Conceptual similarity alters the impact of context shifts on temporal memory
Bibliographic record
Abstract
Past work has shown that changes in encoding contexts (context shifts) act as boundaries across encountered items and can impair temporal memory. We address two questions about this effect: whether conceptual similarity among contexts creating a boundary can alleviate temporal memory impairments and if this effect holds for different forms of contexts (spatial vs. categorical). In a between-subjects design, participants studied the order of sequentially presented faces (items), each presented with an associated context. One group was shown images of a room (spatial) and the other images of a dessert (categorical) as the context. For both, boundaries between contexts with overlapping (similar) or non-overlapping (distinct) conceptual features were introduced. At test, participants performed a recency judgment for pairs of items that crossed or did not cross a context boundary at encoding and recalled whether they were encoded within the same, similar, or distinct context. For both groups, recency judgments were more accurate for item pairs from similar than distinct contexts, but memory for the context relationship between items was more accurate for items from distinct than similar contexts. Our findings suggest that conceptual knowledge impacts how events are parsed during encoding and affects temporal associations formed in episodic memory.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".