Examining the effects of negative emotion and interviewing procedure on eyewitness recall
Bibliographic record
Abstract
Abstract Witnessing or experiencing a crime can be emotionally distressing and this emotional reaction can affect the formation and retrieval of event‐related memory. Extant eyewitness research, however, has generated inconsistent conclusions regarding the effects of emotional arousal on eyewitness memory. In the present experiment, we used a mock witness paradigm to attempt to remedy several methodological limitations that have persisted in the literature and shed light on the effects of emotional memory within an investigative interviewing context. Participants (N = 132) viewed either a Negative or Neutral video and either immediately or one week later provided their account of the video in a virtual interview procedure, consisting of either cognitive interview‐ based instructions or a free recall. Negative emotion was associated with selectively enhanced recall for the central aspects of the video. Participants who viewed the Negative video reported more details that were central to the target video than did those who viewed the Neutral video. The present findings highlight the potential for negative emotional events to lead to focally enhanced recall performance.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".