Hemifield Specificity of Attention Response Functions during Multiple-Object Tracking
Bibliographic record
Abstract
The difficulty of tracking multiple moving objects among identical distractors increases with the number of tracked targets. Previous research has shown that the number of targets tracked (i.e., load) modulates activity in brain areas related to visuospatial attention, giving rise to so-called attention response functions (ARFs). While the hemifield/hemispheric effects of spatial attention (e.g., hemispatial neglect, hemifield capacity limits) are well described, it had not previously been tested whether a hemispheric or hemifield imbalance exists among ARFs. By recording blood oxygenation level-dependent activity from human brains ( n = 19, female and male) in a multiple-object tracking paradigm, we show that the number of tracked objects modulates activity in a large network of areas bilaterally. A significant effect of contralateral load was found in earlier areas throughout the dorsal and ventral visual streams, while the effects of ipsilateral load emerged in later areas. Both contra- and ipsilateral load significantly influenced activity in the parietal and frontal lobes, specifically the dorsal attention network. In addition, some brain regions in the occipital lobe were significantly more sensitive to contralateral than ipsilateral load. Our results are consistent with findings showing that a diverse set of brain areas contributes to tracking multiple targets. In particular, we extend the canonical view of load-based ARFs to include hemifield bias. Given the hemifield-specific nature of speed and capacity limits to multiple-object tracking, we conjecture that areas that show a strong hemifield preference may impose a bottleneck on processing that results in limits on the capacity and speed of tracking.
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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 | 0.001 |
| 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 | 0.000 |
| 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".