Action intentions result in the task-specific integration of object features
Bibliographic record
Abstract
Abstract Theories of object-based attention suggest that attending to an object binds its features together. Yet, there is a growing body of work to suggest that the intention to grasp an object can alter the representation of features such that they are separately represented during different stages of motor planning and execution, whereas some object features such as shape and size might form integrated representations when afforded by motor control. However, it remains untested whether these features were integrated as an outcome of the requirements of grasping motor control, or due to attention towards the object in general. Therefore, here we investigated how task-relevancy modulates the integration of grasp-relevant object features. To this end, we recorded electroencephalography while human participants grasped or reached for objects that varied in their orientation and size. Using multivariate analyses, we found a superadditive integration of object orientation and size during action planning for grasping but not reaching. These integrated representations likely facilitated the calculation of stable grasp points as further evidenced by the representations of grasp-specific visual size and grip size emerging at similar times. Our results provide novel insights into the vital role of action intention on cognitive representations in the human brain.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".