The role of overt attention in emotion-modulated memory.
Bibliographic record
Abstract
The presence of emotional stimuli results in a central/peripheral tradeoff effect in memory: memory for central details is enhanced at the cost of peripheral items. It has been assumed that emotion-modulated differences in memory are the result of differences in attention, but this has not been tested directly. The present experiment used eye movement monitoring as an index of overt attention allocation and mediation analysis to determine whether differences in attention were related to subsequent memory. Participants viewed negative and neutral scenes surrounded by three neutral objects and were then given a recognition memory test. The results revealed evidence in support of a central/peripheral tradeoff in both attention and memory. However, contrary with previous assumptions, whereas attention partially mediated emotion-enhanced memory for central pictures, it did not explain the entire relationship. Further, although centrally presented emotional stimuli led to decreased number of eye fixations toward the periphery, these differences in viewing did not contribute to emotion-impaired memory for specific details pertaining to the periphery. These findings suggest that the differential influence of negative emotion on central versus peripheral memory may result from other cognitive influences in addition to overt visual attention or on postencoding processes.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".