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.

Parents Fine-Tune Their Speech to Children's Vocabulary Knowledge.

Young children learn language at an incredible rate. Although children come prepared with powerful statistical-learning mechanisms, the statistics they encounter are also prepared for them: Children learn from caregivers motivated to communicate with them. How precisely do parents tune their speech to their children's individual language knowledge? To answer this question, we asked parent-child pairs (N = 41) to play a reference game in which the parents' goal was to guide their child to select a target animal from a set of three. Parents fine-tuned their referring expressions to their children's knowledge at the lexical level, producing more informative references for animals they thought their children did not know. Further, parents learned about their children's knowledge over the course of the game and tuned their referring expressions accordingly. Child-directed speech may thus support children's learning not because it is uniformly simplified but because it is tuned to individual children's language development.

Characterizing the Typical Information Curves of Diverse Languages.

Optimal coding theories of language predict that speakers will keep the amount of information in their utterances relatively uniform under the constraints imposed by their language, but how much do these constraints influence information structure, and how does this influence vary across languages? We present a novel method for characterizing the information structure of sentences across a diverse set of languages. While the structure of English is broadly consistent with the shape predicted by optimal coding, many languages are not consistent with this prediction. We proceed to show that the characteristic information curves of languages are partly related to a variety of typological features from phonology to word order. These results present an important step in the direction of exploring upper bounds for the extent to which linguistic codes can be optimal for communication.

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.

Preschoolers Flexibly Adapt to Linguistic Input in a Noisy Channel.

Because linguistic communication is inherently noisy and uncertain, adult language comprehenders integrate bottom-up cues from speech perception with top-down expectations about what speakers are likely to say. Further, in line with the predictions of ideal-observer models, past results have shown that adult comprehenders flexibly adapt how much they rely on these two kinds of cues in proportion to their changing reliability. Do children also show evidence of flexible, expectation-based language comprehension? We presented preschoolers with ambiguous utterances that could be interpreted in two different ways, depending on whether the children privileged perceptual input or top-down expectations. Across three experiments, we manipulated the reliability of both their perceptual input and their expectations about the speaker's intended meaning. As predicted by noisy-channel models of speech processing, results showed that 4- and 5-year-old-but perhaps not younger-children flexibly adjusted their interpretations as cues changed in reliability.

Social cues modulate the representations underlying cross-situational learning.

Because children hear language in environments that contain many things to talk about, learning the meaning of even the simplest word requires making inferences under uncertainty. A cross-situational statistical learner can aggregate across naming events to form stable word-referent mappings, but this approach neglects an important source of information that can reduce referential uncertainty: social cues from speakers (e.g., eye gaze). In four large-scale experiments with adults, we tested the effects of varying referential uncertainty in cross-situational word learning using social cues. Social cues shifted learners away from tracking multiple hypotheses and towards storing only a single hypothesis (Experiments 1 and 2). In addition, learners were sensitive to graded changes in the strength of a social cue, and when it became less reliable, they were more likely to store multiple hypotheses (Experiment 3). Finally, learners stored fewer word-referent mappings in the presence of a social cue even when given the opportunity to visually inspect the objects for the same amount of time (Experiment 4). Taken together, our data suggest that the representations underlying cross-situational word learning of concrete object labels are quite flexible: In conditions of greater uncertainty, learners store a broader range of information.

Beyond naïve cue combination: salience and social cues in early word learning.

Children learn their earliest words through social interaction, but it is unknown how much they rely on social information. Some theories argue that word learning is fundamentally social from its outset, with even the youngest infants understanding intentions and using them to infer a social partner's target of reference. In contrast, other theories argue that early word learning is largely a perceptual process in which young children map words onto salient objects. One way of unifying these accounts is to model word learning as weighted cue combination, in which children attend to many potential cues to reference, but only gradually learn the correct weight to assign each cue. We tested four predictions of this kind of naïve cue combination account, using an eye-tracking paradigm that combines social word teaching and two-alternative forced-choice testing. None of the predictions were supported. We thus propose an alternative unifying account: children are sensitive to social information early, but their ability to gather and deploy this information is constrained by domain-general cognitive processes. Developmental changes in children's use of social cues emerge not from learning the predictive power of social cues, but from the gradual development of attention, memory, and speed of information processing.

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.

Statistical word learning at scale: the baby's view is better.

A key question in early word learning is how children cope with the uncertainty in natural naming events. One potential mechanism for uncertainty reduction is cross-situational word learning - tracking word/object co-occurrence statistics across naming events. But empirical and computational analyses of cross-situational learning have made strong assumptions about the nature of naming event ambiguity, assumptions that have been challenged by recent analyses of natural naming events. This paper shows that learning from ambiguous natural naming events depends on perspective. Natural naming events from parent-child interactions were recorded from both a third-person tripod-mounted camera and from a head-mounted camera that produced a 'child's-eye' view. Following the human simulation paradigm, adults were asked to learn artificial language labels by integrating across the most ambiguous of these naming events. Significant learning was found only from the child's perspective, pointing to the importance of considering statistical learning from an embodied perspective.

Probabilistic cue combination: less is more.

Learning about the structure of the world requires learning probabilistic relationships: rules in which cues do not predict outcomes with certainty. However, in some cases, the ability to track probabilistic relationships is a handicap, leading adults to perform non-normatively in prediction tasks. For example, in the dilution effect, predictions made from the combination of two cues of different strengths are less accurate than those made from the stronger cue alone. Here we show that dilution is an adult problem; 11-month-old infants combine strong and weak predictors normatively. These results extend and add support for the less is more hypothesis: limited cognitive resources can lead children to represent probabilistic information differently from adults, and this difference in representation can have important downstream consequences for prediction.

Using contrastive inferences to learn about new words and categories.

In the face of unfamiliar language or objects, description is one cue people can use to learn about both. Beyond narrowing potential referents to those that match a descriptor (e.g., "tall"), people could infer that a described object is one that contrasts with other relevant objects of the same type (e.g., "the tall cup" contrasts with another, shorter cup). This contrast may be in relation to other objects present in the environment (this cup is tall among present cups) or to the referent's category (this cup is tall for a cup in general). In three experiments, we investigate whether people use such contrastive inferences from description to learn new word-referent mappings and learn about new categories' feature distributions. People use contrastive inferences to guide their referent choice, though size - and not color - adjectives prompt them to consistently choose the contrastive target over alternatives (Experiment 1). People also use color and size description to infer that a novel object is atypical of its category (Experiments 2 and 3): utterances like "the blue toma" prompt people to infer that tomas are less likely to be blue in general. However, these two inferences do not trade off substantially: people infer a described referent is atypical even when the descriptor was necessary to establish reference. We model these experiments in the Rational Speech Act (RSA) framework and find that it predicts both of these inferences. Overall, people are able to use contrastive inferences from description to resolve reference and make inferences about a novel object's category, letting them learn more about new things than literal meaning alone allows.

Visual Data Mining: An Exploratory Approach to Analyzing Temporal Patterns of Eye Movements.

Infant eye movements are an important behavioral resource to understand early human development and learning. But the complexity and amount of gaze data recorded from state-of-the-art eye-tracking systems also pose a challenge: how does one make sense of such dense data? Toward this goal, this article describes an interactive approach based on integrating top-down domain knowledge with bottom-up information visualization and visual data mining. The key idea behind this method is to leverage the computational power of the human visual system. Thus, we propose an approach in which scientists iteratively examine and identify underlying patterns through data visualization and link those discovered patterns with top-down knowledge/hypotheses. Combining bottom-up data visualization with top-down human theoretical knowledge through visual data mining is an effective and efficient way to make discoveries from gaze data. We first provide an overview of the underlying principles of this new approach of human-in-the-loop knowledge discovery and then show several examples illustrating how this interactive exploratory approach can lead to new findings.

Learning Communicative Acts in Children's Conversations: A Hidden Topic Markov Model Analysis of the CHILDES Corpora.

Over their first years of life, children learn not just the words of their native languages, but how to use them to communicate. Because manual annotation of communicative intent does not scale to large corpora, our understanding of communicative act development is limited to case studies of a few children at a few time points. We present an approach to automatic identification of communicative acts using a hidden topic Markov model, applying it to the conversations of English-learning children in the CHILDES database. We first describe qualitative changes in parent-child communication over development, and then use our method to demonstrate two large-scale features of communicative development: (a) children develop a parent-like repertoire of our model's communicative acts rapidly, their learning rate peaking around 14 months of age, and (b) this period of steep repertoire change coincides with the highest predictability between parents' acts and children's, suggesting that structured interactions play a role in learning to communicate.

Cross-situational Learning From Ambiguous Egocentric Input Is a Continuous Process: Evidence Using the Human Simulation Paradigm.

Recent laboratory experiments have shown that both infant and adult learners can acquire word-referent mappings using cross-situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different than the real-world contexts in which learning occurs. Thus, the feasibility of generalizing cross-situational learning beyond the laboratory is in question. Adapting the Human Simulation Paradigm, we conducted a series of experiments examining cross-situational learning from children's egocentric videos captured during naturalistic play. Focusing on individually ambiguous naming moments that naturally occur during toy play, we asked how statistical learning unfolds in real time through accumulating cross-situational statistics in naturalistic contexts. We found that even when learning situations were individually ambiguous, learners' performance gradually improved over time. This improvement was driven in part by learners' use of partial knowledge acquired from previous learning situations, even when they had not yet discovered correct word-object mappings. These results suggest that word learning is a continuous process by means of real-time information integration.

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.

A Collaborative Approach to Infant Research: Promoting Reproducibility, Best Practices, and Theory-Building.

The ideal of scientific progress is that we accumulate measurements and integrate these into theory, but recent discussion of replicability issues has cast doubt on whether psychological research conforms to this model. Developmental research-especially with infant participants-also has discipline-specific replicability challenges, including small samples and limited measurement methods. Inspired by collaborative replication efforts in cognitive and social psychology, we describe a proposal for assessing and promoting replicability in infancy research: large-scale, multi-laboratory replication efforts aiming for a more precise understanding of key developmental phenomena. The ManyBabies project, our instantiation of this proposal, will not only help us estimate how robust and replicable these phenomena are, but also gain new theoretical insights into how they vary across ages, linguistic communities, and measurement methods. This project has the potential for a variety of positive outcomes, including less-biased estimates of theoretically important effects, estimates of variability that can be used for later study planning, and a series of best-practices blueprints for future infancy research.

Competition between multiple words for a referent in cross-situational word learning.

Three experiments investigated competition between word-object pairings in a cross-situational word-learning paradigm. Adults were presented with One-Word pairings, where a single word labeled a single object, and Two-Word pairings, where two words labeled a single object. In addition to measuring learning of these two pairing types, we measured competition between words that refer to the same object. When the word-object co-occurrences were presented intermixed in training (Experiment 1), we found evidence for direct competition between words that label the same referent. Separating the two words for an object in time eliminated any evidence for this competition (Experiment 2). Experiment 3 demonstrated that adding a linguistic cue to the second label for a referent led to different competition effects between adults who self-reported different language learning histories, suggesting both distinctiveness and language learning history affect competition. Finally, in all experiments, competition effects were unrelated to participants' explicit judgments of learning, suggesting that competition reflects the operating characteristics of implicit learning processes. Together, these results demonstrate that the role of competition between overlapping associations in statistical word-referent learning depends on time, the distinctiveness of word-object pairings, and language learning history.

Statistical Word Learning is a Continuous Process: Evidence from the Human Simulation Paradigm.

In the word-learning domain, both adults and young children are able to find the correct referent of a word from highly ambiguous contexts that involve many words and objects by computing distributional statistics across the co-occurrences of words and referents at multiple naming moments (Yu & Smith, 2007; Smith & Yu, 2008). However, there is still debate regarding how learners accumulate distributional information to learn object labels in natural learning environments, and what underlying learning mechanism learners are most likely to adopt. Using the Human Simulation Paradigm (Gillette, Gleitman, Gleitman & Lederer, 1999), we found that participants' learning performance gradually improved and that their ability to remember and carry over partial knowledge from past learning instances facilitated subsequent learning. These results support the statistical learning model that word learning is a continuous process.

An integrative account of constraints on cross-situational learning.

Word-object co-occurrence statistics are a powerful information source for vocabulary learning, but there is considerable debate about how learners actually use them. While some theories hold that learners accumulate graded, statistical evidence about multiple referents for each word, others suggest that they track only a single candidate referent. In two large-scale experiments, we show that neither account is sufficient: Cross-situational learning involves elements of both. Further, the empirical data are captured by a computational model that formalizes how memory and attention interact with co-occurrence tracking. Together, the data and model unify opposing positions in a complex debate and underscore the value of understanding the interaction between computational and algorithmic levels of explanation.

The role of partial knowledge in statistical word learning.

A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: partial knowledge of one word-object mapping can speed up the acquisition of other word-object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word-object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data.