TY - JOUR

T1 - Self-reports map the landscape of task states derived from brain imaging

AU - Mckeown, Brontë

AU - Goodall-Halliwell, Ian

AU - Wallace, Raven

AU - Chitiz, Louis

AU - Mulholland, Bridget

AU - Karapanagiotidis, Theodoros

AU - Hardikar, Samyogita

AU - Strawson, Will

AU - Turnbull, Adam

AU - Vanderwal, Tamara

AU - Ho, Nerissa

AU - Wang, Hao-Ting

AU - Xu, Ting

AU - Milham, Michael

AU - Wang, Xiuyi

AU - Zhang, Meichao

AU - Gonzalez Alam, Tirso Rj

AU - Vos de Wael, Reinder

AU - Bernhardt, Boris

AU - Margulies, Daniel

AU - Wammes, Jeffrey

AU - Jefferies, Elizabeth

AU - Leech, Robert

AU - Smallwood, Jonathan

N1 - © 2025. The Author(s).

PY - 2025/1/22

Y1 - 2025/1/22

N2 - 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.

AB - 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.

U2 - 10.1038/s44271-025-00184-y

DO - 10.1038/s44271-025-00184-y

M3 - Article

C2 - 39843761

VL - 3

SP - 8

JO - Communications Psychology

JF - Communications Psychology

SN - 2731-9121

IS - 1

ER -