Examining Cultural Differences in Recognition Memory Response Bias: An Extension of the MBBE
Bibliographic record
Abstract
According to signal detection theory, people tested on an old/new recognition memory test adopt a liberal, conservative, or neutral response criterion. Several prior studies in our lab demonstrated that subjects showed a clear conservative bias when presented with complex images (e.g., paintings, photographs of scenes) as stimuli. When stimuli were English words, bias tended to be liberal or neutral. The reasons for these materials-based differences in response bias remain ambiguous. Our efforts have focused on understanding response bias variation across materials and individuals. Specifically, we have explored whether Canadian and Japanese participants show differences in response bias for new materials called “diffeomorphs”. We conducted an earlier study with Lebanese participants with a smaller sample and materials size that served as a pilot study for our later studies. The materials-based bias effect cannot be applied to all visual stimuli because, even though both pictures and diffeomorphs are visual stimuli, the response bias for each is different. For example, we found that while Japanese elicit a conservative bias for diffeomorphs, Canadians have a neutral response bias. Besides the observed cross-cultural difference in response bias, this work refuted the hypothesis that novelty, colorfulness, and richness are behind the MBBE. It seemed that neither the semantics (line drawings) nor the colorfulness (diffeomorphs) of the stimuli appear to generate a bias towards conservatism. The MBBE and its cross-cultural generalization have been better understood because of the fresh insights offered by this thesis.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".