Amazon’s Mechanical Turk (MTurk) is arguably one of the most important research tools of the past decade. The ability to rapidly collect large amounts of high-quality human subjects data has advanced multiple fields, including personality and social psychology. Beginning in summer 2018, concerns arose regarding MTurk data quality leading to questions about the utility of MTurk for psychological research. We present empirical evidence of a substantial decrease in data quality using a four-wave naturalistic experimental design: pre-, during, and post-summer 2018. During and to some extent post-summer 2018, we find significant increases in participants failing response validity indicators, decreases in reliability and validity of a widely used personality measure, and failures to replicate well-established findings. However, these detrimental effects can be mitigated by using response validity indicators and screening the data. We discuss implications and offer suggestions to ensure data quality.

Determining the referent of a novel name is a critical task for young language learners. The majority of studies on children’s referent selection focus on manipulating the sources of information (linguistic, contextual and pragmatic) that children can use to solve the referent mapping problem. Here, we take a step back and explore how children’s endogenous biases towards novelty and their own familiarity with novel objects influence their performance in such a task. We familiarized 2-year-old children with previously novel objects. Then, on novel name referent selection trials children were asked to select the referent from three novel objects: two previously seen and one completely novel object. Children demonstrated a clear bias to select the most novel object. A second experiment controls for pragmatic responding and replicates this finding. We conclude, therefore, that children’s referent selection is biased by previous exposure and children’s endogenous bias to novelty.

In this article, we review literature on word learning and propose a theoretical account of how lexical knowledge and word use emerge and develop over time. We contend that the developing lexical system is built on processes that support children’s in-the-moment word usage interacting with processes that create long-term learning. We argue for a new characterization of word learning in which simple mechanisms like association and competition, and the interaction between the two, guide children’s selection of referents and word use in the moment. This in turn strengthens and refines the network of relationships in the lexicon, improving referent selection and use in future encounters with words. By integrating in-the-moment word use with long-term learning through simple domain-general mechanisms, this account highlights the dynamic nature of word learning and creates a broader framework for understanding language and cognitive development more generally.

Recent research demonstrated that although twenty-four month-old infants do well on the initial pairing of a novel word and novel object in fast-mapping tasks, they are unable to retain the mapping after a five-minute delay. The current study examines the role of familiarity with the objects and words on infants' ability to bridge between the initial fast mapping of a name and object, and later retention in the service of slow mapping. Twenty-four-month-old infants were familiarized with either novel objects or novel names prior to the referent selection portion of a fast-mapping task. When familiarized with the novel objects, infants retained the novel mapping after a delay, but not when familiarized with the novel words. This suggests familiarity with the object versus the word form leads to differential encoding of the name-object link. We discuss the implications of this finding for subsequent slow mapping.

Identifying the referent of novel words is a complex process that young children do with relative ease. When given multiple objects along with a novel word, children select the most novel item, sometimes retaining the word-referent link. Prior work is inconsistent, however, on the role of object novelty. Two experiments examine 18-month-old children’s performance on referent selection and retention with novel and known words. The results reveal a pervasive novelty bias on referent selection with both known and novel names and, across individual children, a negative correlation between attention to novelty and retention of new word-referent links. A computational model examines possible sources of the bias, suggesting novelty supports in-the-moment behavior but not retention. Together, results suggest that when lexical knowledge is weak, attention to novelty drives behavior, but alone does not sustain learning. Importantly, the results demonstrate that word learning may be driven, in part, by low-level perceptual processes.

Theories of cognitive development must address both the issue of how children bring their knowledge to bear on behavior in-the-moment, and how knowledge changes over time. We argue that seeking answers to these questions requires an appreciation of the dynamic nature of the developing system in its full, reciprocal complexity. We illustrate this dynamic complexity with results from two lines of research on early word learning. The first demonstrates how the child’s active engagement with objects and people supports referent selection via memories for what objects were previously seen in a cued location. The second set of results highlights changes in the role of novelty and attentional processes in referent selection and retention as children’s knowledge of words and objects grows. Together this work suggests understanding systems for perception, action, attention, and memory and their complex interaction is critical to understand word learning. We review recent literature that highlights the complex interactions between these processes in cognitive development and point to critical issues for future work.

Purpose The particular statistical approach researchers choose is intimately connected to the way they conceptualize their questions, which, in turn, can influence the conclusions they draw. One particularly salient area in which statistics influence our conclusions is in the context of atypical development. Traditional statistical approaches such as t tests or analysis of variance lend themselves to a focus on group differences, downplaying the heterogeneity that exists within so many atypically developing populations. Understanding such variability is important—classification of what a disorder is, an individual's diagnosis, and whether or not a child receives intervention all directly relate to an accurate classification of the disorder and individual's abilities compared to their typically developing peers. Method Here, we use word learning biases (i.e., shape and material biases) in late-talking children as a sample case and employ a variety of statistical approaches to compare the conclusions those approaches might warrant. Results We argue that advanced statistical approaches, such as mixed-effects regression, can help us make sense of heterogeneity and are more consistent with a modern dimensional view of language disorders. Conclusions Accurate characterization of late-talking children (and others at risk for delays) and their prognoses is necessary for accurate diagnosis and implementation of appropriate target interventions. It therefore requires rigorous statistical analyses that can capture and allow for interpretation of the heterogeneity inherent in populations with language delays and disorders.

The goal of science is to advance our understanding of particular phenomenon. However, in the case of understanding development, the phenomena of interest are complex, multifaceted, and change over time. We use three decades of research on the shape bias to argue for a focus not on replication of single studies, but rather an integration across findings to create a coherent understanding of the thoughts and behaviors of young children. The "shape bias", or the tendency to generalize a novel label to novel objects of the same shape, is a reliable and robust behavioral finding and has been shown to predict future vocabulary growth and possible language disorders. Despite the reliability of the phenomenon, the way in which the shape bias is defined and tested has varied across studies and laboratories. The current review argues that differences in performance that come from even seemingly minor changes to the participants or task can offer critical insight to underlying mechanisms, and that working to incorporate data from multiple labs is an important way to reveal how task variation and a child's individual pathway create behavior-a key issue for understanding developmental phenomena.