Table Of ContentShedding Computational
Light on Human
Creativity
Subrata Dasgupta
UniversityofLouisianaatLafayette
Eversince1956whendetailsoftheLogicTheoristwerepublishedbyNewell
andSimon,alargeliteraturehasaccumulatedoncomputationalmodelsand
theoriesofthecreativeprocess,especiallyinscience,inventionanddesign.But
what exactly do these computational models/theories tell us about the way
that humans have actually conducted acts of creation in the past? What
lighthascomputationshedonourunderstandingofthecreativeprocess?Ad-
dressing these questions, we put forth three propositions: (I) Computational
models of the creative process are fundamentally ºawed as theories of human
creativity. Rather, the universal power of computational models lies else-
where: (II) Computational models of particular acts of creation can serve as
effective experiments to test universal hypotheses about creative processes and
mechanisms; and (III) Computation-based architectures of the creative mind
provide metaphorical frameworks that, like all good metaphors, can serve as
rich sources of insight into aspects of the creative process. In this paper, we
provideevidenceforthesethreepropositionsbydrawinguponsomeparticular
episodesinthecognitivehistoryofscience,technology,andart.
1. Introduction
The research program for understanding and explaining the cognitive
natureofcreativityinvolvesabroadspectrumofempirical,theoreticaland
disciplinary approaches. Roughly speaking, these frameworks draw on a
numberofdimensions.
(1) Cognitive historical case studies of episodes and persons based on
detailed historical, biographical and autobiographical evidence (e.g.,
Lowes1927;Shahn,1957;Gruber1981;Miller1986,2001;Chadwick&
de Courtivron 1993; Giesen 1995; Dasgupta 1999; Ippolito & Tweney
2003). (2) Computational theorizing and modeling (e.g., Langley et al
PerspectivesonScience2008,vol.16,no.2
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121
122 SheddingComputationalLightonHumanCreativity
1987; Thagard 1988). (3) Laboratory experiments on human subjects
given tasks/problems deemed to entail creative thought (e.g., Qin &
Simon 1990; Finke, Ward & Smith 1992; Klahr 2000). (4) Field studies
ofcontemporarycreativepeopleatwork(e.g.,Dunbar1999).(5)‘Clinical’
approaches involving interviews, questionnaires and personal life histo-
ries (John-Steiner 1987; Rothenberg 1990; Csikszentmihalyi 1996).
(6) Historiometric and psychometric studies that capture quantitative
aspects of both past and present creative persons, domains, and domain-
speciªccommunities(e.g.,Simonton1988;Ludwig1995).(7)Psychoana-
lytic studies of creative people and work, past and present (e.g., Kris
1952;Storr1972)
Some frameworks combine a subset of these approaches. Thus, our
studyofHerbertSimon’s‘multidisciplinarycreativity’combinedthecog-
nitive historical case study and the clinical approaches (Dasgupta 2003a,
2003b). A more recent study of the painter George Rodrigue utilized the
case study, clinical and ªeld study approaches (Dasgupta 2004b). Our in-
vestigation of the invention of microprogramming in computer design
(Dasgupta 1994) and Kulkarni’s and Simon’s (1988) study of the discov-
eryoftheornithinecycleinbiochemistrybothentailedthemethodofthe
casestudyandcomputationalmodeling.
Our focus in this paper relates to the computational and case study
approaches (1 & 2 above). In particular, we raise the question: what light
does computational modeling really shed on actual human creativity as it
is manifested in speciªc historical episodes in science, technology and
other creative traditions? We address this question by putting forth three
propositions.
(I) Computational models of particular acts of creation (i.e., computa-
tional case studies) are fundamentally ºawed as theories that explain these
creativeacts.Rather:
(II) Computational case studies serve as experiments to test (corroborate/
falsify)generalhypothesesaboutcreativeprocessesandmechanisms.And
(III)Computation-basedarchitecturesofthecreativemindcanserveas
metaphoricalframeworksthatofferrichsourcesofinsightintoaspectsofthe
creativeprocess.
In other words, we suggest that it is in these two latter aspects—as
sources of singular experiments and as metaphor—that the universal
power of computational modeling lies in shedding light on human cre-
ativity.
In the sections that follow, we consider each of the above propositions
andprovideevidenceintheirsupport.
Perspectives on Science 123
2. Computation Models as Theories of Particular Creative Processes.
In1957,HerbertSimonannouncedthat“themysteryhasnowbeenpretty
well stripped away from the higher mental processes”; and since “creativ-
itywasjustalittlemoreofthesame,”itwasnolonger“asmysteriousand
inexplicable”assomemadeout.1
Simon’s assertion came in the wake of the success he and Newell had
achieved with the Logic Theorist (Newell & Simon 1956). Later, Newell,
Shaw and Simon (1962) followed up this claim by addressing the “pro-
cesses of creative thinking” in computational terms. The basic thesis was
thatbyprogrammingthecomputeronecouldattempttosimulatespeciªc
higher level cognitive tasks such as proving theorems and the like. If the
simulation produced behavior consistent with what was predicted, the
program itself became a theory of how humans might carry out relevant
cognitiveprocessesforthatparticularproblemclass.Furthermore,byextract-
ingfromtheprogramitsmostgeneralfeatures,onecouldconstructathe-
oryofthegeneralfeaturesofhumancreativeprocesses.
Inthefourdecadessincethenaconsiderableliteraturehasaccumulated
on work based on this thesis, especially in respect to understanding
scientiªc and technological creativity (Simon 1977; Lenat 1982; Langley,
Simon, Bradshaw & Zytkow 1987; Kulkarni & Simon 1988; Thagard
1988; Shrager & Langley 1990; Thagard 1992; Dartnall 1994; Dasgupta
1994;Gooding1999).Butwhatexactlydothesestudiestellusaboutthe
way humans have actually discovered scientiªc laws, invented technologi-
cal artifacts, produced signiªcant art, or performed other acts of creation?
In other words, how valid are computational models as explanatory theo-
riesofthehumancreativeprocessestheypurporttomodel?
In constructing the BACON family of programs (Langley, Simon,
Bradshaw & Zytkow 1987), Langley et al took up the challenge of con-
structing computer models to discover some of the classic laws in physics
and chemistry. This work was also notable for the comprehensiveness and
breadthofitsscope.
However, if we examine the knowledge base of BACON.1, the ªrst of
these programs which discovered versions of Johannes Kepler’s third law
of planetary motion, and compare it with the historical and biographical
details of how Kepler arrived at his third law, circa 1619 (Caspar 1959;
Holton 1973)—we ªnd that BACON.1’s knowledge base did not mirror
theparticularknowledgethatKeplerhadpossessed.Thus,BACON.1did
notdowhatKeplerdid.OnecannotclaimthatBACON.1wasatheoryof
1. H. A. Simon to G. A. Miller, April 30, 1957. Herbert Simon Papers, Carnegie
MellonUniversity(CMU)Archives,Pittsburgh,Pennsylvania.
124 SheddingComputationalLightonHumanCreativity
how Kepler had arrived at the third law, because the program was
ahistoricalinitscapacityforscientiªcdiscovery.
In contrast, the program KEKADA (Kulkarni & Simon 1988) was
muchclosertothehistoricalsituationittriedtomodel—viz.,thecircum-
stances under which the biochemist Hans Krebs discovered (in 1931–32)
the biochemical process known as the ornithine cycle, the metabolic pro-
cessbywhichanimalcellssynthesizeurea.
Similarly,in(Dasgupta1994),Idevelopedanexplanation,incomputa-
tional terms, of the cognitive process by which a technique in computer
design called microprogramming might have been invented (in 1951) by
the computer pioneer Maurice Wilkes. Here, I followed the paradigm
usedinthedesignofKEKADA;thatisIadheredascloselyaspossibleto
the historical evidence. Indeed, keeping in mind that the plausibility of
suchacomputationalaccountwouldhingecriticallyonthevalidityofthe
assumptions about the contents of the worldview/belief/knowledge sys-
tem, a great deal of effort was expended in justifying the various kinds of
knowledge (facts, concepts, operational principles, rules, heuristics) that
enteredintothecomputation-basedexplanation.
Aswewillseeinthenextsection,computerprogramssuchasBACON
and KEKADA, or computational models of the sort proposed to explain
theinventionofmicroprogrammingdoyieldrichdividendsinilluminat-
ingaspectsofcreativity.Yetnomatterhowdetailedandprecisethemod-
els are, they are fundamentally ºawed as theories of human creativity—for two
reasons.
2.1 The Contingency of the Creative Process
First, as theories of individual acts of creation, there are severe limits to
the extent to which one can test them against the actual historical events
theypurporttobetheoriesof.TheonlywaythatKEKADAasatheoryof
Hans Kerbs’s ornithine cycle discovery process, or my computation-based
modelofMauriceWilkes’smicroprogramminginventionprocesscouldbe
corroboratedwasbyappealingtothehistoricalevidence—thatis,seeking
evidence that things really happened the way they did through archival
research into the person’s correspondence, notes, journals, diaries, and—if
the person is still living—by way of interviews. In other words, by con-
ducting cognitive historical investigations (Nersessian 1995; Dasgupta
2003a).
The problem is that creativity is not a deterministic phenomenon. Consider,
as an example, the actual process whereby a person arrived at an impor-
tant,originalidea.Ifwecould,insomefashion,placethepersoninexactly
the same initial conditions (that is, the same state of her world view/
belief/knowledge system, same state of the goal space, same emotional
Perspectives on Science 125
condition,andidenticalproblemsituation),thereisnoguaranteethatthe
same cognitive process (or even a close approximation to it) would unfold
or,indeed,thatthesameresultwouldensue.
In the context of biological evolution, Gould (1990) has argued that
the evolutionary process is a contingent act: a tape on which a (natural)
creative process is ‘recorded’, when rewound and restarted with the same
initial conditions would not necessarily record the same process as before.
Human creative processes, though not Darwinian in nature (Dasgupta
2004a), are, likewise, contingent processes—if only because the creative
being, like all people, is boundedly rational (Simon 1996), and such pro-
cesses entail making sequences of choices of mental actions that may vary
from one situation, one time, one initial condition, to another. Because of
this nondeterminstic, contingent nature of creative processes, a computa-
tional model that claims to be a theory of such a particular process can
only be corroborated if the historical and biographical records tell us ex-
plicitlythatthepersonusedtheparticularknowledgeandreasoningrules
speciªedinthetheoryinthatspeciªedorder.Otherwise,wecannotclaim
that the computational theory is even a plausible theory, let alone the
‘right’one.Thetheoryastheoryisleftinastateoflimbo:onecanneither
corroborateitwiththeevidencenorfalsifyit.
2.2 The Dilemma of the Unconscious
Itiswidelydocumentedthatmanyhighlycreativepersons—poets,paint-
ers, musicians, scientists, inventors—simply cannot account for their
thought processes. Ideas would appear ‘out of the blue,’ ‘unexpectedly,’
‘suddenly’—as recorded, for example, in the cases of poets William Blake
(Harding1942,p.14),AmyLowellandStephenSpender(Ghiselin1985,
110–126), the story-teller/poet Lewis Carroll (Harding 1942, p. 59), the
artist Pablo Picasso (Ashton 1972, 7–13), the composer Edward Elgar
(Harding 1942, p. 14), the scientist Alfred Russel Wallace (Harding
1942, p. 15), the mathematician Henri Poincaré (Ghiselin, 1985, 22–31)
andtheinventor/engineerJamesWatt(Dasgupta,2004a).
This mystery of where an idea, a poem, a picture, a musical composi-
tion comes from is thus a recurrent theme in autobiographical statements
madebycreativepeopleinmanydifferentªelds.Anditposestheproblem
of we call here the ‘dilemma of the unconscious’—which constitutes the
second serious barrier to testing computational theories of creative pro-
cesses.
For our present purposes, the ‘unconscious’ constitutes those contents
ofthemind(facts,images,acts,processes,desires,beliefs,biases)ofwhich
thepersonisunaware.Thisunconsciousmightinclude,butnotnecessarily
so,whatFreudtermedthe‘dynamicallyunconscious’(‘Ucs’)(Freud1920/
126 SheddingComputationalLightonHumanCreativity
1989,5–6),whichistheresultofrepression.ItalsoincludestheFreudian
‘preconscious’ (‘Pcs’)—entities one is not conscious of but which can be
easily summoned into consciousness. The unconscious also includes those
cognitiveprocessesthatareneitherUcsnorPcsbutwhicharenonetheless
not easily accessible. Linguistic skills in generating and understanding
speech, computational skills in judging distance, depth, speed are in-
stances of such processes, called the ‘cognitive unconscious’ (Kihlstrom
1987;Allen&Weber1999).
The dilemma of the unconscious lies in that anecdotal and autobio-
graphical reports suggest (as cited earlier) that much of creative thinking
takes place in the unconscious, and so it resists our efforts to comprehend
the act of creation. So compelling is the autobiographical/anecdotal evi-
dence that Wallas (1926, p. 86) suggested the term incubation to describe
the mental process in which “. . . we do not voluntarily or consciously
thinkonaparticularproblemand...aseriesofunconsciousandinvolun-
tarymentaleventsmaytake.”
Though incubation as a distinct process has resisted attempts to be
demonstrated in the laboratory (Eindhoven & Vinake 1952; Olton &
Johnson1979),thereseemslittledoubtofitsexistence(Guildford1979).
The problem is that the idea that a great deal of one’s thinking in acts of
creation might occur unconsciously means that (a) any archival evidence
or interview records will necessarily be incomplete, since the information
willberelatedonlytowhatthepersonis/wasconsciousof;andthus(b)it
willthwartanyattempttocorroborateorfalsifyacomputation-basedthe-
oryofthatcreativeact.
Becauseofthesetworeasons,viz.,thecontingent,nondeterministicna-
ture of the creative process, and the presence of unconscious processing
(speciªcally, incubation), we must conclude that a general hypothesis that
computational case studies/models of particular acts of creation are ex-
planatory theories of those creative acts must be rejected (proposition I),
since, generally speaking, we may not be able to either corroborate or fal-
sify the theory. To take speciªc examples, neither Kulkarni’s KEKADA
(Kulkarni & Simon 1988) nor my model of the invention of micropro-
gramming (Dasgupta 1994) can serve as explanatory theories for the ac-
tual creative processes performed by Krebs and Wilkes, respectively. Nei-
ther of the computational models can be corroborated or falsiªed by the
historicalevidenceontheseepisodes.
3. Computational Models/Case Studies as Experiments
There is a way, however, in which computational accounts (either in the
formofrunningprogramsorasmodels‘onpaper’)canserveamuchmore
Perspectives on Science 127
scientiªcallyimportantroleinunderstandingcreativity.Theycanserveas
experiments to test universal hypotheses or theories about the creative pro-
cess(propositionII,section1above).Inotherwords,theverysingularityof
a computational model of a particular act of creation, or a class of such
acts,suggeststhatsuchmodelscanserveasexperimentalmeanstocorrob-
orate or refute some general proposition. We illustrate this with three
examples.
3.1 BACON.1 as a Corroborating Experiment
This view affords a very different signiªcance to the BACON programs.
Consider,inparticularthefollowinggeneralhypothesis:
(H1)“Insomedomainsofscientiªcpractice,neitherdomain—spe-
ciªcknowledgenorphysicalintuitionnormetaphysicalcommit-
mentsnecessarilymatters.Rather,newideasmaybeproducedby
treatingproblemsasexercisesinformalsymbolmanipulation.”
As noted earlier, BACON.1 did not derive Kepler’s third law in the
way Kepler did, since the program had no understanding of the physical
signiªcanceofthevariablesanddataitmanipulated.Itutilizedasmallset
ofheuristicsthatsimplyformallymanipulatedthevariables;theheuristics
didnottakecognizanceofthephysicalsigniªcanceofthevariables—that
they denoted the mean radius of a planet’s distance from the sun, and the
orbital time period of the planet. In fact, using this same set of heuristics,
BACON.1alsoderivedthreeotherclassicalquantitativelawsofphysicsin
very different domains, namely Boyle’s law (for gases), Galileo’s law (in
mechanics)andOhm’slawinelectricity.Inotherwords,BACON.1’sruns
inderivingthesefourlawseachconstitutedanexperimentthatcorroborated
hypothesis(H1).
3.2 Corroborating a Hypothesis about Bisociation with the
Computational Model of the Invention of Microprogramming
Inthepastseveralwritersoncreativityhaveheldthatanessentialingredi-
ent of the creative act is the effective combination of seemingly uncon-
nected or disparate concepts or ideas ( Lowes 1927; Hadamard 1945;
Gruber 1981; Perkins 1981; Johnson-Laird 1988). Koestler (1964) re-
ferred to this process as bisociation. But Koestler did not explain the me-
chanics of bisociation. The computational case study/model offers a way of
elucidating this process. In particular, consider, now the following
hypothesis:
128 SheddingComputationalLightonHumanCreativity
(H2).“Bisociationisexplicableintermsofcommonplacerulesof
reasoning.”
Intheparticularcaseoftheinventionofmicroprogramming,itsohap-
penedthattheideaofmicroprogrammingresultedfromacombinationof
two essential and very unrelated ideas. One was the use of a particular
kindofcircuitcalledthe‘diodematrix’;theotherwastheemploymentof
idea of the stored program computer architecture (Dasgupta 1994,
p. 196). In other words, Wilkes’s invention of microprogramming was an
instanceofthebisociativeprocess.
Whatissigniªcantforthisdiscussionisthatthemainbodyofthecom-
putation-based model of the invention of microprogramming was taken
withthedetailedexplicationofsuchabisociativeprocessintermsofsuch
ordinary rules of reasoning as generalization, analogical inferencing, ab-
duction, and deduction (Dasgupta 1994, chapter 5 & 6). In other words,
thecomputationalmodeloftheinventionofmicroprogrammingservedas
anexperimentthatcorroboratedorsupportedhypothesis(H2).
3.3 KEKADA as an Experiment to Refute the Darwinian Hypothesis
on Creativity
Campbell (1960) advanced an explicitly Darwinian model for creative
thinking.ThisDarwinianmodelofhownewknowledgecomesintobeing
has,since,cometobeknownas‘evolutionaryepistemology’(Radnitzky&
Bartley 1987), and has since been embraced by several others (Simonton
1988, 1999; Gero, 1994; Lumsden 1999). A very recent—and exten-
sive—version of this view is due to Simonton (1999 p. 69), which can be
statedasthehypothesis
(H3)“Thecreativeprocessmustbeviewedasaformof...
Darwinism.”
Space does not permit us to articulate in detail what a ‘Darwinian cre-
ative process’ should look like. This has been dealt with elsewhere
(Dasgupta 2004a). But stated very brieºy, if the process underlying any
actofcreationisDarwinian,—if(H3)istobetakentobeagenuineuniver-
salhypothesisaboutthecreativeprocess—thenanycognitiveprocessthat
iscreativemustexhibit,attheveryleast,thetwomostsigniªcantfeatures
of natural selection: there must be evidence of superfecundity—that is, the
productionofalargenumberofvariationsofthought-productsonwhicha
selectionprocesscanworktopruneoutallthosethought-productsthatdo
not demonstrate a ‘ªt’ against some predetermined goal (‘environment’);
and there must be evidence of blind variations—that is, (a) the alternative
thought-products produced as variations are independent of the environ-
Perspectives on Science 129
ment in which they arise, (b) ‘correct’ variations are no more likely to oc-
cur than ‘incorrect’ ones, and (c) the ‘incorrect’ variations in any stage of
theprocessdonotservetodirecttheprocesstoa‘correct’variationinthe
nextstageoftheprocess(Campbell1960,91–92).
Computational models of particular acts of creation are also experi-
ments that may put the Darwinian hypothesis to test. In fact, both
KEKADA (Kulkarni & Simon 1988) viewed as an experiment that con-
structs the ornithine cycle and the computational model of the invention
of mocroprogramming (Dasgupta 1994) refute the Campbell-Simonton
hypothesis, for in neither of these models is there any evidence of blind
variations in the generation of thought-products (ideas) or of super-
fecundity. Elsewhere, we have presented other cognitive-historical case
studies which also refute the universality of hypothesis (H3) (Dasgupta
2004a).
Insum,ourthreeexamplesabovedemonstratetheefªcacyofcomputa-
tionalmodels/casestudiesofparticularactsofcreationasexperimentsthat
cancorroborateorfalsifygeneraltheoriesorhypothesesaboutthecreative
process(propositionII,section1).
4. Computation-Based Architectures of the Creative Mind as Metaphorical
Frameworks
The inºuence of computer architectures on the development of certain
models of cognitive architectures such as SOAR and ACT is well known
(Anderson1983;Newell,Rosenbloom&Laird1989;Newell1990).Some
researchers on creativity have either adopted one or more of these
computationally-inºuenced cognitive architectures as the foundation for
their studies of the creative mind, or have tailored such architectures for
theirownneeds.Forexample,inconstructingexplanationsoftechnologi-
cal creativity, I adopted a slightly modiªed version of Newell’s knowledge
level system (Newell 1982) as the underlying architecture (Dasgupta
1996,chapter4).
We suggest here that the real power of such computation-based archi-
tecturesofthecreativemindliesinthattheyprovideametaphoricalframe-
workforunderstandingcreativity(propositionIII,section1).
Metaphors, of course, play a common, important, and often profound
role not only in ordinary thought, speech and understanding (Richards
1936; Black 1962; Ortony 1983) but also in more select arenas such as
scientiªc reasoning (Gruber 1981; Holmes 1985; Osowski 1989). They
are used in many different ways and serve different uses. Of most interest
hereiswhatBlack(1962)termed‘metaphor-as-interaction’inwhich,asa
result of the observation of an analogy between two situations, one of
whichisbetterunderstood,theconcepts,knowledgeandideaspertaining
