EXTENDING EVIDENCE ACCRUAL MODELS OF TWO-ALTERNATIVE FORCED-CHOICE DECISION MAKING TO THE n-ALTERNATIVE CASE
Bibliographic record
Abstract
Stimuli in a series of four experiments were 20x20 matrices, each element of which was a square coloured red, green, blue, or black. Systematic manipulation of the frequencies with which each colour appeared allowed for the generation of two, three, and four coloured stimuli. By asking participants to evaluate the stimuli and decide which colour was represented either 'most ' or 'least', response time, accuracy, mean confidence, and mean time to render confidence data was generated for a series of n-alternative forced-choice (nAFC) decisions where 2≤n≤4. A new nAFC model is discussed in light of these data. Many modellers of two-alternative forced choice (2AFC) sensory-based decision-making assume judgements follow from a series of discrete evidence accrual events (for example, Vickers, 1979; Petrusic, 1992; Van Zandt, 2000). Under these models, a decision is made only after a criterion amount of evidence has been collected either in support of one possible alternative choice or the other. Few researchers have attempted to extend these models beyond the limited 2AFC case and into the realm of nAFC decision-making. Vickers (1979; see also Vickers and Lee, 1998)
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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 | 0.060 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".