Bibliographic record
Abstract
Stimulus probabilities affect detection performance: Rare targets, even if important (e.g. bombs, abnormal medical scans, etc.), are missed more often than their higher-probability counterparts. To minimize such probability-related costs, there is a need to understand how probability expectations develop and how they interact with attentional and perceptual processes. A previous experiment demonstrated that observers made smaller judgement errors when estimating orientations of exogenously-cued versus non-cued spatial gabors, suggesting that attentional deployment affects perceptual representations of target stimuli. Using the same paradigm, but with endogenous probability cuing (e.g. having right-positioned gabors likely being right-tilting), we replicated the effect: In Experiment 1a, observers were more precise (i.e. made smaller errors and had a more kurtotic distribution of angular errors) in their estimates for high-probability tilts than low-probability tilts. In Experiment 1b, where different probability distributions were conditionally cued (i.e. having the position-to-probability relation dependent on cue colour), the kurtosis measure again differentiated observers performance between orientation probabilities. Across both experiments, changes in kurtosis rapidly developed despite observers not being instructed on the underlying probability distributions. Curiously, observers were also more precise when judging gabors with near-vertical rather than near-horizontal orientations, while simultaneously displaying judgement errors that were systematically skewed towards the vertical rather than the horizontal meridian. These findings on kurtosis and vertical-bias coalesce in Experiment 2, which tested graded probabilities instead of a binary high/low probability distinction. Particularly, there appears to be a synergistic effect of having near vertical-tilts on the kurtosis measure for higher versus lower-probability tilts. In short, endogenous probability cuing, even if relatively complex, results in behavioral performance closely aligned to what one would expect from traditional attentional manipulations such as exogenous cuing. Possibly, the learning of stimulus probabilities might interact with pre-existing perceptual biases to weight perceptual processing towards expected targets and/or away from less expected targets. Meeting abstract presented at VSS 2014
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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 | 0.001 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".