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Learning-dependent plasticity with and without training in the human brain

Jiaxiang Zhang Orcid Logo, Zoe Kourtzi

Proceedings of the National Academy of Sciences, Volume: 107, Issue: 30, Pages: 13503 - 13508

Swansea University Author: Jiaxiang Zhang Orcid Logo

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DOI (Published version): 10.1073/pnas.1002506107

Abstract

Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of ob...

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Published in: Proceedings of the National Academy of Sciences
ISSN: 0027-8424 1091-6490
Published: Proceedings of the National Academy of Sciences 2010
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URI: https://cronfa.swan.ac.uk/Record/cronfa61332
first_indexed 2022-10-11T13:45:00Z
last_indexed 2023-01-13T19:22:02Z
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spelling 2022-10-11T14:45:02.5963531 v2 61332 2022-09-26 Learning-dependent plasticity with and without training in the human brain 555e06e0ed9a87608f2d035b3bde3a87 0000-0002-4758-0394 Jiaxiang Zhang Jiaxiang Zhang true false 2022-09-26 MACS Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes. Journal Article Proceedings of the National Academy of Sciences 107 30 13503 13508 Proceedings of the National Academy of Sciences 0027-8424 1091-6490 27 7 2010 2010-07-27 10.1073/pnas.1002506107 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University his work was supported by a Biotechnology andBiological Sciences Research Council Grant BB/D52199X/1 and the CognitiveForesight Initiative BB/E027436/1 (to Z.K.) 2022-10-11T14:45:02.5963531 2022-09-26T11:31:31.0658058 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jiaxiang Zhang 0000-0002-4758-0394 1 Zoe Kourtzi 2
title Learning-dependent plasticity with and without training in the human brain
spellingShingle Learning-dependent plasticity with and without training in the human brain
Jiaxiang Zhang
title_short Learning-dependent plasticity with and without training in the human brain
title_full Learning-dependent plasticity with and without training in the human brain
title_fullStr Learning-dependent plasticity with and without training in the human brain
title_full_unstemmed Learning-dependent plasticity with and without training in the human brain
title_sort Learning-dependent plasticity with and without training in the human brain
author_id_str_mv 555e06e0ed9a87608f2d035b3bde3a87
author_id_fullname_str_mv 555e06e0ed9a87608f2d035b3bde3a87_***_Jiaxiang Zhang
author Jiaxiang Zhang
author2 Jiaxiang Zhang
Zoe Kourtzi
format Journal article
container_title Proceedings of the National Academy of Sciences
container_volume 107
container_issue 30
container_start_page 13503
publishDate 2010
institution Swansea University
issn 0027-8424
1091-6490
doi_str_mv 10.1073/pnas.1002506107
publisher Proceedings of the National Academy of Sciences
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
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description Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes.
published_date 2010-07-27T02:32:33Z
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score 11.04748

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