Judgements of relative order: Mechanisms underlying subspan versus supraspan lists - eScholarship
Bibliographic record
Abstract
Judgements of relative order: Mechanisms underlying subspan versus supraspan lists Yang Liu University of Alberta Michelle Chan University of Alberta Jeremy Caplan University of Alberta Abstract: Judging the relative order of materials is a core function of human memory. In short, subspan consonant lists with immediate judgments of relative recency (JOR), instruction wording (”which item was presented earlier?” versus ”which item was presented later?”) could flip around memory search direction (Chan et al., 2009). We wondered whether instruction wording could have an analogous influence on the JOR judgement in supraspan lists. However, supraspan lists typically show a very different behavioural pattern - distance effects (e.g., Yntema & Trask, 1963). Our participants performed JOR judgements on ”short” (LL=8) supraspan noun lists. We evaluate whether it is possible to reconcile the subspan and supraspan data by assuming that the judgement in both sub- and supra-span regimes are influenced by the same factors, positional discriminability and attentional bias across serial positions, and that speed-accuracy tradeoffs combined with ceiling in subspan lists account for the observed qualitative differences in behaviour.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".