Different definitions of the nonrecollection-based response option(s) change how people use the “remember” response in the remember/know paradigm
Bibliographic record
Abstract
In the remember/know paradigm, a "know" response can be defined to participants as a high-confidence state of certainty or as a low-confidence state based on a feeling of familiarity. To examine the effects of definition on use of responses, in two experiments, definitions of "remember" and "guess" were kept constant, but definitions of "know" and/or "familiar" were systematically varied to emphasize (a) a subjective experience of high confidence without recollection, (b) a feeling of familiarity, (c) both of these subjective experiences combined within one response option, or (d) both of these experiences as separate response options. The confidence expressed in "know" and/or "familiar" definitions affected how participants used response options. Importantly, this included use of the "remember" response, which tended to be used more frequently when the nonrecollection-based middle response option emphasized a feeling of familiarity rather than an experience of "just knowing." The influence of the definitions on response patterns was greater for items that had undergone deep rather than shallow processing, and was greater when deep-encoded and shallow-encoded items were mixed, rather than blocked, at test. Our findings fit with previous research suggesting that the mnemonic traces underlying subjective judgments are continuous and that the remember/know paradigm is not a pure measure of underlying processes. Findings also emphasize the importance of researchers publishing the exact definitions they have used to enable accurate comparisons across studies.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".