Bibliographic record
Abstract
Motion perception relies on two fundamentally different motion systems. The energy-based system relies on early direction-selective neurons that automatically and pre-attentively extract motion within their receptive fields. The high-level tracking system rather relies on attentively tracking the position of an object. Two types of apparent motion stimuli were used to investigate the two motion systems. Since early direction-selective neurons only operate within a short temporal window, a temporal gap of 100 msec between the two frames was used to probe the tracking system. No temporal gap was used to probe the energy-based system. In order to explore the role of attention on both systems, the attentional resources dedicated to the stimuli were systematically manipulated using a cueing paradigm. Eight dots simultaneously appeared uniformly distributed on an annulus with a radius of 5 degrees of visual angle. Each dot was displaced in a different random direction (up, down, right or left) in a 2-frame sequence. Observers were asked to report the displacement direction of the randomly selected dot (the target) selected by a central cue. The level of attentional resources dedicated to the target was manipulated by varying the timing of the central cue relative to the offset of the target. Results demonstrated that manipulating attention had a similar effect on both types of stimuli: percentage of correct answers was near perfect when the central cue appeared before the target onset and gradually declined when the cue appeared with or after the target). This suggests that attention plays a similar role in both systems. As energy-based processing is pre-attentive, these results propose that attention plays a minor role in tracking and that the drop in performance with a late cue might not be due to motion processing per se. Instead, it could be related to iconic memory.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".