Valenced Cues and Contexts Have Different Effects on Event-Based Prospective Memory
Bibliographic record
Abstract
This study examined the separate influence and joint influences on event-based prospective memory task performance due to the valence of cues and the valence of contexts. We manipulated the valence of cues and contexts with pictures from the International Affective Picture System. The participants, undergraduate students, showed higher performance when neutral compared to valenced pictures were used for cueing prospective memory. In addition, neutral pictures were more effective as cues when they occurred in a valenced context than in the context of neutral pictures, but the effectiveness of valenced cues did not vary across contexts that differed in valence. The finding of an interaction between cue and context valence indicates that their respective influence on event-based prospective memory task performance cannot be understood in isolation from each other. Our findings are not consistent with by the prevailing view which holds that the scope of attention is broadened and narrowed, respectively, by positively and negatively valenced stimuli. Instead, our findings are more supportive of the recent proposal that the scope of attention is determined by the motivational intensity associated with valenced stimuli. Consistent with this proposal, we speculate that the motivational intensity associated with different retrieval cues determines the scope of attention, that contexts with different valence values determine participants' task engagement, and that prospective memory task performance is determined jointly by attention scope and task engagement.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".