Skill learning involves optimizing the linking of action phases
Bibliographic record
Abstract
Many manual tasks involve object manipulation and are achieved by an evolving series of actions, or action phases, recruited to achieve task subgoals. The ability to effectively link action phases is an important component of manual dexterity. However, our understanding of how the effective linking of sequential action phases develops with skill learning is limited. Here, we addressed this issue using a task in which participants applied forces to a handle to move a cursor on a computer screen to successively acquire visual targets. Target acquisition required actively holding the cursor within the target zone (hold phase) for a required duration before moving to the next target (transport phase). If the transport phase was initiated prematurely, before the end of the required hold duration, participants had to return to the target to acquire it. The goal was to acquire targets as quickly as possible. Distinct visual and auditory sensory events marked goal completion of each action phase. During initial task performance, the transport phase was reactively triggered by sensory events signaling hold phase completion. However, with practice, participants learned to initiate the transport phase based on a prediction of the time of hold phase completion. Simulations revealed that participants learned to near-optimally compensate for temporal uncertainty, presumably related to estimation of time intervals and execution of motor commands, so as to reduce the average latency between the end of the required hold phase duration and the start of the transport phase, while avoiding an excess of premature exits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".