Who Defines Embodiment? Cultural Bias in Interoceptive Wellness Technologies
Bibliographic record
Abstract
Interoception-the perception of internal bodily states such as heartbeat, hunger, and emotion-is foundational to well-being.Despite its significance in wellness technologies within Human-Computer Interaction (HCI), existing designs often impose a universalized model of bodily awareness, shaped by Western-centric assumptions, and overlook cultural variability.This paper integrates perspectives from neuroscience, cultural psychology, Science and Technology Studies (STS), and HCI to critically examine how culture shapes interoception.Through a thematic analysis of the literature, we identify key cultural and contextual dimensions that influence interoceptive experiences and their implications for wellness technologies.Rather than prescribing design solutions, this work challenges dominant paradigms in wellness technology, emphasizing interoception as culturally shaped rather than biologically universal.We highlight overlooked complexities in interoceptive experience and raise critical questions for the development of more inclusive, contextually responsive wellness technologies-technologies that do not simply monitor bodies, but support people in reconnecting with them on their own terms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".