Multiple Object Tracking: Support for Hemispheric Independence
Bibliographic record
Abstract
Using a variant of the multiple object tracking paradigm, Alvarez and Cavanagh (2005) showed that as the task became more demanding, tracking performance was significantly more accurate when targets were distributed between the left and right hemifields compared to when they were presented within a single hemifield. Based on this result, they proposed a hemispheric independence capacity account suggesting that there are independent resources for tracking in each hemifield. In the current study, we tested an alternative distribution account which suggested that the spatial distribution of the tracked objects was the factor underlying their results. In sixteen conditions, we manipulated the distribution of the targets (vertical or horizontal), the positioning of the distribution (within one side, both sides central, or both sides peripheral), the motion of the targets (within- or cross-hemifield), and the number of tracked objects (2 or 4). While the distribution of objects had a small influence on tracking performance, the largest factor influencing tracking performance was whether all stimuli were presented within the same hemifield or not. In sum, the results were largely inconsistent with the distribution account and provided support for the hemispheric independence account. Meeting abstract presented at VSS 2013
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".