There is a commonly held belief that some people have brains that are pre-wired for mathematical excellence, while everyone else is doomed to struggle with the subject. This toxic myth needs to be put deep in the ground and buried in molten lead. It is as destructive as it is self-fulfilling.
The myth equally encourages people who are good at math to falsely believe (Murayama et al., 2012) they are more intelligent than those who are not, and leaves everyone else inclined to believe they can never improve. This is despite the fact that math ability has very little to do with intelligence (Blair et al., 2007).
The reason this myth exists is well understood. School students who were well prepared by their parents in math prior to starting school find themselves separated in ability from their classmates who were not. The latter group consider the seemingly unachievable abilities of their peers and quickly lose confidence in their own abilities. Once that self-confidence is lost, any attempt at completing a math problem leads to math anxiety (Ashcraft et al., 2002; Devine et al., 2012), where thoughts of self-doubt cloud the mind and make it difficult to concentrate on the task at hand.
Mathematics, like computer programming, is a discipline that requires concentration. The student needs to be able to follow a train of thought where A leads to B leads to C etc. A student who lacks self-confidence struggles to maintain the necessary train of thought due to being repeatedly interrupted by negative thoughts about their abilities. This results in poor performance and reinforces the idea that they are incapable of learning the subject.
It is interesting to see this belief so prevalent among software developers who are perfectly capable of writing an algorithm in a programming language, but suddenly feel that it is impossible to grasp the same algorithm represented by a set of mathematical symbols. There is simply no reason that this should be the case. I’ve yet to meet an experienced programmer who would tell me they find it near-impossible to learn the syntax of a new programming language and yet that is precisely what is entailed in learning how to express an algorithm using linear algebra.
A common point of confusion for many who haven’t done a lot of math since secondary school is in the use of mathematics as a language rather than a set of equations to be solved. In academic computer science, linear algebra, as it is used to express algorithms, is not something to be solved, but rather a language used to describe an algorithm.
Understanding the language of academic computer science is becoming increasingly important as the traditional staples of academia, such as machine learning, increasingly find use in industry. After all, even if a software developer manages to avoid the math in their work, how can they expect to keep up with the latest developments in this fast-moving field without an ability to understand the academic literature? Yet this is precisely what some software developers are attempting to do.
Math inability is not hard wired and software developers are already well practiced in the mental skills required. We use the skill of stepping through a problem and visualising the state changes that occur at each step, every time we read or write a piece of code. Anyone who can do that is capable of becoming proficient enough in mathematics to understand the mathematical components of the computer science literature.
Ashcraft, M. H. (2002). Math anxiety: Personal, educational, and cognitive consequences. Current directions in psychological science, 11(5), 181-185.
Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child development, 78(2), 647-663.
Devine, A., Fawcett, K., Szűcs, D., & Dowker, A. (2012). Gender differences in mathematics anxiety and the relation to mathematics performance while controlling for test anxiety. Behavioral and brain functions, 8(1), 1.
Murayama, K., Pekrun, R., Lichtenfeld, S., & Vom Hofe, R. (2012). Predicting long‐term growth in students’ mathematics achievement: The unique contributions of motivation and cognitive strategies. Child development, 84(4), 1475-1490.
Andreescu, T., Gallian, J. A., Kane, J. M., & Mertz, J. E. (2008). Cross-cultural analysis of students with exceptional talent in mathematical problem solving. Notices of the AMS, 55(10), 1248-1260.
Berger, A., Tzur, G., & Posner, M. I. (2006). Infant brains detect arithmetic errors. Proceedings of the National Academy of Sciences, 103(33), 12649-12653.
13/07/2016 – Added references and see also sections. Updated inline references to show primary sources rather than just linking to secondary sources.
14/07/2016 – Corrected typo in final paragraph “Math ability is not hard wired…” changed to “Math inability is not hard wired”.