DO YOU HEAR WHAT I HEAR? ADVANCES IN WEB-BASED PERCEPTUAL TESTING AND TRAINING
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
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.,
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.015 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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