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to be more “traditional” while styles that serve up more unorthodox surprises are
considered to be “cutting edge.”
Notions of Shannon information and algorithmic complexity have their place.
But in aesthetics it is misleading to treat order and complexity as if they are polar
opposites. My suggestion is that the notion of effective complexity better captures
the balance of order and disorder, of expectation and surprise, so important in the
arts. This offers the challenge and potential benefit that effective complexity can
serve as a measure of quality in computational aesthetic evaluation.
10.3 The Future of Computational Aesthetic Evaluation
As should be obvious by now, computational aesthetic evaluation is a very difficult
and fundamentally unsolved problem. To date any marginal successes have tended
towards narrow application niches using methods that do not generalise very well.
The irony is that aesthetic evaluation is something we all do quite naturally. Could
it be that the solution to the computational aesthetic evaluation problem is within us
and just not yet understood?
Artists and engineers have always learned from nature. There is a significant
and growing literature around the psychology and neurology of aesthetics. But this
challenge to understanding seems no less daunting than the difficulty of machine
evaluation. The human brain that gives rise to the human mind is arguably the most
complex unitary system known. The brain includes approximately 10 15 neural con-
nections. In addition, recent research regarding the brain's glial cells reveals that
they contribute to active information processing rather than, as previously thought,
merely providing mechanical support and insulation for neurones. Glial cells make
up 90 % of the brain and some scientists speculate that they are specifically engaged
in creative thought (Koob 2009 ). Computing hardware can only make up for part
of this gap by exploiting electronic switching speeds that are about 10 7
times faster
than human neurones.
Nevertheless, it seems reasonable that an improved understanding of natural aes-
thetic perception will contribute to computational aesthetic evaluation efforts, and
science has made some significant progress in this regard. Perhaps a good place to
start is recent scientific thinking as to the origins of human aesthetics.
10.3.1 The Origins of Art and the Art Instinct
Denis Dutton notes that evolutionary scientist Stephen Jay Gould claims that art is
essentially a nonadaptive side effect, what Gould calls a spandrel , resulting from
an excess of brain capacity brought about by unrelated adaptations. Dutton ( 2009 )
argues that the universality of both art making behaviour and some aesthetic pref-
erences imply a more direct genetic linkage and something he calls the art in-
stinct .
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