Emotion and list context modulate the impact of expectation on memory formation
Bibliographic record
Abstract
Emotional events are often remembered more vividly than neutral ones, and unexpected events also tend to receive enriched encoding. However, little is known about how emotional valence and expectation violations jointly shape episodic memory. This study examined their combined influence by orthogonally manipulating emotional content and expectation during encoding. Participants first learned predictive contingencies in a rule-learning task. An encoding phase followed, in which some stimuli violated established expectations, while emotional content was manipulated orthogonally to expectation. Recognition memory was then tested for expected and unexpected stimuli. In mixed-valence lists (Experiment 1), unexpected stimuli enhanced recollection for negative and neutral items but not for positive ones. When arousal was matched across valence (Experiment 2), the benefit shifted to unexpected positive and neutral stimuli. In purely emotional lists (Experiment 3), the effect of expectation violations on recollection was diminished, and memory was shaped primarily by valence, with negative stimuli eliciting greater recollection than positive ones. These findings show that the mnemonic benefit of unexpected events is not uniform but varies with valence, arousal, and list composition. They support layered models of distinctiveness, suggesting that memory emerges from the interaction of item-level expectancy, emotional salience, and contextual variability.
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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 | 0.001 | 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".