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Latent Diversity in Human Concepts

Many social and legal conflicts hinge on semantic disagreements. Understanding the origins and implications of these disagreements necessitates novel methods for identifying and quantifying variation in semantic cognition between individuals. We collected conceptual similarity ratings and feature ju...

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Detalles Bibliográficos
Autores principales: Marti, Louis, Wu, Shengyi, Piantadosi, Steven T., Kidd, Celeste
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MIT Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320827/
https://www.ncbi.nlm.nih.gov/pubmed/37416074
http://dx.doi.org/10.1162/opmi_a_00072
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author Marti, Louis
Wu, Shengyi
Piantadosi, Steven T.
Kidd, Celeste
author_facet Marti, Louis
Wu, Shengyi
Piantadosi, Steven T.
Kidd, Celeste
author_sort Marti, Louis
collection PubMed
description Many social and legal conflicts hinge on semantic disagreements. Understanding the origins and implications of these disagreements necessitates novel methods for identifying and quantifying variation in semantic cognition between individuals. We collected conceptual similarity ratings and feature judgements from a variety of words in two domains. We analyzed this data using a non-parametric clustering scheme, as well as an ecological statistical estimator, in order to infer the number of different variants of common concepts that exist in the population. Our results show at least ten to thirty quantifiably different variants of word meanings exist for even common nouns. Further, people are unaware of this variation, and exhibit a strong bias to erroneously believe that other people share their semantics. This highlights conceptual factors that likely interfere with productive political and social discourse.
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spelling pubmed-103208272023-07-06 Latent Diversity in Human Concepts Marti, Louis Wu, Shengyi Piantadosi, Steven T. Kidd, Celeste Open Mind (Camb) Research Article Many social and legal conflicts hinge on semantic disagreements. Understanding the origins and implications of these disagreements necessitates novel methods for identifying and quantifying variation in semantic cognition between individuals. We collected conceptual similarity ratings and feature judgements from a variety of words in two domains. We analyzed this data using a non-parametric clustering scheme, as well as an ecological statistical estimator, in order to infer the number of different variants of common concepts that exist in the population. Our results show at least ten to thirty quantifiably different variants of word meanings exist for even common nouns. Further, people are unaware of this variation, and exhibit a strong bias to erroneously believe that other people share their semantics. This highlights conceptual factors that likely interfere with productive political and social discourse. MIT Press 2023-03-09 /pmc/articles/PMC10320827/ /pubmed/37416074 http://dx.doi.org/10.1162/opmi_a_00072 Text en © 2023 Massachusetts Institute of Technology https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/.
spellingShingle Research Article
Marti, Louis
Wu, Shengyi
Piantadosi, Steven T.
Kidd, Celeste
Latent Diversity in Human Concepts
title Latent Diversity in Human Concepts
title_full Latent Diversity in Human Concepts
title_fullStr Latent Diversity in Human Concepts
title_full_unstemmed Latent Diversity in Human Concepts
title_short Latent Diversity in Human Concepts
title_sort latent diversity in human concepts
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320827/
https://www.ncbi.nlm.nih.gov/pubmed/37416074
http://dx.doi.org/10.1162/opmi_a_00072
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