The influence of social anxiety-provoking contexts on context reinstatement effects
Bibliographic record
Abstract
The context reinstatement (CR) effect is the finding that target stimuli are better remembered when presented in the same context as during initial encoding, compared with a different context. It remains unclear, however, whether emotional features of the context affect this memory benefit. In two experiments, we investigated whether the anxiety-provoking nature of a context scene might influence the CR effect. During encoding, participants viewed target faces paired with scenes validated as either highly anxiety-provoking or not, half of which contained other faces embedded within the scene. During retrieval, target faces were presented again with either the same or a new context scene. In Experiment 1, the expected CR effect was observed when the contexts were low-anxiety scenes or high-anxiety scenes without embedded faces. In contrast, the CR effect was absent when the contexts were high-anxiety scenes containing embedded faces. In Experiment 2, to determine whether the presence of embedded faces or the anxiety level of scenes reduced the CR effect, we included an additional context type: low-anxiety scenes with embedded faces. Once again, the CR effect was absent only when the context scene was highly anxiety-provoking with embedded faces: reinstating this context type failed to benefit memory for targets. Results suggest that the benefit to target memory via reinstating a context depends critically on emotional characteristics of the reinstated context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".