When writing up the post on regularity I was looking for an image that would match the content. As you can see if you go back to that entry the image shows a regular structure, a fragment of architecture. I was quite happy with it, but what turned out to be even more interesting was the article I lifted it from, which I did not look at until now.
It happens that in that post the author explores some of the same ideas I’ve considered when attempting to apply concepts from information theory to yield an interpretation of artistic experience as a learning process. The notions of monotony and complexity are also given a similar (tentative) mathematical formalization in terms of algorithmic information theory, which is how we have just discussed regularity in the previous entry.
Of special interest is the hypothesis of how monotony (at the low complexity end of the spectrum, see below on AIC) is not only boring but in fact distressing, due to a mismatch between what our neural system is tuned to observe and what is actually observed
why is human neurological response actually negative? Some insight into the effect comes from the notion of Biophilia, which asserts that our evolution formed our neurological system within environments defined by a very high measure of a specific type of coherent complexity. That is, our neurological system was created (evolved) to respond directly and exquisitely to complex, fractal, hierarchical geometric environments. When placed in environments that have opposite geometrical features, therefore, we feel ill at ease.
In my brief essay on the matter I mentioned hierarchical nature of processing in the visual cortex, which is reflected in machine learning approaches using recurring neural networks such as HTM’s and deep learning. The hierarchical nature of the processing, as well as the dual top-down and bottom-up flow of information are well established in neuroscience. However, it remains to be seen if there is a neurological explanation for the observation that “we feel ill at ease” with, for example, monotonous geometry.
In any case, all this led me to to try to extend my information-theoretic picture of artistic expression and experience with a grounding in neurological processes. In particular, how does the brain deal with regularity and novelty as relates to learning and satisfaction/pleasure. Knowledge from neuroscience points to dopamine as both related to pleasure and reward and, critically, learning (temporal difference learning).
I’m still thinking many of these ideas through, but I’m going to try to summarize some of them in their crude form so I can return to this as reference, please excuse the lack of polish.
Here are the main points:
* Artistic expression can be characterized in terms of its position in a spectrum whose extremes are the obvious/boring on one side and the unintelligible on the other. This spectrum corresponds to the algorithmic information content of the expression.
* Artistic experience is a learning process where regularities are extracted and predictions are made according to those regularities
* Novelty is the mismatch between prediction and observation, novelty is a departure from regularity
* Novelty, either as reward-prediction error, or as an intrinsic reward (indirect indicator of reward) motivating exploration, activates dopamine in the brain. In the latter case, this is related to the exploration vs exploitation aspect of reinforcement learning.
* Different dopamine response profiles yield different levels of sensation-seeking in individuals, which may partially account for different taste in terms of desired optimum points on the AIC spectrum
* Artistic satisfaction/pleasure is an interplay between novelty and regularity, it may be possible to explain this within some model of reward mediated by dopamine
 Perhaps the canonical example of this line of work is Jurgen Schmidhuber’s treatment
 Dopamine: generalization and bonuses 
 In particular, it is unclear whether a dopaminergic model is applicable to standalone perception which is the case for artistic experience, since actions and explicit reward are absent, and the timescales may be too small to be compatible with such a model.