Perceptual averaging for auditory pro- and antisaccades
Bibliographic record
Abstract
The visual antisaccade task requires the top-down and two-component processing of inhibiting a stimulus-driven prosaccade (i.e., response suppression) and the mirror-symmetrical inversion of a target's visual location (i.e., vector inversion). Notably, recent work by our group (Gillen and Heath 2014: Vis Res; Heath et al. 2015: J Vis) has shown that vector inversion is a perception-based process governed via a statistical summary representation (SSR). In particular, our work showed that antisaccade amplitudes were biased in the direction of the most frequently presented target in a stimulus-set. The present investigation was designed to examine whether a SSR influences auditory-based pro- and antisaccades. To that end, participants completed auditory (i.e., 50 ms burst, 70 dBA white noise) pro- and antisaccades to three target eccentricities (10.5°, 15.5° and 20.5°) in blocks wherein eccentricities were presented with equal frequency (i.e., control-weighting condition) and when the 10.5° (i.e., proximal-weighting condition) and 20.5° (i.e., distal-weighting condition) targets were presented five times as often as the other eccentricities. Results showed that pro- and antisaccade amplitudes were refractory to the different weighting conditions; however, the slope relating amplitude to target eccentricity was markedly shallower for the latter task (prosaccade: b=0.51; antisaccade: b=0.17;). Thus, preliminary results provide no evidence that weighting conditions differentially influenced the specification of pro- and antisaccade amplitudes. That said, the shallower amplitude/target eccentricity slope associated with antisaccades provides some evidence that an auditory vector inversion process is governed via a SSR that is similar to its visually based counterpart. Acknowledgments: Supported by NSERC
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".