Decoding individual identity from brain activity elicited in imagining common experiences
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s41467-020-19630-y.pdf
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Nature ResearchCitation
Anderson, A.J., McDermott, K., Rooks, B. et al. Decoding individual identity from brain activity elicited in imagining common experiences. Nat Commun 11, 5916 (2020).Journal
Nature CommunicationsRights
Copyright © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Everyone experiences common events differently. This leads to personal memories that presumably provide neural signatures of individual identity when events are reimagined. We present initial evidence that these signatures can be read from brain activity. To do this, we progress beyond previous work that has deployed generic group-level computational semantic models to distinguish between neural representations of different events, but not revealed interpersonal differences in event representations. We scanned 26 participants’ brain activity using functional Magnetic Resonance Imaging as they vividly imagined themselves personally experiencing 20 common scenarios (e.g., dancing, shopping, wedding). Rather than adopting a one-size-fits-all approach to generically model scenarios, we constructed personal models from participants’ verbal descriptions and self-ratings of sensory/motor/cognitive/spatiotemporal and emotional characteristics of the imagined experiences. We demonstrate that participants’ neural representations are better predicted by their own models than other peoples’. This showcases how neuroimaging and personalized models can quantify individual-differences in imagined experiences. © 2020, The Author(s).Note
Open access journalISSN
2041-1723PubMed ID
33219210Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1038/s41467-020-19630-y
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Except where otherwise noted, this item's license is described as Copyright © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License.
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