Comparing apples to manzanas and oranges to naranjas: A new measure of English-Spanish vocabulary for dual language learners.

The valid assessment of vocabulary development in dual-language-learning infants is critical to developmental science. We developed the Dual Language Learners English-Spanish (DLL-ES) Inventories to measure vocabularies of U.S. English-Spanish DLLs. The inventories provide translation equivalents for all Spanish and English items on Communicative Development Inventory (CDI) short forms; extended inventories based on CDI long forms; and Spanish language-variety options. Item-Response Theory analyses applied to Wordbank and Web-CDI data (n = 2603, 12-18 months; n = 6722, 16-36 months; half female; 1% Asian, 3% Black, 2% Hispanic, 30% White, 64% unknown) showed near-perfect associations between DLL-ES and CDI long-form scores. Interviews with 10 Hispanic mothers of 18- to 24-month-olds (2 White, 1 Black, 7 multi-racial; 6 female) provide a proof of concept for the value of the DLL-ES for assessing the vocabularies of DLLs.

The BabyView camera: Designing a new head-mounted camera to capture children's early social and visual environments.

Head-mounted cameras have been used in developmental psychology research for more than a decade to provide a rich and comprehensive view of what infants see during their everyday experiences. However, variation between these devices has limited the field's ability to compare results across studies and across labs. Further, the video data captured by these cameras to date has been relatively low-resolution, limiting how well machine learning algorithms can operate over these rich video data. Here, we provide a well-tested and easily constructed design for a head-mounted camera assembly-the BabyView-developed in collaboration with Daylight Design, LLC., a professional product design firm. The BabyView collects high-resolution video, accelerometer, and gyroscope data from children approximately 6-30 months of age via a GoPro camera custom mounted on a soft child-safety helmet. The BabyView also captures a large, portrait-oriented vertical field-of-view that encompasses both children's interactions with objects and with their social partners. We detail our protocols for video data management and for handling sensitive data from home environments. We also provide customizable materials for onboarding families with the BabyView. We hope that these materials will encourage the wide adoption of the BabyView, allowing the field to collect high-resolution data that can link children's everyday environments with their learning outcomes.

Estimating Demographic Bias on Tests of Children's Early Vocabulary.

Children's early language skill has been linked to later educational outcomes, making it important to measure early language accurately. Parent-reported instruments, such as the Communicative Development Inventories (CDIs), have been shown to provide reliable and valid measures of children's aggregate early language skill. However, CDIs contain hundreds of vocabulary items, some of which may not be heard (and thus learned) equally often by children of varying backgrounds. This study used a database of American English CDIs to identify words demonstrating strong bias for particular demographic groups of children, on dimensions of sex (male vs. female), race (white vs. non-white), and maternal education (high vs. low). For each dimension, many items showed bias; removing these items slightly reduced the magnitude of race- and education-based group differences, but did not eliminate them. Additionally, we investigated how well the relative frequency of words spoken to young girls versus boys predicted sex-based word learning bias, and discuss possible sources of demographic differences in early word learning.

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.

A longitudinal analysis of the social information in infants' naturalistic visual experience using automated detections.

The faces and hands of caregivers and other social partners offer a rich source of social and causal information that is likely critical for infants' cognitive and linguistic development. Previous work using manual annotation strategies and cross-sectional data has found systematic changes in the proportion of faces and hands in the egocentric perspective of young infants. Here, we validated the use of a modern convolutional neural network (OpenPose) for the detection of faces and hands in naturalistic egocentric videos. We then applied this model to a longitudinal collection of more than 1,700 head-mounted camera videos from three children ages 6 to 32 months. Using these detections, we confirm and extend prior results from cross-sectional studies. First, we found a moderate decrease in the proportion of faces in children's view across age and a higher proportion of hands in view than previously reported. Second, we found variability in the proportion of faces and hands viewed by different children in different locations (e.g., living room vs. kitchen), suggesting that individual activity contexts may shape the social information that infants experience. Third, we found evidence that children may see closer, larger views of people, hands, and faces earlier in development. These longitudinal analyses provide an additional perspective on the changes in the social information in view across the first few years of life and suggest that pose detection models can successfully be applied to naturalistic egocentric video data sets to extract descriptives about infants' changing social environment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Online Computerized Adaptive Tests of Children's Vocabulary Development in English and Mexican Spanish.

PURPOSE: Measuring the growth of young children's vocabulary is important for researchers seeking to understand language learning as well as for clinicians aiming to identify early deficits. The MacArthur-Bates Communicative Development Inventories (CDIs) are parent report instruments that offer a reliable and valid method for measuring early productive and receptive vocabulary across a number of languages. CDI forms typically include hundreds of words, however, and so the burden of completion is significant. We address this limitation by building on previous work using item response theory (IRT) models to create computer adaptive test (CAT) versions of the CDIs. We created CDI-CATs for both comprehension and production vocabulary, for both American English and Mexican Spanish. METHOD: Using a data set of 7,633 English-speaking children ages 12-36 months and 1,692 Spanish-speaking children ages 12-30 months, across three CDI forms (Words & Gestures, Words & Sentences, and CDI-III), we found that a 2-parameter logistic IRT model fits well for a majority of the 680 pooled vocabulary items. We conducted CAT simulations on this data set, assessing simulated tests of varying length (25-400 items). RESULTS: Even very short CATs recovered participant abilities very well with little bias across ages. An empirical validation study with N = 204 children ages 15-36 months showed a correlation of r = .92 between language ability estimated from full CDI versus CDI-CAT forms. CONCLUSION: We provide our item bank along with fitted parameters and other details, offer recommendations for how to construct CDI-CATs in new languages, and suggest when this type of assessment may or may not be appropriate.

A Critical Period for Robust Curriculum-Based Deep Reinforcement Learning of Sequential Action in a Robot Arm.

Many everyday activities are sequential in nature. That is, they can be seen as a sequence of subactions and sometimes subgoals. In the motor execution of sequential action, context effects are observed in which later subactions modulate the execution of earlier subactions (e.g., reaching for an overturned mug, people will optimize their grasp to achieve a comfortable end state). A trajectory (movement) adaptation of an often-used paradigm in the study of sequential action, the serial response time task, showed several context effects of which centering behavior is of special interest. Centering behavior refers to the tendency (or strategy) of subjects to move their arm or mouse cursor to a position equidistant to all stimuli in the absence of predictive information, thereby reducing movement time to all possible targets. In the current study, we investigated sequential action in a virtual robotic agent trained using proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm. The agent was trained to reach for appearing targets, similar to a serial response time task given to humans. We found that agents were more likely to develop centering behavior similar to human subjects after curricularized learning. In our curriculum, we first rewarded agents for reaching targets before introducing a penalty for energy expenditure. When the penalty was applied with no curriculum, many agents failed to learn the task due to a lack of action space exploration, resulting in high variability of agents' performance. Our findings suggest that in virtual agents, similar to infants, early energetic exploration can promote robust later learning. This may have the same effect as infants' curiosity-based learning by which they shape their own curriculum. However, introducing new goals cannot wait too long, as there may be critical periods in development after which agents (as humans) cannot flexibly learn to incorporate new objectives. These lessons are making their way into machine learning and offer exciting new avenues for studying both human and machine learning of sequential action.

Word Learning: Associations or Hypothesis Testing?

Children learn thousands of words in the first years of life, but the process supporting this feat is largely unknown. New neuroimaging results indicate that learning a word may be sudden rather than gradual, supported by hippocampal memory.

Predictive Movements and Human Reinforcement Learning of Sequential Action.

Sequential action makes up the bulk of human daily activity, and yet much remains unknown about how people learn such actions. In one motor learning paradigm, the serial reaction time (SRT) task, people are taught a consistent sequence of button presses by cueing them with the next target response. However, the SRT task only records keypress response times to a cued target, and thus it cannot reveal the full time-course of motion, including predictive movements. This paper describes a mouse movement trajectory SRT task in which the cursor must be moved to a cued location. We replicated keypress SRT results, but also found that predictive movement-before the next cue appears-increased during the experiment. Moreover, trajectory analyses revealed that people developed a centering strategy under uncertainty. In a second experiment, we made prediction explicit, no longer cueing targets. Thus, participants had to explore the response alternatives and learn via reinforcement, receiving rewards and penalties for correct and incorrect actions, respectively. Participants were not told whether the sequence of stimuli was deterministic, nor if it would repeat, nor how long it was. Given the difficulty of the task, it is unsurprising that some learners performed poorly. However, many learners performed remarkably well, and some acquired the full 10-item sequence within 10 repetitions. Comparing the high- and low-performers' detailed results in this reinforcement learning (RL) task with the first experiment's cued trajectory SRT task, we found similarities between the two tasks, suggesting that the effects in Experiment 1 are due to predictive, rather than reactive processes. Finally, we found that two standard model-free reinforcement learning models fit the high-performing participants, while the four low-performing participants provide better fit with a simple negative recency bias model.

Observing and Modeling Developing Knowledge and Uncertainty during Cross-situational Word Learning.

Being able to learn word meanings across multiple scenes consisting of multiple words and referents (i.e., cross-situationally) is thought to be important for language acquisition. The ability has been studied in infants, children, and adults, and yet there is much debate about the basic storage and retrieval mechanisms that operate during cross-situational word learning. It has been difficult to uncover the learning mechanics in part because the standard experimental paradigm, which presents a few words and objects on each of a series of training trials, measures learning only at the end of training, after several occurrences of each word-object pair. Diverse models are able to match the final level of performance of the standard paradigm, while the rich history and context of the learning trajectories remain obscured. This study examines accuracy and uncertainty over time in a version of the cross-situational learning task in which words are tested throughout training, as well as in a final test. With similar levels of performance to the standard task, we examine how well the online response trajectories match recent hypothesis- and association-based computational models of word learning.eing able to learn word meanings across multiple scenes consisting of multiple words and referents (i.e., cross-situationally) is thought to be important for language acquisition. The ability has been studied in infants, children, and adults, and yet there is much debate about the basic storage and retrieval mechanisms that operate during cross-situational word learning. It has been difficult to uncover the learning mechanics in part because the standard experimental paradigm, which presents a few words and objects on each of a series of training trials, measures learning only at the end of training, after several occurrences of each word-object pair. Diverse models are able to match the final level of performance of the standard paradigm, while the rich history and context of the learning trajectories remain obscured. This study examines accuracy and uncertainty over time in a version of the cross-situational learning task in which words are tested throughout training, as well as in a final test. With similar levels of performance to the standard task, we examine how well the online response trajectories match recent hypothesis- and association-based computational models of word learning.B.

Desirable difficulties during the development of active inquiry skills.

This study explores developmental changes in the ability to ask informative questions, hypothesizing a link between the ability to update beliefs in light of evidence and the ability to ask informative questions. Five- to ten-year-old children played an iPad game asking them to identify a hidden insect. Learners could either ask about individual insects, or make a series of feature queries (e.g., "Does the hidden insect have antenna?") that could more efficiently narrow the hypothesis space. Critically, the task display either helped children integrate evidence with the hypothesis space or required them to perform this operation themselves. Our prediction was that assisting children with belief updating would help them formulate more informative queries. This assistance improved some aspects of children's active inquiry behavior; however, despite making some updating mistakes, children required to update their own beliefs asked questions that were more context-sensitive and thus informative. The results show how making a task more difficult can improve some aspects of children's active inquiry skills, thus illustrating a type of "desirable difficulty" for reasoning.

A Bootstrapping Model of Frequency and Context Effects in Word Learning.

Prior research has shown that people can learn many nouns (i.e., word-object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model (Kachergis, Yu, & Shiffrin, 2012), we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings.

A continuous-time neural model for sequential action.

Action selection, planning and execution are continuous processes that evolve over time, responding to perceptual feedback as well as evolving top-down constraints. Existing models of routine sequential action (e.g. coffee- or pancake-making) generally fall into one of two classes: hierarchical models that include hand-built task representations, or heterarchical models that must learn to represent hierarchy via temporal context, but thus far lack goal-orientedness. We present a biologically motivated model of the latter class that, because it is situated in the Leabra neural architecture, affords an opportunity to include both unsupervised and goal-directed learning mechanisms. Moreover, we embed this neurocomputational model in the theoretical framework of the theory of event coding (TEC), which posits that actions and perceptions share a common representation with bidirectional associations between the two. Thus, in this view, not only does perception select actions (along with task context), but actions are also used to generate perceptions (i.e. intended effects). We propose a neural model that implements TEC to carry out sequential action control in hierarchically structured tasks such as coffee-making. Unlike traditional feedforward discrete-time neural network models, which use static percepts to generate static outputs, our biological model accepts continuous-time inputs and likewise generates non-stationary outputs, making short-timescale dynamic predictions.

Cross-situational word learning is both implicit and strategic.

For decades, implicit learning researchers have examined a variety of cognitive tasks in which people seem to automatically extract structure from the environment. Similarly, recent statistical learning studies have shown that people can learn word-object mappings from the repeated co-occurrence of words and objects in individually ambiguous situations. In light of this, the goal of the present paper is to investigate whether adult cross-situational learners require an explicit effort to learn word-object mappings, or if it may take place incidentally, only requiring attention to the stimuli. In two implicit learning experiments with incidental tasks directing participants' attention to different aspects of the stimuli, we found evidence of learning, suggesting that cross-situational learning mechanisms can operate incidentally, without explicit effort. However, performance was superior under explicit study instructions, indicating that strategic processes also play a role. Moreover, performance under instruction to learn word meanings did not differ from performance at counting co-occurrences, which may indicate these tasks engage similar strategies.

Everyday robotic action: lessons from human action control.

Robots are increasingly capable of performing everyday human activities such as cooking, cleaning, and doing the laundry. This requires the real-time planning and execution of complex, temporally extended sequential actions under high degrees of uncertainty, which provides many challenges to traditional approaches to robot action control. We argue that important lessons in this respect can be learned from research on human action control. We provide a brief overview of available psychological insights into this issue and focus on four principles that we think could be particularly beneficial for robot control: the integration of symbolic and subsymbolic planning of action sequences, the integration of feedforward and feedback control, the clustering of complex actions into subcomponents, and the contextualization of action-control structures through goal representations.

Actively learning object names across ambiguous situations.

Previous research shows that people can use the co-occurrence of words and objects in ambiguous situations (i.e., containing multiple words and objects) to learn word meanings during a brief passive training period (Yu & Smith, 2007). However, learners in the world are not completely passive but can affect how their environment is structured by moving their heads, eyes, and even objects. These actions can indicate attention to a language teacher, who may then be more likely to name the attended objects. Using a novel active learning paradigm in which learners choose which four objects they would like to see named on each successive trial, this study asks whether active learning is superior to passive learning in a cross-situational word learning context. Finding that learners perform better in active learning, we investigate the strategies and discover that most learners use immediate repetition to disambiguate pairings. Unexpectedly, we find that learners who repeat only one pair per trial--an easy way to infer this pair-perform worse than those who repeat multiple pairs per trial. Using a working memory extension to an associative model of word learning with uncertainty and familiarity biases, we investigate individual differences that correlate with these assorted strategies.

An associative model of adaptive inference for learning word-referent mappings.

People can learn word-referent pairs over a short series of individually ambiguous situations containing multiple words and referents (Yu & Smith, 2007, Cognition 106: 1558-1568). Cross-situational statistical learning relies on the repeated co-occurrence of words with their intended referents, but simple co-occurrence counts cannot explain the findings. Mutual exclusivity (ME: an assumption of one-to-one mappings) can reduce ambiguity by leveraging prior experience to restrict the number of word-referent pairings considered but can also block learning of non-one-to-one mappings. The present study first trained learners on one-to-one mappings with varying numbers of repetitions. In late training, a new set of word-referent pairs were introduced alongside pretrained pairs; each pretrained pair consistently appeared with a new pair. Results indicate that (1) learners quickly infer new pairs in late training on the basis of their knowledge of pretrained pairs, exhibiting ME; and (2) learners also adaptively relax the ME bias and learn two-to-two mappings involving both pretrained and new words and objects. We present an associative model that accounts for both results using competing familiarity and uncertainty biases.

Toward a scalable holographic word-form representation.

Phenomena in a variety of verbal tasks--for example, masked priming, lexical decision, and word naming--are typically explained in terms of similarity between word-forms. Despite the apparent commonalities between these sets of phenomena, the representations and similarity measures used to account for them are not often related. To show how this gap might be bridged, we build on the work of Hannagan, Dupoux, and Christophe, Cognitive Science 35:79-118, (2011) to explore several methods of representing visual word-forms using holographic reduced representations and to evaluate them on their ability to account for a wide range of effects in masked form priming, as well as data from lexical decision and word naming. A representation that assumes that word-internal letter groups are encoded relative to word-terminal letter groups is found to predict qualitative patterns in masked priming, as well as lexical decision and naming latencies. We then show how this representation can be integrated with the BEAGLE model of lexical semantics (Jones & Mewhort, Psychological Review 114:1-37, 2007) to enable the model to encompass a wider range of verbal tasks.