Evaluations of a sequence of affective events presented simultaneously
Bibliographic record
Abstract
Purpose A key finding in the affect integration literature is that for a sequence of events that unfolds sequentially, individuals attend to specific aspects of these events, such as the spread, peak, end, or trend. Due to recent findings of deviations from the peak-end rule, this study closely examines the integration processes of affective events presented sequentially and simultaneously. Design/methodology/approach Three experimental studies were conducted. Study 1a (financial dashboard) and Study 1b (charity advertisement) examine consumers’ overall evaluation for a sequence of mixed affective events. Using eye trackers, Study 2 examines individuals’ attention to particular affective moments presented sequentially and simultaneously. Findings The present research provides additional support for the peak–end rule for the sequential presentation of mixed-valence affective events. However, in the simultaneous mode of presentation, the flexibility to view various affective events decreases the disproportionate weights given to specific events, a divergence from the peak–end rule. Research limitations/implications Although the tempering effect of simultaneous presentation can be concluded, further studies are required to discern how individuals process these events and develop a predictive rule. Practical implications The results of the present study provide clear and actionable directions for application developers and advertising agencies: when communicating information or developing an advertisement, consideration should be given to how each affective event is being communicated. Originality/value It is argued that in the simultaneous mode of presentation, the flexibility to view various affective events allows greater shifts in attention that increase the salience of interconnections and thereby decrease the disproportionate weights given to specific events.
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How this classification was reachedexpand
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 | 0.006 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".