Scanning of Face-Scene or Object-Scene Pairs Reveals Implicit Relational Memory
Bibliographic record
Abstract
The hippocampus is thought to play a role in the formation of memories of relations among items in a scene (Cohen and Eichenbaum, 1993). Recently, we described a change detection task in which visual scanning of objects in a scene indicated explicit memory for those objects, and is thought to require hippocampal function (Chau, Murphy, Rosenbaum, Ryan, & Hoffman, 2010). In contrast, a task pairing faces and scenes revealed that the scanning of faces can be used as a measure of implicit memory, yet it, too, is associated with hippocampal function (Hannula & Ranganath, 2009). One difference between tasks is that the latter was never tested with objectscene pairs. In this study, we replicated the face-scene task, and added an object-scene condition to determine if the difference in scanning of previously shown pairs exists for objects-scene pairs and if, as with faces, this bias exists in the absence of explicit recall. Paired items were viewed preferentially, whether the items were faces or objects, and irrespective of whether recall was implicit or explicit. The bias towards the paired image emerged within the first 500 ms of viewing for all pairs, and the protracted response was stronger for explicit than implicit pairs. These results suggest that this task is effective whether using face or object stimuli, and could be used to tease apart the role of the hippocampus in explicit and implicit memory formation. Furthermore, its use of non-verbal measurements makes it amenable for use in animal models.Authors: Branfield Day, Leora R.; Bartlett, Adrian M.; Leonard, Timothy K. and Hoffman, Kari L.
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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 | 0.000 | 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) | 0.002 | 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".