How do learners gather new information during word learning? One possibility is that learners selectively sample items that help them reduce uncertainty about new word meanings. In a series of cross-situational word learning tasks with adults and children, we manipulated the referential ambiguity of label-object pairs experienced during training and subsequently investigated which words participants chose to sample additional information about. In the first experiment, adult learners chose to receive additional training on object-label associations that reduce referential ambiguity during cross-situational word learning. This ambiguity-reduction strategy was related to improved test performance. In two subsequent experiments, we found that, at least in some contexts, children (3-8 years of age) show a similar preference to seek information about words experienced in ambiguous word learning situations. In Experiment 2, children did not preferentially select object-label associations that remained ambiguous during cross-situational word learning. However, in a third experiment that increased the relative ambiguity of two sets of novel object-label associations, we found evidence that children preferentially make selections that reduce ambiguity about novel word meanings. These results carry implications for understanding how children actively contribute to their own language development by seeking information that supports learning.
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http://dx.doi.org/10.1111/desc.13064 | DOI Listing |
J Am Med Inform Assoc
December 2024
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, United States.
Objective: To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.
Materials And Methods: We first created a lexicon and regular expression lists from literature-driven stem words for linguistic features of stigmatizing patient labels, doubt markers, and scare quotes within EHRs. The lexicon was further extended using Word2Vec and GPT 3.
Disabil Rehabil Assist Technol
December 2024
New Technologies Platform, Raymond Poincaré Hospital, APHP. Université Paris Saclay, Garches, France.
Purpose: Information and communication technologies are crucial for social and professional integration, but access to technology can be difficult for people with physical impairments. Text entry can be slow and tiring. We developed a free and open-source module called for use with AAC (augmentative/alternative communication) software in French language.
View Article and Find Full Text PDFPsychon Bull Rev
December 2024
Laboratoire Cognition Langage & Développement (LCLD), Centre de Recherche Cognition et Neurosciences (CRCN), Université Libre de Bruxelles (ULB), Av. F. Roosevelt, 50 /CP 191, 1050, Brussels, Belgium.
Lexical competition between newly acquired and already established representations of written words is considered a marker of word integration into the mental lexicon. To date, studies about the emergence of lexical competition involved mostly artificial training procedures based on overexposure and explicit instructions for memorization. Yet, in real life, novel word encounters occur mostly without explicit learning intent, through reading texts with words appearing rarely.
View Article and Find Full Text PDFHuman aging affects the ability to remember new experiences, in part, because of altered neural function during memory formation. One potential contributor to age-related memory decline is diminished neural selectivity -- i.e.
View Article and Find Full Text PDFDigit Health
December 2024
School of Computer Science, University of Birmingham, Birmingham, UK.
Objective: The study aims to present an active learning approach that automatically extracts clinical concepts from unstructured data and classifies them into explicit categories such as Problem, Treatment, and Test while preserving high precision and recall and demonstrating the approach through experiments using i2b2 public datasets.
Methods: Initially labeled data are acquired from a lexical-based approach in sufficient amounts to perform an active learning process. A contextual word embedding similarity approach is adopted using BERT base variant models such as ClinicalBERT, DistilBERT, and SCIBERT to automatically classify the unlabeled clinical concept into explicit categories.
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