We offer evidence that the productive use of English regular past tense morphology (e.g., drived) results from competitions among lexical-level features within a single mechanism associative system. W e present error data from: (1) on-line elicited productions by adult native speakers (N = 51), and (2) cormectionist back-propagation networks trained to map stems and past tenses of 552 English verbs. The frequency of regularizations is analyzed in terms of item frequency, stem final alveolar consonant, and similarity in past tense mapping across "friends" and "enemies" in phonologically defined neighborhoods. All items were compiled from a lexicon of 1,191 verbs which represents a near-exhaustive listing of monosyllabic stem-past tense pairs in current American English. Results revealed striking similarities between the hiunan and simulation data. Regularizations were significantly correlated with item frequency, as well as phonological attributes of the stem. Crucially, regularization was a function of phonological similarity to frequent suffixed items, especially for irregulars that normally undergo a vowel-change. These results are incompatible with the view that regularization applies by default, independently of inter-item similarities which support the acquisition and processing of lexical items in associative systems.

The degree to which the behavior of PDP models of pattern associations (Rumelhart &McClelland, 1986; 1987) approximates children's acquisition of inflectional morphology has recently been highlighted in discussions of the applicability of P D P to the study of human cognition and language (Pinker & Mehler, 1988). In this paper, we attempt to eliminate many of the limitations of the R & M model, adopting an empirical approach to the analysis of learning(hit rate and error type) in two sets of simulations in which vocabulary structure (token frequency)and the presence of phonological subregularities are manipulated. A 3-layer back propagation network is used to implement a pattern association task with strings that are analogous to four types of present and past tense English verbs. W e overview resulting"competitions" when strings are randomly assigned to verb classes, in particular, the conditions under which different overgeneralization errors (both " pure" and " blended") are produced.In a second set of simulations, identical type and token frequencies are used, but strings are assigned to the identity and vowel change classes on the basis of phonological shape of the stem. Phonological cues are exploited by the system leading to overall improved performance.However, overgeneralizations continue to be observed in similar conditions. Token frequency works together with phonological subregularities to determine patterns of learning,including the conditions under which " rule-like"behavior will and will not emerge. The results are discussed with reference to behavioral data on children's acquisition of the English past tense.

In learning morphology, do children generalize from their vocabularies on an item-by-item basis, or do they form globalrules on a developmental timetable? We use large-scale parent-report data to address this question by investigating relationsamong morphological development, vocabulary growth, and age. For three languages, we examine irregular verbs (e.g.go) and predict childrens correct inflection (went) and overregularization (goed/wented). Morphology knowledge relatesstrongly to vocabulary, more so than to age. Further, this relation is modulated by age: for two children with the samevocabulary size, the older is more likely to correctly inflect and overregularize, and the effect of vocabulary on morphologydecreases with age. Lastly, correct inflection and overregularization rates rise in tandem over age, and vocabulary effectson them are correlated across items. Our findings support that morphology learning is strongly coupled to lexical learningand that correct inflection and overregularization are related, verb-specific, processes.

How does abstract structure emerge during language learning? On some accounts, children’s early syntax emerges from direct generalizations from particular lexical items, while on others, syntactic structure is acquired independently and follows its own timetable. Progress on differentiating these views requires detailed developmental data. Using parental reports of vocabulary and grammar abilities, previous analyses have shown that early syntactic abstraction strongly depends on the growth of the lexicon, providing support for lexicalist and emergentist theories. Leveraging a large cross-linguistic dataset, we replicate and extend these findings, demonstrating similar patterns in each of four languages. Moreover, the power of our dataset reveals that there are measurable effects of age over and above those attributable to vocabulary size, and that these effects are greater for aspects of language ability more closely tied to syntax than morphology. These findings suggest non-lexical contributions to the growth of syntactic abstraction that all theories must address.

Language comprehension in grounded, social contexts in-volves integrating information from both the visual and the lin-guistic signals. But how should listeners prioritize these differ-ent information sources? Here, we test the hypothesis that evenyoung listeners flexibly adapt the dynamics of their gaze toseek higher value visual information when the auditory signalis less reliable. We measured the timing and accuracy of adults(n=31) and 3-5 year-old children’s (n=39) eye movements dur-ing a real-time language comprehension task. Both age groupsdelayed the timing of gaze shifts away from a speaker’s facewhen processing speech in a noisy environment. This delayresulted in listeners gathering more information from the vi-sual signal, more accurate gaze shifts, and fewer random eyemovements to the rest of the visual world. These results pro-vide evidence that even young listeners adjust to the demandsof different processing contexts by seeking out visual informa-tion that supports language comprehension.

Why do children learn some words earlier than others? Reg-ularities and differences in the age of acquisition for wordsacross languages yield insights regarding the mechanismsguiding word learning. In a large-scale corpus analysis,we estimate the ages at which 9,200 children learn 300-400words in seven languages, predicting them on the basis ofindependently-derived linguistic, environmental, and concep-tual factors. Predictors were surprisingly consistent across lan-guages, but varied across development and as a function oflexical category (e.g., concreteness predicted nouns while lin-guistic structure predicted function words). By leveraging dataat a significantly larger scale than previous work, our analyseshighlight the power that emerges from unifying previously dis-parate theories, but also reveal the amount of reliable variationthat still remains unexplained.

Early language skill is predictive of later life outcomes, and is thus of great interest to developmental psychologists and clinicians. The Communicative Development Inventories (CDIs), parent-reported inventories of early-learned vocabulary items, have proven to be valid and reliable instruments for measuring children's early language skill. CDIs have been painstakingly adapted to dozens of languages, and cross-linguistic comparisons thus far show both consistency and variability in language acquisition trajectories. However, thousands of languages do not yet have CDIs, posing a significant barrier to increasing the diversity of languages that are studied. Here, we propose a method for selecting candidate words to include on new CDIs, leveraging analysis of psychometric properties of translation-equivalent concepts that are frequently included on existing CDIs. Leveraging 26 datasets from existing CDIs, we propose a list of 229 concepts that have low variability in their cross-linguistic learning difficulty. This pool of common concepts---analogous to the "Swadesh" lists used in glottochronology---can be used as a starting point for future CDI adaptations. We test how well the proposed list generalizes to data from 8 additional languages.

Language comprehension in grounded contexts involves in-tegrating visual and linguistic information through decisionsabout visual fixation. But when the visual signal also con-tains information about the language source – as in the caseof written text or sign language – how do we decide where tolook? Here, we hypothesize that eye movements during lan-guage comprehension represent an adaptive response. Usingtwo case studies, we show that, compared to English-learners,young signers delayed their gaze shifts away from a languagesource, were more accurate with these shifts, and produced asmaller proportion of nonlanguage-driven shifts (E1). Next,we present a well-controlled, confirmatory experiment, show-ing that English-speaking adults produced fewer nonlanguage-driven shifts when processing printed text compared to spokenlanguage (E2). Together, these data suggest that people adaptto the value of seeking different information in order to in-crease the chance of rapid and accurate language understand-ing.

What structures children's early language environment? Large corpora of child-centered naturalistic recordings provide an important window into this question, but most available data centers on young children within the home or in lab contexts interacting primarily with a single caregiver. Here, we characterize children's language experience in a very different kind of environment: the preschool classroom. Children ages 3 – 5 years (N = 26) wore a head-mounted camera in their preschool class, yielding a naturalistic, egocentric view of children's everyday experience across many classroom activity contexts (e.g., sand play, snack time), with >30 hours of video data. Using semi-automatic transcriptions (227,624 words), we find that activity contexts in the preschool classroom vary in both the quality and quantity of the language that children both hear and produce. Together, these findings reinforce prior theories emphasizing the contribution of activity contexts in structuring the variability in children's early learning environments.