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1. How Children Learn From Others: An Analysis of Selective Word Learning

   https://doi.org/doi.org/10.1111/cdev.13415  

   Sobel, D. M.; Finiasz, Z. (November 2020, Child development)

   null (Ed.)

   One way children are remarkable learners is that they learn from others. Critically, children are selective when assessing from whom to learn, particularly in the domain of word learning. We conducted an analysis of children’s selective word learning, reviewing 63 papers on 6,525 participants. Children’s ability to engage in selective word learning appeared to be present in the youngest samples surveyed. Their more metacognitive understanding that epistemic competence indicates reliability or that others are good sources of knowledge has more of a developmental trajectory. We also found that various methodological factors used to assess children influence performance. We conclude with a synthesis of theoretical accounts of how children learn from others. 

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2. How Children Learn From Others: An Analysis of Selective Word Learning

   https://doi.org/10.1111/cdev.13415  

   Sobel, David_M; Finiasz, Zoe (July 2020, Child Development)

   One way children are remarkable learners is that they learn from others. Critically, children are selective when assessing from whom to learn, particularly in the domain of word learning. We conducted an analysis of children’s selective word learning, reviewing 63 papers on 6,525 participants. Children’s ability to engage in selective word learning appeared to be present in the youngest samples surveyed. Their more metacognitive understanding that epistemic competence indicates reliability or that others are good sources of knowledge has more of a developmental trajectory. We also found that various methodological factors used to assess children influence performance. We conclude with a synthesis of theoretical accounts of how children learn from others. 

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3. Mapping words to the world: Adults prioritize grammar, but children prioritize descriptions

   Brody, Gabor; Feiman, Roman (January 2022, Proceedings of the Annual Conference of the Cognitive Science Society)

   How do children learn to connect expressions (e.g “that red apple”) to the real-world objects they refer to? The dominant view in developmental psychology is that children rely primarily on descriptive information encoded in content words (red, apple). In contrast, linguistic semantic theories of adult language attribute primacy to the grammar (e.g. words like that, another), which first establish the status of potential referents within the discourse context (old, new) before descriptive information can factor in. These theories predict that reference can succeed even when the description does not match the referent. We explore this novel prediction in adults and children. Over three experiments, we found that (i) adults relied on the articles to establish the referent, even when the noun description did not fit, consistent with grammar-first accounts; (ii) consistent with description-first accounts, and contrary to adult behavior, 3-5yo children prioritized the descriptions provided by the nouns, despite being sensitive to grammatical information. 

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4. Integrating emotional expressions with utterances in pragmatic inference

   Wu, Yang; Tessler, Michael Henry; Asaba, Mike; Zhu, Peter; Gweon, Hyo; Frank, Michael C (January 2021, Proceedings of the Annual Conference of the Cognitive Science Society)

   null (Ed.)

   Human communication involves far more than words; speak- ers’ utterances are often accompanied by various kinds of emo- tional expressions. How do listeners represent and integrate these distinct sources of information to make communicative inferences? We first show that people, as listeners, integrate both verbal and emotional information when inferring true states of the world and others’ communicative goals, and then present computational models that formalize these inferences by considering different ways in which these signals might be generated. Results suggest that while listeners understand that utterances and emotional expressions are generated by a bal- ance of speakers’ informational and social goals, they addi- tionally consider the possibility that emotional expressions are noncommunicative signals that directly reflect the speaker’s in- ternal states. These results are consistent with the predictions of a probabilistic model that integrates goal inferences with linguistic and emotional signals, moving us towards a more complete formal theory of human communicative reasoning. 

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5. Quantifying the role of vocabulary knowledge in predicting future word learning

   https://doi.org/10.1109/TCDS.2019.2928023  

   Beckage, Nicole M.; Mozer, Michael C.; Colunga, Eliana (July 2019, IEEE transactions on cognitive and developmental systems)

   Can we predict the words a child is going to learn next given information about the words that a child knows now? Do different representations of a child’s vocabulary knowledge affect our ability to predict the acquisition of lexical items for individual children? Past research has often focused on population statistics of vocabulary growth rather than prediction of words an individual child is likely to learn next. We consider a neural network approach to predict vocabulary acquisition. Specifically, we investigate how best to represent the child’s current vocabulary in order to accurately predict future learning. The models we consider are based on qualitatively different sources of information: descriptive information about the child, the specific words a child knows, and representations that aim to capture the child’s aggregate lexical knowledge. Using longitudinal vocabulary data from children aged 15-36 months, we construct neural network models to predict which words are likely to be learned by a particular child in the coming month. Many models based on child-specific vocabulary information outperform models with child information only, suggesting that the words a child knows influence prediction of future language learning. These models provide an understanding of the role of current vocabulary knowledge on future lexical growth. 

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