Movement trajectories as a window into the dynamics of emerging neural representations.
- Publisher:
- NATURE PORTFOLIO
- Publication Type:
- Journal Article
- Citation:
- Sci Rep, 2024, 14, (1), pp. 11499
- Issue Date:
- 2024-05-20
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Koenig-Robert, R | |
dc.contributor.author | Quek, GL | |
dc.contributor.author | Grootswagers, T | |
dc.contributor.author | Varlet, M | |
dc.date.accessioned | 2024-12-02T01:20:00Z | |
dc.date.available | 2024-05-14 | |
dc.date.available | 2024-12-02T01:20:00Z | |
dc.date.issued | 2024-05-20 | |
dc.identifier.citation | Sci Rep, 2024, 14, (1), pp. 11499 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | http://hdl.handle.net/10453/182229 | |
dc.description.abstract | The rapid transformation of sensory inputs into meaningful neural representations is critical to adaptive human behaviour. While non-invasive neuroimaging methods are the de-facto method for investigating neural representations, they remain expensive, not widely available, time-consuming, and restrictive. Here we show that movement trajectories can be used to measure emerging neural representations with fine temporal resolution. By combining online computer mouse-tracking and publicly available neuroimaging data via representational similarity analysis (RSA), we show that movement trajectories track the unfolding of stimulus- and category-wise neural representations along key dimensions of the human visual system. We demonstrate that time-resolved representational structures derived from movement trajectories overlap with those derived from M/EEG (albeit delayed) and those derived from fMRI in functionally-relevant brain areas. Our findings highlight the richness of movement trajectories and the power of the RSA framework to reveal and compare their information content, opening new avenues to better understand human perception. | |
dc.format | Electronic | |
dc.language | eng | |
dc.publisher | NATURE PORTFOLIO | |
dc.relation.ispartof | Sci Rep | |
dc.relation.isbasedon | 10.1038/s41598-024-62135-7 | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Movement | |
dc.subject.mesh | Magnetic Resonance Imaging | |
dc.subject.mesh | Electroencephalography | |
dc.subject.mesh | Brain Mapping | |
dc.subject.mesh | Brain | |
dc.subject.mesh | Male | |
dc.subject.mesh | Adult | |
dc.subject.mesh | Female | |
dc.subject.mesh | Visual Perception | |
dc.subject.mesh | Photic Stimulation | |
dc.subject.mesh | Brain | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Magnetic Resonance Imaging | |
dc.subject.mesh | Electroencephalography | |
dc.subject.mesh | Brain Mapping | |
dc.subject.mesh | Photic Stimulation | |
dc.subject.mesh | Visual Perception | |
dc.subject.mesh | Movement | |
dc.subject.mesh | Adult | |
dc.subject.mesh | Female | |
dc.subject.mesh | Male | |
dc.subject.mesh | Humans | |
dc.subject.mesh | Movement | |
dc.subject.mesh | Magnetic Resonance Imaging | |
dc.subject.mesh | Electroencephalography | |
dc.subject.mesh | Brain Mapping | |
dc.subject.mesh | Brain | |
dc.subject.mesh | Male | |
dc.subject.mesh | Adult | |
dc.subject.mesh | Female | |
dc.subject.mesh | Visual Perception | |
dc.subject.mesh | Photic Stimulation | |
dc.title | Movement trajectories as a window into the dynamics of emerging neural representations. | |
dc.type | Journal Article | |
utslib.citation.volume | 14 | |
utslib.location.activity | England | |
pubs.organisational-group | University of Technology Sydney | |
pubs.organisational-group | University of Technology Sydney/Faculty of Health | |
pubs.organisational-group | University of Technology Sydney/Faculty of Health/Graduate School of Health | |
pubs.organisational-group | University of Technology Sydney/Faculty of Health/Graduate School of Health/GSH.Clinical Psychology | |
utslib.copyright.status | open_access | * |
dc.rights.license | This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ | |
dc.date.updated | 2024-12-02T01:19:54Z | |
pubs.issue | 1 | |
pubs.publication-status | Published online | |
pubs.volume | 14 | |
utslib.citation.issue | 1 |
Abstract:
The rapid transformation of sensory inputs into meaningful neural representations is critical to adaptive human behaviour. While non-invasive neuroimaging methods are the de-facto method for investigating neural representations, they remain expensive, not widely available, time-consuming, and restrictive. Here we show that movement trajectories can be used to measure emerging neural representations with fine temporal resolution. By combining online computer mouse-tracking and publicly available neuroimaging data via representational similarity analysis (RSA), we show that movement trajectories track the unfolding of stimulus- and category-wise neural representations along key dimensions of the human visual system. We demonstrate that time-resolved representational structures derived from movement trajectories overlap with those derived from M/EEG (albeit delayed) and those derived from fMRI in functionally-relevant brain areas. Our findings highlight the richness of movement trajectories and the power of the RSA framework to reveal and compare their information content, opening new avenues to better understand human perception.
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