Individual differences in incorrect responding and the ability to discriminate the source of the products of retrieval.
Bibliographic record
Abstract
When memory is tested, researchers are often interested in the items that were correctly recalled or recognized, while ignoring or factoring out trials where one "recalls" or "recognizes" a nonstudied item. However, intrusions and false alarms are more than nuisance data and can provide key insights into the memory system. The present article reports 2 experiments demonstrating that people are remarkably consistent in the rate at which they incorrectly report memory for nonstudied items, even across a range of differing stimuli and test features. Experiment 1 found that individual differences in false alarms and intrusions were strongly related, even while controlling for the shared influence of memory ability on these incorrect response types. Furthermore, intrusion rate was found to be related to response bias as well, but this effect was suppressed by the shared influence of memory ability, demonstrating that an independent measure of memory ability is an important control in investigations into individual differences in response bias. Experiment 2 revealed that the relations between intrusions and false alarm rate and response bias were entirely explained by one's ability to discriminate episodic memories from semantic generation. This work links together previous work on individual differences in intrusions and in false alarms, and highlights the ability to identify the source of a memory as the key cognitive trait underlying incorrect response styles on various memory tests. (PsycINFO Database Record
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".