Bibliographic record
Abstract
Encoding of false visual memories can have severe negative consequences (e.g. incorrectly identifying foraged berries; mistaken eyewitness testimony). This phenomenon remains unclear, as most research focuses on verbal episodic stimuli, or nonrepresentational drawings when studied in the visual domain. Here, we complement this prior work by probing for whether false memories could be induced for unusual object-scene pairings in realistic photographs. Participants (N = 60) studied photographs of household scenes (SCEGRAM database: Öhlschläger & Võ, 2017). Later, we added unusual objects (e.g. ketchup in shower) to some scenes and removed them from others, and participants indicated whether each scene was an exact match with a studied scene. We investigated whether participants would report false memories for scenes where the unusual object was added and whether the frequency of these endorsements would differ from other false alarms (e.g., to brand-new, object-removed scenes). The hit rate for exact-match scenes was high, indicating strong memory formation. Interestingly, the false memory rate for object-added scenes was greater than for brand-new scenes. However, participants were also highly likely to false alarm to object-removed scenes, indicating a reliance on gist-based memory that overlooks incongruous objects. We also investigated the downstream fate of memories for the changed objects themselves. We found a boost to memory for objects that, via their addition or removal, violated predictions from prior memories. In contrast to previous work, we showed that false memories can be generated even for out-of-place objects, where the error cannot be attributed to scene schemas. Though participants endorsed schema violations, they were also likely to overlook these violations when the object was removed at test, highlighting the malleability of visual memory. Current follow-ups identify periods of off-task thought in real-time to trigger trials that gauge the association between susceptibility to false memory and ongoing attentional states.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".