Test Position Effects on Hit and False Alarm Rates in Recognition Memory for Paintings and Words
Bibliographic record
Abstract
When old/new recognition memory is tested with equal numbers of studied and non-studied items and no rewards or instructions that favour one response over the other, there is no obvious reason for response bias. In line with this, Canadian undergraduates have shown, on average, a neutral response bias when we tested them on recognition of common English words. By contrast, most subjects we have tested on recognition of richly detailed images have shown a conservative bias: they more often erred by missing a studied image than by judging a non-studied image as studied. Here, in an effort to better understand these materials-based bias effects (MBBEs), we examined changes in hit and false alarm (FA) rates (and in sensitivity and bias) from the first to fourth quartile of a recognition memory test in eight experiments in which undergraduates studied words and/or images of paintings. Response bias for images tended to increase across quartiles, whereas bias for words showed no consistent pattern across quartiles. This pattern could be described as an increase in the MBBE over the course of the test, but the underlying patterns for hits and FAs are not easily reconciled with this interpretation. Hit rates decreased over the course of the test for both materials types, with that decline tending to be steeper for images than words. For words, FA rates tended to increase across quartiles, whereas for paintings FA rates did not increase across quartiles. We discuss implications of these findings for theoretical accounts of the MBBE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".