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© Rupkatha Journal on Interdisciplinary Studies in Humanities
Calculation in Art: The Inconspicuous Heuristics of
Computation
Judson Wright
Pump Orgin, New York, USA
Abstract
How might computers, as tools for automatic execution of formal boolean logic, as opposed to
their popular use as rote presentation appliances for presenting various pre-packaged media, be
integrated into art? Though the latter use may only yield discussion relevant to a subset of media
theory, and we have no argument in or with that forum, the former represents a fundamental
inquiry regarding cognition, perception, biology, mathematics, and anthropology, which applies
to a far broader issue that bleeds across to far more disciplines of study. The direct approach,
algorithmically generating modal events, is hardly sufficient. It is the well-known, but hardly ever
analyzed basis of the Turing test. As an example, computers taking input from distinct
biosensors tend to create what can be called “cat-walking-on-the-piano” music. This is not to say
that an individual cannot come to develop an appreciation for such an aesthetic, but merely that
our species seem to be equipped with a general organic art-ness detecting sense.
keywords: cognition, Constructivism, development, modeling, perception
Introduction
Both practices, the use of computers and the experience of art, are generally
exploitations of perceptual gestalt rules. Yet gestalt can be applied much more deeply,
through metaphor, to conceptualization. These gestalt rules, which allow our species to
both perceive and enjoy the use of our bloated pre-frontal cortices, are products of
Evolution. But can we say that the current results are optimizations of our design? With
evolutionary competition as a model, beginning with number theory and the Turing
machine (Ash, 1965; Shannon & Weaver, 1940; Turing, 1936), and culminating in the
weighting of options that is fundamental to neural networking schemes (Adbi, Valentin,
& Edelman, 1999; Sporns, 2011), the computer is implemented as a means of
optimization. Optimization is somewhat synonymous with a common approach to
computation. However, deep in the background of this underlying theoretical approach,
remains the implication that Evolution itself is a means of optimizing individual species. It
is not (Bjorklund & Pellegrini, 2001). It is a means of optimizing systems (relative to the
current state of other systems)—not instances.
To this end, we will be discussing computer programs which tease apart aesthetic
sensory experiences, of what is described as an objet d’art (i.e. embodied by
recognizable media)—from cognitive effects, of which art-ness (in a much wider variety
of forms) is a byproduct. As this certainly bears further explanation. Suffice it to say,
these works are not intended to be conclusive of a hypotheses, but initial indicators
answering what exactly is to be investigated. The former paradigm might be likened to
aiming at an elusive target, while the later is more akin to turning on the lights before
Rupkatha Journal on Interdisciplinary Studies in Humanities (ISSN 0975-2935), Vol. VII, No. 1, 2015.
Ed. Tirtha Prasad Mukhopadhyay &Tarun Tapas Mukherjee
URL of the Issue: http://rupkatha.com/v7n1.php
URL of the article: http://rupkatha.com/V7/n1/14_Heuristics_Computation_Art.pdf
Kolkata, India. Copyrighted material. www.rupkatha.com
121
Rupkatha Journal on Interdisciplinary Studies in Humanities, V7N1, 2015
entering an unfamiliar room. However, there is a more fundamental obstacle.
This division of labor [between scientific fields] is … popular: Natural scientists deal with
the nonhuman world and the “physical” side of human life, while social scientists are the
custodians of human minds, human behavior, and, indeed, the entire human mental,
moral, political, social, and cultural world. Thus, both social scientists and natural
scientists have been enlisted in what has become a common enterprise: the resurrection
of a barely disguised and archaic physical/mental, matter/spirit, nature/human dualism, in
place of integrated scientific monism. (Tooby and Cosmides, 1992, p. 49)
Sarah Shettleworth (1998) makes an important point about cognition in nonhuman
animals, that whatever their available behavioral and motor features, these mental
abilities only tend to be idiosyncratic strategies the organism uses given its own
embodied resources, rather than any reflection of the degree of that organism's
comprehension of the environment. There is no ideal vantage point from which to
observe the universe. It must be taken as a premise on faith, that a world beyond the
mind exists, roughly in the way humans describe it to themselves as conceptual metaphor
(Feldman, 2008; Lakoff and Johnson, 1980). So too we humans must appreciate the
effects of being human on our interpretations of our art and our computers.
This is no simple matter. We highlight a subtle distinction between physical
properties (and the possible organization thereof), and the inconsistent detection of
such physical dynamics by human sensory systems, as rendered within the Cartesian
theater of the detector’s mind (as it is discussed in Dennett, 1991). That humans so easily
confuse these two entities, that they are so often only considered as a single
phenomenon, is ultimately essential to the utility of the computer, and coincidentally as
well as art (this subtle distinction for art was recognized in Dewey, 1935).
Actually, this coincidence may be rather trivial. We are designed to overlook this
slight discrepancy, and, in turn, design artifacts to accommodate this idiosyncrasy (as
discussed in Deacon, 1997; see also Greve 2013). We do not experience the word
objectively, rather the protocol/medium between mind and body is affect (to site various
domains Clancey, 2005; Cobb, 2005; Grafton & Cross, 2008; Hohenberger, 2011; Lakoff &
Núñez, 2000; LeDoux, 2002), subject to—or perhaps shared in the common adherence
with—gestalt and grouping (regarding nonhuman animal linguistics, see Cheney, 1984;
Seyfarth, 1984). Our species developed its complex processes of perception and
conceptualization in response to evolutionary pressures (Herrero, 2005). One
conspicuous byproduct of these features is art. Moreover, inventions of mechanical
prosthetic devices, from the wheel to the computer, are ultimately embodied artifacts of
those very same cognitive processes. We invent machines to satisfy our species-specific
needs in this respect, and not to answer to an unknowable, ultimate objective
perspective. It would be perhaps more accurate to say that the construction and
employment of any technology (including paint brushes and the piano) is art, an
expression of this creative impulse.
The practice of making and experiencing artwork is entirely an exercise in passing
on, and actively sculpting this delicate and elusive impulse of contextualization (Wright,
2014a). This is not to trivialize art as being chaos imbued with illusory projected meaning,
nor to decree that the computer is inherently inartistic, but to point out the genetic
component of the creative impulse (Wright, 2012). That such an impulse is common
122 Calculation in Art: The Inconspicuous Heuristics of Computation
among our species, though slight degrees have dramatic effects (i.e. autism), further
indicates that it must carry some small but significant benefit to fitness (again, Deacon,
1997; Tooby and Cosmides, 1992).
Formalization
Conceptual models of minds often derive from one of three distinct domains: the study
of human behavior and cognition (psychology and neurology), the study of nonhuman
behavior (ethology and biology) and the study of computational behavior (computer
science). Unfortunately, these models are often assumed by their authors to apply to all
minds in general, and often lack conceptual integration with one another, or the larger
schemes of physics, evolution, and biology (Tooby and Cosmides, 1992). The general
model proposed by computer science is particularly insufficient in a very specific way.
Claude Shannon's (Ash, 1965; Shannon & Weaver, 1949; von Neumann & Morgenstern,
1944) conception of communication applies well enough between machines, but cannot
be generalized further to organic instances of communication. Digital computation is
prohibitively restricted from development, and thus learning in any biological-like sense.
Digital computation is restricted by the von Neumann bottleneck, that all code must be
resolved sequentially, one command at a time, with the result that all threads are
condensed to some final point (Nissan & Schocken, 2005; Wright, 2012).
This argument often boils down to the objection that consistent computation
necessitates serial processing. The issue is far less interesting, but often becomes mired
in technical misunderstandings (by both sides) regarding the machine processes, as well
as the terms being used. The bottleneck remains. Suffice it to say, in order to achieve a
final and consistent result, all processing must terminate at a final on/off binary state
simply because it must be rendered by a machine. Thus far, engineers merely design
machines to display multimedia data serially because that is the general model of human
perception used. By and large, this model works well enough in most cases, where the
data does not obviously depend on biological cognitive influences. This paper aims to
provide an initial point from which engineers might explore non-serial presentations.
Nonetheless, in practice, serial presentation is physically akin to an arrangement of
dominos (Wright, 2013; Wright, 2014b). Whether this design consists of only a few tiles or
millions, the system does not accumulate qualitatively new information beyond any
single physical reaction at a time. From the machine's perspective, unlike the human
operator's, the registration of each bit is a long series of independent phenomena (see
Figure 1 below).
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Figure 1. This depicts our triadic relationship with computers. The computer has no sense of its own
holism, its mechanical parts, or its own behavior. As a middle, fairly optional, part of a three-step process,
the computer can only aspire to indirectly encourage the rudiments of meaning within cooperating human
minds.
Only by considering consciousness as being a static, all-or-nothing binary state,
and the exclusively automatic (impulsive) computation described above as defining all
there might be to consciousness, there might justifiably remain hope that machines
could eventually come to problem-solve as animals do (Rumelhart & Zipster, 1985).
A popular approach to perception and cognition, as solely being a process of
cataloguing sensory data, disregarding the “friction” of idiosyncratic errors, such as
psychology, within individual brains, prevails in much of academia as well as mainstream
belief (Arkin, 2005; Castefranci, 2011). Likely this extremely problematic notion should
have been abandoned in the early 1990’s (which include biological topics discussed in
Bshary, DiLascio, Pinto, and van de Waal, 2011), with a wave of interest in the topic of
consciousness (of note Dennett, 1991; Edelman, 1992; Searle, 1994). However,
disregarding that assumptions about the mind are implicit in the orthodox view, further
accounts of the mind could generally be discounted as being speculative (see the
epistemological debate in Changeux & Connes, 1995). Hence, these widely held
assumptions could not freely be questioned. However, a formidable quantitative
challenge to this view arose in the form of the issue of neuroplasticity (Bach-y-Rita, 1972;
Doidge, 2007; Gregory & Ramachandran, 1991; Schwartz & Begley, 2002). To a surprising
degree sensory information can be interchangeable, and therefore must be inconsistent.
Notwithstanding, in the face of mounting, contrary empirical data, and the absolute lack
of evidence, many staunch followers of the traditional defend the cataloguing model of
perception with alarming passion.
A subsequent programming technique, employed to model evolutionary
competition is the Turing machine (Horzyyk & Tadeusiewicz, 2005; Winston, 1984; the
core concept has remained fairly unchanged since Turing, 1936). While theoretically
exciting for programmers, the strict limits of application are seldom acknowledged or
124 Calculation in Art: The Inconspicuous Heuristics of Computation
put to any formidable test. Thus, aimed at distilling the methodological paradigm at its
barest, we concoct the following program. The computer generates two lists of
randomly mutating pixel colors to compare to the model list. The most similar list is
retained and the other discarded and the competition continues. Ideally, because the
number of pixels is finite, the machine should reach a point where one if not both lists
are recognizably similar to the model.
Unfortunately, though it is common among to ignore, calculation is hardly
instantaneous. Though the compiler pre-formulates complex mathematical problems
into a series of simple arithmetic problems, thus saving a formidable run-time processing
task, and these simplified arithmetic calculations can be performed in tiny fractions of a
second, there are now so many, that the processing time becomes rather significant. To
analyze a 360 x 240 pixel list, the program running at 200 cycles per second, would take
several hundred lifetimes to achieve. As the resolution of the model image decreases, to
yield a more viable time requirement, the model data becomes less descriptive to us,
until it is entirely dependent on context for meaning. The figures below (see Figures 2a-c,
d-e) illustrate how this virtual competition, which remains a fundamental technique for
neural networking (Adbi, Valentin & Edelman, 1999; Sporns, 2011), which would seem to
attain optimal results for output that is contextualized externally to the machine, is
absurdly impractical.
In short, there is an inversely proportional ratio: the finer and more useful the
result, based on finer premises, and more intricately specified algorithms, the less
practical it becomes to implement computer processing. Far from saying that computers
give incorrect answers, the answers an ideally maintained machine could come up with,
within the finite lifetime of the questioner, must be considered imprecise. However, let’s
say, we need directions to a store across town. Perhaps the machine gives us a precise
address, but as instructions, this data alone is hardly consistently useful for all cases. Of
course, with the advent of the GPS system, textual data is merely augmented with a
visual translation. But the human mind is expected to make that final, and most crucial
leap from the arbitrary matrix of pixel hues to understanding (reading the map). This
subtle step often goes unnoticed by techno-enthusiasts, for instance (as in Berners-Lee,
Hendler, & Ora, 2001). Text strings directions may be explicitly included in piecemeal
parts, as included in the GPS systems of many cars today. These can be listed and
regurgitated automatically, without ever knowing what they say, just as sentences can
be assembled from indexed lists of vocabulary (Fitch & Friederici, 2012, Winter, 2010). We
humans then assemble the text into meaning and apply it intelligently, but again
displaying is not problem-solving. The GPS system at no time actually solves the direction
problem, as is noticeable when obvious shorter routes are ignored, that appear to defy
some algorithm. It is certainly not for a lack of data, which the system even displays on
the screen.
In all cases, the computer provides no indication of how generally its results
should be taken, or in what way. In this case, the address does not indicate where the
door will be, nor which planet, nor if the address is applicable to your goal (e.g. a
restaurant that is closed). But computer users are often accustomed to assuming that
the degree to which input is accurate, the output should be accurate. At the detailed
level of how the activity of specific neurons and synapses might result in a meaningful
mental image (which is our general concern in creating “digital poetry”), the level of
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imprecision renders any result irrelevant.
(2a-c)
(3a-c)
Figures 2a-c, 3a-c. This is a simple evolutionary computer process for creating a 'portrait.' On the left is the
(static) model, with two competing imitations. The progress (or lack thereof) is shown here after a few
seconds and after 48 hours.
Experiments
The following experiments were chosen among almost twenty years of pieces now.
Obviously, many might qualify as examples for our purposes. However, for this
discussion, it would seem less important to show examples of computer art, which might
solely satisfy computer art enthusiasts. Rather, we wish to show examples where
computer art might be integrated into other art media, in such a way that the work must
stand up to a wider range of audience members’ unpredictable tastes, patience for any
real or perceived stylistic or technical shortcomings of the computer itself, as well as
isolated laws discovered in scientific fields, disciplines which implicitly champion devoutly
self-imposed introversion (as in conceptual integration discussed previously, quoted from
Tooby & Cosmides, 1992, p. 49).
Animal Magnetism
This play performed by legendary avant garde theater group Mabou Mines at Saint Ann's
in New York and under circus tents in the Czech Republic and Poland. The story sets out
to blur distinctions between what we assume to be 'real' and what we assume to be
imaginary. For this play, I created a cartoon that would interact with the events on stage.
Actually, I sat next to the soundboard, where I could see the stage (and monitor the
personal mics when actors were not easily visible) and pressed buttons to trigger various
actions on the screen. These actions and transitions also included faux lip-synching and
dancing to live music. The animation on my laptop screen was then projected back onto
the stage with the actors. This setup is now fairly routine for laptop performers, but was
126 Calculation in Art: The Inconspicuous Heuristics of Computation
highly unusual at the time. The point being that the audience was not expecting it, could
not easily see how this was done, blurring the distinction between the cartoon and
reality all the more.
Figure 3. Animal Magnetism (2001, 2002, 2004) http://pump.org.in/arch/animal. This photo (by the author)
was from a performance at Arts at St. Anne’s in New York. It was also presented under circus tents, in the
Czech Republic and Poland.
At various times it would appear that the real actor cartoon character would
dialogue. Technically, no attempt was made to actually render realistic, lip-synched
mouth movements. Rather, the high-speed cartoon lip action, which occurred very
approximately simultaneous with the sound of the speech (often performed by musical
instruments). The mind of audience members then interpreted a correspondence,
ignoring errors and reinforcing the meaningfulness of the random stimuli (related to the
Perceptual Magnet Effect in music perception, as discussed in Patel, 2008; see also Pierce,
1999).
Figure 4. Computer animation resources from the animation for Animal Magnetism.Ritual for a Nonrepeating Universe (2002–2005, 2007)
Ritual for a Non-Repeating Universe
Philippa Kaye choreographed this performance for six dancers (see Figures 5a-d and 6).
It uses a camera to analyze movement on stage and a computer to create visual
animation from the live dataset. Taking images from a live camera feed, the computer
records the coordinates of the pixels from each frame sent, to determine where
movement occurred. Provided the camera is left alone on its tripod, the number of
points corresponding to the dancers is always significantly less than total points of
motion. About 12 times per second, an essential number for perceiving animation (Lutz,
1920:13–17; Taylor, 1996:34–41), a randomly chosen subset of the points are depicted on
the screen.
As there are never nearly enough of them displayed simultaneously to sufficiently
draw a recognizable image, a single frame would hardly depict anything coherent. Some
of these points also appear at random locations and do not correspond to actual
dancers, but to errant light reflections, etcetera, and are ignored, or might be said to lose
in this evolutionary competition. Only when these points' constant disappearance and
reappearance reinforce each other, via redintegration (Gregory, 1966:43–145; Hunt,
1993:442–460), do we recognizable something figurative in the animation (i.e. the
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Rupkatha Journal on Interdisciplinary Studies in Humanities, V7N1, 2015
movement of the dancers). What makes the motion of bodies more meaningful to us
than errant reflections of light? In the end, audience members experience seeing the
form of the dancers in these constellations, yet no such forms actually exist outside the
minds of the individual spectators.
(5a-d)
(6)
Figures 5a-d, 6. Ritual for a Non-repeating Universe (2002-2005, 2007) http://pump.org.in/arch/again. The
photo 5e above is cheated a bit in order to illustrate what the audience might see. The image is actually an
overlaying of several sequential frames, as in photo 5a-d, where no single still would capture anything
noticeable for the reader.
Conclusion
A key problem lies in the sense of the term “meaning.” For programmers—essentially
boolean logicians, this word is seen as rather synonymous with “predication,” while for
biologists, the word is not so concisely summarized, and there is much still to be
debated. Nonetheless, meaning is credited with directing attention, not simply
perception, as in redintegration, but the active focus that clarifies vague ideas (Millikan,
2000). Again, without the aid of attention to 'curate' the data (Chang, 2002; Schmeichal
& Baumeister, 2010), the computer simply does not have this ability.
This requires us to take a step backwards perhaps, to revisit computers, not as
synthesizers, manipulators, or teachers of concepts, but as tools, within a larger process.
In regards to tool use, “man-machine symbiosis” remains extremely useful (Licklider,
1960; Waldrop, 2001; Weiner, 1950). While the essence of imaging techniques is as a
prosthetic for limitations in our vision, it only addresses limitations of our eyeballs, not
vision itself. Computer imaging techniques assume the brain does a consistent but
incidental job of interpreting whatever sensations the visual cortex is provided.
However, “We see with our brains, not with our eyes.” (Bach-y-Rita quoted in Doidge,
2007, p. 16)
The actual implementation of this alternative conceptual frame would certainly
not be obvious at first, particularly for those experienced, who must unlearn orthodox
conceptual models. No doubt, especially for most readers here, while the ubiquitous
disdain for computers in art is unwarranted, uncritical love of technology is equally
problematic (as has been shown over history as in Marvin, 1988; see also Nadis, 2005).
128 Calculation in Art: The Inconspicuous Heuristics of Computation
However, it may prove helpful to at least acknowledge the profound ramifications of an
inherent shortcoming of computation, in order to put a novel spin on an old attitude.
The strict limits of the computation can become potential strengths for machines-astools, to be incorporated in a constructive way into one’s work. Though it is equally as
unlikely that we could build a thinking brain from bricks, as with binary digits, it is no less
handy to configure bricks in order to build useful things—such as a gallery for computer
art—which may inspire thought.
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judsoN (Judson Wright) makes Behavioral Art, programming computers in order to study
cognition. His software experiments/artwork, papers, music and performances have been
featured extensively around the world since 1996 (circus tents in Europe, the Smithsonian
International Art Gallery, the Brooklyn Museum of Art with the Brooklyn Philharmonic, the
809 International Art District in China, the Journal of Science and Technology of the
Arts…). He graduated from Brown University and has an MA from the Interactive
Telecommunications Program at New York University. portfolio: http://pump.org.in