Feature distinctiveness effects in language acquisition and lexical processing: Insights from megastudies

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.

Global and Local Feature Distinctiveness Effects in Language Acquisition

“…In layperson terms, Manhattan distance is sensitive to the extent to which features differ between two words. Although there are other ways of computing distance, previous investigations by Engelthaler and Hills (2017) and Siew (2020) showed that feature distinctiveness computed with Manhattan distance was a stronger predictor of behavioral data as compared to other distance measures such as Jaccard distance.…”

Lexico-semantic structure in vocabulary and its links to lexical processing in toddlerhood and language outcomes at age three.

“…A number of studies have sought to model early vocabulary networks to describe early patterns in lexical acquisition in relation to productive vocabulary size. These studies have varied in their approach to establishing semantic connections among words, variously using: free association norms (Fourtassi et al, 2020; Hills et al, 2009b); child-directed speech corpora derived co-occurrence statistics (Beckage et al, 2011); semantic features (Hills et al, 2009a, 2009b; Peters & Borovsky, 2019; Siew, 2019; Yurovsky et al, 2012); or a combination of all of these (Stella et al, 2017). All of these approaches yield appropriate measures of semantic structure in early lexicon, and studies that have sought to compare multiple semantic modeling methods in psycholinguistic studies have found a number of redundancies in among feature norm and distributional approaches (Riordan & Jones, 2011).…”

Applications of Network Science to Education Research: Quantifying Knowledge and the Development of Expertise through Network Analysis

“…Again, large databases of feature production norms are readily available for researchers to analyze the internal conceptual structure of words [38]. Feature networks can be constructed by connecting concepts that share the same features, and network analysis of feature networks have provided new insights into language development [39,40] and semantic processing [41]. Within the context of the education sciences, students could be asked to list features or properties of key concepts.…”