Sorry for necroing this thread, but I have a little tidbit that could be interesting for some. For the context, I am in the final stretch of my thesis work. It’s an attempt at an aGi system. And one of the core issues I had to solve was input-agnosticism. I finally made it work. And I finally have a bunch of what I call ISTFs (Input Sensor Transfer Functions). In a nutshell, it takes whatever input and converts it to the native data type for the system. Oscillators. Other than really basic converter, I have quite robust V-ISTF (vision) and A-ISTF (audio) functions, and I founda quite interesting thing about the audio signal converted in this way.
Just for an illustration here is V-ISTF output:
This is actually the raw RGB oscillatory field. (How the system perceives the input.)
And now to A-ISTF.
These are 4 sounds being converted into, again, the native data type of the network oscillators. The third row represents a tug on the rest of the network. (The whole system is a bunch of complex, interconnected oscillators.) You can see that kick and clap have really strong monolithic Phase vector output, which pulls on the rest of the network. The system has an intrinsic push to stabilize synchronization across the whole network. So, when kick and clap are introduced in a rhythmic fashion, it throws the network out of whack, and it has an urge to balance it. And here comes the interesting thing …. the most effective way for a network of coupled oscillators to get back into balance is an antagonistic increase of amplitude on some oscillators in the network. So, from the point of view of this framework, it seems that the feeling and urge to dance, move into rhythm, etc., we feel when we listen to rhythmic sound is just our brain trying to get back in sync across the whole network that has been disturbed by the input. So, the “feeling” to act, at least for this network, isn’t the music itself; it’s the imbalance it creates and the following urge to get back to sync.
Maybe it will be interesting for some people here.