CREATIVITY AND PROBLEM SOLVING: ELEMENTS FOR A MODEL OF CREATIVITY
by
Bruno Carvalho Castro Souza
Abstract
The human creativity has already been theorized in practically all sciences. Some privilege the cognitive aspects, emphasizing the architecture of the thought, while others focus the empiric approach, describing the creativity as a productive technique. This paper tries to reconcile these two visions, offering glimpses of the creativity as technique and of the involved mental processes. Finally, it proposes an "architecture of the creativity" focused on an integration in artificial intelligence systems for the learning of the creativity.
Keywords:
creativity, artificial intelligence, cognitive models, problems (solution of)
Introduction
The creativity, for applied sciences as Management, Engineering, Publicity and Marketing, identifies itself with no-trivial problem solutions. A no-trivial problem is that in which the solution is not obvious at the beginning, nor are the means to reach it (Kim, 1990). Its approach, therefore, is elusive to the subject of the problem.
The traditional focus of the creation process for the resolution of such problems involves four identifiable phases: preparation, incubation, inspiration and verification (Wallas, 1926).
The preparation phase consists of collecting information on the problem to be solved, including researches, readings, annotations, inquiries, explorations – in short, the conscious effort (in the psychoanalytic sense) of finding a solution. After a preparation period without results (in other words, without the solution of the problem), the individual enters in the incubation phase, in which unconscious mental processes are put to work. The unconscious, "free from the literal intellect, makes the unexpected connections that constitute the essence of creation." Wallas identifies this "essence of creation" as the inspiration phase: it happens when the idea appears in the mind, in a ready way – it is the brilliant solution to the problem. This solution, however, should still be tested to verify its validity in practical terms, and that is the object of the verification phase.
Although several scientists, thinkers, professionals and general people have identified in their own creative actions the same "script'' – always in an individual way and without any research or previous references – it was never really explained why it happens in that exact way. The effects are observed and each person deduces a beginning for the individual creation without observing the causes. The process is deductive, in the Aristotelian sense, using a top-down approach. However, which we should look for is the opposite way: what causes the creative behavior or, in other words, which are the semantic components and each individual's cognitive universe that contribute to the creative thought. That is a typical application of the bottom-up approach.
A first step in that direction would be to identify and to classify the problem in accordance to its relevance. Live urgent problems, under the individual's perspective, exercise pressure towards their solution, while irrelevant problems are postponed or ignored. Once the problem is identified, we can delimit an initial field of performance – or a space of research for the solutions of the problem. In trivial problems and, for the presented definition, non-creative, the solution will appear in this first space of research. However, in problems that require creativity, the answer won't be found so easily, resulting in unacceptable answers (errors) on the initial space.
The mental associations shot by relative semantic proximity to the approached subject characterize the problem field. For instance, a problem in a Physics area can include concepts related to the Mathematics, to the Engineering, to the Chemistry, to the Computation and to the own Physics, but it won't usually include notions belonging to the Arts, to the Management or to the Economics, for instance. Another factor that composes the problem field is constituted in the experiences lived by the subject. It is known that great part of the human learning is due to those experiences and that decisions regarding situations found in the day by day are strongly influenced by such experiences.
In a no-trivial problem, the answer cannot be linked directly to the initial field of the problem. In that case, the efforts in search of the solution will constantly be frustrated, resulting in inadequate answers to the situation that originated the problem. To each new attempt, the brain executes alterations in the parameters of the field (regulations), trying to modify several variables that compose the problem, seeking to reach satisfactory results.
Piaget identifies that process as the balancing mechanism. Pict. 1 can demonstrate it
:

Picture 1: Piaget’s Balancing Mechanism
This approach of the creative process explains why creators experience moments of anguish and anxiety when involved in the solution of complex problems: such emotions are caused by the frustrated attempts of the brain in reaching the balanced state. As the emotions are important regulation instruments in the mental processes, the greater is the pressure and the more urgent the problem becomes (in other words, as larger the anguish and the anxiety provoked by the flaws of the brain in reaching a new balanced state, the stronger those emotions get). Another conclusion is that the creativity capacity grows because of mistakes by the cognitive human architecture in adapting to complex problems.
The construction of solutions through mistakes
Piaget affirms that the human mental growth is "a continuous passage of a state of smaller balance to a state of superior balance." When the brain comes across a problem, it enters in an unbalance state. If dealing with a no-trivial problem, therefore creative, the space of initial research won’t be enough for the solution, causing an impasse: all of the strategies known to obtain an answer were tried without results. As consequence, the mental mechanism needs to expand the research space. Such expansion opens new exploration possibilities, forcing the connection (including physiologic ones, involving the formation of neural connections) of initially no-related concepts. On that moment, the structuring of new processes (in the piagetian sense) begin to happen, creating new knowledge, which was obtained as a final result of a new (and enhanced) space of research. That enhancement process and restructuring continues until a satisfactory solution for the problem is obtained or the emotional aspects intervene in the control of the process, forcing its interruption.
Once reached a satisfactory solution for the problem, the mental process and the research space used are incorporate in a definitive way in the long term memory, starting to constitute the individual’s scope of lived experiences and enlarging his/her cognitive universe. On that moment, it can be said that it was reached a new balance state, superior to the one that existed before the problem, forming what Piaget defines as "major balancing of the beta type", according to Pict. 2:

Picture 2: Piaget’s Major Balancing Mechanism
A poetic form of putting the situation is that, in the creation process, in the search for the new, happiness is failure.
Creative architectures for problems solutions
Based on the exposed, it becomes relatively simple to model a "model of creativity" seeking to make possible its implementation in artificial intelligence system. Pict. 3 presents a general vision of this architecture:

Picture 3: Model of Creativity
This architecture offers a linear visualization of the creative process regarding the solution of no-trivial problems. The visualization is offered this way to facilitate the didactic apprehension of the model. In the real life, the mental processes happen in parallel, in several areas of the brain. They were classified in three categories: the everyday problems, corresponding to the situations of daily life (how to change a lamp? how to tie the shoes?); the difficult problems, which use the mental powers of convergent thought (Guilford, 1950) and find solutions by logical-deductive processes; and the complex problems, which require the creative capacity for their resolution.
The "Domain" box represents the abilities and the individual's competences, according to the classification of Czikszentmihalyi (1988). In the initial evaluation and categorization of a problem, the domain is of fundamental relevance, because it will be a decisive factor in the definition of its priority ("urgency" degree). Usually, the smaller the domain involving a specific problem is, the smaller the individual's interest in solving it will be.
The box "Problems Fields" includes the concepts and individual representations of the experienced problems (the scripts we use to live). It is a kind of "mental index" that classifies lived situations and related them to general concepts "learned" through instruction or experience. As example, the professional of Management that needs to increase the sales through the communication of a sales promotion can classify this problem as belonging to the field "advertisement".
The "Cognitive Universe" is associated to the long-term memory. In it are stored all the lived experiences, as well as the acquired knowledge along the years.
The box of "Emotions" represents the emotional factor, and it is present in all moments of the process: it participates in the prioritization of the actions, in the control of the activities, in the decisions about continuity of the process. It is also influenced by the final solution.
The diagram of the process facilitates the visualization of three types of problems: trivial, whose answer is easily found already in the initial processing, corresponding to the routine situations that we found in the daily life; the difficult problems, whose solution, although no-apparent, can be deduced through the use of a subsequent processing (that Piaget identifies as "reflecting abstraction") that would structure the knowledge without alter the space of researches; and the complex problems.
The problems considered by this article as complex require an enhancement of the research space, because they result of successive inadequate answers as much of the deductive logic as of the previous experiences in relation to the initial problem. That is a delicate moment in the structuring of reasoning, because it is the point that emotions exercise larger pressure, forcing the individual to continually take decisions on the viability of finding a solution or decide to abandon the problem.
In that new, enhanced problem space, enlarged by the impasse caused by the lack of answers of the deductive logic and of the previous experiences, the brain possesses new factors, experiences and abilities, "borrowed" from other areas not considered initially, to apply to the problem, provoking the restructuring of the old mental connections and forming new ones, propitiating the emergence of a majoring balancing: once solved satisfactorily, the problem generates relief and pleasure (in the emotional extent), it produces new experiences (which would be stocked in the long term memory, increasing the individual's cognitive universe), it restructures the fields of problems (through the incorporation of new concepts and representations of problems and possible solutions) and it enlarges the general domain (for the formation of new relationships among fields initially not related).
That modeling facilitates the implementation of artificial intelligence systems that make use or that intend to simulate human creativity. It’s linear approach allows the visualization of the creative process as a flow, especially facilitating its adaptation for use with expert systems, although other techniques can also be used depending on the final objectives of the system.
Creativity and artificial intelligence
The artificial intelligence concept as it is accepted today in the computational sciences was born in 1956, in a summer conference in Dartmouth College, in New Hampshire, United States. It’s base, however, is much older, including disciplines like Philosophy, especially the Logic, Mathematics and Psychology.
The central objective of artificial intelligence is simultaneously theoretical – the creation of theories and models that explains cognitive capacity – and practical – the implementation of computational systems based in those models. For that, cognitive models are developed, as the model of creativity presented in this paper; and computational tools (softwares) that allow the developed cognitive models to be experienced in the proposed implementations are build.
An artificial intelligence implementation that intends to simulate the human creativity should take into account the aspects proposed in the presented model, as well to consider the effects of learning of new situations.
For that, the system should provide conditions for:
An interesting problem to solve (complex, according to the criterion adopted in this article) is the subject of the amount and types of competencies and abilities that should be introduced in the system, as well as the subject of how to combine them according to the experiences. In other words, how to solve the problem of integration among Domain, Problems Fields, Cognitive Universe and Emotions, as well as the ways that each one should interact with the problem to form the space of initial research.
Another pertinent subject is which cognitive strategies are adopted by the user to build the reasoning – in other words, which are the parameters used to the initial and to the subsequent processing. A person that thinks preponderantly using visual thought (artists, geographers, advertising people etc.) possesses different mental resources than somebody that thinks using mostly natural language, for instance. So, it can be deduced that the more cognitive strategies the individual possess, the more easiness he/she will have in the transition among the fields, therefore the greater will be the creative potential. A system of artificial intelligence that intends "to teach" the creativity should promote the facilitation of transitional process among the fields and domains, through the placement of "obstacles" in the cognitive strategies commonly used by each user. The insistence in the usual strategies will deliver inadequate or unsatisfactory answers and, finally, the system "will motivate" the user to appeal to new fields in search of new solutions.
Conclusion
It is objective of this article that these considerations can inspire new research lines in the sense of enlarging the concepts and the uses of the artificial intelligence technologies in the study and teaching of creativity. For that, some interesting issues were pointed out, resulting in an outline for the construction of a model of creativity. The implementation of this model for a artificial intelligence system seeking the teaching of creativity should consider technical subjects (which would the fields pre-defined? how should the interaction with the user be? which emotional answers will be considered valid for the system? in what forms the user will participate in the formulation of those emotional responses? how to implement such system?) and it indicates some studies to be developed (cognitive strategies can be drawn from the concepts of mental abilities proposed by Gardner). In these aspects, the present study is important for the formation of new research lines and for the development of new Architecture of Creativity, reconciling the cognitive and empiric aspects.
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