Sports team participation in high school and adolescent and young adult depression and anxiety across sex, race/ethnicity, and socioeconomic status.

Interoception, the sensing and appraisal of internal bodily states, has emerged as a key focus in psychology and psychiatry, linking brain–body signaling to cognition, emotion, and self-awareness (Cameron, ; Craig, ). For example, interoceptive monitoring of cardiac signals has been linked to emotional arousal and fear processing, and it is frequently investigated in relation to anxiety, panic, and psychosis (Adams et al., ; Ehlers et al., ; Jeganathan et al., ). Despite this potential as a psychiatric biomarker, methods for quantifying and modeling interoception remain limited. To address this issue, we present a hierarchical Bayesian modeling approach for interoceptive psychophysics, spanning the respiratory and cardiac domains. Interoception research faces several methodological limitations (Desmedt et al., ). In particular, commonly used methods such as the heartbeat counting task or self-report questionnaires suffer from psychometric issues related to construct validity and other related shortcomings (Desmedt, Heeren et al., ; Desmedt, Van Den Houte et al., ; Zamariola et al., ). Classical psychophysical tasks address some of these limitations, but require a substantial number of trials for reliable estimates, resulting in long testing times and high cognitive burdens that are often impractical for pediatric or clinical populations. Even in healthy volunteers, extended testing sessions risk reducing data quality due to distraction, habituation, or fatigue. This trade-off between validity and feasibility indicates the need for methods that yield valid and reliable estimates while minimizing burden on participants. Without such advances, rigorous investigation of the proposed link between interoception and mental health will remain substantially constrained. To this end, we recently developed and validated two new tasks: the Heart Rate Discrimination Task (HRDT) (Legrand et al., ) and the Respiratory Resistance Sensitivity Task (RRST) (Nikolova et al., ). These tasks enable the estimation of individual participants’ cardiac and respiratory interoceptive psychometric functions, whose thresholds and slopes provide indices of perceptual sensitivity or bias and perceptual precision, respectively. Unlike classical tasks, the HRDT and RRST use an adaptive Bayesian algorithm to select stimulus intensity at each trial based on the participant’s previous responses, drastically reducing the number of trials required to obtain reliable estimates (Kontsevich & Tyler, ). Adoption of these tasks partially helps resolve the trade-off, improving both theoretical validity and estimation efficiency.