Human fMRI Reveals That Delayed Action Re-Recruits Visual Perception
Bibliographic record
Abstract
Behavioral and neuropsychological research suggests that delayed actions rely on different neural substrates than immediate actions; however, the specific brain areas implicated in the two types of actions remain unknown. We used functional magnetic resonance imaging (fMRI) to measure human brain activation during delayed grasping and reaching. Specifically, we examined activation during visual stimulation and action execution separated by a 18-s delay interval in which subjects had to remember an intended action toward the remembered object. The long delay interval enabled us to unambiguously distinguish visual, memory-related, and action responses. Most strikingly, we observed reactivation of the lateral occipital complex (LOC), a ventral-stream area implicated in visual object recognition, and early visual cortex (EVC) at the time of action. Importantly this reactivation was observed even though participants remained in complete darkness with no visual stimulation at the time of the action. Moreover, within EVC, higher activation was observed for grasping than reaching during both vision and action execution. Areas in the dorsal visual stream were activated during action execution as expected and, for some, also during vision. Several areas, including the anterior intraparietal sulcus (aIPS), dorsal premotor cortex (PMd), primary motor cortex (M1) and the supplementary motor area (SMA), showed sustained activation during the delay phase. We propose that during delayed actions, dorsal-stream areas plan and maintain coarse action goals; however, at the time of execution, motor programming requires re-recruitment of detailed visual information about the object through reactivation of (1) ventral-stream areas involved in object perception and (2) early visual areas that contain richly detailed visual representations, particularly for grasping.
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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) | 0.001 | 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".