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expectation of easily confirming these predictions that we undertook the experiments. The
results are clear-cut. They do not support the theory.
The debate pitting collative effects versus prototypicality would dominate ex-
perimental aesthetics for almost 20 years (North and Hargreaves 2000 ). For some
Berlyne's notion of collative effects was especially problematic. First it was odd
for a behaviourist like Berlyne to make an appeal to a concept so much about the
inner state of the individual. Additionally, terms like novelty and complexity were
problematic both in specification and mechanism.
However, Martindale's primary critique was empirical. For example, contrary to
Berlyne's model he found that psychophysical, ecological, and collative properties
are not additive, nor can they be traded off. Significantly more often than not empir-
ically measured responses do not follow the inverted-U of the Wundt curve, but are
monotonically increasing. Finally, a number of studies showed that meaning rather
than pure sensory stimulation is the primary determinant of aesthetic preference
(Martindale et al. 1990 ; 2005 , Martindale 1988b ).
In a series of publications Martindale ( 1981 ; 1984 ; 1988a ; 1991 ) developed a
natural neural network model of aesthetic perception that is much more consistent
with experimental observation. Martindale first posits that neurones form nodes that
accept, process, and pass on stimulation from lower to higher levels of cognition.
Shallow sensory and perceptual processing tends to be ignored. It is the higher se-
mantic nodes, the nodes that encode for meaning, that have the greatest strength in
determining preference. Should the work carry significant emotive impact the limbic
system can become engaged and dominate the subjective aesthetic experience.
Nodes are described as specialised recognition units connected in an excitatory
manner to nodes corresponding to superordinate categories. So, for example, while
one is reading nodes that extract features will excite nodes for letters, and they will
in turn excite nodes for syllables or letter groupings, leading to the excitation of
nodes for words, and so on. Nodes at the same level, however, will have a lateral in-
hibitory effect. Nodes encoding for similar stimuli will be physically closer together
than unrelated nodes. So nodes encoding similar and related exemplars will tend to-
wards the centre of a semantic field . The result is that the overall nervous system
will be optimally activated when presented an unambiguous stimulus that matches a
prototypically specific and strong path up the neural hierarchy (Martindale 1988b ).
Commenting on prototypicality North and Hargreaves ( 2000 ) explain:
. . . preference is determined by the extent to which a particular stimulus is typical of its
class, and explanations of this have tended to invoke neural network models of human cog-
nition: this approach claims that preference is positively related to prototypicality because
typical stimuli give rise to stronger activation of the salient cognitive categories.
Martindale's neural network prototypicality model carries with it great explana-
tory and predictive power. Towards the end of his life he penned a chapter describing
the results of 25 widely disparate empirical studies, and how his single model can
provide a foundation for understanding all of them (Martindale 2007 ).
While most in the field agree that Martindale's prototypicality model explains
more of the empirical data than Berlyne's collative effect model, some cases re-
main where prototypicality is the weaker explanation. Some have suggested ways
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