Does Perception Asymmetrically Influence Motor Production in Upper and Lower Visual Fields?
Bibliographic record
Abstract
The authors examined if previously reported anatomical asymmetries between the upper (uVF) and lower visual fields (lVF) influence the preparation and control of visually and memory-guided reaching movements. To manipulate visual field, participants maintained their visual gaze on a cue position presented above or below the location of a target object, thus resulting in reaches completed in respective uVF and lVF of peripersonal. In Experiment 1, participants performed reaches to four targets with indices of difficulty ranging from 3.1 to 5.1 bits under five visual-memory conditions: full vision and memory-guided conditions entailing 0, 2, 5, and 10 s of delay. In Experiment 2, participants reached to the vertex of Müller-Lyer figures in 3 visual-memory conditions: full vision, and memory-guided conditions entailing 0, and 2 s of delay. In accord with duplex theories of vision (e.g., Milner & Goodale, 1992), it was hypothesized that the introduction of a visual delay and/or the introduction of context-dependent illusory structure would differentially bias the efficiency and effectiveness of uVF and lVF reaches. Although data displayed mixed supported for the existence of an lVF advantage for movement execution, neither the introduction of delay nor contextual illusions succeeded in differentiating visual fields. Thus, performance advantages for movements made in the lower visual field do not appear associated with preferential connections to parietal (i.e., dorsal-action) and temporal (i.e., ventral-perception) architectures.
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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".