Will get fooled again: Emotionally intelligent people are easily duped by high‐stakes deceivers
Bibliographic record
Abstract
Purpose. There is major disagreement about the existence of individual differences in deception detection or naturally gifted detection ‘wizards’ (see vs. ). This study aimed to elucidate the role of a specific, and seemingly relevant individual difference – emotional intelligence (EI) and its subcomponents – in detecting high‐stakes, emotional deception. Methods. Participants ( N = 116) viewed a sample of 20 international videos of individuals emotionally pleading for the safe return of their missing family member, half of whom were responsible for the missing person's disappearance/murder. Participants judged whether the pleas were honest or deceptive, provided confidence ratings, reported the cues they utilized, and rated their emotional response to each plea. Results. EI was associated with overconfidence in assessing the sincerity of the pleas and greater self‐reported sympathetic feelings to deceptive targets (enhanced gullibility). Although total EI was not associated with discrimination of truths and lies, the ability to perceive and express emotion (a component of EI), specifically, was negatively related to detecting deceptive targets (lower sensitivity [ d′ ]). Combined, these patterns contributed negatively to the ability to spot emotional lies. Conclusions. These findings collectively suggest that features of EI, and subsequent decision‐making processes, paradoxically may impair one's ability to detect deceit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; both teacher heads agree on what is shown here.
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".