“You're wrong, I'll switch, I'm wrong, I'll stay”: How task-switching strategies are modulated by a partner in a multi-task learning protocol
Bibliographic record
Abstract
Individuals given control over practice variables make practice decisions based on their current performance. When individuals practice in pairs, the question as to if and how a partner's performance impacts these decisions is of theoretical and practical interest. Here, we evaluated this question in a multi-task learning protocol, where individuals and dyads practiced three, differently timed keystroke sequences. Dyad participants alternated turns with a partner so we could study the immediate consequences of the partner's performance on practice choice. Only one of the partners had choice over the sequence order, the other partner practiced the sequences in either a predetermined blocked or random order. Practice with a partner that had a random-schedule promoted more task-switching in the other partner and had some benefit for retention accuracy. Distinct "own-error" and "partner-error" switching strategies were evidenced, with partners choosing to repeat the same sequence on their next turn when they performed poorly or when their partner performed well. These data show that an individual's practice decisions are influenced by their social context, particularly the practice schedule and patterns of errors in a partner's performance.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".