A flicker change detection task reveals object-in-scene memory across species
Bibliographic record
Abstract
Tests of recognition memory in macaques typically assay memory for objects or isolated images, over time spans of seconds to hours from stimulus presentation, and/or require extensive training. Here, we propose a new application of the flicker change detection task that could measure object-in-scene memory days after single-trial exposures. In three experiments, participants searched for a changing object - or "target" - embedded within a scene as their eye movements were tracked. For new targets-in-scenes, the change is difficult to detect and requires extensive search. Once the target is found, however, the change becomes obvious. We reasoned that the decreased times required to find a target in a repeated scene would indicate memory for the target. In humans, targets were found faster when the targets-and-scenes were explicitly remembered than when they were forgotten, or had never been seen before. This led to faster repeated-trial compared to novel-trial search times. Based solely on repeated-trial search times, we were able to select distributions comprised of predominantly remembered or predominantly forgotten trials. Macaques exhibited the same repetition effects as humans, suggesting that remembered trials could be dissociated from novel or forgotten trials using the same procedures we established in humans. Finally, an anterograde amnesic patient with damage that included the medial temporal lobe (MTL) showed no search time differences, suggesting that memory revealed through search times on this task requires MTL integrity. Together, these findings indicate that the time required to locate a changing object reveals object-in-scene memory over long retention intervals in humans and macaques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".