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 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".