Bibliographic record
Abstract
The task in the present experiments was to reach out and grasp a novel object that afforded two possible grips. Different versions of the object were created that biased subjects to use one grip or the other. The dependent variable was the repetition effect, the tendency to repeat the grip that was used on the previous trial. In Experiment 1, two qualitatively different objects were used, and it was found that the repetition effect was specific to the object being grasped: There was much less tendency to use the same grip as the previous trial if the object being grasped was different. Moreover, the effect lasted over intervening trials and was even present with more than five intervening trials. In Experiment 2, the global context was manipulated, so that in some blocks one grip was much more likely than the other. However, this manipulation had little effect on the choice of grip and did not interact with the repetition effect. In Experiment 3, the hand used to grasp the object was manipulated, and there was little change in the repetition effect. Thus, a grip was more likely to be used if it was used on the previous trial, regardless of whether the previous grasp was performed with the left or right hand. In Experiment 4, a similar result was found for a manipulation of object location and orientation. Our interpretation of these results is that subjects prepare for an action by retrieving action features from memory, and that the object to be grasped provides a critical cue for that memory retrieval. In this view, the repetition effect is essentially a memory recency effect.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".