Lindsay, GW;
(2020)
Attention in Psychology, Neuroscience, and Machine Learning.
Frontiers in Computational Neuroscience
, 14
, Article 29. 10.3389/fncom.2020.00029.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Attention in Psychology, Neuroscience, and Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fncom.2020.00029 |
| Publisher version: | https://doi.org/10.3389/fncom.2020.00029 |
| Language: | English |
| Additional information: | © 2020 Lindsay. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | attention, artificial neural networks, machine learning, vision, memory, awareness |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > The Sainsbury Wellcome Centre |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10108431 |
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