Action intentions reactivate representations of task-relevant cognitive cues
Bibliographic record
Abstract
Abstract Recent research shows that the intention to act on an object alters its neural representation in ways as afforded by underlying sensorimotor processes. For example, the intention to grasp and pick up an object results in representations of the object’s weight. But these representations become grasp-specific only immediately before object lift if weight information is relayed through object material. This feature triggers earlier representations regardless of intention probably because material-weight contingencies are overlearned. By contrast, recently learned weight cues should be recalled deliberately during grasp planning resulting in early grasp-specific representations. Here, we examined how action intentions affect the representation of newly acquired colour-weight contingencies. We recorded electroencephalography while human participants grasped or reached for objects that varied in shape and density as indicated by their colour. Multivariate analyses revealed a grasp-specific reactivation of colour during planning that was mirrored in beta band. This suggests that task-relevancy influences the representation of colour such that previously encoded colour-weight contingencies may be reactivated as required for grasping, mediated top-down via working memory. Grasp-specific representations of shape and colour were also present in theta band, perhaps reflecting attentional activity. These results provide novel insights into the interplay between cognition and motor planning processes. Significance Statement Recent research shows that the object feature weight, conveyed via highly overlearned material-weight contingencies, is more prominently represented within electrophysiological signals when people intend to grasp an object to lift it vs. reach for it. However, such differences occur late, immediately before object lift. Our study is the first to show that newly learned colour-weight contingencies yield early task-specific representations during action planning; we find that colour/weight representations, forming 140-210 ms after object onset, reactivate around 270 ms during grasp, but not reach planning, with the same pattern arising for beta band. These data suggest that the brain deliberately processes weight information depending on intention, arguably involving working memory representations. Our results highlight the interplay of cognition and motor planning.
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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 | 0.002 |
| 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 | 0.000 |
| 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".