A viewing time account for robust spatial cueing effects in all attentional paradigms
Bibliographic record
Abstract
Both predictive spatial cues and leptokurtic reward distributions may lead to increased response precision. Is this because of changes in the efficiency with which sensory information is evaluated at prioritized locations? Such an account would be consistent with attentional theories that emphasize noise exclusion and reductions in sensory uncertainty. To distinguish cue and reward effects on perceptual efficiencies from other attentional effects we devised a gaze contingent paradigm where we could equate stimulus viewing time across cue and reward conditions. We had participants report the orientation of Gabors presented in the periphery at contrasts too low for orientation to be reliably reported without fixation. Participants were instructed to look at the Gabor as quickly as possible and then report its orientation. By tracking a participant's gaze, we were able to limit Gabor fixation time to a standard 60ms across cue and reward conditions. Spatial cues appeared on 80% of trials and predicted the location of the Gabor with 50% validity. Reward conditions were either leptokurtic (peaked) for a block of 250 trials followed by a platykurtic (flat) reward block or a platykurtic block followed by a leptokurtic reward block. For one third of participants they began with a no reward condition followed by leptokurtic rewards. With a fixed viewing time there was no effect of any cue or reward condition on response precision. However, fixation response times were consistently faster for validly cued trials and pupil diameter was greatest for leptokurtic rewards. Thus, we conclude that the robust spatial cueing effects seen in essentially all attentional paradigms reflect additional viewing time that succeeds a valid cue when the stimulus is presented for a fixed duration or the viewing time is terminated by the participant's response. Meeting abstract presented at VSS 2016
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".