Serial and joint processing of conjunctive predictions
Why this work is in the frame
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Bibliographic record
Abstract
When two jointly presented cues predict different outcomes, people respond faster to the conjunction/overlap of outcomes. Two explanations exist. In the joint account, people prioritize conjunction. In the serial account, people process cues serially and incidentally respond faster to conjunction. We tested these accounts in three experiments using novel web based attention-tracking tools. Participants learned colour-location associations where colorus predicted target locations (Experiment 1). Afterward, two cues appeared jointly and targets followed randomly. Exploratory data showed participants initially prioritized locations consistent with the conjunction, shifting later. Experiment 2 presented complex color-category associations during exposure. Upon seeing joint cues, participants' responses indicated both serial and joint processing. Experiment 3, with imperfect cue-outcome associations during exposure, surprisingly showed robust conjunctive predictions, likely because people expected exceptions to their predictions. Overall, strong learning led to spontaneous conjunctive predictions, but there were quick shifts to alternatives like serial processing when people were not expecting exceptions.
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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 | 0.000 |
| 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 it