Interpolated task effects on direct and mediated false recognition: Effects of initial recall, recognition, and the ironic effect of guessing.
Bibliographic record
Abstract
In two experiments, participants studied two types of word lists. Direct lists were taken from the Deese-Roediger-McDermott (DRM) paradigm (e.g., water, bridge, run) and contained words directly related to a nonpresented critical item (CI; e.g., river, Roediger & McDermott, 1995). Mediated lists (e.g., faucet, London, jog) contained words related to the CI through a nonpresented mediator. After each study list, participants completed either a recall test, a recall test with a warning about the CI, arithmetic problems, or a recognition test, or they guessed the CI. On a final recognition test, both warning and guessing decreased direct false recognition but increased mediated false recognition, an ironic effect of guessing. An initial recognition test also increased final mediated false recognition. We argue that warning and guessing tasks strengthened associative pathways to the CI, increased the accessibility of associated mediators, and increased monitoring for the CI at test. Increased monitoring was able to reduce CIs from direct, but not mediated, lists.
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".