Genius is 1% inspiration and 99% perspiration … or is it? An investigation of the impact of motivation and feedback on deception detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it