Lateral peri-hand bias affects the horizontal but not the vertical distribution of attention
Bibliographic record
Abstract
It has been demonstrated that humans exhibit an attention bias towards the lower visual field (e.g., faster target detection for targets appearing below eye level). This bias has been interpreted as reflecting the visual motor demand in near space below eye level. In this study, we examined whether this spatial bias could be affected by participants' hand position at the time of testing. Specifically, if the hand position is held at eye level at the time of target detection, whether the bias toward the lower visual field would be reduced if the bias is directly related to the motor demand at the time of testing. Using a modified spatial cueing paradigm, in Experiment 1, we found a downward bias in reaction time measures and cueing effects in a target detection task. In Experiment 2, using the same stimulus used in Experiment 1, we compared attention performance when participants' dominant (right) hand was positioned close to the right side of the visual display with the conditions where their hand was in their laps. We revealed that despite an influence on the horizontal distribution of attention (lateral peri-hand effect), the downward bias in attention remained regardless of the hand position. This revealed that lateral peri-hand manipulation is insufficient to override the attention advantage for stimuli appearing in the lower visual field.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".