‘You caught ’em!’…or not? Feedback affects investigators’ recollections of speech cues thought to signal honesty and deception
Bibliographic record
Abstract
Purpose. When eyewitnesses to crime receive feedback about their choice of a suspect from a line‐up (or post‐identification feedback ), such information can substantially alter their recollections of the witnessing experience. This study examined whether feedback exerts similar effects on investigators’ recollections of a suspect's behaviours. Methods. Participant‐investigators received training on speech cues that they were told, when present in a speaker's account, signal either honesty or deception. After hearing a suspect's account of a theft, participants decided whether the suspect was lying or telling the truth. One‐third of participants subsequently received immediate confirming feedback about their performance, while another third received disconfirming feedback. The remaining one‐third of participants did not receive feedback about their decision. Finally, participants rated the frequencies of speech cues that they had been instructed to detect in the suspect's account. Results. Disconfirming feedback significantly altered retrospective judgments about the characteristics of the suspect's account. Specifically, when told that the decision they made about the speaker's credibility was incorrect, participants judged the speaker as having exhibited fewer behaviours consistent with the credibility decision they had made, relative to those who either received no feedback or confirming feedback. Conclusions. Biases in recollections of a suspect may have consequences in real‐world interrogations wherein investigators assess credibility on the basis of numerous behavioural cues. Results are discussed in light of findings of post‐identification feedback studies on eyewitnesses.
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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 | 0.003 | 0.043 |
| 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) | 0.004 | 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".