Intentionally forgetting other-race faces: Costs and benefits?
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
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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