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Record W4415961995 · doi:10.1037/emo0001601

Human psychophysiology is influenced by physical touch with a “breathing” robot.

2025· article· en· W4415961995 on OpenAlexafffund
Zachary Witkower, Laura Cang, Paul Bucci, Karon E. MacLean, Jessica L. Tracy

Bibliographic record

VenueEmotion · 2025
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychophysiologyBreathingRobotHeart rateHuman–robot interactionTest (biology)

Abstract

fetched live from OpenAlex

People often physically cling to others when afraid and doing so can downregulate negative emotional experiences (e.g., Coan et al., 2006). However, in some situations, physical touch may fail to downregulate emotional experiences-such as when an individual being touched is physiologically aroused themselves. To test this hypothesis, we built plush robots with motorized plastic ribcages that were manipulated to contract and expand to simulate human breathing patterns. Participants held these robots while we measured their heart rate before, during, and after watching a fear-eliciting stimulus. Consistent with our hypothesis, participants who interacted with robots that exhibited accelerated-breathing patterns experienced a pronounced increase in their own heart rate, compared to participants who held stable-breathing and nonbreathing robots. These results indicate that holding or clinging to others engaged in accelerated breathing may be ineffective or detrimental for downregulating one's own physiological arousal. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.015
GPT teacher head0.381
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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