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

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  • Brontë Mckeown
    Queens's University, Kingston, Canada
  • Ian Goodall-Halliwell
    Queens's University, Kingston, Canada
  • Raven Wallace
    Queens's University, Kingston, Canada
  • Louis Chitiz
    Queens's University, Kingston, Canada
  • Bridget Mulholland
    Queens's University, Kingston, Canada
  • Theodoros Karapanagiotidis
    University of Sussex
  • Samyogita Hardikar
    Queens's University, Kingston, Canada
  • Will Strawson
    University of Sussex
  • Adam Turnbull
    Stanford University
  • Tamara Vanderwal
    University of British Columbia
  • Nerissa Ho
    University of Plymouth
  • Hao-Ting Wang
    Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM)
  • Ting Xu
    Child Mind Institute
  • Michael Milham
    Child Mind Institute
  • Xiuyi Wang
    Chinese Academy of Sciences
  • Meichao Zhang
    Chinese Academy of Sciences
  • Tirso Rj Gonzalez Alam
  • Reinder Vos de Wael
    McGill University, Montreal
  • Boris Bernhardt
    McGill University, Montreal
  • Daniel Margulies
    Integrative Neuroscience and Cognition Center (UMR 8002
  • Jeffrey Wammes
    Queens's University, Kingston, Canada
  • Elizabeth Jefferies
    University of York
  • Robert Leech
    King's College London
  • Jonathan Smallwood
    Queens's University, Kingston, Canada

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.

Original languageEnglish
Pages (from-to)8
JournalCommunications Psychology
Volume3
Issue number1
Early online date22 Jan 2025
DOIs
Publication statusE-pub ahead of print - 22 Jan 2025
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