Role of the Primary Motor and Dorsal Premotor Cortices in the Anticipation of Forces during Object Lifting
Bibliographic record
Abstract
When lifting small objects, people apply forces that match the expected weight of the object. This expectation relies in part on information acquired during a previous lift and on associating a certain weight with a particular object. Our study examined the role of the primary motor and dorsal premotor cortices in predicting weight based either on information acquired during a previous lift (no-cue experiment) or on arbitrary color cues associated with a particular weight (cue experiment). In the two experiments, subjects used precision grip to lift two different weights in a series of trials both before and after we applied low-frequency repetitive transcranial magnetic stimulation over the primary motor and dorsal premotor cortices. In the no-cue experiment, subjects did not receive any previous information about which of two weights they would have to lift. In the cue experiment, a color cue provided information about which of the two weights subjects would have to lift. Our results demonstrate a double dissociation in the effects induced by repetitive stimulation. When applied over the primary motor cortex, repetitive stimulation disrupted the scaling of forces based on information acquired during a previous lift. In contrast, when applied over the dorsal premotor cortex, repetitive stimulation disrupted the scaling of forces based on arbitrary color cues. We conclude that the primary motor and dorsal premotor cortices have unique roles during the anticipatory scaling of forces associated with the lifting of different weights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".