Using high‐preference and low‐preference music in a synchronous reinforcement treadmill preparation: A further extension
Bibliographic record
Abstract
We conducted a series of studies on the effects of synchronous reinforcement. Study 1 presented 30 participants with their high-preference (HP) music, identified via a conjugate assessment, for walking on a treadmill during three synchronous reinforcement (SYNC) components. The results indicated that HP music produced schedule control of walking speed for 20 participants (66.7%). In addition, 80% of the participants who displayed schedule control also displayed variable walking speeds when music was withheld. Study 2 extended Study 1 by providing 30 new participants with their low-preference (LP) music, again using the same conjugate assessment, for walking on a treadmill during the same three SYNC components. The results indicated that LP music produced schedule control of walking to (a) avoid music for 13 participants (43.33%) and (b) access music for two participants (6.67%). Study 3 compared group results across components for HP and LP participants from Studies 1 and 2, respectively. The results indicated that that the HP group walked significantly faster than the LP group during three components; however, heart rates did not differ statistically between the two groups for any component. The results across the studies indicate that both positive and negative synchronous reinforcement with music increased the walking speeds and heart rates of participants.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".