The distributional properties of exemplars affect category learning and generalization

Abstract What we learn about the world is affected by the input we receive. Many extant category learning studies use uniform distributions as input in which each exemplar in a category is presented the same number of times. Another common assumption on input used in previous studies is that exempla...

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Autores principales:	Paulo F. Carvalho, Chi-hsin Chen, Chen Yu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7d8735da03ee486fa6c6a38b9c2ca6af
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spelling	oai:doaj.org-article:7d8735da03ee486fa6c6a38b9c2ca6af2021-12-02T15:49:35ZThe distributional properties of exemplars affect category learning and generalization10.1038/s41598-021-90743-02045-2322https://doaj.org/article/7d8735da03ee486fa6c6a38b9c2ca6af2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90743-0https://doaj.org/toc/2045-2322Abstract What we learn about the world is affected by the input we receive. Many extant category learning studies use uniform distributions as input in which each exemplar in a category is presented the same number of times. Another common assumption on input used in previous studies is that exemplars from the same category form a roughly normal distribution. However, recent corpus studies suggest that real-world category input tends to be organized around skewed distributions. We conducted three experiments to examine the distributional properties of the input on category learning and generalization. Across all studies, skewed input distributions resulted in broader generalization than normal input distributions. Uniform distributions also resulted in broader generalization than normal input distributions. Our results not only suggest that current category learning theories may underestimate category generalization but also challenge current theories to explain category learning in the real world with skewed, instead of the normal or uniform distributions often used in experimental studies.Paulo F. CarvalhoChi-hsin ChenChen YuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Paulo F. Carvalho Chi-hsin Chen Chen Yu The distributional properties of exemplars affect category learning and generalization
description	Abstract What we learn about the world is affected by the input we receive. Many extant category learning studies use uniform distributions as input in which each exemplar in a category is presented the same number of times. Another common assumption on input used in previous studies is that exemplars from the same category form a roughly normal distribution. However, recent corpus studies suggest that real-world category input tends to be organized around skewed distributions. We conducted three experiments to examine the distributional properties of the input on category learning and generalization. Across all studies, skewed input distributions resulted in broader generalization than normal input distributions. Uniform distributions also resulted in broader generalization than normal input distributions. Our results not only suggest that current category learning theories may underestimate category generalization but also challenge current theories to explain category learning in the real world with skewed, instead of the normal or uniform distributions often used in experimental studies.
format	article
author	Paulo F. Carvalho Chi-hsin Chen Chen Yu
author_facet	Paulo F. Carvalho Chi-hsin Chen Chen Yu
author_sort	Paulo F. Carvalho
title	The distributional properties of exemplars affect category learning and generalization
title_short	The distributional properties of exemplars affect category learning and generalization
title_full	The distributional properties of exemplars affect category learning and generalization
title_fullStr	The distributional properties of exemplars affect category learning and generalization
title_full_unstemmed	The distributional properties of exemplars affect category learning and generalization
title_sort	distributional properties of exemplars affect category learning and generalization
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7d8735da03ee486fa6c6a38b9c2ca6af
work_keys_str_mv	AT paulofcarvalho thedistributionalpropertiesofexemplarsaffectcategorylearningandgeneralization AT chihsinchen thedistributionalpropertiesofexemplarsaffectcategorylearningandgeneralization AT chenyu thedistributionalpropertiesofexemplarsaffectcategorylearningandgeneralization AT paulofcarvalho distributionalpropertiesofexemplarsaffectcategorylearningandgeneralization AT chihsinchen distributionalpropertiesofexemplarsaffectcategorylearningandgeneralization AT chenyu distributionalpropertiesofexemplarsaffectcategorylearningandgeneralization
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The distributional properties of exemplars affect category learning and generalization

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