Language is a way of composing functions by making them low dimensional and discrete. I think of artificial languages (programming and math) as bottoming out in logical functions, natural languages in perceptual categories (which includes the perception of transitions and agency)
There is one dimension where the signal is continuous (loudness), and another one where it is not (pitch), and depending on where you focus, you get a different classification result?
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But the output has to be continuous because experience and behavior are continuous. Somehow in that output the discrete thing is represented. Single var continuous input --> some computation --> single var continuous output that contains the discrete classification.
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Yet neural representations are discrete, at timescales that we can often even resolve! At least one trick seems to be that we represent continuous motion via keyframes and operators that allow to derive the next keyframe.
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