Testing the impact of emotional mood and cue characteristics on detailed autobiographical memory retrieval.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it