I don't get it. How those hypervectors are different from matrices in neural networks? Ok, having not just 0 and 1 for each separate shape and encoding each set

I don't get it. How those hypervectors are different from matrices in neural networks? Ok, having not just 0 and 1 for each separate shape and encoding each set of mutually exclusive attributes with just one parameter would make it more compact and fast, probably, but also sounds very limiting, and making it more close to Prolog than to neural networks. We, humans, can deal with "roundish squares".