Things that go grunt in the flight: Auditory-visual SR compatibility affects the perception of tennis shots
Bibliographic record
Abstract
Background: Sinnett and Kingstone (2010) found that when novices were asked to predict the terminal location of tennis shots, they made more errors when the shot was accompanied by an auditory stimulus (suggesting that grunting tennis players may have a competitive advantage). However, beneficial predictive cues might be available in situations where the auditory and visual stimuli are compatible (e.g. loud noise paired with a hard shot and vice versa). Conversely, performance would be hindered in incompatible situations (e.g., loud noise/soft shot). The purpose of this study is to investigate whether the amplitude (volume) of an auditory stimulus differentially affects the predictability of a tennis shot. Method: 13 participants viewed 480 clips of a player hitting a tennis ball to one of four locations on the court. Each clip was accompanied with a loud (74db), quiet (62db), or no sound stimulus occurring simultaneously with ball contact. Participants were asked to predict the terminal location of the viewed shot (left/right; short/deep) by pushing a number key corresponding to the area of the court to which the ball was being hit. Results: Consistent with our hypotheses, a significant interaction, F (2,24) = 6.149, p<.05, between auditory stimulus and terminal location was revealed for predictive accuracy. Participants were significantly less accurate in predicting the location of the shot when it was incompatible with the auditory stimulus with which it was paired (e.g. short shots paired with “loud” sounds/deep shots with “quiet” sounds). Results are discussed in the context of cross modal S-R compatibility.Acknowledgments: NSERC Dundas Tennis Club
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 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 | 0.000 |
| 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 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".