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Archive for the ‘Nersessian Response’ Category

Reading Dr.Nersesian’s book and attending the group meeting followed by her lecture last Thursday was a valuable experience for me. Apart from the insights derived from her talk capturing the way scientists think, it helped me reflect upon the thought process involved in problem solving. This session was especially beneficial following Dr.Simonton’s talk the week earlier with his views on the big “C”s referring to the pschycology of scientific and creative minds.
Dr.Nersesian discussed how throughout history we have evidence of scientists using analogical reasoning in the “social-cognitive-cultural-context” where they applied what they already knew to new domains to reach breakthroughs in their fields of work. What I found unvaluable was the outline she offered the group to follow for drawing analogies, both near or distant and applying it to imaginary models. Simply put, she advised the use of analogy or imagery and to look at the reasoning process in an integrated manner. She stressed on the initial part of the process as the stage to begin constructing from basic concepts, followed by developing physical or relative concepts. This would precede the step of deconstructing the information obtained when an individual  needed to start thinking intentionally and self-question “What am I trying to do?” She encouraged the listeners to break free of constraints and dare to be different from the existing situation at that moment. And, finally she suggested the use of diverse advancements in technology to find futuristic solutions, and test for plausibility. Throughout her talk, she reinforced the concept of using concrete imagery to introduce abstract concepts.
I agree with Dr.Nersesian’s views on creative inventions happenning as a result of model-based reasoning for problem solving. Though her talk focused on scientists and scientific discoveries (she offered examples of Maxwell and Rutherford’s work), I tend to believe the reasoning process would apply to other fields as well. As a graphic designer, I have always relied on the ‘design process’ as a guide to arrive at visual communication solutions for creative projects. I would hesitate to entirely eliminate the circumstance of the birth of a new concept arising in a flash of inspiration. It does happen, though it is definitely not an everyday occurence.

Reading Dr.Nersesian’s book and attending the group meeting followed by her lecture last Thursday was a valuable experience for me. Apart from the insights derived from her talk capturing the way scientists think, it helped me reflect upon the thought process involved in problem solving. This session was especially beneficial following Dr.Simonton’s talk the week earlier with his views on the big “C”s referring to the pschycology of scientific and creative minds.
Dr.Nersesian discussed how throughout history we have evidence of scientists using analogical reasoning in the “social-cognitive-cultural-context” where they applied what they already knew to new domains to reach breakthroughs in their fields of work. What I found unvaluable was the outline she offered the group to follow for drawing analogies, both near or distant and applying it to imaginary models. Simply put, she advised the use of analogy or imagery and to look at the reasoning process in an integrated manner. She stressed on the initial part of the process as the stage to begin constructing from basic concepts, followed by developing physical or relative concepts. This would precede the step of deconstructing the information obtained when an individual  needed to start thinking intentionally and self-question “What am I trying to do?” She encouraged the listeners to break free of constraints and dare to be different from the existing situation at that moment. And, finally she suggested the use of diverse advancements in technology to find futuristic solutions, and test for plausibility. Throughout her talk, she reinforced the concept of using concrete imagery to introduce abstract concepts.
I agree with Dr.Nersesian’s views on creative inventions happenning as a result of model-based reasoning for problem solving. Though her talk focused on scientists and scientific discoveries (she offered examples of Maxwell and Rutherford’s work), I tend to believe the reasoning process would apply to other fields as well. As a graphic designer, I have always relied on the ‘design process’ as a guide to arrive at visual communication solutions for creative projects. I would hesitate to entirely eliminate the circumstance of the birth of a new concept arising in a flash of inspiration. It does happen, though it is definitely not an everyday occurence.

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For those interested in reading more of Nancy Nersessian’s work, she has many of her publications available on her homepage.  Of particular interest for those following our discussion from afar are the preface to her new book, Creating Scientific Concepts and her articles “Hybrid Analogies in Conceptual Innovation in Science” and “How do engineering scientists think? Model-based simulation in biomedical engineering laboratories.”

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I enjoyed talking with Nancy Nersessian even though many of her examples were beyond my comprehension. What I enjoy the most is how it felt like a sequential extension of what I enjoyed last week with Dr. Simonton. Many of the questions I had last week to better elaborate on the idea of Blind Variation, Selective Retention was addressed in Nersessian’s model based reasoning. The idea of mental modeling is exactly what Kukle was doing in Dr. Simonton’s example of BVSR. Nersessian argues that even what appears to be sudden jumps of scientific creativity are the result of many small incremental steps. This enlightens the idea of BVSR eliminating the seemingly random and “luck” based victories of people like Kukle because of the understanding that it only appears to be random and lucky because we are not privy to the many small incremental steps that led Kukle to relate the Oroborus to the Benzine structure. This leads me to believe that, yes, creativity can be cultivated and taught, but by using a much slower process.

Many of these small incremental steps come in the form of analogies of the problem using various mental and physical models. This is encouraging for me because all games are metaphors for interpreting a basic mathematical rule structure. This means that there is in fact potential to help cultivate this idea of relating models to a problem using an interactive game. If you could create a mathematic problem that drives a rule structure and have the player use a variety of unrelated metaphors for explaining that system then the player may start to gain the literacy of using analogies and models as tools for creative problem solving. Ofcourse, this is easier said then done since most of these rule structures you see in games only make sense with a very limited set of metaphors which is why most video games deal primarily with physics based problems of jumping and aiming, etc.

It’s an interesting challenge, but based on the readings of both Simonton and Nersessian it may be an endeavor worth tackling.

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Similar to others, I was quite dismayed by the technical aspects of her work and found myself quite lost at moments. However, there was a great deal that I did find valuable…

The readings, particularly where she addresses the idea of a metacognitive awareness and the need for an element of play, while keeping in mind an ultimate target, were interesting. The notion that one can “play” with structures, imagined and centered around source and model constraints, reaffirmed for me the notion of “homo ludens.”  I myself have noticed a lot of these strategies at work while playing scrabble with opponents on my cell phone. I often have to “bootstrap” and move letters around until I get to the point where one emerges finally.

I do, however, have problems with figuring exactly out how selectivity figures into model constructing. A great deal of what I read makes me think that this notion of bracketing “irrelevent features” is counter-productive to creative work in my field. Analogies and relational comparisons, as representation-building and simulative processing, sound all to limiting to me.  Not exactly sure if they would explain how the idea of snakes mating could have inspired Watson and Crick. But then again.

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Professor Nersessian mentioned in her talk that visual representations by scientists were not considered important.  This is remarkable since many different scientists, as many visual artists, have relied on some sort of visual representation to exemplify and expand their ideas.  For instance, as mentioned by Dr. Neressian, Newton relied on diagrams for visual representations of analogy, gravity, and projectile.  Also, Johannes Kepler created a diagram of the cosmos, Machina mundi artificialis,  that depicted a universe made of geometric mechanisms.   His reliance on visual aids reached beyond the two-dimensional diagram, for he had a sculptural model made out of gold for the Duke Friedrich of Wurttemberg  (The Lure of Antiquity and the Cult of the Machine, p. 37).  Neressian said that visual representations were crucial in conveying the message or theory.  But what about using visual representation as a means to reach their ideas?  Perhaps, individuals like Descartes, Newton, and Kepler used visual representation to formulate their ideas as well as convey them.

The models Dr. Nersessian mentioned are also a representation; my question is if they are visual representations as well, or are they purely conceptual?  Nersessian stated that these models facilitated building an understanding of the system in competent terms and of how these interact dynamically to produce a certain behavior; these models are incrementally built towards serving as source analogies; and they represent a visualization and dynamical simulation that played a generative role.  How are these models different than models that might be formulated by an artist, designer, or architect?  And would the model based reasoning promote innovation in the arts the same as it would for the sciences, or is it a completely different process?

Regardless if they are visual or conceptual, if they are for the arts or the sciences, models allow manipulation, shifting back and forth, and altering representations.  This fluidity is one of the components of creativity, for if one stayed within the constraints and did not play with the boundaries, then there would probably be less innovation.

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In her talk at the Jonnson auditorium, Nancy J. Neressian discussed the importance of the use of physical models for working out problems in scientific theory. Neressian argued that scientific innovations did not occur from a “Eureka” moment as is popularly believed. Rather, these breakthroughs came about through a combination of the scientists working on say, mathematical formulas, and creating physical models and diagrams that express the ideas found in these formulas in a material form. As an example, Neressian points to Sir Isaac Newton and his use of diagrams of projectiles that displayed the force of gravity, as well as the model of the double helix form taken by DNA.  Neressian said that the gradual working out of the problems encountered in perfecting scientific theories benefit from the use of models as both a physical realization of the problem in question, and as a break from monotonously repeating one type of activity. Neressian points out that while educators once believed that learning was best achieved through repetition alone, it is now commonly believed (and backed up by recent studies showing how learning retention is improved by recess in elementary school children). While most of her presentation was too technical, and specialized for me to grasp, what I found compelling about her talks with the class was her belief that a form of play was benficial to research. She even gave an example of how one group of scientists benefited from the use of models,  while another was held back.  the case of the French scientists working on the one theory while strictly working on mathematical formulas, while the English scientists used a combination of models, and mathematical formulas. The English scientists made the breakthrough, and the French scientists (who apparently thought the English approach was childish, and frivolous), failed.   All work and no play makes Jacques a dull boy, indeed!!!

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I have decided to look at Nersessian’s description of what a Concept is, as defined in Chapter 6.  First, I want to add to everyone’s comments by saying that I agree with Nersessian’s logical basis for model-based reasoning in conceptual innovation.  She disagrees with classical definitions of “necessary and sufficient conditions” concerning the representation of a concept, but she also adds that she embraces a more “flexible notion[s]” consistent with “empirical evidence on human categorization.”  In other words, she is saying that there is a cultural and social component to defining what a concept is and is not.  She further states that there is no consensus of what a concept is in “cognitive science or philosophy of science.”  I want to point out that Concept is not just a philosophy of science or cognitive science problem, but is more generally a philosophical problem without regard to just the philosophy of science.  The first chapter in the book mentions Wittgenstein and his identification of general ideas of language and conceptual categories; this is just a contemporary argument that began in western thought with Plato or even Heraclitus, and in the east (China) is represented in the warring states period by the logicians, of School of Names.  Given this caveat, I will move on to her arguments.

Specifically, since this is a short response, I want to relate her ideas of “descendants” with conceptual organization.  This idea of hers is an argument grounded in logic, but really in a methodology or inheritance relationship.  For instance, one can look at the structure of computer languages and understand this idea easily.  In computer science, one constructs objects with two main attributes:

  1. Data/information
  2. Methods or functions

Data is exactly as it sounds; it’s information.  Methods, however, are the relationships that other objects have with the data stored in a particular object.  These objects have “relationships” defined as methods, and methods are able to be transfered to other objects by a process known as polymorphism.  This is the same as Nersessian’s definition of “descendants”.  So her argument is that concepts are less static and more dynamic, but that there is a social or cultural aspect that influences the propogation of given concepts.

One major issue that can not be settled, as yet, is the idea of “abstraction”.  Since the time of Plato, people have thought of abstractions as having a more general form than an instantiation of something in the physical world.  Many social constructivists say that the definition is just the opposite.  Given Nersessian’s definition of model-based that they “satisfy constraints,” one must understand that those constraints are socially identified.  This seems less problematic when talking of “purer” models such as mathematical models, but model-based reasoning only helps in scientific conceptualizing.  When applied to concepts such as Justice, absolute morals, etc, then models do not serve such a constructive function and need to be developed in a less scientific way, though not any less logical.

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