Action planning modulates the representation of object features in human fronto‐parietal and occipital cortex
Bibliographic record
Abstract
The visual cortex has been extensively studied to investigate its role in object recognition but to a lesser degree to determine how action planning influences the representation of objects' features. We used functional MRI and pattern classification methods to determine if during action planning, object features (orientation and location) could be decoded in an action-dependent way. Sixteen human participants used their right dominant hand to perform movements (Align or Open reach) towards one of two 3D-real oriented objects that were simultaneously presented and placed on either side of a fixation cross. While both movements required aiming towards target location, Align but not Open reach movements required participants to precisely adjust hand orientation. Therefore, we hypothesized that if the representation of object features is modulated by the upcoming action, pre-movement activity pattern would allow more accurate dissociation between object features in Align than Open reach tasks. We found such dissociation in the anterior and posterior parietal cortex, as well as in the dorsal premotor cortex, suggesting that visuomotor processing is modulated by the upcoming task. The early visual cortex showed significant decoding accuracy for the dissociation between object features in the Align but not Open reach task. However, there was no significant difference between the decoding accuracy in the two tasks. These results demonstrate that movement-specific preparatory signals modulate object representation in the frontal and parietal cortex, and to a lesser extent in the early visual cortex, likely through feedback functional connections.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".