When a sports performance is at its peak, it is akin to a musical performance in the sense that each player seems to perform their part effortlessly, creating a rhythmic flow of counterparts all moving as one. Rhythm and timing are vital elements in sports like basketball in which syncopated passing and shooting appear to facilitate accuracy. This study tests if shooting baskets “in rhythm,” as measured by the catch-to-release time, reliably enhances shooting accuracy. It then tests if an “in rhythm” timing is commonly detected and agreed upon by observers, and if observer timing ratings are related to shooting accuracy. Experiment 1 tests the shooting accuracy of two amateur basketball players after different delays between catching a pass and shooting the ball. Shots were taken from the three-point line (180 shots). All shots were recorded and analyzed for accuracy as a function of delay time, and the recordings were used to select stimuli varying in timing intervals for observers to view in Experiment 2. In Experiment 2, 24 observers each reviewed 17 video clips of the shots to test visual judgment of shooting-in-rhythm. The delay times ranged from 0.3 to 3.2 seconds, with a goal of having some of the shots taken too fast, some close to in rhythm, and some too slow. Observers rated if each shot occurs too fast, in rhythm slightly fast, in rhythm slightly slow, or too slow. In Experiment 1, shooters exhibited a significant cubic fit with better shooting performance in the middle of the timing distribution (1.2 sec optimal delay) between catching a pass and shooting. In Experiment, 2 observers reliably judged shots to be in rhythm centered at 1.1 ± 0.2 seconds, which matched the delay that leads to optimal performance for the shooters found in Experiment 1. The pattern of findings confirms and validates that there is a common “in rhythm” catch-to-shoot delay time of a little over 1 second that both optimizes shooter accuracy and is reliably recognized by observers.
Method: A finger oscillation (Finger Tapping) test was administered to both ASD (n=21) and TD (n=20) participants aged 40-70 year old participants as a test of fine motor speed. Magnetic resonance (MR) images were collected using a Philips 3 Tesla scanner. 3D T1-weighted and diffusion tensor images (DTI) were obtained to measure gray and white matter volume and white matter integrity, respectively. FreeSurfer, an automated volumetric measurement software, was used to determine group volumetric differences. Mean, radial, and axial diffusivity, fractional anisotropy, and local diffusion homogeneity were measured from DTI images using PANDA software in order to evaluate white matter integrity.
Results: All participants were right-handed and there were no significant differences in demographic variables (ASD/TD, means) including age (51.9/49.1 years), IQ (107/112) and years education (15/16). Total brain volume was not significantly different between groups. No statistically significant group differences were observed in finger tapping speed. ASD participants compared to TDs showed a trend of slower finger tapping (taps/10 seconds) speed on the dominant hand (47.00 (±11.2) vs. (50.5 (±6.6)) and nondominant hand (44.6 (±7.6) vs. (47.2 (±6.6)). However, a large degree of variability was observed in the ASD group, and the Levene’s test for homogeneity of variance approached significance (p=0.053) on the dominant, but not the nondominant, hand. No significant group differences in gray matter regional volume were found for brain regions associated with performing motor tasks. In contrast, group differences were found on several measures of white matter including the corticospinal tract, anterior internal capsule and middle cerebellar peduncle. Brain-behavior correlations showed that dominant finger tapping speed correlated with left hemisphere white matter integrity of the corticospinal tract and right hemisphere cerebellar white matter in the ASD group.
Conclusions: No significant differences were observed between groups in finger tapping speed but the high degree of variability seen in the ASD group. Differences in motor performance appear to be associated with observed brain differences, particularly in the integrity of white matter tracts contributing to motor functioning.