Testing the impact of emotional mood and cue characteristics on detailed autobiographical memory retrieval.
Bibliographic record
Abstract
Autobiographical memory retrieval is impacted by emotion, whether from an individual's mood state or a retrieval cue. Here, we addressed two questions concerning how emotion from these two sources affects the remembering of autobiographical experiences. The first question concerns whether emotional mood and retrieval cues both reliably impact the details and content of a recalled autobiographical memory. The second question concerns to what extent distinct emotional dimensions of retrieval cues-valence and arousal-individually impact the way these memories are recalled. Across three experiments, young adult participants described the details of autobiographical experiences in response to cue words that varied in emotional valence (Experiment 1) or both emotional valence and arousal (Experiments 2 and 3) under two mood states (happy or sad). Memory descriptions were scored for the number of specific episodic (internal) and nonepisodic (external) details as well as for overall emotional tone. Experiment 1 demonstrated that cue valence more reliably predicted the number of episodic details and tone of the memories than mood. Experiment 2 and 3 further explored this reported cue effect by comparing memory recollection to cues that systematically varied in both valence and arousal. Generally, we found that highly arousing cues led to memories described with more episodic details while cue valence predicted the emotional tone of the memory. We discuss how these dimensions of emotionality (i.e., valence and arousal) bias cued autobiographical memory recall and the implications these results have on models of memory. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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) | 0.000 | 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".