Science, Clave, and Understanding

When Dr. Eben Alexander defended, in one of the major news magazines, his book (“proof[i]”) [1] about a spiritual non-physical afterlife realm, part of his argument was that he is a surgeon, and therefore a scientist. Surgeons are highly trained, highly specialized people who perform a very difficult and critically important service. It would be absurd not to recognize their value. Their work is without a doubt science-based, but does that make it “science”? (There are, of course, surgeons who publish scholarly work (although, I’ve noticed that in some cases, it’s not about surgery, but on fields as distant as music), and thus function as scholars, and therefore scientists.)

A scientist is not anyone who functions as a professional practitioner of a difficult and science-based field; a scientist is someone who sets up, tests, and evaluates (mostly via statistical data analysis) testable hypotheses (about anything, including the afterlife and spiritual realms, if necessary), and more importantly, does so within the guidelines of rigor, accuracy, objectivity, skepticism, and open-mindedness [2][ii]. It is worrisome to imagine that surgeons are setting up double-blinded clinical trials of surgical practices as part of their work, choosing to apply a known good technique on one patient and an as-yet-unsupported one on another patient. (In other words, I really hope surgeons do not act as scientists.) Maybe they do; I’d like to know, so please give me feedback on this question.

Assuming, though, that they don’t endanger patients’ lives for the sake of science, as we tend not to do anymore, it seems safe to assume that surgeons are highly trained specialists who practice state-of-the-art medicine. In this sense, they are not scientists. They use the findings and results of science in their practical, applied work (medicine). They must, then, fall somewhere between applied scientists and technologists (inclusive).

To say that someone who practices a specialty that is based on scientific findings is therefore a scientist is like saying a sandwich-shop employee is a farmer because they use bacon, lettuce, and tomato in their work. (The fact that surgery is far more specialized does not invalidate the argument.)

The professions that discover, invent, develop, and apply are all different. The roles can overlap—scientists do develop and build new equipment to perform their experiments, but these are not mass-produced. Anything we can purchase repeatedly on amazon or at Best Buy, say, was not made by scientists. It was designed, developed, tested, and manufactured by engineers, technologists, technicians, and other professionals, not by scientists, even if scientists were involved in the early stages. As for applied scientists, including those who work at laboratories, characterizing soil samples, say, or performing tests, they are also highly trained specialists of scientific background who are not doing science at that point. As one XKCD comic suggested [3], you can simply order a lab coat from a catalog; no one will check your publication record. Science is not solely about what you’re wearing or what degrees you have; it’s about what, exactly, you’re doing.

The public’s idea of what science is seems to be “mathy and difficult, preferable done in a lab coat while uttering multisyllabic words you don’t want to see in your cereal’s list of ingredients.” This may be a decent shortcut for pop-culture purposes, but it is not what science really is. I will not go into the inductive-method-vs-hypothetico-deductive-method-vs-what-have-you debate here because there are people who do that professionally, and do it very well. (I have been enjoying Salmon’s The Foundations of Scientific Inference [4] immensely.) What I do want to do is draw two parallels in succession, first from the preceding discussion to explanation and understanding, and from those concepts, to explanations and understanding of clave (in music).

The former has been done quite successfully in Paul Dirac-medal-and-prize-winning physicist Deutsch’s earlier book The Fabric of Reality [5]. I am not concerned here with the bulk and main point of his book, but only with his opening argument about the role of science (explanation) and what it means to understand. Deutsch criticizes instrumentalism because of its emphasis on prediction at the cost of explanation (pp. 3–7). He gives rather good examples of situations in which no scientist (or layperson, for that matter) would be satisfied with good predictions without explanations (p. 6, for example). He does not deny the role and importance of predictions, but argues that “[t]o say that prediction is the purpose of scientific theory is [. . .] like saying that the purpose of a spaceship is to burn fuel” (similar to another author’s argument that the purpose of a car is not to make vrooom–vrooom noises; they just happen to do that as part of their operation[iii]). Deutsch states that just like spaceships have to burn fuel to do what they’re really meant to do, theories have to pass experimental tests in order “to achieve the real purpose of science, which is to explain the world.” (Think about it: Why did we all, as children, get excited about science? To understand the world!)

He then moves on to explain that theories with greater explanatory power than the ones they’ve replaced are not necessarily more difficult to understand, and certainly do not necessarily add to the list of theories one has to understand the be a scientist (or an enthusiast). Theories with better explanatory power can be simpler. Furthermore, not everything that could be learned and understood needs to be: See his example of multiplication with Roman numerals (pp. 9–10). It might be fun, and occasionally necessary to have some source in which to look it up (for purposes of the history of mathematics, say), but it’s not something anyone today needs a working knowledge of; it has been superseded. His example for this is how the Copernican system superseded the Ptolemaic system, and made astronomy simpler in the process (p. 9). All of this is discussed in order to make the point that there is a distinction between “understanding and ‘mere’ knowing” (p. 10), which is where my interest in clave comes into play.

Several “explanations” of clave (sometimes even with that word in the title) that were published in recent years have been of the “mere knowing” type in which clave patterns are listed, without any explanation as to how and why they indicate what other patterns are allowed or disallowed in the idiom. Telling someone that x..x..x…x.x… is 3-2, and ..x.x…x..x..x. is its opposite, so 2-3, and (essentially) “there you go, you now know clave” does nothing towards explaining why a certain piano pattern played over one is “sick” (good) and over the other, sickening (bad) within the idiom.

Imagine if the natural sciences went about education the way we musicians do with clave. A chapter in a high-school biology book would contain a diagram of the Krebs cycle, with all the inputs, outputs (sorry for the electrical-engineer language), and enzymes given by name and formula, followed by “and now you know biochemical pathways,” without any explanation as to how it has anything to do with an organism being alive. I’m flabbergasted that musicians and music scholars find mere listings of clave son, clave rumba, [and . . . you know, the other one that won’t be named[iv]] sufficient as so-called explanations[v].

All of this reminds me of an argument I once had with a very intelligent person. I had said, in my talk at Tuesday Talks, that science is concerned with ‘why’ and ‘how’, not just ‘how’. He disagreed, which I think is because he thought of a different type of ‘why’: the theological ‘why’. I, instead, had in mind Deutsch’s type of ‘why’: “about what must be so, rather than what merely happens to be so; about laws of nature rather than rules of thumb” (p. 11). I would add, about consistency (even given Goedel, because I’m Bayesian like that, and not so solipsistic), which Deutsch mentions immediately afterwards, calling it ‘coherence’.[vi]

I understand that Hume, Goedel, and others have shown us that our confidence in science, or even math, ought not to be infinite. It isn’t. Even in a book like The God Delusion, even Richard Dawkins makes it clear that he is not absolutely certain. Scientific honesty requires that we not be absolutely certain. But we can examine degrees of (un)certainty, and specifically because of the solipsists, we have to ignore them[vii], and be imperfect pursuers of an imperfect truth, improving our understanding, all the while knowing that it could all be wrong.

To that end, I continue to test my clave hypothesis under different genres. Even if it’s wrong, it definitely is elegant.

[1] Alexander, M.D., E., Proof of Heaven: A Neurosurgeon’s Near-Death Experience and Journey into the Afterlife, Simon & Schuster, 2012.

[2] Baron, R. A., and Kalsher, M. J., Essentials of Psychology, Needham, MA: Allyn & Bacon, A Pearson Education Company, 2002.

[3] (last accessed 12/25/2015).

[4] Salmon, W. C., The Foundations of Scientific Inference, Pittsburgh, Pennsylvania: University of Pittsburgh Press, 1966.

[5] Deutsch, D., The Fabric of Reality: A leading scientist interweaves evolution, theoretical physics, and computer science to offer a new understanding of reality, New York: Penguin Books, 1997.

[i] Scientists do not speak of proof; they deal with evidence. Proofs are limited to the realm of mathematics. There are no scientific proofs; there are just statistically significant results, which are presented to laypersons as ‘proof’ because even scientists have quite a lot of difficulty interpreting measures of statistical significance, and the average person has no patience for or interest in the details of philosophy of science.

[ii] The authors of [2] give the following excellent definitions for these precise terms. Accuracy: “gathering and evaluating information in as careful, precise, and error-free a manner as possible”; objectivity: “obtaining and evaluating such information in a manner as free from bias as possible” [Ibid.]. ‘Bias’ in this case refers to the cognitive biases that are natural to human thinking and judgment, such as confirmation bias, Hawthorne effect[ii], selection bias, etc.; skepticism: the willingness to accept findings “only after they have been verified over and over”; and open-mindedness: not resisting changing one’s own views—even those that are strongly held—in the face of evidence that they are inaccurate [2]. To these we can add principles like transferability and falsifiability, and the key tools of double-blinding, randomization, blocking, and the like. Together, all these techniques and principles constitute science. Simply being trained in science and carrying out science-based work is not sufficient.

[iii] I think it was Philips in The Undercover Philosopher, but I’m not sure.

[iv] If you’ve read my post about running into cool people from SoundCloud at NIPS ’15, you’ll know what pattern I’m talking about: the English-horn-like-named pattern.

[v] Fortunately, we do have work from the likes of Mauleón and Lehmann that show causal relationships between individual notes or phrases in different instrumental lines, but since their work and mine, the trend has reverted to listing three patterns, and calling that an explanation.

[vi] Perhaps this paragraph needs its own blog post. . .

[vii] Because, according to them, they don’t exist.


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