A signal detection analysis of contingency data
Bibliographic record
Abstract
There are many psychological tasks that involve the pairing of binary variables. The various tasks used often address different questions and are motivated by different theoretical issues and traditions. Upon closer examination, however, the tasks are remarkably similar in structure. In the present paper, we examine two such tasks, the contingency judgment task and the signal detection task, and we apply a signal detection analysis to contingency judgment data. We suggest that the signal detection analysis provides a novel interpretation of a well-established but poorly understood phenomenon of contingency judgments—the outcome-density effect. We must often make a decision even though the information we have is ambiguous or uncertain. One such situation is illustrated by a patient being treated by an allergist. The patient sometimes, but not always, develops hives after eating strawberries. Moreover, the patient sometimes develops hives even when strawberries are not eaten. Although the relationship between eating strawberries and developing hives is uncertain, the allergist must decide whether or not to recommend that the patient stop eating strawberries. Another type of ambiguous situation is illustrated by the task confronted by the radiologist. The radiologist must decide whether or not an X-ray indicates the presence of lung cancer. The signals seen in the X-ray are ambiguous, some consistent with lung cancer and others inconsistent with lung cancer. Even though the correct diagnosis is unclear, the radiologist must decide whether or not to recommend treatment. Despite the obvious similarities between the tasks, they have been treated quite differently. The allergy task has often been used by researchers interested in contingency assessment; that is, how humans judge that a cue (strawberry ingestion) imperfectly signals an outcome (see
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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 | 0.025 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".