Behavioral effects of visual field location on processing motion- and luminance-defined form
Bibliographic record
Abstract
Traditional theories posit a ventral cortical visual pathway subserving object recognition regardless of the information defining the contour. However, functional magnetic resonance imaging (fMRI) studies have shown dorsal cortical activity during visual processing of static luminance-defined (SL) and motion-defined form (MDF). It is unknown if this activity is supported behaviorally, or if it depends on central or peripheral vision. The present study compared behavioral performance with two types of MDF [one without translational motion (MDF) and another with (TM)] and SL shapes in a shape matching task where shape pairs appeared in the upper or lower visual fields or along the horizontal meridian of central or peripheral vision. MDF matching was superior to the other contour types regardless of location in central vision. Both MDF and TM matching was superior to SL matching for presentations in peripheral vision. Importantly, there was an advantage for MDF and TM matching in the lower peripheral visual field that was not present for SL forms. These results are consistent with previous behavioral findings that show no field advantage for static form processing and a lower field advantage for motion processing. They are also suggestive of more dorsal cortical involvement in the processing of shapes defined by motion than luminance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".