How Did Humans Become So Creative? A Computational Approach
Liane Gabora
Department of Psychology
University of British Columbia, 3333 University Way
Kelowna BC, CANADA, V1V 1V7
liane.gabora@ubc.ca
Steve DiPaola
Department of Cognitive Science / SIAT
Simon Fraser University, 250-13450 102 Ave
Surrey BC, CANADA, V3T 0A3
sdipaola@sfu.ca
Abstract
This paper summarizes efforts to computationally model
two transitions in the evolution of human creativity:
its origins about two million years ago, and the ‘big
bang’ of creativity about 50,000 years ago. Using a
computational model of cultural evolution in which
neural network based agents evolve ideas for actions
through invention and imitation, we tested the hypothesis
that human creativity began with onset of the capacity
for recursive recall. We compared runs in which
agents were limited to single-step actions to runs in
which they used recursive recall to chain simple actions
into complex ones. Chaining resulted in higher diversity,
open-ended novelty, no ceiling on the mean fitness
of actions, and greater ability to make use of learning.
Using a computational model of portrait painting, we
tested the hypothesis that the explosion of creativity in
the Middle/Upper Paleolithic was due to onset of contextual
focus: the capacity to shift between associative
and analytic thought. This resulted in faster convergence
on portraits that resembled the sitter, employed
painterly techniques, and were rated as preferable. We
conclude that recursive recall and contextual focus provide
a computationally plausible explanation of how
humans evolved the means to transform this planet.
Introduction
To gain insight into the mechanisms underlying creativity,
one might start by testing peoples’ creative abilities, perhaps
using technologies such as fMRI, or dissect the brains
of people who were known to be particularly creative during
their lifetimes. However, to gain insight into the evolution
of creativity, these options do not exist. All that is left
of our prehistoric ancestors are their bones and artifacts
such as stone tools that resist the passage of time. Thus to
understand the evolution of creativity, computational modeling
is virtually the only scientific tool we have.
Humans are not only creative; we put our own spin on
the inventions of others, such that new inventions build
cumulatively on previous ones. This cumulative cultural
change is referred to as the ratchet effect (Tomasello, Kruger,
& Ratner, 1993), and it has been suggested that it is
uniquely human (Donald, 1991). A mathematical model of
two transitions in the evolution of the cognitive mechanisms
underlying creativity has been put forward (Gabora
& Aerts, 2009). Computational models of these mechanisms
have also been developed (DiPaola & Gabora, 2007,
2009; Gabora, 1995, 2008a,b; Gabora & Leijnen, 2009;
Leijnen & Gabora, 2009, 2010; Gabora & Saberi, 2011).
However, these efforts used different modeling platforms,
and because the aims underlying them have been part scientific
and part artistic, their relevance to each other, and to
an overarching research program has not previously been
made clear. The goal of this paper is to explain how, together,
they constitute an integrated effort to computationally
model the evolution of human creativity.
First Transition:
The Earliest Signs of Creativity
The minds of our earliest ancestors, Homo habilis, have
been referred to as episodic because there is no evidence
that they deviated from the present moment of concrete
sensations (Donald, 1991). They could encode perceptions
of events in memory, and recall them in the presence of a
cue, but had little voluntary access to memories without
environmental cues. They were therefore unable to shape,
modify, or practice skills and actions, and unable to invent
or refine complex gestures or vocalizations.
Homo erectus lived between approximately 1.8 and 0.3
million years ago. The cranial capacity of the Homo erectus
brain was approximately 1,000 cc, about 25% larger
than that of Homo habilis, at least twice as large as that of
living great apes, and 75% that of modern humans (Ruff et
al., 1997). This period is widely referred to as the beginnings
of cumulative culture. Homo erectus exhibited many
indications of enhanced intelligence, creativity, and ability
to adapt to their environment, including sophisticated, taskspecific
stone hand axes, complex stable seasonal home
bases, and long-distance hunting strategies involving large
game, and migration out of Africa.
This period marks the onset of the archaeological record
and it is thought to be the beginnings of human culture. It
is widely believed that this cultural transition reflects an
underlying transition in cognitive or social abilities. Some
have suggested that they owe their achievements to onset
of theory of mind (Mithen, 1998) or the capacity to imitate
(Dugatkin, 2001). However, there is evidence that other
species possess theory of mind and the capacity to imitate
(Heyes, 1998), yet do not compare to modern humans in
intelligence and cultural complexity.
International Conference on Computational Creativity 2012 203
Evolutionary psychologists have suggested that the intelligence
and cultural complexity of the Homo line is due to
the onset of massive modularity (Buss, 1999, 2004; Barkow,
Cosmides, &Tooby, 1992). However, although the
mind exhibits an intermediate degree of functional and
anatomical modularity, neuroscience has not revealed vast
numbers of hardwired, encapsulated, task-specific modules;
indeed, the brain has been shown to be more highly
subject to environmental influence than was previously
believed (Buller, 2005; Byrne, 2000; Wexler, 2006).
A Promising and Testable Hypothesis
Donald (1991) proposed that with the enlarged cranial capacity
of Homo erectus, the human mind underwent the
first of three transitions by which it—along with the cultural
matrix in which it is embedded—evolved from the
ancestral, pre-human condition. This transition is characterized
by a shift from an episodic to a mimetic mode of
cognitive functioning, made possible by onset of the capacity
for voluntary retrieval of stored memories, independent
of environmental cues. Donald refers to this as a selftriggered
recall and rehearsal loop. Self-triggered recall
enabled information to be processed recursively with respect
to different contexts or perspectives. It allowed our
ancestor to access memories voluntarily and thereby act
out1 events that occurred in the past or that might occur in
the future. Thus not only could the mimetic mind temporarily
escape the here and now, but by miming or gesture,
it could communicate similar escapes in other minds. The
capacity to mime thus ushered forth what is referred to as a
mimetic form of cognition and brought about a transition to
the mimetic stage of human culture. The self-triggered
recall and rehearsal loop also enabled our ancestors to engage
in a stream of thought. One thought or idea evokes
another, revised version of it, which evokes yet another,
and so forth recursively. In this way, attention is directed
away from the external world toward one’s internal model
of it. Finally, self-triggered recall allowed for voluntary
rehearsal and refinement of actions, enabling systematic
evaluation and improvement of skills and motor acts.
Computational Model
Donald’s hypothesis is difficult to test directly, for if correct
it would leave no detectable trace. It is, however, possible
to computationally model how the onset of the capacity
for recursive recall would affect the effectiveness, diversity,
and open-endedness of ideas generated in an artificial
society. This section summarizes how we tested Donald’s
hypothesis using an agent-based computational model
of culture referred to as ‘EVOlution of Culture’, abbreviated
EVOC. Details of the modeling platform are provided
elsewhere (Gabora, 2008b, 2008c; Gabora & Leijnen,
2009; Leijnen & Gabora, 2009).
 1 The term mimetic is derived from “mime,” which means
“to act out.”
The EVOC World. EVOC uses neural network based
agents that (i) invent new ideas, (ii) imitate actions implemented
by neighbors, (iii) evaluate ideas, and (iv) implement
successful ideas as actions. Invention works by modifying
a previously learned action using learned trends
(such as that more overall movement tends to be good) to
bias the invention process. The process of finding a neighbor
to imitate works through a form of lazy (non-greedy)
search. An imitating agent randomly scans its neighbors,
and adopts the first action that is fitter than the action it is
currently implementing. If it does not find a neighbor that
is executing a fitter action than its own action, it continues
to execute the current action. Over successive rounds of
invention and imitation, agents’ actions improve. EVOC
thus models how descent with modification occurs in a
purely cultural context. Agents do not evolve in a biological
sense–they neither die nor have offspring–but do in a
cultural sense, by generating and sharing ideas for actions.
Following Holland (1975) we refer to the success of an
action in the artificial world as its fitness, with the caveat
that unlike its usage in biology, here the term is unrelated
to number of offspring (or ideas derived from a given
idea). The fitness function (FF) was originally chosen because
it allows investigation of biological phenomena such
as underdominance and epistasis in a cultural context (see
Gabora, 1995); the one used here is one over several used
in EVOC (see Gabora, 2008 for others). The FF rewards
head immobility and symmetrical limb movement. Fitness
of actions starts out low because initially all agents are
entirely immobile. Soon some agent invents an action that
has a higher fitness than doing nothing, and this action gets
imitated, so fitness in- creases. Fitness increases further as
other ideas get invented, assessed, implemented as actions,
and spread through imitation. The diversity of actions initially
increases due to the proliferation of new ideas, and
then decreases as agents hone in on the fittest actions.
We used was a toroidal lattice with 100 nodes, each occupied
by a single, stationary agent, and a von Neumann
neighborhood structure (agents only interacted with their
four adjacent neighbors). During invention, the probability
of changing the position of any body part involved in an
action was 1/6. On each run, creators and imitators were
randomly dispersed.
Chaining. This gives agents the opportunity to execute
multi-step actions. For the experiments reported here with
chaining turned on, if in the first step of an action an agent
was moving at least one of its arms, it executes a second
step, which again involves up to six body parts. If, in the
first step, the agent moved one arm in one direction, and in
the second step it moved the same arm in the opposite direction,
it has the opportunity to execute a three-step action.
And so on. The agent is allowed to execute an arbitrarily
long action so long as it continues to move the same
arm in the opposite direction to the direction it moved previously.
Once it does not do so, the chained action comes
to an end. The longer it moves, the higher the fitness of this
International Conference on Computational Creativity 2012 204
multi-step chained action. Where n is the number of
chained actions, the fitness, Fc, is calculated as follows:
Fc = Fnc+ (n – 1)
The fitness function with chaining provides a simple means
of simulating the capacity for recursive recall.
‘Origins of Creativity’ Results
As shown in Figure 1, the capacity to chain simple actions
into more complex ones increases the mean fitness of actions
in the society. This is most evident in the later phase
of a run. Without chaining, agents converge on optimal
actions, and the mean fitness of action reaches a plateau.
With chaining, however, there is no ceiling on the mean
fitness of actions. By the 100th iteration it reached almost
15, indicating a high incidence of chaining.
Figure 1. Mean fitness of actions in the artificial society with
chaining versus without chaining.
As shown in Figure 2, chaining also increases the diversity
of actions. This is most evident in the early phase of a run
before agents begin to converge on optimal actions. Although
in both cases there is convergence on optimal actions,
without chained actions, this is a static set (thus
mean fitness plateaus) whereas with chained actions the set
of optimal actions is always changing, as increasingly fit
actions are found (thus mean fitness keeps increasing).
Figure 2. Mean number of different actions in the artificial
society with chaining (continuous line) versus without chaining
(dashed line).
Recall that agents can learn trends from past experiences
(using the knowledge-based operators), and thereby bias
the generation of novelty in directions that have a greater
than chance probability of being fruitful. Since chaining
provides more opportunities to capitalize on the capacity to
learn, we hypothesized that chaining would accentuate the
impact of learning on the mean fitness of actions, and this
too turned out to be the case (Gabora & Saberi, 2011).
Second Transition:
The ‘Big Bang’ of Human Creativity
The European archaeological record indicates that a truly
unparalleled cultural transition occurred between 60,000
and 30,000 years ago at the onset of the Upper Paleolithic
(Bar-Yosef, 1994; Klein, 1989; Mellars, 1973, 1989a,
1989b; Soffer, 1994; Stringer & Gamble, 1993). Considering
it “evidence of the modern human mind at work,”
Richard Leakey (1984:93-94) describes the Upper Palaeolithic
as follows: “unlike previous eras, when stasis dominated,
... [with] change being measured in millennia rather
than hundreds of millennia.” Similarly, Mithen (1996) refers
to the Upper Paleaolithic as the ‘big bang’ of human
culture, exhibiting more innovation than in the previous six
million years of human evolution. We see the more or less
simultaneous appearance of traits considered diagnostic of
behavioral modernity. It marks the beginning of a more
organized, strategic, season-specific style of hunting involving
specific animals at specific sites, elaborate burial
sites indicative of ritual and religion, evidence of dance,
magic, and totemism, the colonization of Australia, and
replacement of Levallois tool technology by blade cores in
the Near East. In Europe, complex hearths and many forms
of art appeared, including cave paintings of animals, decorated
tools and pottery, bone and antler tools with engraved
designs, ivory statues of animals and sea shells, and personal
decoration such as beads, pendants, and perforated
animal teeth, many of which may have indicated social
status (White, 1989a, 1989b).
Whether this period was a genuine revolution culminating
in behavioral modernity is hotly debated because
claims to this effect are based on the European Palaeolithic
record, and largely exclude the African record (McBrearty
& Brooks, 2000); Henshilwood & Marean, 2003). Indeed,
most of the artifacts associated with a rapid transition to
behavioral modernity at 40–50,000 years ago in Europe are
found in the African Middle Stone Age tens of thousands
of years earlier. However the traditional and currently
dominant view is that modern behavior appeared in Africa
between 50,000 and 40,000 years ago due to biologically
evolved cognitive advantages, and spread replacing existing
species, including the Neanderthals in Europe (e.g.,
Ambrose, 1998; Gamble, 1994; Klein, 2003; Stringer &
Gamble, 1993). Thus from this point onward there was
only one hominid species: modern Homo sapien. Despite
lack of overall increase in cranial capacity, the prefrontal
International Conference on Computational Creativity 2012 205
cortex, and particularly the orbitofrontal region, increased
significantly in size (Deacon, 1997; Dunbar, 1993; Jerison,
1973; Krasnegor, Lyon, and Goldman-Rakic, 1997;
Rumbaugh, 1997) and it was likely a time of major neural
reorganization (Klein, 1999; Henshilwood, d’Errico,
Vanhaeren, van Niekerk, and Jacobs, 2000; Pinker, 2002).
Given that the Middle/Upper Palaeolithic was a period
of unprecedented creativity, what kind of cognitive processes
may have been involved?
A Testable Hypothesis
Converging evidence suggests that creativity involves the
capacity to shift between two forms of thought (Finke,
Ward, & Smith, 1992; Gabora, 2003; Howard-Jones &
Murray, 2003; Martindale, 1995; Smith, Ward, & Finke,
1995; Ward, Smith, & Finke, 1999). Divergent or associative
processes are hypothesized to occur during idea generation,
while convergent or analytic processes predominate
during the refinement, implementation, and testing of an
idea. It has been proposed that the Paleolithic transition
reflects a mutation to the genes involved in the fine-tuning
of the biochemical mechanisms underlying the capacity to
subconsciously shift between these modes, depending on
the situation, by varying the specificity of the activated
cognitive receptive field. This is referred to as contextual
focus2 because it requires the ability to focus or defocus
attention in response to the context or situation one is in.
Defocused attention, by diffusely activating a broad region
of memory, is conducive to divergent thought; it enables
obscure (but potentially relevant) aspects of the situation
thus come into play. Focused attention is conducive to
convergent thought; memory activation is constrained
enough to hone in and perform logical mental operations
on the most clearly relevant aspects.
Support from Computational Model
Again, because it would be difficult to empirically determine
whether Paleolithic humans became capable of contextual
focus, we decided to begin by determining whether
the hypothesis is at least computational feasible. To do so
we used an evolutionary art system that generated progressively
evolving sequences of artistic portraits, with no human
intervention once initiated. We sought to determine
whether incorporating contextual focus into the fitness
function would play a crucial role in enabling the computer
system to generate art that humans find "creative" (i.e. possessing
qualities of novelty and aesthetic value typically
ascribed to the output of a creative artistic process).
We implemented contextual focus in the evolutionary art
algorithm by giving the program the capacity to vary its
level of fluidity and control over different phases of the
creative process in response to the output it generated. The
 2 In neural net terms, contextual focus amounts to the capacity
to spontaneously vary the shape of the activation
function, flat for divergent thought and spiky for analytical.
creative domain of portrait painting was chosen because it
requires both focused attention and analytical thought to
accomplish the primary goal of creating a resemblance to
the portrait sitter, as well as defocused attention and associative
thought to deviate from resemblance in a way that
is uniquely interesting, i.e., to meet the broad and often
conflicting criteria of aesthetic art. Since judging creative
art is subjective, rather than use quantitative analysis, a
representative subset of the automatically produced artwork
from this system was selected, output to high quality
framed images, and submitted to peer reviewed and commissioned
art shows, thereby allowing it to be judged positively
or negatively as creative by human art curators, reviewers
and the art gallery going public.
Our strategy for modeling contextual focus may raise
questions about the ability of computers to "truly" be creative,
and the role of the human system designer in the creative
output. Several researchers in computational creativity,
have addressed such questions by outlining different dimensions
of creativity and proposing schema for evaluating
a "level of creativity" of a given system, for example
(Ritchie, 2007; Jennings, 2010; Colton, Pease, & Charnley,
2011). We are interested in applying such analyses to our
portrait-system as a possibility for future work; indeed, the
mechanics of contextual focus might be clarified by the
computational creativity literature. In particular we are
interested in further exploring the link between systemmodified
fitness constraints and the idea of transformational
creativity (Boden, 2003; Wiggins 2006).
However, for the purposes of the current paper, it is less
important to address the question of designer involvement
in system creativity, or to try and quantify the amount of
creativity displayed. Rather, we concentrate on the qualitative
impact made by the explicit incorporation of contextual
focus into the system as a whole, and its ability to elevate
the perceived quality and novelty of system output to
a level audiences judged reminiscent of successful "artistic,
human-style" creativity.
Generative Art Systems: Creative evolutionary systems
are a class of search algorithms inspired by Darwinian
evolution, the most popular of which are genetic algorithms
(GA) and genetic programming (GP) (Koza, 1993).
These techniques solve complex problems by encoding a
population of randomly generated potential solutions as
‘genetic instruction sets’, assessing the ability of each to
solve the problem using a predefined fitness function, mutating
and/or marrying (applying crossover to) the best to
yield a new generation, and repeating until one of the offspring
yields an acceptable solution. We are not claiming
that contextual focus is Darwinian, but simply that for our
computational modeling purposes, Genetic Programming
proved a convenient foundational aggregator to support our
contextual focus fitness function module.
Typically these systems allow a human user to pick
those individuals that will be mated – making the human
the creative judge. In contrast, our system used a function
trigger mechanism within the contextual focus fitness funcInternational
Conference on Computational Creativity 2012 206
tion which allowed the process to run automatically, without
any human intervention once the process was started. It
was not until the evolutionary art process came to completion
that humans looked at and evaluated the art. Others
have begun to use creative evolutionary systems with an
automatic fitness function in design and music, as well as
in a creative invention machine (Bentley, Corne, 2002).
What is unique in our approach is that it incorporates several
techniques that enable it to shift to processing artistic
content in a more divergent or associative manner, and
employs a form of GP called Cartesian Genetic Programming
(Miller, 2011), detailed in the next section.
Implementation: The GP function set has 13 functions
which use unitized x and y positions of the portrait image
as variables and additional parameter variables (noted PM)
that can be affected by adaptive mutation. Functions are
low level in nature which aids in a large ‘creative’ search
space, and output HSV color space values between 0 and
255. An individual in our population is manifested as one
program that runs successively for every pixel in the output
image, which is then tested against our creative fitness
function. This allows correlated painterly effects as one
moves through the image. Functions 1 through 5 use simple
logical or arithmetic manipulations of the positions
(low level functions create a larger ‘creative’ search space),
whereas 7 through 14 use trigonometric or logical functions
that are more related to geometric shapes and color
graduations. The 13 functions of the function set are:
1: x|y;
2: PM & x;
3: (x ? y) % 255;
4: if (x[y) x - y; else y - x;
5: 255 - x;
6: abs (cos (x) * 255);
7: abs (tan (((x % 45) * pi)/180.0) * 255));
8: abs (tan (x) * 255) % 255);
9: sqrt ((x - PM)2 ? (y - PM) 2); (thresholded at 255)
10: x % (PM? 1) ? (255 - PM);
11: (x ? y)/2;
12: if (x[y)255*((y ? 1)/(x ? 1)); else 255*((x ? 1)/(y ? 1));
13: abs (sqrt (x – PM2? y – PM2) % 255);
The contextual focus based fitness function varies fluidly
from tightly focusing on resemblance (similarity to the
sitter image, which in this case is an image of Charles
Darwin), to swinging (based on functional triggers) toward
a more associative process of the intertwining, and at times
contradicting, ‘rules’ of abstract portrait painting. Different
genotypes map to the same phenotype. This allows us to
vary the degree of creative fluidity because it offers the
capacity to move though the search space via genotype
(small ordered movement) or phenotype (large movement
but still related). For example, in one set of experiments
this is implemented as follows: if the fittest individual of a
population is identical to an individual in the previous generation
for more than three iterations, meaning the algorithm
is stuck in analytic mode and needs to open up, other
genotypes that map to this same phenotype are chosen over
the current non-progressing genotype, allowing divergent
open movement through the landscape of possibilities.
The automatic fitness function partly uses a ‘portrait to
sitter’ resemblance. Since the advent of photography (and
earlier), portrait painting has not just been about accurate
reproduction, but also about using modern painterly goals
to achieve a creative representation of the sitter. The fitness
function primarily rewards accurate representation, but in
certain situations also rewards visual painterly aesthetics
using simple rules of art creation as well as a portrait
knowledge space. Specifically, the divergent painterly portion
of the fitness function takes into account: (1) face versus
background composition, (2) tonal similarity over exact
color similarity, matched with a sophisticated artistic color
space model that weighs for warm-cool color temperature
relationships based on analogous and complementary color
harmony rules, and (3) unequal dominate and subdominant
tone and color rules, and other artistic rules based on a portrait
painter knowledge domain as detailed in (DiPaola,
2009) and illustrated in Figure 3. The system is biased toward
resemblance, which gives it structure, but can, under
the influence of functional triggers, exhibit artistic flair.
Figure 3. The contextual focus fitness function mimics human
creativity by moving between restrained focus (resemblance)
to more unstructured associative focus (resemblance + ambiguous
art rules of composition, tonality and color theory).
The fitness function calculates four scores (resemblance
and the three painterly rules), and then combines them in
different ways to mimic human creativity, shifting between
unstructured associative focus (rules of composition, tonality
and color theory) and restrained focus (resemblance). In
its default state, the fitness function uses a more analytic
1 2 3
Structured
Focus
Innovative
Exploration
Fitness:
Similarity
Fitness:
Artistically
Expressive
Resemblance
Fuzzy Rules of Art:
1: composition, 2: tonality, 3: color
F
I
T
N
E
S
S
T
E
S
T
F
L
U
I
D
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form of processing, specifically, a ratio of 80% resemblance
to 20% non-proportional scoring of the three painterly
rules. Several functional triggers can alter this ratio in
different ways, but the main trigger is when the system is
“stuck”. Within any run, for instance as long as an adaptive
percentage of 80–20 resemblance bias is maintained (resemblance
patriarchs), the system will allow very high
scoring of painterly rule individuals to be accepted into the
next population. Those with high painterly scores
(weighted non-proportionally including for a very high
score with respect to just one rule) are saved separately,
and mated with the current 80/20 population. Unless other
triggers exist, their offspring are still tested with the 80–20
resemblance test. System wide functional changes occur
when redundancy triggers affect the default ratio for all
individuals. As mentioned previously, when a plateau or
local minimum is reached for a certain number of populations,
the fitness function ratio switches such that painterly
rules are weighted higher than resemblance (on a sliding
scale), and work in conjunction with redundancy at the
input, node, and functional levels. Similarly, but now in
reverse, to the default resemblance situation, high scoring
resemblance individuals can pass into the next population
when a percentage of painterly rule individuals is met. Using
this more associative mode, high resemblance individuals
are always part of the mix, and when these individuals
show a marked improvement, a trigger is set to return to
the more focused 80/20 resemblance ratio.
As the fitness score increases, portraits look more like
the sitter. This gives us a somewhat known spread from
very primitive (abstract) all the way through to realistic
portraits. Thus in effect the system has two ongoing processes:
(1) those ‘most fit’ portraits that pass on their portrait
resemblance strategies, making for more and more
realistic portraits—the family ‘resemblance’ patriarchs,
and (2) the creative ‘strange uncles’: related to the current
‘resemblance fit’, but portraits that are more artistically
creative or ‘artistically fit’. This dual evolving technique of
‘patriarchs and strange uncles’ mimics the interplay between
freedom and constraint that is so central to creativity.
Paradoxically, novelty often benefits from the existence
of a known framework reference system to rebel and innovate
from. Creative people use some strong structural rules
(as in the templates of a sonnet, tragedy, or in this case, a
resemblance to the sitter image) as a resource or base to
elaborate new variants beyond that structure (in this case,
an abstracted variation of the sitter image).
‘Big Bang of Creativity’ Results
The automatic creative output was generated over thirty
days of continuous, un-supervised computer use. The images
in Figure 4 show a selection of representative portraits
produced by the system. While the overall population improves
at resembling Darwin’s portrait, what is more interesting
to us is the variety of recurring, emergent and
merged creative strategies that evolve as the programs in
different ways to become better abstract portraitists.
Figure 4. These images have been seen by thousands in the
last 2 years and have been perceived as creative art works on
their own by the art public, including above at the MIT Museum
in Cambridge, MA.
Humans rated the portraits produced by this version of
the portrait painting program with contextual focus as
much more creative and interesting than a previous version
that did not use contextual focus, and unlike its predecessor,
the output of this program generated public attention
worldwide. Example pieces were framed and submitted to
galleries as a related set of work. Care was taken by the
author to select representational images of the evolved unsupervised
process, however creative human bias obvious
exists in the representational editing process. Output has
been accepted and exhibited at six major galleries and museums
including the TenderPixel Gallery in London, Emily
Carr Galley in Vancouver, and Kings Art Centre at Cambridge
University as well as the MIT Museum, and the
High Museum in Atlanta, all either peer reviewed, juried or
commissioned shows from institutions that typically only
accept human art work. A typical gallery installation consisted
of 40-70 related portraits produced in time order
over a given run. Gallery showings focus on “best resemblances”
and those that are artistically compelling from an
abstract portrait perspective. This gallery of work has been
seen by tens of thousands of viewers who have commented
that they see the artwork as an aesthetic piece that ebb and
flows through seemly creative ideas even though they were
solely created by an evolutionary art computer program
using contextual focus. Note that no attempt to create a
pure ‘creativity Turning Test’ was attempted. Besides the
issues surrounding the validity of such a test (Pease, Colton,
2011), it was not feasible in such reputable and large
art venues. However most of the thousands of causal viewers
assumed they were looking at human created art. The
work was also selected for its aesthetic value to accompany
an opinion piece in the journal Nature (Padian, 2008), and
was given a strong critical review by the Harvard humaniInternational
Conference on Computational Creativity 2012 208
ties critic, Browne (2009). While these are subjective
measures, they are standard in the art world. The fact that
the computer program produced novel creative artifacts,
both as single art pieces and as a gallery collection of pieces
with interrelated themes, using contextual focus as a key
element of its functioning, is compelling evidence of the
effectiveness of contextual focus.
Discussion and Conclusions
Many species engage in acts that could be said to be creative.
However, humans are unique in that our creative ideas
build on each other cumulatively; indeed it is for this reason
that culture is widely construed as an evolutionary process
(e.g. Bentley, Ormerod, & Batty, 2011; Cavalli-Sforza
& Feldman, 1981; Gabora, 1996, 2008; Hartley, 2009;
Mesoudi, Whiten & Laland, 2006; Whiten, Hinde, Laland,
& Stringer, 2011). Our creativity is evident in all walks of
life. It has transformed the planet we live on.
We discussed two transitions in the evolution of uniquely
cumulative form of creativity, discussed cognitive
mechanisms that have been proposed to underlie these
transitions, and summarized efforts to computationally
simulate them. Using an agent based computer model of
cultural evolution, we obtained support for the hypothesis
that the onset of cumulative, open-ended cultural evolution
can be attributed to the evolution of a self-triggered recall
and rehearsal loop, enabling the recursive chaining of
thoughts and actions. Using a generative genetic programming
system, we used a computational model of contextual
focus to automatically produce a related series of art output
that received critical acclaim usually given to human art
work supporting the hypothesis that the capacity to shift
between analytic and associative modes of thought plays
an important role in the creative process.
Our results suggest that the evolution of chaining and
contextual focus made possible the open-ended cumulative
creativity exhibited by computational models of language
evolution (e.g. Kirby, 2001). Note that in chaining versus
no chaining conditions the size of the neural network is the
same, but how it is used differs. This suggests that it was
not larger brain size per se that initiated the onset of cumulative
culture, but that larger brain size enabled episodes to
be encoded in more detail, allowing more routes for reminding
and recall, thereby facilitating recursive redescription
of information encoded in memory (KarmiloffSmith,
1992), thereby tailor it to the situation at hand. Our
results suggest that it is reasonable to hypothesize that this
in turn is vastly accentuated by the capacity to shift between
associative and analytic different processing modes.
We wish to acknowledge some limitations of this work.
Chaining does not work, as in humans, by considering an
idea in light of one perspective, seeing how that perspective
modifies the idea, seeing how this modification suggests
a new perspective from which to consider the idea,
and so forth. We are planning a more sophisticated implementation
of that works more along these lines. Second,
there is some irony in using an art program based on the
genetic algorithm as a starting point to implement contextual
focus, which we have claimed is unique to the cultural
evolution of ideas and has no counterpart in biological evolution.
Our goal here was to see if contextual focus ‘works’
at all; since this was successful, we will now move on to
more cognitively plausible implementations. One of the
projects currently underway is to implement contextual
focus in the EVOC model of cultural evolution that was
used for the ‘origin of creativity’ experiments. This is being
carried out as follows. The fitness function will change
periodically, so that agents find themselves no longer performing
well. They will be able to detect that they are not
performing well, and in response, increase the probability
of change to any component of a given action. This temporarily
makes them more likely to “jump out of a rut” resulting
in a very different action, thereby simulating the capacity
to shift to a more associative form of thinking. Once
their performance starts to improve, the probability of
change to any component of a given action will start to
decrease to base level, making them less likely to shift to a
dramatically different action. This helps them perfect the
action they have already settled upon, thereby simulating
the capacity to shift to a more associative form of thinking.
Acknowledgements
We are grateful to Graeme McCaig and grants from Natural
Sciences and Engineering Research Council of Canada
and the Fund for Scientific Research of Flanders, Belgium.
<references_biblio/>
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