Lexical processing is influenced by a word's semantic diversity, as estimated by corpus-derived metrics. Although this suggests that contextual variation shapes verbal learning and memory, it is not clear what semantic diversity represents and why this influences lexical processing. Word learning experiments and simulations offer an opportunity to manipulate contextual variation directly and measure the effects on processing. In Experiment 1, adults read novel words in six naturalistic passages spanning one familiar topic (low semantic diversity) or six familiar topics (high semantic diversity). Words experienced in the low-diversity condition showed better learning, an effect replicated by simulating spreading activation in lexical networks differing in semantic diversity. We attributed these findings to "anchoring", a process of stabilizing novel word representations by securing them onto a familiar topic in long-term memory. Simulation 2 and Experiment 2 tested whether word learning might be better placed to take advantage of diversity if novel words were first anchored before diversity was introduced. Simulations and behavioural data both showed that after an anchoring opportunity, novel words forms were better learned in the high-diversity condition. These findings show that anchoring and contextual variation both influence the early stages of word learning.

Frequency exerts a powerful influence on lexical processing but it is possible that at least part of its effect is caused by high frequency words being experienced in more diverse contexts over an individual's language experience. To capture this variability, we applied Latent Semantic Analysis on a 35-million-word corpus of texts written for children, deriving a measure of semantic diversity that quantifies the similarity of all the contexts a word appears in. Across three experiments with 6-13-year-old children involving reading aloud and lexical decision, we found a main effect of semantic diversity: high diversity words were responded to faster and read more accurately than low diversity words. Frequency, document count and age of acquisition were also significant predictors of reading behavior. These findings demonstrate that contextual variability contributes to word learning and the development of lexical quality, beyond the effect of frequency. Keywords semantic diversity; contextual diversity; frequency effect; lexical quality; reading development Highlights • We examined the influence of semantic diversity on children's word reading • Semantic diversity was calculated from a corpus of material written for children • Semantic diversity predicted children's word naming and lexical decision • The effect of semantic diversity was independent of frequency, document count and age of acquisition

Sentences containing relative clauses are well known to be difficult to comprehend, and they have long been an arena in which to investigate the role of working memory in language comprehension. However, recent work has suggested that relative clause processing is better described by ambiguity resolution processes than by limits on extrinsic working memory. We investigated these alternative views with a Simple Recurrent Network (SRN) model of relative clause processing in Mandarin Chinese, which has a unique pattern of word order across main and relative clauses and which has yielded mixed results in human comprehension studies. To assess the model's ability to generalize from similar sentence structures, and to observe effects of ambiguity through the sentence, we trained the model on several different sentence types, based on a detailed corpus analysis of Mandarin relative clauses and simple sentences, coded to include patterns of noun animacy in the various structures. The model was evaluated on 16 different relative clause subtypes. Its performance corresponded well to human reading times, including effects previously attributed to working memory overflow. The model's performance across a wide variety of sentence types suggested that the seemingly inconsistent results in some prior empirical studies stemmed from failures to consider the full range of sentence types in empirical studies. Crucially, sentence difficulty for the model was not simply a reflection of sentence frequency in the training set; the model generalized from similar sentences and showed high error rates at points of ambiguity. The results suggest that SRNs are a powerful tool to examine the complicated constraint-satisfaction process of sentence comprehension, and that understanding comprehension of specific structures must include consideration of experiences with other similar structures in the language.

a b s t r a c tThis article investigates the relationship between production and comprehension of relative clauses in Mandarin Chinese. In a picture description task, we find strong head noun animacy effects on relative clause production despite the fact that Mandarin has headfinal relative clauses ([[relative clause] head noun]), so that the animate/inanimate head noun is uttered late. These and other production results have implications for theories of incremental language planning. We then used corpus analyses to investigate the distribution of structure-message pairings in the language that result from these animacy-based production biases. Mandarin is particularly interesting from the language comprehension side, as there is an extensive literature on relative clause comprehension, with conflicting results. A gated sentence completion task reveals comprehenders' animacy-linked expectations in relative clause interpretation and also shows the substantial amount of syntactic ambiguity in Mandarin relative clauses, owing to their head-final structure. The completion data were reliable predictors of comprehenders' self-paced reading times, but the distance between syntactically-dependent elements in the sentences was not. We argue that these results argue against accounts of sentence comprehension that posit that some sentence types are inherently more difficult than others. Instead we suggest that sentence types with which comprehenders have little experience are difficult, and we link the results ultimately to producers' different production choices in different animacy configurations and consequent variation in language patterns that comprehenders experience.Ó 2015 Elsevier Inc. All rights reserved. IntroductionIn order to test theories of how people comprehend language, researchers often measure comprehension difficulty of certain sentence types via some combination of reading times and accuracy, typically via responses to comprehension questions. Because differences in time/accuracy across sentence conditions cannot by themselves indicate why one sentence type is harder than another, data patterns are given different interpretations in various theoretical accounts of comprehension processes. Recent sentence processing research has been divided between experience-based approaches, including both constraintbased accounts . Briefly, experience-based approaches suggest that comprehenders gain skill at some or all levels of language interpretation from past sentence comprehension experience, so that comprehension difficulty is predicted to vary with past experience with similar language http://dx.

Two experiments explored two-to five-year-old Mandarin-speaking children's acquisition of classifiers, mandatory morphemes for expressing quantities in many Asian languages. Classifiers are similar to measure words in English (e.g., a piece of apple; a cup of apples), with the main difference being that classifiers are also required when counting sortals (e.g., yi ge pinguo or ''one unit apple'' in Mandarin means ''one apple''). The current study extended prior studies (e.g., Chien et al., J East Asian Linguist 12:91-120, 2003) to examine Mandarin-speaking children's understanding of classifiers as indicating units of quantification. Children were also tested on their knowledge of numerals to assess the relationship between children's acquisition of numerals and classifiers. The findings suggest that children first notice that sortal classifiers specify properties such as shape. Only after learning some numerals do they begin to work out how classifiers indicate units of quantification. By age four, children scored above chance on most classifiers tested.