Hierarchical Bayesian modeling of interoceptive psychophysics

Area of Science:

• Psychology and Psychiatry
• Neuroscience
• Computational Biology

Background:

• Interoception, sensing internal body signals, is crucial in psychology and psychiatry.
• Quantifying interoceptive abilities (sensitivity, bias, precision) uses psychophysical tasks.
• Current methods often require extensive trials or participants, limiting clinical research.

Purpose of the Study:

• To present novel hierarchical Bayesian models for interoceptive psychophysics.
• To efficiently estimate individual and group-level interoceptive parameters (sensitivity, bias, precision).
• To reduce participant burden while maintaining statistical rigor in interoceptive research.

Main Methods:

• Development of hierarchical Bayesian models for cardiac and respiratory interoceptive psychophysics.
• Validation using computational simulations and empirical datasets.
• Provision of R tutorial and sample-size justification app for accessibility.

Main Results:

• The proposed Bayesian models efficiently estimate interoceptive sensitivity, bias, and precision.
• These models offer enhanced statistical inference compared to conventional methods.
• Validated models demonstrate robust, efficient, and generalizable interoceptive performance estimation.

Conclusions:

• Hierarchical Bayesian models provide a powerful tool for interoceptive psychophysics.
• These models facilitate rigorous interoceptive studies with reduced participant burden.
• Openly accessible resources promote wider adoption and advance interoceptive research.