Chatting in the face of the eyewitness: The impact of extraneous cell-phone conversation on memory for a perpetrator.
Bibliographic record
Abstract
Cell-phone conversation is ubiquitous within public spaces. The current study investigates whether ignored cell-phone conversation impairs eyewitness memory for a perpetrator. Participants viewed a video of a staged crime in the presence of 1 side of a comprehensible cell-phone conversation (meaningful halfalogue), 2 sides of a comprehensible cell-phone conversation (meaningful dialogue), 1 side of an incomprehensible cell-phone conversation (meaningless halfalogue), or quiet. Between 24 and 28 hr later, participants freely described the perpetrator's face, constructed a single composite image of the perpetrator from memory, and attempted to identify the perpetrator from a sequential lineup. Further, participants rated the likeness of the composites to the perpetrator. Face recall and lineup identification were impaired when participants witnessed the staged crime in the presence of a meaningful halfalogue compared to a meaningless halfalogue, meaningful dialogue, or quiet. Moreover, likeness ratings showed that the composites constructed after ignoring the meaningful halfalogue resembled the perpetrator less than did those constructed after experiencing quiet or ignoring a meaningless halfalogue or a meaningful dialogue. The unpredictability of the meaningful content of the halfalogue, rather than its acoustic unexpectedness, produces distraction. The results are novel in that they suggest that an everyday distraction, even when presented in a different modality to target information, can impair the long-term memory of an eyewitness. (PsycINFO Database Record
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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 | 0.000 | 0.007 |
| 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 | 0.000 |
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".