How does this model explain/deal with the facts that people have different musical tastes and that (at least some) people have no taste in music at all, but rather appreciate music based on weather or not their peers like it.
The paper isn’t particularly long, if you haven’t read it.
It doesn’t attempt to explain music at a cultural level, only an individual one. You don’t need a theory of aesthetics to explain why people would decide to like whatever their peers do, there’s plenty of general psychology to cover that.
As for different musical tastes, the compression algorithm that the model is based around is subjective and adaptive. So mine can be different from yours (though there’s a fair amount that humans on the whole will tend to have in common), and yours can change over time (esp in response to new data).
In particular, if you’ve been exposed to a lot of e.g. reggae music, then your algorithm will likely be especially efficient at compressing reggae. So it will seem more accessible to you. If I’ve been exposed to only a little reggae, it will likely seem less accessible, but more interesting: the compression algorithm can detect the presence of order and structure, but still has work to do in uncovering and utilizing all the regularity that’s there. And if someone has never heard any music but classical, reggae could be incomprehensible gibberish to that person (read: they won’t like it), because it clashes with their existing model and expectations so drastically.
How does this model explain/deal with the facts that people have different musical tastes and that (at least some) people have no taste in music at all, but rather appreciate music based on weather or not their peers like it.
The paper isn’t particularly long, if you haven’t read it.
It doesn’t attempt to explain music at a cultural level, only an individual one. You don’t need a theory of aesthetics to explain why people would decide to like whatever their peers do, there’s plenty of general psychology to cover that.
As for different musical tastes, the compression algorithm that the model is based around is subjective and adaptive. So mine can be different from yours (though there’s a fair amount that humans on the whole will tend to have in common), and yours can change over time (esp in response to new data).
In particular, if you’ve been exposed to a lot of e.g. reggae music, then your algorithm will likely be especially efficient at compressing reggae. So it will seem more accessible to you. If I’ve been exposed to only a little reggae, it will likely seem less accessible, but more interesting: the compression algorithm can detect the presence of order and structure, but still has work to do in uncovering and utilizing all the regularity that’s there. And if someone has never heard any music but classical, reggae could be incomprehensible gibberish to that person (read: they won’t like it), because it clashes with their existing model and expectations so drastically.