Self-reports map the landscape of task states derived from brain imaging
Abstract Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychol...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Psychology |
| Online Access: | https://doi.org/10.1038/s44271-025-00184-y |
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| author | Brontë Mckeown Ian Goodall-Halliwell Raven Wallace Louis Chitiz Bridget Mulholland Theodoros Karapanagiotidis Samyogita Hardikar Will Strawson Adam Turnbull Tamara Vanderwal Nerissa Ho Hao-Ting Wang Ting Xu Michael Milham Xiuyi Wang Meichao Zhang Tirso RJ Gonzalez Alam Reinder Vos de Wael Boris Bernhardt Daniel Margulies Jeffrey Wammes Elizabeth Jefferies Robert Leech Jonathan Smallwood |
| author_facet | Brontë Mckeown Ian Goodall-Halliwell Raven Wallace Louis Chitiz Bridget Mulholland Theodoros Karapanagiotidis Samyogita Hardikar Will Strawson Adam Turnbull Tamara Vanderwal Nerissa Ho Hao-Ting Wang Ting Xu Michael Milham Xiuyi Wang Meichao Zhang Tirso RJ Gonzalez Alam Reinder Vos de Wael Boris Bernhardt Daniel Margulies Jeffrey Wammes Elizabeth Jefferies Robert Leech Jonathan Smallwood |
| author_sort | Brontë Mckeown |
| collection | DOAJ |
| description | Abstract Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity. |
| format | Article |
| id | doaj-art-f758e0bb1ecc473b933c4757bdfa4e76 |
| institution | Kabale University |
| issn | 2731-9121 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Psychology |
| spelling | doaj-art-f758e0bb1ecc473b933c4757bdfa4e762025-01-26T12:51:04ZengNature PortfolioCommunications Psychology2731-91212025-01-013111610.1038/s44271-025-00184-ySelf-reports map the landscape of task states derived from brain imagingBrontë Mckeown0Ian Goodall-Halliwell1Raven Wallace2Louis Chitiz3Bridget Mulholland4Theodoros Karapanagiotidis5Samyogita Hardikar6Will Strawson7Adam Turnbull8Tamara Vanderwal9Nerissa Ho10Hao-Ting Wang11Ting Xu12Michael Milham13Xiuyi Wang14Meichao Zhang15Tirso RJ Gonzalez Alam16Reinder Vos de Wael17Boris Bernhardt18Daniel Margulies19Jeffrey Wammes20Elizabeth Jefferies21Robert Leech22Jonathan Smallwood23Department of Psychology, Queens University, KingstonDepartment of Psychology, Queens University, KingstonDepartment of Psychology, Queens University, KingstonDepartment of Psychology, Queens University, KingstonDepartment of Psychology, Queens University, KingstonSchool of Psychology, University of SussexDepartment of Psychology, Queens University, KingstonDepartment of Neuroscience, Brighton and Sussex Medical School (BSMS), University of SussexDepartment of Psychiatry and Behavioral Sciences, Stanford UniversityDepartment of Psychiatry, Faculty of Medicine, University of British ColumbiaSchool of Psychology, University of PlymouthCentre de recherche de l’institut Universitaire de gériatrie de Montréal (CRIUGM)Centre for the Developing Brain, Child Mind InstituteCentre for the Developing Brain, Child Mind InstituteCAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of SciencesCAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of SciencesSchool of Psychology, Bangor UniversityMontreal Neurological Institute, McGill UniversityMontreal Neurological Institute, McGill UniversityIntegrative Neuroscience and Cognition Center (UMR 8002, Centre National de la Recherche Scientifique (CNRS) and Université de ParisDepartment of Psychology, Queens University, KingstonDepartment of Psychology, University of YorkCentre for Neuroimaging Science, King’s CollegeDepartment of Psychology, Queens University, KingstonAbstract Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity.https://doi.org/10.1038/s44271-025-00184-y |
| spellingShingle | Brontë Mckeown Ian Goodall-Halliwell Raven Wallace Louis Chitiz Bridget Mulholland Theodoros Karapanagiotidis Samyogita Hardikar Will Strawson Adam Turnbull Tamara Vanderwal Nerissa Ho Hao-Ting Wang Ting Xu Michael Milham Xiuyi Wang Meichao Zhang Tirso RJ Gonzalez Alam Reinder Vos de Wael Boris Bernhardt Daniel Margulies Jeffrey Wammes Elizabeth Jefferies Robert Leech Jonathan Smallwood Self-reports map the landscape of task states derived from brain imaging Communications Psychology |
| title | Self-reports map the landscape of task states derived from brain imaging |
| title_full | Self-reports map the landscape of task states derived from brain imaging |
| title_fullStr | Self-reports map the landscape of task states derived from brain imaging |
| title_full_unstemmed | Self-reports map the landscape of task states derived from brain imaging |
| title_short | Self-reports map the landscape of task states derived from brain imaging |
| title_sort | self reports map the landscape of task states derived from brain imaging |
| url | https://doi.org/10.1038/s44271-025-00184-y |
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