Page 252 - A Mind For Numbers: How to Excel at Math and Science

Chapter 12: Learning to Appreciate Your Talent

1 Jin et al. 2014.
2 Partnoy 2012, p. 73. Partnoy goes on to note: “Sometimes having an understanding of precisely what we
are doing unconsciously can kill the natural spontaneity. If we are too self-conscious, we will impede
our instincts when we need them. Yet if we aren’t self-conscious at all, we will never improve on our
instincts. The challenge during a period of seconds is to be aware of the factors that go into our
decisions . . . but not to be so aware of them that they are stilted and ineffectual” (Partnoy 2012, p. 111).
3 Partnoy 2012, p. 72, citing Klein 1999.
4 Klein 1999, p. 150, citing Klein and Klein 1981. But note the small sample size in Klein and Klein 1981.
5 Mauro Pesenti and colleagues (2001, p. 103) note, “We demonstrated that calculation expertise was not
due to increased activity of processes that exist in non-experts; rather, the expert and the non-experts
used different brain areas for calculation. We found that the expert could switch between short-term
effort-requiring storage strategies and highly efficient episodic memory encoding and retrieval, a
process that was sustained by right prefrontal and medial temporal areas.”
Already in 1899 brilliant psychologist William James wrote, in his classic Talks to Teachers on
Psychology: “You now see why ‘cramming’ must be so poor a mode of study. Cramming seeks to stamp
things in by intense application immediately before the ordeal. But a thing thus learned can form but few
associations. On the other hand, the same thing recurring on different days, in different contexts, read,
recited on, referred to again and again, related to other things and reviewed, gets well wrought into the
mental structure. This is the reason why you should enforce on your pupils habits of continuous
application” (William 2008, [1899], p. 73).
6 In a classic study, William Chase and Herbert Simon (1973) found that the intuitive generation of next
moves by chess experts is based on the superior, quick perception of patterns that has been achieved
through practice. Fernand Gobet and colleagues (2001, p. 236) define a chunk as “a collection of
elements having strong associations with one another, but weak associations with elements within other
chunks.”
7 Amidzic et al. 2001; Elo 1978; Simon 1974. A figure of 300,000 chunks was cited by Gobet and Simon
2000.
8 Gobet 2005. Gobet goes on to note that expertise in one domain doesn’t transfer to another. That’s true—
certainly if you learned Spanish, it’s not going to help you when you go to order sauerkraut in Germany.
But the metaskills are important. If you learn how to learn a language, you can pick up a second
language more easily.
That, again, is where developing an expertise in something like chess can be quite valuable—it
provides a set of neural structures that are similar to those you need when learning math and science.
Even if the neural structures are as simple as you need to internalize the rules of the game—that’s a
valuable insight.
9 Beilock 2010, pp. 77–78; White and Shah 2006.
10 Indeed, there is modest support for this type of finding in the research literature. See Simonton 2009.
11 Carson et al. 2003; Ellenbogen et al. 2007; White and Shah 2011.
12 Merim Bilalić and colleagues (2007) point out that some players with an IQ of between 108 and 116 fell
into the elite player group by virtue of their extra practice. The elite group had an average IQ of 130. See
also Duckworth and Seligman 2005.
Nobel Prize winner Richard Feynman liked to tout his relatively low IQ score of 125 as evidence
that you could go pretty far whatever tests might indicate about your intelligence. Feynman clearly had
natural smarts, but even as a youngster he was practicing obsessively in developing his mathematical
and physical knowledge and intuition (Gleick 1992).
13 Klingberg 2008.
14 Silverman 2012.
15 Felder 1988. See also Justin Kruger and David Dunning (1999), who note “the miscalibration of the