Genius is 1% inspiration and 99% perspiration … or is it? An investigation of the impact of motivation and feedback on deception detection
Bibliographic record
Abstract
Purpose . Although most people perform around the level of chance in making credibility judgments, some researchers have hypothesized that high motivation and the provision of accurate feedback could lead to a higher accuracy rate. This study examined the influence of these factors on judgment accuracy and whether any improvement following feedback was related to social facilitation, a gradual incorporation of successful assessment strategies, or a re‐evaluation of ‘tunnel vision’ decision‐making. Methods . Participants ( N = 151) were randomly assigned to conditions according to motivation level (high/low) and feedback (accurate, inaccurate or none). They then judged the credibility of 12 videotaped speakers either lying or telling the truth about a personal experience. Results . Highly motivated observers performed less accurately ( M = 46.0%), but more confidently, than those in the low‐motivation condition ( M = 60.0%). Although there was no main effect of feedback, the provision of any feedback (accurate or inaccurate) served to diminish the motivational impairment effect. Further, high motivation was associated with a relatively low ‘hit’ rate and high ‘false‐alarm’ rate. This suggested that in the absence of feedback the judgments of highly motivated participants were made through tunnel vision. Conclusions . The results suggest that it is important for lie‐catchers to monitor their motivation level to ensure that over‐enthusiasm is not clouding their judgments. It may be useful for professionals engaged in deception detection to regularly discuss their judgments with colleagues as a form of feedback in order to re‐evaluate their own decision‐making strategies.
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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 | 0.005 | 0.057 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".