Imitation performance biases are moderated by perceived accuracy in golf putting
Bibliographic record
Abstract
Watching others act can cause unintentional biases in the observer's next action. This "contagion" occurs due to the shared processes engaged during observation and execution, interfering with performance after observation. Biases can be imitative or compensatory, with the direction thought to be dependent on the presence of observed-induced prediction errors (PE: difference between predicted/expected and observed performance). To replicate and test the proposed PE mechanism, we compared golfers watching "on-target" and errorful putts. Twenty-three experienced golfers alternated putting a ball to a centre target on a nine-square grid (without outcome feedback) and watching videos of an actor putting to the same grid. Across four conditions, we covaried the location of the actor's putts (centre, corner) and expectations about the actor's aiming location, to manipulate the presence of a PE. As expected, imitative execution biases emerged after watching "correct" putts to corner squares, but not when these same corner putts were perceived as errors. Compensatory biases from PEs were also absent after watching "misses" to the centre square. These data provide evidence for different behaviours after the observation of correct versus errorful actions but raise questions about the types and/or stability of errors that underpin these effects.
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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 | 0.020 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".