DO YOU HEAR WHAT I HEAR? ADVANCES IN WEB-BASED PERCEPTUAL TESTING AND TRAINING
Bibliographic record
Abstract
This paper describes a newly developed online perceptual testing and training tool called the Perceptual Chronograph.This tool was initially developed to test the reaction of salespeople to customer verbal and nonverbal cues in an experimentally designed 'sales transaction'.The Chronograph records the correct or incorrect identification of target or manipulated information, (signal detection theory), records the sensor's evaluation of the information, and also records reactions that a sensor would have in response to the identified information.The Chronograph has virtually unlimited potential for media richness: verbal, nonverbal, visual, contextual or even temporal information can be included.It also has the potential to be used as a training device, whereby exemplary sensors are tested and their response patterns analyzed to create a 'fuzzy gold' standard of behavior.Novice or less perceptually astute sensors (the 'novice') can be tested and their results analyzed.The differences between the exemplar and the novice can then be compared, either at the time of testing, or in a later training session.After feedback is given, the novice can be re-tested to ensure learning.Although the Chronograph was developed and tested in the sales context, it has learning and testing applications in many areas of research where a sensor (person or system) must perceive, evaluate, and respond to uncertain or conflicting information: (e.g., social perception, forecasting, and medical diagnosis).
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".