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in the very biological systems that present us with our greatest opportunities and
challenges. Hence there is great survival value in having a sensory system optimised
for the processing of such complexity. There is also additional survival value in our
experiencing such processing as being pleasurable. As in other neurological reward
systems such pleasure directs our attention to where it is needed most.
The fields of psychology and neurology have been noted as possible sources
of help for future work in computational aesthetic evaluation. Models of aesthetic
perception such as those from Arnheim, Berlyne, and especially Martindale invite
computational adaptation. Results from empirical studies of human aesthetics can
stimulate our thinking about computational evaluation. At the same time they warn
us that aesthetic evaluation in humans is highly variable depending on setting, con-
text, training, expectations, presentation, and likely dozens of other factors.
Will robust human-like computational aesthetic evaluation be possible someday?
There is currently no deductive proof that machine evaluation either is or isn't pos-
sible in principle. Presumably an argument for impossibility would have to estab-
lish as key an aspect of the brain or human experience that goes beyond mechani-
cal cause and effect. Others might argue that because the brain itself is a machine
our aesthetic experience is proof enough that computational aesthetic evaluation is
possible. These in-principle arguments parallel philosophical issues regarding phe-
nomenology and consciousness that are still in dispute and far from settled.
As a practical matter, what is currently possible is quite limited. The one con-
sistent thread that for some will suggest a future direction relates to connectionist
approaches. The current leading psychological model, Martindale's prototypicality,
presents natural aesthetic evaluation as a neural network phenomenon. We know
that animals with natural neural systems much simpler than those in the human
brain are capable of some forms of aesthetic evaluation. In software, new connec-
tionist computing paradigms such as hierarchical temporal memory show promise
for both higher performance and closer functional equivalency with natural neural
systems. In hardware we are beginning to see systems that can dynamically adapt
to problem domains at the lowest gate level. Perhaps this will all someday lead to a
synergy of hardware, software, and conceptual models yielding success in compu-
tational aesthetic evaluation.
Acknowledgements
My interest in writing this chapter began at the “Computational Creativity:
An Interdisciplinary Approach” seminar in July of 2009 at the Schloss Dagstuhl—Leibniz Center
for Informatics. I would like to thank Margaret Boden, Mark d'Inverno and Jon McCormack for
organising the seminar. In addition my thanks go to my fellow members of the “Evaluation” dis-
cussion group at the seminar including Margaret Boden, David Brown, Paul Brown, Harold Cohen,
and Oliver Deussen. Finally I enjoyed and appreciated the lively post-seminar e-mail discussion of
related topics with David Brown, Paul Brown, Harold Cohen, Jon McCormack, and Frieder Nake.
Please note, however, that any matters of opinion or error in this chapter are purely my own.
References
Aguilar, C., & Lipson, H. (2008). A robotic system for interpreting images into painted artwork.
