Decision-making models of remember-know judgments: Comment on Rotello, Macmillan, and Reeder (2004).
Bibliographic record
Abstract
The sum-difference theory of remembering and knowing (STREAK) provides a sophisticated account of many interactions in the remember-know (R-K) area (C. M. Rotello, N. A. Macmillan, & J. A. Reeder, 2004). It assumes 2 orthogonal strength dimensions and oblique criterion planes. Another dual-process model (J. T. Wixted & V. Stretch, 2004) with one decision axis has also been applied to R-K judgments with considerable success and provides new insights into the processes involved. An analysis of the 4 major R-K interactions can also be explained by a simpler one-dimensional signal detection theory (J. C. Dunn, 2004a). However these models do not make contact with standard work on recognition memory, so their scope is limited. To bridge this gap, a global-matching model (a theory of distributed associative memory [TODAM]) for R-K judgments is proposed. This model can produce good fits to the data, and there are established experimental manipulations with which to test it. It provides further support for the idea that R judgments are based on associative information, whereas K judgments are based on item information.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 0.013 |
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".