Intentionally forgetting other-race faces: Costs and benefits?
Bibliographic record
Abstract
Eyewitnesses to events with multiple actors might be aware that during a subsequent investigation some actors will need to be remembered and others can be forgotten. Research on the directed-forgetting procedure suggests that when some information is cued to be forgotten, retention of other information is enhanced. In three experiments, directed-forgetting conditions were compared with control conditions to assess potential costs and benefits of forgetting other-race faces. In Experiment 1, undergraduate students (N = 148; mostly Caucasian) viewed all Black faces or all Asian faces followed by overt remember or forget cues. Participants in the directed-forgetting conditions of Experiments 2 and 3 received more covert cues instructing them to remember the faces of one race and to forget the faces of another race. In Experiment 2, undergraduate students (N = 116; all Caucasian) viewed Black and Asian faces within the context of a criminal storyline. In Experiment 3, undergraduate students (N = 94; all Caucasian) again viewed Black and Asian faces; however, the remember and forget cues were embedded in a noncriminal narrative. Although faces generally were forgotten on cue, forgetting some faces did not enhance memory for other faces. Furthermore, recognition of remember-cued faces was impaired by exposure to forget-cued faces. These findings indicate that faces can be forgotten on cue, but that doing so confers no benefit for remembering other faces. Eyewitnesses are advised that exposure to irrelevant faces reduces the likelihood that relevant faces will be remembered, even when effort is allocated to forgetting the irrelevant faces.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".