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System alignment supports cross-domain learning and zero-shot generalisation
Recent findings suggest conceptual relationships hold across modalities. For instance, if two concepts occur in similar linguistic contexts, they also likely occur in similar visual contexts. These similarity structures may provide a valuable signal for alignment when learning to map between domains...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469439/ https://www.ncbi.nlm.nih.gov/pubmed/35717766 http://dx.doi.org/10.1016/j.cognition.2022.105200 |
_version_ | 1785099440736960512 |
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author | Aho, Kaarina Roads, Brett D. Love, Bradley C. |
author_facet | Aho, Kaarina Roads, Brett D. Love, Bradley C. |
author_sort | Aho, Kaarina |
collection | PubMed |
description | Recent findings suggest conceptual relationships hold across modalities. For instance, if two concepts occur in similar linguistic contexts, they also likely occur in similar visual contexts. These similarity structures may provide a valuable signal for alignment when learning to map between domains, such as when learning the names of objects. To assess this possibility, we conducted a paired-associate learning experiment in which participants mapped objects that varied on two visual features to locations that varied along two spatial dimensions. We manipulated whether the featural and spatial systems were aligned or misaligned. Although system alignment was not required to complete this supervised learning task, we found that participants learned more efficiently when systems aligned and that aligned systems facilitated zero-shot generalisation. We fit a variety of models to individuals' responses and found that models which included an offline unsupervised alignment mechanism best accounted for human performance. Our results provide empirical evidence that people align entire representation systems to accelerate learning, even when learning seemingly arbitrary associations between two domains. |
format | Online Article Text |
id | pubmed-10469439 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-104694392023-09-01 System alignment supports cross-domain learning and zero-shot generalisation Aho, Kaarina Roads, Brett D. Love, Bradley C. Cognition Article Recent findings suggest conceptual relationships hold across modalities. For instance, if two concepts occur in similar linguistic contexts, they also likely occur in similar visual contexts. These similarity structures may provide a valuable signal for alignment when learning to map between domains, such as when learning the names of objects. To assess this possibility, we conducted a paired-associate learning experiment in which participants mapped objects that varied on two visual features to locations that varied along two spatial dimensions. We manipulated whether the featural and spatial systems were aligned or misaligned. Although system alignment was not required to complete this supervised learning task, we found that participants learned more efficiently when systems aligned and that aligned systems facilitated zero-shot generalisation. We fit a variety of models to individuals' responses and found that models which included an offline unsupervised alignment mechanism best accounted for human performance. Our results provide empirical evidence that people align entire representation systems to accelerate learning, even when learning seemingly arbitrary associations between two domains. Elsevier 2022-10 /pmc/articles/PMC10469439/ /pubmed/35717766 http://dx.doi.org/10.1016/j.cognition.2022.105200 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Aho, Kaarina Roads, Brett D. Love, Bradley C. System alignment supports cross-domain learning and zero-shot generalisation |
title | System alignment supports cross-domain learning and zero-shot generalisation |
title_full | System alignment supports cross-domain learning and zero-shot generalisation |
title_fullStr | System alignment supports cross-domain learning and zero-shot generalisation |
title_full_unstemmed | System alignment supports cross-domain learning and zero-shot generalisation |
title_short | System alignment supports cross-domain learning and zero-shot generalisation |
title_sort | system alignment supports cross-domain learning and zero-shot generalisation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469439/ https://www.ncbi.nlm.nih.gov/pubmed/35717766 http://dx.doi.org/10.1016/j.cognition.2022.105200 |
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