Brazilian norms for the Bank of Standardized Stimuli (BOSS)
Bibliographic record
Abstract
Norms for visual stimuli are critical for designing reliable psychological and neuroscientific studies. However, such normative sets of stimuli are scarce for the Brazilian population. Here, we report norms for the Bank of Standardized Stimuli (BOSS) for Brazilian college students. Sixty-five Brazilian university students rated the initial normative set of BOSS images for familiarity and visual complexity, and produced a name for each object. Data analysis focused on comparing the present norms to prior BOSS norms (English-Canadian, French-Canadian, and Thai) across four normative dimensions: familiarity, visual complexity, modal name agreement, and H value, and considered these dimensions according to whether items pertained to living or non-living domains. Correlation analyses revealed that the present norms show strong similarities to prior BOSS norms, although objects were scored as more familiar in the Brazilian relative to all prior norms, especially relative to the Thai norms. In addition, familiarity was greater for living than for non-living items in the English- and French-Canadian norms, but such difference was absent in the Brazilian and Thai norms, suggesting that familiarity is more strongly affected by culture than by semantic domain. In sum, even when cultural differences are considered, the current study reveals that the images of the BOSS data set are in general well known for Brazilians, demonstrating that they can be useful for psychological and neuroscientific research in Brazil.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".