Bibliographic record
Abstract
The visual system readily detects statistical relationships where multiple cues jointly predict a specific outcome (e.g., two co-authors publishing a paper, or two co-founders starting a company). What is less known is how the visual system generates predictions when only a single cue is present, after learning that the two cues were previously jointly associated with an outcome. Here we examine three hypotheses: (1) complete inheritance hypothesis where the single cue predicts 100% of the outcome previously associated with the two cues, (2) proportional inheritance hypothesis where the single cue predicts 50% of the outcome, and (3) subadditive hypothesis where the single cue predicts more than 50% but less than 100% of the outcome, consistent with support theory (Tversky & Koehler, 1994). To test these hypotheses, we used a statistical learning paradigm where participants were exposed to two objects (e.g., red and blue squares) that were followed by a circle with a specific size. After exposure, participants viewed a single object (e.g., a red square) at a time and were asked to estimate the circle size that was associated with the object. Afterwards, participants recalled the size of the circle that followed the two objects during exposure. We found that the estimated size associated with the single object was significantly smaller than the recalled size associated with the two objects, but significantly larger than 50% of the recalled size (Experiment 1), or larger than 33% of the recalled size in case of three objects (Experiment 2). This confirms the subadditive hypothesis. Importantly, no participants were consciously aware of the association between the objects and the circle size. The results reveal a new consequence of statistical learning on visual inferences: when multiple objects predict a specific outcome, the single object is implicitly expected to predict an outcome in a subadditive fashion.
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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 | 0.000 |
| 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 | 0.001 |
| 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".