Bibliographic record
Abstract
Can probability improve the precision of perceptual judgments? Participants were briefly shown individual tilted gabor patches, and asked to reproduce the tilt. Unbeknownst to the participants some tilt angles were more likely than others; right tilts on the right and left tilts on the left (or vice versa) occured on 80% of the trials. Stimuli tilted in the high probability range were defined as "validly cued." Statistical analysis was with linear mixed effects models treating participant as a random effect. All three experiments demonstrated a reduced magnitude of absolute tilt error for validly cued stimuli, and no bias for orientation judgments. In Experiment 1 we had different participants use either their right or left hand for the matching task in order to assess for a congruency effect (greater effect of probability on the side of the responding hand). The valid cue effect was significant (p = 0.01), but not the congruency effect. This was confirmed by a within subjects manipulation in Experiment 2 where we had participants switch their responding hand midway through the task. Validly cued stimuli were judged more precisely (p = 0.01) and accuracy was not modulated by the side of the responding hand. Experiment 3 flipped the direction of high probability tilts midway through the task. Validly cued stimuli were still judged more precisely (p = 0.003), and a factor coding for the initial direction of high probability tilts was not statistically significant. Additionally, despite instructions emphasizing accuracy, all three experiments showed that validly cued trials were judged more quickly. In summary, using probability to cue an orientation range results in stimuli within that range being judged, on average, more precisely and more quickly. This pattern is similar to that seen with conventional attentional cues, consistent with their having a common mechanism. Meeting abstract presented at VSS 2013
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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 | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".