Statistical learning generates implicit conjunctive predictions
Bibliographic record
Abstract
The visual system readily detects statistical relationships where the presence of an object predicts a specific outcome. What is less known is how the visual system generates predictions when multiple objects predicting different outcomes are present simultaneously. Here we examine the rules with which predictions are made in the presence of two objects that are associated with two distinct outcomes. In a visual search paradigm, participants first viewed one color dot and then searched for a target (a rotated T) in an array during the exposure phase. Each color predicted a specific location of the target. For example, after a blue dot the target would appear only in the top half of the array; and after a red dot the target would appear only in the left half of the array. The question is: Where was the target expected to appear when both the blue dot and the red dot were present? A conjunctive prediction would mean that the target was expected to appear in the top left quadrant of the array, whereas a disjunctive prediction would mean that the target was expected to appear in the top half or the left half of the array. Importantly at the test phase when both dots were present, the target was equally likely to appear in any half of the array. We found that participants were reliably faster to find the target when it appeared in the conjunctive quadrant. This was true even if participants were not consciously aware of the association between the color dots and target locations during debriefing. This effect was equally strong whether participants implicitly learned the association or were explicitly told about the association. The results suggest that in the presence of multiple predictors, statistical learning generates implicit expectations about the outcomes in a conjunctive fashion. Meeting abstract presented at VSS 2018
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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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".