respyra: A general-purpose respiratory motor control tracking toolbox for interoception research

Respiratory interoception is increasingly recognized as a key modality linking bodily sensation to emotional experience and self-regulation. However, few tools exist for studying the sensorimotor dynamics of voluntary breathing control. Here we present respyra, an open-source Python toolbox that integrates a Vernier Go Direct Respiration Belt with PsychoPy to enable real-time respiratory motor control tracking experiments. Participants follow a sinusoidal target with their breathing while receiving continuous visual biofeedback. The toolbox supports configurable experimental conditions including multi-frequency target waveforms and visuomotor perturbations (visual gain manipulation), in which the displayed breathing trace is amplified or attenuated relative to ground truth. We report proof-of-concept data from a single participant completing six trials alternating between veridical feedback and a 1.5× gain perturbation condition. Results demonstrate that the participant achieved accurate respiratory tracking (overall MAE = 0.174N) and that the gain perturbation reliably increased tracking error (perturbed MAE = 0.240N) relative to veridical feedback (MAE = 0.107N), while remaining learnable within the session. The toolbox is freely available and designed to facilitate future research on respiratory sensorimotor control, interoceptive learning, and breathing-based interventions.