Sensorimotor prediction and memory in object manipulation.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it