Biomedical Engineering Reference
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microscopic level for intracellular information processing and a macroscopic level of
interneuronal communications. Following the lead of Conrad's ideas, the author subse-
quently conducted a detailed survey of biocomputing schemes and found that both
“shape-based” and “switch-based” information processing are utilized alternatingly [130].
Furthermore, biocomputing is neither absolutely deterministic, as in digital computing, nor
completely random, as suggested by molecular diffusion and collisions in solution-phase
biochemistry. At all levels, extending from submolecular protein folding all the way to
intermolecular communications, information process exhibits the combination of a “top-
down” constraint and a “bottom-up” exploration—apparently a compromise between
absolute determinism and extreme randomness. It is the hierarchical separation that pre-
vents randomness from propagating beyond bound and keeps randomness at bay. This
insight was extended to investigate creative problem-solving. Again, human cognition at
the system-level mirrors events at lower hierarchical levels. Creative problem-solvers do
not resort to a highly random process of trial and error. Nor do they follow predictable dog-
matic and algorithmic processes of finding the answers to a novel problem. Our new
insights into geniuses' thinking process have only recently appeared in print [131,132].
What we did was treat creative problem-solving as a process of
pattern recognition
: rec-
ognizing potential solutions to match a given problem. The idea of treating creative prob-
lem-solving as pattern recognition could be traced back to 1988 Peano Lecture presented
by economist and computer scientist Simon [133]. Recently, high-tech entrepreneur
Hawkins [134] also invoked pattern recognition to interpret his personal experience in
design innovations. Here, we consider two kinds of pattern recognition: analog and digi-
tal pattern recognition. To make a long story short, these two terms correspond to visual
thinking and verbal thinking, respectively, in the psychology literature. Visual thinking is
nothing new [135,136]. It was frequently advocated by scientific geniuses, such as Albert
Einstein [137], Richard Feynman, [138], and Stephen Hawking [139]. Inventor Tesla [140]
had a vivid account of how he had been tormented by persistent visual imagery and
claimed that he could visualize how an electric motor turned without actually carrying out
an experimental test. However, mainstream psychologists have dismissed these intro-
spective reports as mere anecdotes. In contrast, verbal thinking, which is synonymous as
logical thinking, is regarded as being objective and reliable by conventional wisdom, and
is often heavily reinforced by higher education. In fact, dogmatism, which was character-
ized by thinking along a predictable, well-trodden path, is a manifestation of verbal think-
ing, whereas keyword matching, which was often invoked by students to answer a
standardized test, is an extreme form of verbal thinking.
Obviously, digital pattern recognition requires strict matching of criteria, which are often
expressed in terms of explicit symbolic or verbal rules. Analog pattern recognition requires
holistic judgment, which is often based on ill-defined criteria and gut feeling; the matches
are often imprecise and loose rather than rigorous and strict. Obviously, digital pattern
recognition is more reliable than analog pattern recognition, and it is also far more objec-
tive than analog one. However, there is a serious drawback of digital pattern recognition.
Because of the use of strict criteria, potential solutions tend to be excluded prematurely. In
contrast, analog pattern recognition is known to be highly subjective and error-prone. What
then is the merit of visual thinking that had lured so many geniuses of the past?
Most models of creative problem-solving reported in the literature include a
solution-
generating phase
and a
solution-verifying phase
(so-called double-checking). Scientific
geniuses used intuition to generate solution while invoked strict logic for solution verifi-
cation, as mathematician Henri Poincaré [135] aptly proclaimed, “It is by logic that we
prove, but by intuition that we discover.” Here, we identify intuition with visual thinking
(see [131,132] for detailed evidence). The saving grace for the peril of visual thinking is
that its fallibility can be rescued and rectified by subsequent verification based on strict
