How does a baseball outfielder know where to run to catch a fly ball? The “outfielder problem” remains unresolved, and its solution would provide a window into the visual control of action. It may seem obvious that human action is based on an internal model of the physical world, such that the fielder predicts the landing point based on a mental model of the balls trajectory (TP). However, two alternative theories, Optical Acceleration Cancellation (OAC) and Linear Optical Trajectory (LOT), propose that fielders are led to the right place at the right time by coupling their movements to visual information in a continuous “online” manner. All three theories predict successful catches and similar running paths. We provide a critical test by using virtual reality to perturb the vertical motion of the ball in mid-flight. The results confirm the predictions of OAC but are at odds with LOT and TP.

1/f serial correlations and statistical self-similarity (fractal structure) have been measured in various dimensions of musical compositions. Musical performances also display 1/f properties in expressive tempo fluctuations, and listeners predict tempo changes when synchronizing. Here the authors show that the 1/f structure is sufficient for listeners to predict the onset times of upcoming musical events. These results reveal what information listeners use to anticipate events in complex, non-isochronous acoustic rhythms, and this will entail innovative models of temporal synchronization. This finding could improve therapies for Parkinson's and related disorders and inform deeper understanding of how endogenous neural rhythms anticipate events in complex, temporally structured communication signals.

Multifractal analyses have been used in recent years as a way of studying balance, with the goal of understanding the patterns of movement of the center of pressure at different spatial scales. A multifractal detrended fluctuation analysis was used to compare obese and nonobese children to investigate the cause of previously demonstrated deficiencies in balance for obese children. Twenty-two children (11 obese and 11 nonobese), aged 8–15 years, performed 30-s trials of bilateral static balance on a plantar pressure distribution measuring device. Both the obese and nonobese groups demonstrated greater persistence for small fluctuations, but the effect was greater in the obese group. This was particularly evident with the eyes closed, where significant differences between the obese and nonobese were observed for small fluctuations. These results demonstrate that balance deficiencies in obese children may be the result of decreased proprioceptive abilities in obese children.

Sedentary behavior increases risk for non-communicable diseases; associations may differ within different contexts (e.g., leisure time, occupational). This study examined associations between different types of sedentary behavior and disease risk factors in women, using objectively measured accelerometer-derived sedentary data. A validation study (n = 20 women) classified sedentary behavior into four categories: lying down; sitting (non-active); sitting (active); standing. A cross-sectional study (n = 348 women) examined associations between these classifications and disease risk factors (body composition, metabolic, inflammatory, blood lipid variables). Participants spent an average of 7 h 42 min per day in sedentary behavior; 58% of that time was classified as non-active sitting and 26% as active sitting. Non-active sitting showed significant (p ≤ 0.001) positive correlations with BMI (r = 0.244), body fat percent (r = 0.216), body mass (r = 0.236), fat mass (r = 0.241), leptin (r = 0.237), and negative correlations with HDL-cholesterol (r = −0.117, p = 0.031). Conversely, active sitting was significantly (p ≤ 0.001) negatively correlated with BMI (r = −0.300), body fat percent (r = −0.249), body mass (r = −0.305), fat mass (r = −0.320), leptin (r = −0.259), and positively correlated with HDL-cholesterol (r = 0.115, p = 0.035). In summary, sedentary behavior can be stratified using objectively measured accelerometer-derived activity data. Subsequently, different types of sedentary behaviors may differentially influence disease risk factors. Public health initiatives should account for sedentary classifications when developing sedentary behavior recommendations.