Right and left-handed pitch-type recognition among hitters and pitchers in baseball: Testing the motor simulation hypothesis
Bibliographic record
Abstract
Both visual and motor experiences impact action prediction, yet through potentially different mechanisms. Having physical experience with an action is thought to engage motor simulative processes, potentially activating the watcher’s motor system in an effector-specific manner, aiding prediction. Leveraging baseball’s unique specialization demands, we assessed pitch discrimination in athletes with predominantly visual experience seeing pitches (hitters, n = 41; 7 = left-handed hitters) and motor experience producing pitches (pitchers, n = 42; 9 = left-handed). Videos of right-handed (RH) pitches, with temporal occlusion applied at or after ball release, of three different pitches, were used to assess pitch prediction accuracy. Two versions of each video clip were shown; the original RH clips were “flipped” to make the pitcher appear to throw left-handed (LH). Pitchers and hitters had reliable responses and showed high accuracy (~70%) and discriminability. Pitchers were more discriminatory than hitters when contrasting two pitch types with the same initial trajectory and speed, but different postural cues (curveballs and changeups; d’ pitchers = 2.18, hitters = 1.88). Although there was the predicted interaction for the pitchers when comparing LH and RH pitchers watching LH and RH videos (p<.05), only LH pitchers showed a trend for a same-side, LH video advantage (p=.06). Because pitchers did not differ in accuracy from hitters, despite a lack of visual experience seeing and responding, this suggests that their motor experience aided predictions. However, there was only partial support for a simulation explanation based on effector specificity (due to absence of any differences in RH pitchers).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".