It is not what you say but how you say it: Evidence from Russian shows robust effects of the structural prior on noisy channel inferences.

Under the noisy-channel framework of language comprehension, comprehenders infer the speaker's intended meaning by integrating the perceived utterance with their knowledge of the language, the world, and the kinds of errors that can occur in communication. Previous research has shown that, when sentences are improbable under the meaning prior (implausible sentences), participants often interpret them nonliterally. The rate of nonliteral interpretation is higher when the errors that could have transformed the intended utterance into the perceived utterance are more likely. However, previous experiments on noisy channel processing mostly relied on implausible sentences, and it is unclear whether participants' nonliteral interpretations were evidence of noisy channel processing or the result of trying to conform to the experimenter's expectations in an experiment with nonsensical sentences. In the current study, we used the unique properties of Russian, an understudied language in the psycholinguistics literature, to test noisy-channel comprehension using only simple plausible sentences. The prior plausibility of sentences was tied only to their word order; subject-verb-object (SVO) sentences were more probable under the structural prior than object-verb-subject (OVS) sentences. In two experiments, we show that participants often interpret OVS sentences nonliterally, and the probability of nonliteral interpretations depended on the Levenshtein distance between the perceived sentence and the (potentially intended) SVO version of the sentence. The results show that the structural prior guides people's final interpretation, independent of the presence of semantic implausibility. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Peekbank: An open, large-scale repository for developmental eye-tracking data of children's word recognition.

The ability to rapidly recognize words and link them to referents is central to children's early language development. This ability, often called word recognition in the developmental literature, is typically studied in the looking-while-listening paradigm, which measures infants' fixation on a target object (vs. a distractor) after hearing a target label. We present a large-scale, open database of infant and toddler eye-tracking data from looking-while-listening tasks. The goal of this effort is to address theoretical and methodological challenges in measuring vocabulary development. We first present how we created the database, its features and structure, and associated tools for processing and accessing infant eye-tracking datasets. Using these tools, we then work through two illustrative examples to show how researchers can use Peekbank to interrogate theoretical and methodological questions about children's developing word recognition ability.

Consistency and Variability in Children's Word Learning Across Languages.

Why do children learn some words earlier than others? The order in which words are acquired can provide clues about the mechanisms of word learning. In a large-scale corpus analysis, we use parent-report data from over 32,000 children to estimate the acquisition trajectories of around 400 words in each of 10 languages, predicting them on the basis of independently derived properties of the words' linguistic environment (from corpora) and meaning (from adult judgments). We examine the consistency and variability of these predictors across languages, by lexical category, and over development. The patterning of predictors across languages is quite similar, suggesting similar processes in operation. In contrast, the patterning of predictors across different lexical categories is distinct, in line with theories that posit different factors at play in the acquisition of content words and function words. By leveraging data at a significantly larger scale than previous work, our analyses identify candidate generalizations about the processes underlying word learning across languages.

childes-db: A flexible and reproducible interface to the child language data exchange system.

The Child Language Data Exchange System (CHILDES) has played a critical role in research on child language development, particularly in characterizing the early language learning environment. Access to these data can be both complex for novices and difficult to automate for advanced users, however. To address these issues, we introduce childes-db, a database-formatted mirror of CHILDES that improves data accessibility and usability by offering novel interfaces, including browsable web applications and an R application programming interface (API). Along with versioned infrastructure that facilitates reproducibility of past analyses, these interfaces lower barriers to analyzing naturalistic parent-child language, allowing for a wider range of researchers in language and cognitive development to easily leverage CHILDES in their work.

Promoting Replicability in Developmental Research Through Meta-analyses: Insights From Language Acquisition Research.

Previous work suggests that key factors for replicability, a necessary feature for theory building, include statistical power and appropriate research planning. These factors are examined by analyzing a collection of 12 standardized meta-analyses on language development between birth and 5 years. With a median effect size of Cohen's d = .45 and typical sample size of 18 participants, most research is underpowered (range = 6%-99%; median = 44%); and calculating power based on seminal publications is not a suitable strategy. Method choice can be improved, as shown in analyses on exclusion rates and effect size as a function of method. The article ends with a discussion on how to increase replicability in both language acquisition studies specifically and developmental research more generally.

Wordbank: an open repository for developmental vocabulary data.

The MacArthur-Bates Communicative Development Inventories (CDIs) are a widely used family of parent-report instruments for easy and inexpensive data-gathering about early language acquisition. CDI data have been used to explore a variety of theoretically important topics, but, with few exceptions, researchers have had to rely on data collected in their own lab. In this paper, we remedy this issue by presenting Wordbank, a structured database of CDI data combined with a browsable web interface. Wordbank archives CDI data across languages and labs, providing a resource for researchers interested in early language, as well as a platform for novel analyses. The site allows interactive exploration of patterns of vocabulary growth at the level of both individual children and particular words. We also introduce wordbankr, a software package for connecting to the database directly. Together, these tools extend the abilities of students and researchers to explore quantitative trends in vocabulary development.