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Humans use Compression Heuristics to Improve the Recall of Social Networks

The ability of primates, including humans, to maintain large social networks appears to depend on the ratio of the neocortex to the rest of the brain. However, observed human network size frequently exceeds predictions based on this ratio (e.g., “Dunbar’s Number”), implying that human networks are t...

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Autor principal: Brashears, Matthew E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3604710/
https://www.ncbi.nlm.nih.gov/pubmed/23515066
http://dx.doi.org/10.1038/srep01513
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author Brashears, Matthew E.
author_facet Brashears, Matthew E.
author_sort Brashears, Matthew E.
collection PubMed
description The ability of primates, including humans, to maintain large social networks appears to depend on the ratio of the neocortex to the rest of the brain. However, observed human network size frequently exceeds predictions based on this ratio (e.g., “Dunbar’s Number”), implying that human networks are too large to be cognitively managed. Here I show that humans adaptively use compression heuristics to allow larger amounts of social information to be stored in the same brain volume. I find that human adults can remember larger numbers of relationships in greater detail when a network exhibits triadic closure and kin labels than when it does not. These findings help to explain how humans manage large and complex social networks with finite cognitive resources and suggest that many of the unusual properties of human social networks are rooted in the strategies necessary to cope with cognitive limitations.
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spelling pubmed-36047102013-03-21 Humans use Compression Heuristics to Improve the Recall of Social Networks Brashears, Matthew E. Sci Rep Article The ability of primates, including humans, to maintain large social networks appears to depend on the ratio of the neocortex to the rest of the brain. However, observed human network size frequently exceeds predictions based on this ratio (e.g., “Dunbar’s Number”), implying that human networks are too large to be cognitively managed. Here I show that humans adaptively use compression heuristics to allow larger amounts of social information to be stored in the same brain volume. I find that human adults can remember larger numbers of relationships in greater detail when a network exhibits triadic closure and kin labels than when it does not. These findings help to explain how humans manage large and complex social networks with finite cognitive resources and suggest that many of the unusual properties of human social networks are rooted in the strategies necessary to cope with cognitive limitations. Nature Publishing Group 2013-03-21 /pmc/articles/PMC3604710/ /pubmed/23515066 http://dx.doi.org/10.1038/srep01513 Text en Copyright © 2013, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Article
Brashears, Matthew E.
Humans use Compression Heuristics to Improve the Recall of Social Networks
title Humans use Compression Heuristics to Improve the Recall of Social Networks
title_full Humans use Compression Heuristics to Improve the Recall of Social Networks
title_fullStr Humans use Compression Heuristics to Improve the Recall of Social Networks
title_full_unstemmed Humans use Compression Heuristics to Improve the Recall of Social Networks
title_short Humans use Compression Heuristics to Improve the Recall of Social Networks
title_sort humans use compression heuristics to improve the recall of social networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3604710/
https://www.ncbi.nlm.nih.gov/pubmed/23515066
http://dx.doi.org/10.1038/srep01513
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