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Attention in Psychology, Neuroscience, and Machine Learning
Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several dom...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177153/ https://www.ncbi.nlm.nih.gov/pubmed/32372937 http://dx.doi.org/10.3389/fncom.2020.00029 |
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author | Lindsay, Grace W. |
author_facet | Lindsay, Grace W. |
author_sort | Lindsay, Grace W. |
collection | PubMed |
description | Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored. |
format | Online Article Text |
id | pubmed-7177153 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71771532020-05-05 Attention in Psychology, Neuroscience, and Machine Learning Lindsay, Grace W. Front Comput Neurosci Neuroscience Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored. Frontiers Media S.A. 2020-04-16 /pmc/articles/PMC7177153/ /pubmed/32372937 http://dx.doi.org/10.3389/fncom.2020.00029 Text en Copyright © 2020 Lindsay. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Lindsay, Grace W. Attention in Psychology, Neuroscience, and Machine Learning |
title | Attention in Psychology, Neuroscience, and Machine Learning |
title_full | Attention in Psychology, Neuroscience, and Machine Learning |
title_fullStr | Attention in Psychology, Neuroscience, and Machine Learning |
title_full_unstemmed | Attention in Psychology, Neuroscience, and Machine Learning |
title_short | Attention in Psychology, Neuroscience, and Machine Learning |
title_sort | attention in psychology, neuroscience, and machine learning |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177153/ https://www.ncbi.nlm.nih.gov/pubmed/32372937 http://dx.doi.org/10.3389/fncom.2020.00029 |
work_keys_str_mv | AT lindsaygracew attentioninpsychologyneuroscienceandmachinelearning |