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Fluent speech contains no known acoustic analog of the blank spaces between printed words. Early research presumed that word learning is driven primarily by exposure to isolated words. In the last decade there has been a shift to the view that exposure to isolated words is unreliable and plays little if any role in early word learning. This study revisits the role of isolated words. The results show (a) that isolated words are a reliable feature of speech to infants, (b) that they include a variety of word types, many of which are repeated in close temporal proximity, (c) that about three fourths of the words infants produce are words that mothers speak in isolation, and (d) that the frequency with which a child hears a word in isolation predicts whether that word will be learned better than the child's total frequency of exposure to that word. Thus, exposure to isolated words may significantly facilitate vocabulary development at its earliest stages.

In the novel noun generalization task, 2 1/2-year-old children display generalized expectations about how solid and nonsolid things are named, extending names for never-before-encountered solids by shape and for never-before-encountered nonsolids by material.This distinction between solids and nonsolids has been interpreted in terms of an ontological distinction between objects and substances.Nine simulations and behavioral experiments tested the hypothesis that these expectations arise from the correlations characterizing early learned noun categories.In the simulation studies, connectionist networks were trained on noun vocabularies modeled after those of children.These networks formed generalized expectations about solids and nonsolids that match children’s performances in the novel noun generalization task in the very different languages of English and Japanese.The simulations also generate new predictions supported by new experiments with children.Implications are discussed in terms of children’s development of distinctions between kinds of categories and in terms of the nature of this knowledge. Concepts are hypothetical constructs, theoretical devices hypothesized to explain data, what people do, and what people say. The question of whether a particular theory can explain children’s concepts is therefore semantically strange because strictly speaking this question asks about an explanation of an explanation.We begin with this reminder because the goal of the research reported here is to understand the role of associative processes in children’s systematic attention to the shape of solid things and to the material of nonsolid things in the task of forming new lexical categories. These attentional biases have been interpreted in terms of children’s concepts about the ontological kinds of object and substance

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this paper I (1) examine three levels of inferential strength supported by typical social science data-gathering methods, and call for a greater degree of explicitness, when HMs and other models are applied, in identifying which level is appropriate; (2) reconsider the use of HMs in school effectiveness studies and meta-analysis from the perspective of causal inference; and (3) recommend the increased use of Gibbs sampling and other Markov-chain Monte Carlo (MCMC) methods in the application of HMs in the social sciences, so that comparisons between MCMC and better-established fitting methods---including full or restricted maximum likelihood estimation based on the EM algorithm, Fisher scoring or iterative generalized least squares---may be more fully informed by empirical practice.

How do characteristics of caregiver speech contribute to a child’s early word learning? We explore the relationship between a single child’s vocabulary growth and the distributional and prosodic characteristics of the speech he hears using data collected for the Human Speechome Project, an ecologically valid corpus collected from the home of a family with a young child. We measured F0, intensity, phoneme duration, usage frequency, recurrence, and MLU for caregivers ’ production of each word that the child learned during the period of recording. When all variables are considered, we obtain a model of word acquisition as a function of caregiver input speech. Coefficient estimates in the model help to illuminate which factors are relevant to learning classes of words. In addition, words that deviate from the model’s prediction are of interest as they may suggest important social, contextual and other cues relevant to word learning.

Three exploratory studies were carried out to determine if there was continuity in the development of language in young children at the upper and lower extremes of the normal continuum, and if it was possible to use variables from an early assessment to predict their language status at a later date. Studies 1 and 2 examined continuity over 6-month period (from approximately 20 to 26 and 13 to 20 months of age, respectively); Study 3 examined continuity from 8 to 30 months of age. Results provided solid evidence for continuity at the group level but no evidence of ability to predict outcome for individual children using the vocabulary production, vocabulary comprehension, and gesture production variables included in this study. 2 Many parents wonder whether their child is normal. They wonder whether he or she is abnormally slow to develop, or so precocious that early celebration is warranted. During their child's first year of life, most parents worry about issues like sleeping, eating, and attainment of motor milestones (especially crawling and walking). During the second year, the focus switches to communication and language. This is true for physicians and other health care professionals

Naturalistic longitudinal recordings of child development promise to reveal fresh perspectives on fundamental questions of language acquisition. In a pilot effort, we have recorded 230,000 hours of audio-video recordings spanning the first three years of one child’s life at home. To study a corpus of this scale and richness, current methods of developmental cognitive science are inadequate. We are developing new methods for data analysis and interpretation that combine pattern recognition algorithms with interactive user interfaces and data visualization. Preliminary speech analysis reveals surprising levels of linguistic fine-tuning by caregivers that may provide crucial support for word learning. Ongoing analyses of the corpus aim to model detailed aspects of the child’s language development as a function of learning mechanisms combined with lifetime experience. Plans to collect similar corpora from more children based on a transportable recording system are underway. Index Terms: language acquisition, rich longitudinal data, human-machine collaborative analysis, computational models

Two experiments explored the communicative bases of preschoolers ’ object appearance–reality (AR) errors. In Experiment 1, 3-, 4-, and 5-year-olds (N 5 36) completed the AR test (with high- and low-deceptive objects), a control test with the same discourse structure but nondeceptive stimuli, and stimulus naming and memory tests. AR performance correlated positively with control (discourse) and naming test performance. Object deceptiveness had little effect. In Experiment 2, 3- and 4-year-olds (N 5 64) completed AR tests that experimentally varied question phrasing and use of exemplar objects. Children also completed memory, vocabulary, and control tests (of verbal perseveration). AR performance variance was predicted by a composite perseveration score from three non-AR tasks, vocabulary, and exemplars. The results indicate that the discourse structure of the AR test elicits a perseverative tendency that is mediated by children’s verbal knowledge. Adults in modern societies are accustomed to illusion. Our surroundings feature disembodied voices floating from stereo speakers, colored light on TV screens showing fantastic creatures and events, refrigerator magnets resembling juicy victuals or cute animals, and magic tricks that transform ordinary objects. Adults are entertained but not fooled by such phenomena; it is less clear how young children understand them. What are the sources of children’s erroneous answers to questions about apparent and real aspects of deceptive objectsFitems that look like one thing but function like another? Such errors often are assumed to stem from an inability to represent two concepts at once, or an inability to represent one’s own changing beliefs about an object’s identity. We explore an alternative account: Children’s errors might be due to specific communicative and linguistic processes. By this account, mature responses to questions about

In this paper, we present an automatic child-directed speech detection system to be used in the study of child language development. Child-directed speech (CDS) is speech that is directed by caregivers towards infants. It is not uncommon for corpora used in child language development studies to have a combination of CDS and non-CDS. As the size of the corpora used in these studies grow, manual annotation of CDS becomes impractical. Our automatic CDS detector addresses this issue. The focus of this paper is to propose and evaluate different sets of features for the detection of CDS, using several offthe-shelf classifiers. First, we look at the performance of a set of acoustic features. We continue by combining these acoustic features with several linguistic and eventually contextual features. Using the full set of features, our CDS detector was able to correctly identify CDS with an accuracy of.88 and F1 score of.87 using Naive Bayes. Index Terms: motherese, automatic, child-directed speech, infant-directed speech, adult-directed speech, prosody, language development 1.