Pre-Crastination Emerges in a Sequential Joint Action Task
Bibliographic record
Abstract
In sequential joint actions, one co-actor performs the first step of a task (the initiator) before the second co-actor finishes the task (the finisher). Studies of sequential joint actions have revealed the initiator plans their movement to facilitate their finisher's action, consistent with the principle of "pre-crastination". Pre-crastination refers to the finding that actors choose to complete more demanding tasks earlier to decrease cognitive and/or motor load later. The present experiments examined the potential for pre-crastination in a sequential joint action task. Partners performed a task wherein an initiator passed a cube with a 3D-protuberance to a finisher so the protuberance could be inserted into a target slot. The initiator could rotate the cube all, some, or none of the way into the final orientation before passing. The results of Experiment 1 were that initiators completed more rotations when working with a partner than actors completed in the first step when working alone. Experiment 2 revealed that visual information about the finisher's task influenced the amount of rotation completed by the initiator. These findings are consistent with the notion of pre-crastination because co-actors facilitated their partner's achievement of a shared goal by doing more of the work earlier on.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".