Opposite effects of emotion and event segmentation on temporal order memory and object-context binding
Bibliographic record
Abstract
Our daily lives unfold continuously, yet our memories are organised into distinct events, situated in a specific context of space and time, and chunked when this context changes (at event boundaries). Previous research showed that this process, termed event segmentation, enhances object-context binding but impairs temporal order memory. Physiologically, peaks in pupil dilation index event segmentation, similar to emotion-induced bursts of autonomic arousal. Emotional arousal also modulates object-context binding and temporal order memory. Yet, these two critical factors have not been systematically studied together. To address this gap, we ran a behavioural experiment using a paradigm validated to study event segmentation and extended it with emotion manipulation. During encoding, we sequentially presented greyscale objects embedded in coloured frames (colour changes defining events), with a neutral or aversive sound. During retrieval, we tested participants’ memory of temporal order memory and object-colour binding. We found opposite effects of emotion and event segmentation on episodic memory. While event segmentation enhanced object-context binding, emotion impaired it. On the contrary, event segmentation impaired temporal order memory, but emotion enhanced it. These findings increase our understanding of episodic memory organisation in laboratory settings, and potentially in real life with perceptual changes and emotion fluctuations constantly interacting.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".