Eye Movement Monitoring Reveals Differential Influences of Emotion on Memory
Bibliographic record
Abstract
Research shows that memory for emotional aspects of an event may be enhanced at the cost of impaired memory for surrounding peripheral details. However, this has only been assessed directly via verbal reports which reveal the outcome of a long stream of processing but cannot shed light on how/when emotion may affect the retrieval process. In the present experiment, eye movement monitoring (EMM) was used as an indirect measure of memory as it can reveal aspects of online memory processing. For example, do emotions modulate the nature of memory representations or the speed with which such memories can be accessed? Participants viewed central negative and neutral scenes surrounded by three neutral objects and after a brief delay, memory was assessed indirectly via EMM and then directly via verbal reports. Consistent with the previous literature, emotion enhanced central and impaired peripheral memory as indexed by eye movement scanning and verbal reports. This suggests that eye movement scanning may contribute and/or is related to conscious access of memory. However, the central/peripheral tradeoff effect was not observed in an early measure of eye movement behavior, i.e., participants were faster to orient to a critical region of change in the periphery irrespective of whether it was previously studied in a negative or neutral context. These findings demonstrate emotion's differential influences on different aspects of retrieval. In particular, emotion appears to affect the detail within, and/or the evaluation of, stored memory representations, but it may not affect the initial access to those representations.
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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 | 0.002 |
| 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 | 0.000 | 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".