Corrigendum: Attention in Psychology, Neuroscience, and Machine Learning

Lindsay, Grace W.

doi:10.3389/fncom.2021.698574

CORRECTION article

Front. Comput. Neurosci. , 26 May 2021

Volume 15 - 2021 | https://doi.org/10.3389/fncom.2021.698574

This article is part of the Research Topic How Can Neuroscience Contribute to the Development of General Artificial Intelligence? View all 9 articles

Corrigendum: Attention in Psychology, Neuroscience, and Machine Learning

This article is a correction to:

Attention in Psychology, Neuroscience, and Machine Learning
1. Read original article

$\nGrace W. Lindsay$ Grace W. Lindsay^*

Gatsby Computational Neuroscience Unit, Sainsbury Wellcome Centre, University College London, London, United Kingdom

A Corrigendum on
Attention in Psychology, Neuroscience, and Machine Learning

by Lindsay, G. W. (2020). Front. Comput. Neurosci. 14:29. doi: 10.3389/fncom.2020.00029

In the original article, there was an error. A portion of the text was repeated unnecessarily.

A correction has been made to 4. Ideas for Future Interaction Between Artificial and Biological Attention, 4.2. How to Deploy Attention, Paragraph 4. The corrected paragraph is shown below.

Activities would likely need to flexibly decide which of several possible goals should be achieved at any time and therefore where attention should be placed. This problem clearly interacts closely with issues around reinforcement learning—particularly hierarchical reinforcement learning which involves the choosing of subtasks—as such decisions must be based on expected positive or negative outcomes. Indeed, there is a close relationship between attention and reward as previously rewarded stimuli attract attention even in contexts where they no longer provide reward (Camara et al., 2013). A better understanding of how humans choose which tasks to engage in and when should allow human behavior to inform the design of a multi-task AI.

The author apologizes for this error and states that this does not change the scientific conclusions of the article in any way. The original article has been updated.

References

Camara, E., Manohar, S., and Husain, M. (2013). Past rewards capture spatial attention and action choices. Exp. Brain Res. 230, 291–300. doi: 10.1007/s00221-013-3654-6

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Keywords: attention, artificial neural networks, machine learning, vision, memory, awareness

Citation: Lindsay GW (2021) Corrigendum: Attention in Psychology, Neuroscience, and Machine Learning. Front. Comput. Neurosci. 15:698574. doi: 10.3389/fncom.2021.698574

Received: 21 April 2021; Accepted: 30 April 2021;
Published: 26 May 2021.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2021 Lindsay. 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.

*Correspondence: Grace W. Lindsay, Z3JhY2V3bGluZHNheUBnbWFpbC5jb20=

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Corrigendum: Attention in Psychology, Neuroscience, and Machine Learning

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