Somatosensory false feedback biases emotional ratings through interoceptive embodiment
Bibliographic record
Abstract
Mismatches between perceived and veridical physiological signals during false feedback (FFB) can bias emotional judgements. Paradigms using auditory FFB suggest perceived changes in heart rate (HR) increase ratings of emotional intensity irrespective of feedback type (increased or decreased HR), implicating right anterior insula as a mismatch comparator between exteroceptive and interoceptive information. However, few paradigms have examined effects of somatosensory FFB. Participants rated the emotional intensity of randomized facial expressions while they received 20 s blocks of pulsatile somatosensory stimulation at rates higher than HR, lower than HR, equivalent to HR, or no stimulation during a functional magnetic resonance neuroimaging scan. FFB exerted a bidirectional effect on reported intensity ratings of the emotional faces, increasing over the course of each 20 s stimulation block. Neuroimaging showed FFB engaging regions indicative of affective touch processing, embodiment, and reflex suppression. Contrasting higher vs. lower HR FFB revealed engagement of right insula and centres supporting socio-emotional processing. Results indicate that exposure to pulsatile somatosensory stimulation can influence emotional judgements though its progressive embodiment as a perceived interoceptive arousal state, biasing how affective salience is ascribed to external stimuli. Results are consistent with multimodal integration of priors and prediction-error signalling in shaping perceptual judgments.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".