I’d be happy to give you some pointers! Most important is to keep the hands low, and the tosses high. Next is to pay a lot of attention to the shape traced by the objects you’re throwing. You want to start with a quite tall parabola shape at first, and you want its width to be about shoulder width. You toss from A great two-object drill is tossing the left object to the right hand, and the right object to the left hand, and then clapping, instead of a throwing a third object (Toss-Toss-Clap). I’d advise working on two in one hand too. There are 3 shapes here. Parabola that starts medially and ends distally (Reverse/Outside Tosses), parabola that starts distally and ends medially (Forwards/Inside Tosses), and a straight line (Columns!). Two in one-hand practice will be good for building up your speed and coordination. I would be willing to do a video call on it sometime if you’re interested. Here are my credentials: IJA Tricks of the Month by Tyfoods of the USA | Poi Juggling
That’s fair! That means you reject all models that are fit to data before a model-system mapping has been established. I believe Artificial Neural Networks, and many other techniques fall into this. Indeed, theoretical and computational neuroscience has largely been about this sort of work. I think this is the case in other fields too.
Right, sometimes we don’t know what that causal structure is, and sometimes it’s because it’s in practice very hard/impossible to measure. I think this is why people actually like models like Artificial Neural Networks and other such models that can simply be fit to data. They can give us insight as to the form of unknown causal structures although we may not know the physiological mechanisms corresponding to it. However, I agree predictive understanding is absolutely not the end all be all.
I think it’s fine to reject data fitting without a solid model-system mapping as an approach to gaining understanding; However, suggesting that a model-system mapping does not exist, even when the model is capable of predicting data, seems to be a very strong claim. Are we really to believe that this data-fitting is only coincidence?
I am reminded of the “Unreasonable Effectiveness of Mathematics”, in particular the opening paragraph:
There is a story about two friends, who were classmates in high school, talking about their jobs.
One of them became a statistician and was working on population trends. He showed a reprint to his
former classmate. The reprint started, as usual, with the Gaussian distribution and the statistician
explained to his former classmate the meaning of the symbols for the actual population, for the
average population, and so on. His classmate was a bit incredulous and was not quite sure whether
the statistician was pulling his leg. “How can you know that?” was his query. “And what is this
symbol here?” “Oh,” said the statistician, “this is pi.” “What is that?” “The ratio of the
circumference of the circle to its diameter.” “Well, now you are pushing your joke too far,” said the
classmate, “surely the population has nothing to do with the circumference of the circle.”
The fact that our symbols on paper, or computers, seem to capture anything about the world we experience is absolutely mysterious and incredible.