Judgments of alphabetical order and mechanisms of congruity effects.
Bibliographic record
Abstract
= 340) produced a clear congruity effect in response time and even error rate (when controlled for response time). The large number of serial positions afforded by the alphabet enabled us to test a repertoire of mathematical models instantiating four distinct mechanisms of the congruity effect, against the empirical serial-position effects. The best-performing model assumed a response bias toward a discrete set of letters conceived of as "early" versus "late," respectively, an account that had previously been ruled out for typical comparative-judgment paradigms. In contrast, models implementing congruity effect mechanisms supported for conventional comparative judgment paradigms (based on reference-point theory or positional discriminability) produced quantitatively poorer fits, with more curvilinear serial-position effects that deviated from the data. The congruity effect thus extends to long, highly directional semantic-memory lists. However, qualitatively different serial-position effects across models suggest that, despite the superficial similarity, there are probably several quite different mechanisms that produce congruity effects, which may, in turn, depend on specific task characteristics. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.002 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.004 |
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".