Master jugglers are clearly very good at multitasking, and since balls aren’t being thrown randomly, each ball need not be tracked and caught independently. But Botvinick-Greenhouse and Shinbrot still wondered how it was possible for jugglers with reaction times of milliseconds to routinely catch balls every milliseconds. “Jugglers rely on making accurate throws and predictions of where the balls will travel,” the authors wrote . “The accuracy required is a measure of how unstable — and thus how difficult — a particular juggling pattern is.”
Juggling has a long and glorious history dating back to ancient Egypt; there are hieroglyphics circa and (BCE that historians consider to be the earliest historical record of juggling. There were juggling warriors in China (801 – BCE) —apparently it was viewed as an effective diversionary tactic — and the practice eventually spread to ancient Greece and Rome. By the mid – s CE, juggling was largely practiced by circus and street performers , and it has fascinated scientists since at least . That’s when Edgar James Swift published a paper Looking at the psychology and physiology of learning in the American Journal of Psychology, which discussed the rate at which students learned to toss two balls in one hand.
Standard particle dynamics works fine as a model for juggling balls, while clubs and rings are best modeled as a rigid body system . Whatever the juggled objects of choice, they essentially follow a classic parabolic motion in periodic cycles — there’s just more than one object at play, and the various paths interweave. Since the number of possible patterns is small, one might think this would make it fairly easy to model the process mathematically. But variables like the angle of release, height of the throw, release velocity, and so forth ensure that no two throws or catches are precisely the same. The best jugglers can control all those variables with impressive consistency.
There are three basic patterns in juggling: the “cascade,” in which an odd number of balls are tossed from one hand to another (the most common pattern); the “fountain,” in which an even number of balls are thrown and caught with the same hand; and the “shower,” in which all the objects are tossed in a circle. There’s also the “multiplex,” where the juggler will throw more than one object from a single hand simultaneously.
The standard mathematical notation for juggling patterns is known as (siteswap theory) (aka quantum juggling, aka the Cambridge Notation), invented in by Paul Klimek and further developed in by Cambridge Mathematicians Colin Wright and Adam Chalcraft (among others). Strings of numbers are used to represent the patterns, and the average of the numbers in the strings is equal to the number of balls being juggled in the pattern. For example, a simple three-ball pattern has a site swap of three (3,3,3), whereby each ball lands three beats after it is thrown.
As Botvinick-Greenhouse and Shinbrot wrote in (their Physics Today article :
In general, the height of the toss is proportional to how much time a juggler has between tosses. Back in , Beek and Lewbel pointed out that the need for either greater speed or height increases increasing with the number of objects being juggled, so the more objects being juggled, the more difficult the feat (and the longer it takes to master a particular trick). You can probably learn to juggle three balls in a few days, but four balls could take weeks or months to master, while five balls could take years.
Botvinick-Greenhouse and Shinbrot performed simulations of parabolic trajectories of five-ball juggling patterns under gravity to investigate how sensitive the different patterns are to deviations in throw speed and angle. For instance, hand motions during a cascade are “left-right asymmetric,” which is why it is only possible to juggle an odd number of balls in that pattern. “It’s impossible to juggle an even number of balls in a cascade without breaking the asymmetry — for example, by throwing with both hands simultaneously or by throwing balls to different heights,” they wrote. A shower pattern, on the other hand, can be performed with even or odd numbers of balls because it lacks symmetry. While it’s simpler, in that regard, it’s more difficult to perform because the pattern is inherently less stable.
Based on their simulations, the duo concluded that cascade patterns can tolerate more variability in throw speed than the shower pattern. “In the shower pattern, each ball travels first through a parabola and then through a quick shuffle, whereas in the cascade, each ball must travel through two parabolas to return to its starting point,” the authors wrote. “So hands must move nearly twice as rapidly in the shower as in the cascade, which makes catches in the shower much more sensitive to timing.” Speed vs. height
That’s why jugglers typically throw balls to greater heights in the shower pattern than in the cascade: they need a little more time between catches. But
There is a tradeoff : higher throws require much more control over the angle than lower throws. “When balls are thrown many meters high, throw angles must be accurate to within 0.1 degree,” they wrote. “That’s more than an order of magnitude tighter than is achievable by world-class athletes.”
Botvinick-Greenhouse and Shinbrot admit that it is still not clear exactly how expert jugglers manage such high accuracy and fast response times , given known physiological limits. But they cite (a) study published in Nature that found human and monkey brains are both capable of computing trajectories via dynamical prediction, thanks to some kind of internal representation of physical laws of motion. “Just as an outfielder predicts where a fly ball will land, a juggler predicts trajectories from how balls are thrown,” they wrote. Muscle memory likely also Plays a role .
In short, “Complex tasks like juggling can be successfully performed without understanding the physiology behind motor control, although a deeper understanding is both intriguing and useful, “Botvinick-Greenhouse and Shinbrot concluded. “Unraveling the secrets behind how our nervous systems pull that off may pave the way for more dexterous robots.”
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