Intention to grasp reactivates shape processing
Bibliographic record
Abstract
Paying attention to object features such as motion, contours, or colours can enhance visual processes throughout the visual field. Relatedly, when we grasp an object, processing visual properties of the object are necessary to allow us to successfully execute the grasp. However, few studies have reported attention-induced changes to the visual computations of object features, specifically in terms of temporal dynamics. Here, we aimed to clarify the time frames of attention to object features using multivariate EEG analyses. We recorded electrophysiological signals from 64 scalp electrodes in human participants while they viewed and acted upon real objects. Objects had one of two shapes (“flower” and “pillow”), and materials (steel and wood), respectively. To manipulate attention to these features, participants either grasped and lifted these objects, or touched them with their knuckle, thus making shape and material more or less relevant to the task. We then performed pattern classification of shape and material based on spatiotemporal EEG data. We found that classifiers reached transient accuracies around 100-200 ms after stimulus presentation. Shape classification was more robust than material, but there was not a marked difference in classification performance between tasks. However, the cross-temporal generalization of shape representations revealed that only for grasping did early and late neural generators reactivate one another during action planning. In contrast, knuckling shape computations involved a chained activation of generators. Our findings suggest that task-related attention does indeed modulate the visual processing of shape such that earlier representations are stored and reactivated during action planning, only if they are important to the task at hand.
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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 | 0.004 |
| 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 | 0.001 |
| 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".