Judgments of alphabetical order and mechanisms of congruity effects.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it