Bank of Standardized Stimuli (BOSS) Phase II: 930 New Normative Photos
Bibliographic record
Abstract
Researchers have only recently started to take advantage of the developments in technology and communication for sharing data and documents. However, the exchange of experimental material has not taken advantage of this progress yet. In order to facilitate access to experimental material, the Bank of Standardized Stimuli (BOSS) project was created as a free standardized set of visual stimuli accessible to all researchers, through a normative database. The BOSS is currently the largest existing photo bank providing norms for more than 15 dimensions (e.g. familiarity, visual complexity, manipulability, etc.), making the BOSS an extremely useful research tool and a mean to homogenize scientific data worldwide. The first phase of the BOSS was completed in 2010, and contained 538 normative photos. The second phase of the BOSS project presented in this article, builds on the previous phase by adding 930 new normative photo stimuli. New categories of concepts were introduced, including animals, building infrastructures, body parts, and vehicles and the number of photos in other categories was increased. All new photos of the BOSS were normalized relative to their name, familiarity, visual complexity, object agreement, viewpoint agreement, and manipulability. The availability of these norms is a precious asset that should be considered for characterizing the stimuli as a function of the requirements of research and for controlling for potential confounding effects.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.015 |
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".