|
Article Excerpt
For many educators in the field of gifted education, the most endearing characteristic of gifted children is their creative intelligence: their capacity for original explanations, insightful questions, elegant proofs, original creations, and quirky humor. Such a basket of cognitive behaviors begs the question of an underlying cognitive generator and, hence, some delineating neural correlates (Kalbfleisch, 2004). Here it is proposed that gifted intelligence is an outcome of an enhanced facility to engage in fluid analogizing--a cognitive-level construct that describes selective intermodule information processing within the brain (Dehaene, Kerszberg, & Changeux, 1998). Fluid analogizing supports a suite of neural functions associated with working memory (Geake & Hansen, 2005). Consequently, a gifted person's high ability at fluid analogizing explains their more efficacious working memory, which in turn supports high levels of creative intelligence (Geake, in press).
Thus, the aim of this article is to present a case for fluid analogizing as a useful construct with which to better understand giftedness. The adequacy of the neural underpinnings of this argument could be judged in the light of two recent extensive reviews of the considerable literature concerning the neural correlates of general intelligence, in particular, the interactive involvement of the frontal and parietal areas (Jung & Haier, 2007), and the neural correlates of high-level fluid reasoning, including an explanation of the Flynn Effect of rising IQ scores (Blair, 2006). It is not the purpose of this article to review these extensive reviews: they are recommended reading for anyone interested in this area of research.
However, in an attempt to illustrate the utility of a fluid analogizing construct of giftedness, the article concludes with a post hoc (and therefore possibly somewhat conjectural) consideration of some earlier research on the information processing characteristics of musical prodigies.
FLUID ANALOGIZING AS A FUNDAMENTAL COGNITIVE PROCESS
Historically, the most enduring conceptualization of human intelligence is that it is essentially analogical. As William James (1895) wrote over a century ago: "A native talent for perceiving analogies is ... the leading fact in genius of every order" (p. 530). That is, the essence of intelligent behavior lies in making insightful metaphors or analogies (Dunbar, 2001; French, 2002; Goswami, 2001; Halford, 1992; Holyoak & Thagard, 1995). Evidence that this is a fundamental cognitive process comes from studies of the conceptual development of young children, which is characteristically analogical (Goswami, 2001). Insightful analogy making is necessary for success in a wide range of endeavors, including pattern recognition, composition of musical variations, producing and appreciating humor, translation between languages, poetry, classroom exercises, and much of everyday speech (Goswami, 2001; Holyoak & Thagard, 1995). In education, a characteristic of good teachers is their ability to create analogies for explanation and clarification (Geake, 2003).
While making an analogy is clearly rooted in perceptual experience, it goes beyond perception in employing relationships (Mitchell, 1993). Moreover, such relationships rarely involve analogical exactness, as in the classic "White is to black as day is to ...?" For real-world higher order categorical relationships, Hofstadter (2001) argues that: "Categories are quintessentially fluid entities; they adapt to a set of incoming stimuli and try to align themselves with it. The process of inexact matching between prior categories and new things being perceived ... is analogy-making par excellence" (p. 499). That is, analogizing as a basic cognitive process is not exact analogizing, but fluid analogizing. In contrast to an exact analogy question where there is one correct response, in a fluid analogy there may be a range of responses, some more plausible or creative than others. For example, to the question: "What is the London of the United States?" plausible responses include "Washington, DC, because it is the capital; New York because it is the largest city; Los Angeles because it is the center of the national film industry," and so on. Importantly, none of these responses are wrong but rather highlight the multidimensional possibilities of categorization. Consequently, it is the judicious (albeit often instinctive) employment of fluid rather than exact analogies that constitutes effective pedagogy enabling efficient categorization and assimilation of new knowledge (Geake & Dobson, 2005). Of relevance here, a notable characteristic of gifted children's metacognitive explanations is their recourse to fluid analogizing (Clark, 1997).
As a pioneering formalization of fluid analogizing, the AI program, Copycat, constructed by Melanie Mitchell and Douglas Hofstadter, sought fluid applications of a implicit transformation rule that was applied to a pair of letter strings (Hofstadter, 1995; Mitchell, 1993). The only internal knowledge required of the program was alphabetical and reverse-alphabetical order, and the boundary conditions of the English alphabet, the letters "a" and "z." The task, given the first transformation pair, was to complete the second in an analogous way. As a simple example, to abc [right arrow] abd, ijk [right arrow] ? most people respond "ijl" (increase the last letter by one), although "ijd" (change the last letter to "d") and other responses are possible. Copycat responded similarly. However, examples can be made arbitrarily more complex, such as abc [right arrow] abd, iijjkk [right arrow] ? or a [right arrow] ab, z [right arrow] ? each of which have a number of plausible responses. The various responses can be analyzed in terms of the number of transformations required to construct it and quantified as a metric: the analogical depth response (ADR; Geake & Hansen, 2005).
For example, in the example abc [right arrow] abd, pqqrrr [right arrow] ?, plausible responses could include:
1. pqqrrr (ADR = 1: new letter sequence);
2. pqqrrd (ADR = 2: new letter sequence, last letter copy);
3. pqqrrs (ADR = 3: new letter sequence, alphabet preservation, letter advance);
4. pqqsss (ADR = 4: new letter sequence, alphabet preservation, grouping, letter advance);
5. pqqssss (ADR = 5: new letter sequence, alphabet preservation, grouping, numerical increase, letter advance).
Consequently, one would expect presentation of fluid analogies to humans (in contrast to an AI computer program such as Copycat) to...
|
