Receivers in American Football Use a Constant Optical Projection Plane Angle to Pursue and Catch Thrown Footballs

An anti-inertial motion bias explains people discounting inertial motion of carried objects

In this paper we propose an anti-inertial motion (AIM) bias that can explain several intuitive physics beliefs including the straight-down belief and beliefs held concerning the pendulum problem. We show how the AIM bias also explains two new beliefs that we explore - a straight-up-and-down belief as well as a straight-out/backward bias that occurs for objects traveling in one plane that are then thrown in another plane, ostensibly affording a greater opportunity for perception of canonical motion. We then show how the AIM bias in general is invariant across perceived/imagined speed of the object carrier, only altering percentages of straight-out from backward responses, and why occluding the carrier once the object is released into a second plane does not result in more veridical perception. The AIM bias serves as a simple explanation for a family of beliefs including those in the current paper as well as those shown in previous work.

Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football

Highly efficient training is a must in professional sports. Presently, this means doing exercises in high number and quality with some sort of data logging. In American football many things are logged, but there is no wearable sensor that logs a catch or a drop. Therefore, the goal of this paper was to develop and verify a sensor that is able to do exactly that. In a first step a sensor platform was used to gather nine degrees of freedom motion and audio data of both hands in 759 attempts to catch a pass. After preprocessing, the gathered data was used to train a neural network to classify all attempts, resulting in a classification accuracy of 93%. Additionally, the significance of each sensor signal was analysed. It turned out that the network relies most on acceleration and magnetometer data, neglecting most of the audio and gyroscope data. Besides the results, the paper introduces a new type of dataset and the possibility of autonomous training in American football to the research community.

Human Navigational Strategy for Intercepting an Erratically Moving Target in Chase and Escape Interactions

Pursuit and interception of moving targets are fundamental skills of many animal species. Although previous studies in human interception behaviors have proposed several navigational strategies for intercepting a moving target, it is still unknown which navigational strategy humans use in chase-and-escape interactions. In the present experimental study, by using two one-on-one tasks as seen in ball sports, we showed that human interception behaviors were statistically consistent with a time-optimal model. Our results provide the insight about the navigational strategy for intercepting a moving target in chase-and-escape interactions, which may be common across species.

The geometry of consciousness

Conscious experience implies a reference-frame or vantage, which is often important in scientific models. Control models of ball-interception are used as an example. Models that use viewer-dependent egocentric reference-frames are contrasted with viewer-independent allocentric ones. Allocentric reference-frames serve well for models like Newtonian physics, which utilize static coordinate-systems that allow forces and object-movements to be compartmentalized. In contrast, egocentric reference-frames are natural for modeling mobile organisms or robots when controlling perception-action behavior. Lower-level perception-action behavior is often characterized using egocentric coordinate-systems that optimize processing-speed, while higher-level cognitive-processes use allocentric frames that provide a stationary spatial reference. Brain-behavior models like the Ventral-Stream What System, and Dorsal-Stream Where-How System, also respectively utilize allocentric and egocentric reference-frames. Reference-frame clarification can resolve disputes about models of control-tasks like running to catch baseballs, and can provide insights for biomimetic-robots. Confusion regarding geometry and reference-frames contributes to a lack of clarity between how and when egocentric versus allocentric geometries are imposed, with perception-actions generally being more egocentric and conscious experience more allocentric.

No free lunch in ball catching: A comparison of Cartesian and angular representations for control

How to run most effectively to catch a projectile, such as a baseball, that is flying in the air for a long period of time? The question about the best solution to the ball catching problem has been subject to intense scientific debate for almost 50 years. It turns out that this scientific debate is not focused on the ball catching problem alone, but revolves around the research question what constitutes the ingredients of intelligent decision making. Over time, two opposing views have emerged: the generalist view regarding intelligence as the ability to solve any task without knowing goal and environment in advance, based on optimal decision making using predictive models; and the specialist view which argues that intelligent decision making does not have to be based on predictive models and not even optimal, advocating simple and efficient rules of thumb (heuristics) as superior to enable accurate decisions. We study two types of approaches to the ball catching problem, one for each view, and investigate their properties using both a theoretical analysis and a broad set of simulation experiments. Our study shows that neither of the two types of approaches can be regarded as superior in solving all relevant variants of the ball catching problem: each approach is optimal under a different realistic environmental condition. Therefore, predictive models neither guarantee nor prevent success a priori, and we further show that the key difference between the generalist and the specialist approach to ball catching is the type of input representation used to control the agent. From this finding, we conclude that the right solution to a decision making or control problem is orthogonal to the generalist and specialist approach, and thus requires a reconciliation of the two views in favor of a representation-centric view.

Catching Objects Thrown to Oneself: Testing Control Strategies for Object Interception in a Novel Domain

A considerable amount of research has been performed to determine the strategies people use to intercept moving objects. Much of this research has been done using target objects such as baseballs and Frisbees that are launched to people from distances ranging from 10 m to 50 m. This research has qualified the range of domains in which each strategy is effective, but there is still controversy regarding which strategy has the most general application. The present research sought to further reduce the range of possible domains in which these strategies work by testing object interception in a situation that had never been tested before, where people launch the target objects to themselves. A strategy based on controlling optical velocity—acting to keep the optical velocity of the object at zero—provided the best match to catcher ground movements and optical trajectories. These results suggest that control of optical velocity is currently the best explanation of effective interception of both other- and self-launched objects.

Navigational strategy used to intercept fly balls under real-world conditions with moving visual background fields

This study explored the navigational strategy used to intercept fly balls in a real-world environment under conditions with moving visual background fields. Fielders ran across a gymnasium attempting to catch fly balls that varied in distance and direction. During each trial, the launched balls traveled in front of a moving background texture that was projected onto an entire wall of a gymnasium. The background texture consisted of a field of random dots that moved together, at a constant speed and direction that varied between trials. The fielder route deviation was defined as the signed area swept out between the actual running path and a straight-line path to the destination, and these route deviation values were compared as a function of the background motion conditions. The findings confirmed that the moving visual background fields systematically altered the fielder running paths, which curved more forward and then to the side when the background gradient moved laterally with the ball, and curved more to the side and then forward when the background gradient moved opposite the ball. Fielder running paths deviated systematically, in a manner consistent with the use of a geometric optical control strategy that helps guide real-world perception-action tasks of interception, such as catching balls.