Commonsense beliefs and the identification of familiar voices
Bibliographic record
Abstract
Abstract Two experiments are reported in which participants attempted to reject the tape‐recorded voice of a stranger and identify by name the voices of three personal associates who differed in their level of familiarity. In Experiment 1 listeners were asked to identify speakers as soon as possible, but were not allowed to change their responses once made. In Experiment 2 listeners were permitted to change their responses over successive presentations of increasing durations of voice segments. Also, in Experiment 2 half of the listeners attempted to identify speakers who spoke in normal‐tone voices, and the remainder attempted to identify the same speakers who spoke in whispers. Separate groups of undergraduate students attempted to predict the performance of the listeners in both experiments. Accuracy of performance depended on the familiarity of speakers and tone of speech. A between‐subjects analysis of rated confidence was diagnostic of accuracy for high familiar and low familiar speakers (Experiment 1), and for moderate familiar and unfamiliar normal‐tone speakers (Experiment 2). A modified between‐subjects analysis assessed across the four levels of familiarity yielded reliable accuracy‐confidence correlations in both experiments. Beliefs about the accuracy of voice identification were inflated relative to the significantly lower actual performance for most of the normal‐tone and whispered‐speech conditions. Forensic significance and generalizations are addressed. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.065 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".