Unconfounding the time to passage, direction of motion and rotation rate of an approaching object: Different early visual processing in expert baseball players and nonplayers
Bibliographic record
Abstract
Observers were presented with a set of 216 simulated approaching textured baseballs in random order. Each had a different combination of time to passage (TTP), direction of motion in depth (dMID), and rate of ball rotation (RR). When required to discriminate TTP, dMID and RR in separate blocks (Expt 1), observers could not discriminate dMID independently of variations in TTP but instead showed a bias towards perceiving objects approaching on a trajectory close to the nose as having a shorter TTP than objects approaching on a trajectory that would miss the face. Judgments of dMID were affected by variations in RR and rotation direction: for simulated overspin the (i.e. the top of the ball spins towards the observer) perceived ball trajectory was biased towards the ground whereas for simulated underspin the perceived ball trajectory was biased towards the sky. RR could be discriminated independently of both TTP and dMID. When required to make all three of these judgments simultaneously on each trial (Expt. 2) discrimination thresholds were not significantly different from those found in Expt. 1. Judgments by expert (college) baseball players (Expt. 3) showed an interaction between TTP and RR (balls with overspin were judged to have a longer TTP) that was not found for non-players. We conclude that information about TTP, dMID and RR are processed in parallel but not completely independently within the human visual system and that the degree of independence can be influenced by sports experience.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".