ArticleCognitive processing2020
Feature distinctiveness effects in language acquisition and lexical processing: Insights from megastudies.
Article in Cognitive processing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Two pathways in vocabulary development: Large-scale differences in noun and verb semantic structure.Cognitive psychology · 2023Article
- Drivers of Lexical Processing and Implications for Early Learning.Annual review of developmental psychology · 2022Article
- Lexico-semantic structure in vocabulary and its links to lexical processing in toddlerhood and language outcomes at age three.Developmental psychology · 2022Article
- Eliciting semantic properties: methods and applications.Cognitive processing · 2020Article
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1 author.
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Abstract
Semantic features are central to many influential theories of word meaning and semantic memory, but new methods of quantifying the information embedded in feature production norms are needed to advance our understanding of semantic processing and language acquisition. This paper capitalized on databases of semantic feature production norms and age-of-acquisition ratings, and megastudies including the English Lexicon Project and the Calgary Semantic Decision Project, to examine the influence of feature distinctiveness on language acquisition, visual lexical decision, and semantic decision. A feature network of English words was constructed such that edges in the network represented feature distance, or dissimilarity, between words (i.e., Jaccard and Manhattan distances of probability distributions of features elicited for each pair of words), enabling us to quantify the relative feature distinctiveness of individual words relative to other words in the network. Words with greater feature distinctiveness tended to be acquired earlier. Regression analyses of megastudy data revealed that Manhattan feature distinctiveness inhibited performance on the visual lexical decision task, facilitated semantic decision performance for concrete concepts, and inhibited semantic decision performance for abstract concepts. These results demonstrate the importance of considering the structural properties of words embedded in a semantic feature space in order to increase our understanding of semantic processing and language acquisition.
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