Higher Emotional Intelligence Is Associated With a Stronger Rubber Hand Illusion
Bibliographic record
Abstract
The aim of the study was to investigate how emotion information processing factors, such as alexithymia and emotional intelligence, modulate body ownership and influence multisensory integration during the ‘rubber hand illusion’ (RHI) task. It was previously shown that alexithymia correlates with RHI, and we suggested that emotional intelligence should also be a top-down factor of body ownership, since it was not shown in previous experiments. We elaborated the study of Grynberg and Pollatos [Front. Hum. Neurosci. 9 (2015) 357] with an additional measure of emotional intelligence, and propose an explanation for the interrelation of emotion and body ownership processing. Eighty subjects took part in the RHI experiment and completed the Toronto Alexithymia Scale and the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT). Only MSCEIT was detected to be a significant predictor of the subjective measure of the RHI. There were no significant correlations between alexithymia scores and the test statements of the RHI or the proprioceptive drift, thus we did not replicate the results of Grynberg and Pollatos. However, alexithymia correlated with the control statements of subjective reports of the illusion, which might be explained as a disruption of the ability to discriminate and describe bodily experience. Therefore, (1) alexithymia seems to be connected with difficulties in conscious or verbal processing of body-related information, and (2) higher emotional intelligence might improve multisensory integration of body-related signals and reflect better predictive models of self-processing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".