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Multitask learning over shared subspaces
This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would be boosted for shared subspaces. Our findings broad...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284664/ https://www.ncbi.nlm.nih.gov/pubmed/34228719 http://dx.doi.org/10.1371/journal.pcbi.1009092 |
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author | Menghi, Nicholas Kacar, Kemal Penny, Will |
author_facet | Menghi, Nicholas Kacar, Kemal Penny, Will |
author_sort | Menghi, Nicholas |
collection | PubMed |
description | This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would be boosted for shared subspaces. Our findings broadly supported this hypothesis with either better performance on the second task if it shared the same subspace as the first, or positive correlations over task performance for shared subspaces. These empirical findings were compared to the behaviour of a Neural Network model trained using sequential Bayesian learning and human performance was found to be consistent with a minimal capacity variant of this model. Networks with an increased representational capacity, and networks without Bayesian learning, did not show these transfer effects. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning. |
format | Online Article Text |
id | pubmed-8284664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82846642021-07-28 Multitask learning over shared subspaces Menghi, Nicholas Kacar, Kemal Penny, Will PLoS Comput Biol Research Article This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would be boosted for shared subspaces. Our findings broadly supported this hypothesis with either better performance on the second task if it shared the same subspace as the first, or positive correlations over task performance for shared subspaces. These empirical findings were compared to the behaviour of a Neural Network model trained using sequential Bayesian learning and human performance was found to be consistent with a minimal capacity variant of this model. Networks with an increased representational capacity, and networks without Bayesian learning, did not show these transfer effects. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning. Public Library of Science 2021-07-06 /pmc/articles/PMC8284664/ /pubmed/34228719 http://dx.doi.org/10.1371/journal.pcbi.1009092 Text en © 2021 Menghi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Menghi, Nicholas Kacar, Kemal Penny, Will Multitask learning over shared subspaces |
title | Multitask learning over shared subspaces |
title_full | Multitask learning over shared subspaces |
title_fullStr | Multitask learning over shared subspaces |
title_full_unstemmed | Multitask learning over shared subspaces |
title_short | Multitask learning over shared subspaces |
title_sort | multitask learning over shared subspaces |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8284664/ https://www.ncbi.nlm.nih.gov/pubmed/34228719 http://dx.doi.org/10.1371/journal.pcbi.1009092 |
work_keys_str_mv | AT menghinicholas multitasklearningoversharedsubspaces AT kacarkemal multitasklearningoversharedsubspaces AT pennywill multitasklearningoversharedsubspaces |