TVA in action: Attention capacity and selectivity during coordinated eye-hand movements
Bibliographic record
Abstract
Goal-directed eye and hand movements are preceded by attention shifts towards the movement targets. Whether attentional resources can be allocated independently towards multiple effector target locations or whether a single attentional system underlies target selection for multiple effectors, is controversially debated. Here, we used the TVA approach (Theory of Visual Attention, Bundesen, 1990) to measure the distribution of attentional resources before single and combined eye-hand movements. We applied a whole report paradigm in which six letters arranged in a semi-circle were briefly (17–167ms) presented. Observers (n=8) performed single eye or hand movements, or combined eye and hand movements to centrally cued locations. Gaze and finger positions were recorded with a video-based eye-tracker and a touch screen. We used letter categorization performance as a proxy of attentional capacity and modelled the data according to the TVA framework. Additionally, we used the TVA-model to estimate the probability of correct categorization at motor targets and non-targets to evaluate attention selectivity. This allowed to directly determine attention capacity (processing speed) at multiple, movement-relevant and -irrelevant locations within a single trial. Our results show that total attention capacity is constant across the different action conditions and does not increase with the number of active effectors. However, attention is predominantly allocated towards the movement-relevant locations. The data demonstrate that attentional resources can be allocated simultaneously and independently towards both, eye and finger targets during the combined movements without competition, although associated with attentional costs occurring at movement-irrelevant locations. Overall, our findings suggest that attention can indeed be allocated towards multiple effector targets in parallel, as long as sufficient attentional resources are available. They also demonstrate, for the first time, that the TVA framework can be used as a sensitive tool to measure action-related shifts of visual attention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".