Literature DB >> 26117487

Recent evolution of learnability in American English from 1800 to 2000.

Thomas T Hills1, James S Adelman2.   

Abstract

Concreteness-the psycholinguistic property of referring to a perceptible entity-enhances processing speed, comprehension, and memory. These represent selective filters for cognition likely to influence language evolution in competitive language environments. Taking a culturomics approach, we use multiple language corpora representing more than 350 billion words combined with concreteness norms for over 40,000 English words and demonstrate a systematic rise in concrete language in American English over the last 200years, both within and across word classes (nouns, verbs, and prepositions). Comparisons between new and old concreteness norms indicate this is not explained by semantic bleaching, but we find some evidence that the rise is related to changes in population demographics and may be associated with increasing numbers of second language learners or attention economics in response to crowding in the language market. We also examine the influence of gender and literacy. In sum, we demonstrate evolution in the psycholinguistic structure of American English, with a well-established impact on cognitive processing, which is likely to permeate modern language use.
Copyright © 2015 Elsevier B.V. All rights reserved.

Keywords:  Concreteness; Culturomics; Historical linguistics; Language evolution; Semantic bleaching

Mesh:

Year:  2015        PMID: 26117487     DOI: 10.1016/j.cognition.2015.06.009

Source DB:  PubMed          Journal:  Cognition        ISSN: 0010-0277


  11 in total

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Journal:  PLoS One       Date:  2019-04-25       Impact factor: 3.240

8.  The Macroscope: A tool for examining the historical structure of language.

Authors:  Ying Li; Tomas Engelthaler; Cynthia S Q Siew; Thomas T Hills
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9.  Semantic Factors Predict the Rate of Lexical Replacement of Content Words.

Authors:  Susanne Vejdemo; Thomas Hörberg
Journal:  PLoS One       Date:  2016-01-28       Impact factor: 3.240

10.  Population Size Predicts Lexical Diversity, but so Does the Mean Sea Level --Why It Is Important to Correctly Account for the Structure of Temporal Data.

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Journal:  PLoS One       Date:  2016-03-03       Impact factor: 3.240

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