Self-reports map the landscape of task states derived from brain imaging

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.

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Publication Details

Journal
Communications Psychology
Published
2025-01-22
DOI
https://doi.org/10.1038/s44271-025-00184-y
Citations
10
Primary Topic
Mind wandering and attention
Type
article
Field-Weighted Citation Impact
12.88

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article

Self-reports map the landscape of task states derived from brain imaging

Boris C. Bernhardt, Bridget Mulholland, Robert Leech, Adam G. Turnbull et al.
10 citations
Communications Psychology
Mind wandering and attention
12.88
article

Self-reports map the landscape of task states derived from brain imaging

Boris C. Bernhardt, Bridget Mulholland, Robert Leech, Adam G. Turnbull, Ian Goodall-Halliwell, Xiuyi Wang, Louis Chitiz, Nerissa Siu Ping Ho, Daniel S. Margulies, Tamara Vanderwal, Theodoros Karapanagiotidis, Brontë Mckeown, Meichao Zhang, Michael Peter Milham, Raven Star Wallace, Elizabeth Jefferies, Hao-Ting Wang, Reinder Vos de Wael, Jeffrey D. Wammes, Samyogita Hardikar, Tirso RJ Gonzalez Alam, Jonathan Smallwood, Will Strawson, Ting Xu
article en
10 citations

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.

Communications PsychologyVol. 3(1)
Brighton and Sussex Medical School (GB), Montreal Neurological Institute and Hospital (CA), Centre National de la Recherche Scientifique (FR), University of British Columbia (CA), Bangor University (GB), University of Sussex (GB), Wellcome Centre for Human Neuroimaging (GB), King's College London (GB), Queen's University (CA), Université Paris Cité (FR), Queens University (BD), Child Mind Institute (US), Institut Universitaire de Gériatrie de Montréal (CA), Max Planck Institute for Human Cognitive and Brain Sciences (DE), Institute of Psychology, Chinese Academy of Sciences (CN), University of Chinese Academy of Sciences (CN), McGill University (CA), University of York (GB), University of Plymouth (GB), Stanford University (US)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 1%
Mind wandering and attention
12.88
DOIOpen Access PDFOpenAlex
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