Stimulus-based mirror effects revisited.
Bibliographic record
Abstract
The mirror effect is the finding that in recognition tests, a manipulation that increases the hit rate also decreases the false alarm rate. For example, low frequency words have a higher hit rate and a lower false alarm rate than high frequency words. Because the mirror effect is held to be a regularity of memory, it has had a pronounced influence on theories of recognition. We took advantage of the recent increase in the number of linguistic databases to create sets of stimuli that differed on one dimension (contextual diversity, frequency, or concreteness) but were more fully equated on other dimensions known to affect memory. Experiment 1 (contextual diversity), Experiment 3 (frequency), and Experiment 5 (concreteness) found no evidence of a mirror effect. We also conducted parallel experiments which used previously published stimuli that could not avail of the new databases and which therefore contained confounds. Experiment 2 (contextual diversity), Experiment 4 (frequency), and Experiment 6 (concreteness) all resulted in mirror effects. If this pattern of results is replicable, it has broad implications for theories of recognition, which typically view the mirror effect as a benchmark finding. Unfortunately, few articles on the mirror effect include the stimuli, rendering the past literature of little use in testing this hypothesis. We encourage researchers to create and assess other pools of highly controlled stimuli to establish whether the stimulus-based mirror effect obtains when confounds are eliminated or whether it is due to the presence of these confounds. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.014 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".