far more abstract level than that used in linguistics, and even more problematic, the mechanisms of learning and change employed in these models often make patently unrealistic assumptions, for example, language spreads like a virus. In this thesis, an initial attempt is made to bridge this gap between ideas and means. First, two variables of interest are identified to quantify the notions of compositional complexity and intimacy - synthesis and the global clustering coefficient, respectively. Synthesis is the average proportion of bound morphemes per word in a representative corpus, and was developed specifically to quantify the degree of morphological composition used in a language. The global clustering coefficient measures the proportion of triangular connections in a complex network, and these triangular connections have been directly connected in the literature to the notion of intimacy. To explore the relationship between these variables, two novel computational models are created, and are to my knowledge the first to incorporate the fundamental observations within linguistics concerning the development of bound morphology. Specifically, both models have distinct diffusion and intergenerational transmission stages. In addition, the learning mechanisms are differentiated to account for the difference between the learning of novel lexical forms through diffusion, and the slow, incremental grammatical changes that accrue through language transmission. More specifically, the three subprocesses of extension, reanalysis, and repetition identified in the linguistics literature as driving the development of bound morphology are independently addressed. First, a high-level model that is computationally cheap is developed. It models the capacity of various social networks to support the repeated application of these processes responsible for creating bound morphology. However, this level of abstraction lacks the structure required to measure synthesis directly, and so a low-level model in which the processes are directly modeled on a symbolic meaning-signal structure is also presented. The results of these two models are in agreement, and find that the physical network measure of the global clustering coefficient supports the processes responsible for bound morphology in the high level model, as well as predicting higher levels of synthesis in the low-level model. In addition, by testing realistic human social network topologies, it was determined that hub agents that emerge as social networks grow in size, counteract this effect of clustering by serving to reshape the input and interrupt the continuation of the transmission process. This result provides both a mechanistic account of the observed correlation of social structure on increases in synthesis, as well as an alternative explanation for decreases in synthesis that do not involve second-language speakers. Beyond these initial insights, this thesis demonstrates that the computational study of typological change helps extend our knowledge beyond what can be naturally observed. In this case, it provides inroads to understanding how the intimacy of small societies mechanistically relates to the observed correlations seen in the typological distribution of synthesis. Doctor of Philosophy