Children's Production of Unfamiliar Word Sequences Is Predicted by Positional Variability and Latent Classes in a Large Sample of Child-Directed Speech

We explore whether children's willingness to produce unfamiliar sequences of words reflects their experience with similar lexical patterns. We asked children to repeat unfamiliar sequences that were identical to familiar phrases (e.g., A piece of toast) but for one word (e.g., a novel instantiation of A piece of X, like A piece of brick). We explore two predictions-motivated by findings in the statistical learning literature-that children are likely to have detected an opportunity to substitute alternative words into the final position of a four-word sequence if (a) it is difficult to predict the fourth word given the first three words and (b) the words observed in the final position are distributionally similar. Twenty-eight 2-year-olds and thirty-one 3-year-olds were significantly more likely to correctly repeat unfamiliar variants of patterns for which these properties held. The results illustrate how children's developing language is shaped by linguistic experience.     

A Computational Model for the Item‐Based Induction of Construction Networks

According to usage‐based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form‐meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item‐based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous context. To determine meanings of constructions starting from ambiguous contexts, we rely on the principle of cross‐situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model, we show how cross‐situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences.     

A Bayesian framework for word segmentation: Exploring the effects of context

Since the experiments of Saffran et al. [Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning in 8-month-old infants. Science, 274, 1926-1928], there has been a great deal of interest in the question of how statistical regularities in the speech stream might be used by infants to begin to identify individual words. In this work, we use computational modeling to explore the effects of different assumptions the learner might make regarding the nature of words - in particular, how these assumptions affect the kinds of words that are segmented from a corpus of transcribed child-directed speech. We develop several models within a Bayesian ideal observer framework, and use them to examine the consequences of assuming either that words are independent units, or units that help to predict other units. We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g. what’s that, do you, in the house) misidentified as individual words. In contrast, when the learner assumes that words are predictive, the resulting segmentation is far more accurate. These results indicate that taking context into account is important for a statistical word segmentation strategy to be successful, and raise the possibility that even young infants may be able to exploit more subtle statistical patterns than have usually been considered.     

From exemplar to grammar: a probabilistic analogy-based model of language learning

While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase-structure trees should be assigned to initial sentences, s/he allows (implicitly) for all possible trees and lets linguistic experience decide which is the "best" tree for each sentence. The best tree is obtained by maximizing "structural analogy" between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus-based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar-based and rule-based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children's language development from item-based constructions to abstract constructions, and that the model can simulate some of the errors made by children in producing complex questions.     

Sexual‐perception processes in acquaintance‐targeted sexual aggression

This study analyzes data from seven published studies to examine whether three performance‐based indices of men's misperception of women's sexual interest (MSI), derived from a self‐report questionnaire, are associated with sexual‐aggression history, rape‐supportive attitudes, sociosexuality, problem drinking, and self‐reported MSI. Almost 2000 undergraduate men judged the justifiability of a man's increasingly unwanted advances toward a woman on the Heterosocial Perception Survey‐Revised. Participants self‐reported any sexual‐aggression history, and some completed questionnaires assessing rape‐supportive attitudes, sociosexuality, problem drinking, and self‐reported MSI. A three‐parameter logistic function was fitted to participants’ justifiability ratings within a non‐linear mixed‐effects framework, which provided precise participant‐specific estimates of three sexual‐perception processes (baseline justifiability, bias, and sensitivity). Sexual‐aggression history and rape‐supportive attitudes predicted: (a) reduced sensitivity to women's affect; (b) more liberal biases, such that the woman's affect had to be more negative before justifiability ratings dropped substantially; and (c) greater baseline justifiability of continued advances after a positive response. Sexual‐aggression history and attitudes correlated more strongly with sensitivity than baseline justifiability; remaining variables showed the opposite pattern. This work underscores the role of sexual‐perception processes in sexual aggression and illustrates the derivation of performance‐based estimates of sexual‐perception processes from questionnaire responses.