The application of statistical decision theory to a perceptual decision-making problem
Bibliographic record
Abstract
The object of this study was to determine whether statistical decision theory, or a special application of it, the theory of signal detection, could be of value in accounting for the behaviour of subjects in a perceptual decision-making task. The amount of information in these tasks was varied to see if the theory could predict changes in subject performance. Five subjects were required to distinguish between fifty percent time compressed recordings of the stimulus words "commination" and "comminution” embedded in "white" noise. Under one treatment, compression was gained by discarding many small letter segments while in the other this same compression value was obtained by discarding a few large letter segments. It was hypothesized that large-discard- interval compression would be more detrimental to stimulus intelligibility than small-discard-interval compression. Five other subjects were asked to distinguish between the two noise-embedded stimulus words which had been time-compressed sixty and seventy-four percent. It was predicted that sixty percent compression would be less detrimental to the intelligibility of the stimulus words than seventy-four percent compression. Concurrently, in both groups, an attempt was made to manipulate the degree of cautiousness or decision criteria of all ten subjects. Such manipulation was attempted in order to permit the separation of each subjects' actual sensitivity from each's variable decision criterion. This manipulation involved varying the costs and fines associated with correct and incorrect decisions as well as the probabilities of each stimulus word's occurrence. Large-discard-interval compression was found to be less detrimental to intelligibility, as inferred from subject performance, than small-discard-interval compression. This finding was contrary to the first hypothesis. Sixty percent compression, as predicted, was less detrimental to intelligibility than seventy-four percent compression. It was observed that the theory of signal detection permitted separation of each subjects' sensitivity from his monetary degree of cautiousness. This cautiousness was also found to be accessible to manipulation. It is suggested that since the approach of statistical decision theory detected changes in subject performance in response to varying amounts of information, it can be profitably applied to the study of perception.
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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 | 0.046 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".