Factors that affect action possibility judgements: Recent experience with the action and the current body state
Bibliographic record
Abstract
It has been suggested that action possibility judgements are formed through a covert simulation of the to-be-executed action. We sought to determine whether the motor system (via a common coding mechanism) influences this simulation, by investigating whether action possibility judgements are influenced by experience with the movement task (Experiments 1 and 2) and current body states (Experiment 3). The judgement task in each experiment involved judging whether it was possible for a person's hand to accurately move between two targets at presented speeds. In Experiment 1, participants completed the action judgements before and after executing the movement they were required to judge. Results were that judged movement times after execution were closer to the actual execution time than those prior to execution. The results of Experiment 2 suggest that the effects of execution on judgements were not due to motor activation or perceptual task experience-alternative explanations of the execution-mediated judgement effects. Experiment 3 examined how judged movement times were influenced by participants wearing weights. Results revealed that wearing weights increased judged movement times. These results suggest that the simulation underlying the judgement process is connected to the motor system, and that simulations are dynamically generated, taking into account recent experience and current body state.
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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 | 0.002 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".