Theory of Visual Attention (TVA) in Action: Assessing Premotor Attention in Simultaneous Eye-Hand Movements
Bibliographic record
Abstract
Abstract Attention shifts that precede goal-directed eye and hand movements are regarded as markers of motor target selection. Whether effectors compete for a single, shared attentional resource during simultaneous eye-hand movements or whether attentional resources can be allocated independently towards multiple target locations is controversially debated. Independent, effector-specific target selection mechanisms underlying parallel allocation of visuospatial attention to saccade and reach targets would predict an increase of the overall attention capacity with the number of active effectors. We test this hypothesis in a modified Theory of Visual Attention (TVA; Bundesen, 1990) paradigm. Participants reported briefly presented letters during eye, hand, or combined eye-hand movement preparation to centrally cued locations. Modeling the data according to TVA allowed us to assess both the overall attention capacity and the deployment of visual attention to individual locations in the visual work space. In two experiments, we show that attention is predominantly allocated to the motor targets – without pronounced competition between effectors. The parallel benefits at eye and hand targets, however, have concomitant costs at non-motor locations, and the overall attention capacity does not increase by the simultaneous recruitment of both effector systems. Moreover, premotor shifts of attention dominate over voluntary deployment of processing resources, yielding severe impairments of voluntary attention allocation. We conclude that attention shifts to multiple effector targets without mutual competition given that sufficient processing resources can be withdrawn from movement-irrelevant locations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".