Comparison of momentary and retrospective soundscape evaluations
Bibliographic record
Abstract
The Peak and End rule describes the effect that retrospective evaluations of temporal events significantly depend on the most extreme affect (peak) experienced during an episode and on the affect at the ending. Other features, like the duration of an event, seem to be widely negligible. We are testing this hypothesis in the context of soundscape evaluation in a series of listening tests conducted in Montréal, Canada, and Duesseldorf, Germany. The soundscapes consisted of recordings of different locations (e.g., railway station, park) and were edited so that there was one presumed emotional “peak moment.” The task of the test group was to indicate momentary judgments by continuously adjusting a slider on a computer interface over the course of the stimulus presentation. Additionally, the participants had to make an overall retrospective rating of the soundscapes after listening to them. To investigate attention effects in the course of the test task a second group was asked to only judge the sounds retrospectively. Preliminary results indicate that the Peak and End rule in combination with the averaged momentary evaluations well predict the retrospective judgments. Within this contribution the results of the experiment will be presented and implications for soundscape design will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".