Contextual effects on prospective person memory
Bibliographic record
Abstract
To assist with missing person investigations, the public may be on the lookout during their everyday activities and alert the authorities if the person is encountered. In this Registered Report, participants encoded posters that included an image of a target person along with relevant, irrelevant, or no contextual information about that person. After viewing a poster, participants watched a video that included either the target or a plausible nontarget, using a new experimental paradigm that kept all other conditions of the encounter constant. Previous findings suggest contextual information could affect prospective person memory in several ways. If contextual cues are relevant, they could direct attention to targets and plausible nontargets without improving face recognition and hence have no effect on discriminability ( sighting bias hypothesis ). Alternatively, any contextual information at encoding (relevant or irrelevant) could encourage deeper processing of each target’s identity and improve sighting discriminability ( elaborative encoding hypothesis ). A third possibility is that associating a target with relevant contextual information improves both face recognition and attention, resulting in greater sighting discrimination compared with irrelevant or no contextual information ( context matching hypothesis ). We tested 396 participants and found that associating target faces with contextual information had no significant effect on discriminating between targets and plausible nontargets. The context manipulation also had no significant effect on response bias. Our findings suggest that the previously reported recognition advantage might depend on the kind of contextual information at encoding, on how targets are encountered during testing, as well as on the type of recognition task.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".