Sensorimotor prediction and memory in object manipulation.
Bibliographic record
Abstract
When people lift objects of different size but equal weight, they initially employ too much force for the large object and too little force for the small object. However, over repeated lifts of the two objects, they learn to suppress the size-weight association used to estimate force requirements and appropriately scale their lifting forces to the true and equal weights of the objects. Thus, sensorimotor memory from previous lifts comes to dominate visual size information in terms of force prediction. Here we ask whether this sensorimotor memory is transient, preserved only long enough to perform the task, or more stable. After completing an initial lift series in which they lifted equally weighted large and small objects in alternation, participants then repeated the lift series after delays of 15 minutes or 24 hours. In both cases, participants retained information about the weights of the objects and used this information to predict the appropriate fingertip forces. This preserved sensorimotor memory suggests that participants acquired internal models of the size-weight stimuli that could be used for later prediction.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".