Progress in trumpet technique: Hora Staccato

Grigoraș Dinicu wrote “Hora Staccato” as a showpiece for violin on the occasion of his graduation ceremony from the Bucharest Conservatory in 1906. Jascha Heifetz adapted it in 1932 and violists have been playing it ever since.

Harry James performed a trumpet adaptation in 1944 in the movie, Bathing Beauty. As far as I know this is the first recording of a trumpet version:

Rafael Mendez recorded “Hora Staccato” in 1957:

His performance is better than James’. The tempo is faster and Mendez's articulation is lighter and more precise. In comparison James’ version feels ‘thick’ and awkward. I attribute the difference to technique. Mendez has more technical skill than James did, though he couldn’t play jazz nearly so well.

Here is a 2015 recording by Brandon Ridenour:

It is comparable to Mendez’s version. I could easily have chosen a half dozen other contemporary performances, all as fleet as Mendez’s and Ridenour’s. I have no idea how many of Mendez’s contemporaries could play it as well as he did. But I suspect that the general level of trumpet technique has gone up over the last half-century so that the number of trumpet players who can perform at that level now is greater than the number in 1957.

Glory reflecting on the Hudson River at night

Rogue AI & the relationship between reality and community orientations

Time once again to chew the AI-Doom bone.

Rohit over at Strange Loop Canon has recent posts about AI risk is modern eschatology and Effective Altruism is a constant fight against Knightian uncertainty. Since AI existential risk (x-risk) is one of EA’s target concerns they are closely related topics. I commented on both. Here’s my comment on EA:

I've spent more time thinking about AI x-risk than about EA in general. But of course they're closely related, as AI x-risk is one of the causes embraced by EA. It's my understanding that EA didn't start out with a focus on long-termism. That emerged.

The problem, as your title indicates, is that we're dealing with radical uncertainty. In the case of AI x-risk the fundamental problem is we don't know how to think about AGI in terms of mechanisms, as opposed to FOOM-like magic. The AI x-risk people respond by creating these elaborate predictive contraptions around something where meaningful quantitative reasoning is impossible. You're arguing that the EA folks are doing this as well.

Why?

At some point it seems to me that the mechanisms of community have overwhelmed the objectives the community was created to address. So now those objectives function as a reason for engaging in this elaborate ritual intellection. The community is now more engaged in elaborating its rituals than in dealing with the world. How does that happen and why?

We've got community orientation (CO) and reality orientation (RO). CO should be subordinate to RO and should serve it. What has happened is that RO has become subordinate to CO. Put your old McKinsey hat on: How do you measure CO and RO of a group and plot their evolution over time? What's going on at the tipping point where CO surpasses RO? I think that happened in the AI x-risk space at about the time Bostrom published Superintelligence.

It’s that last paragraph that has my current attention.

The point is that we’ve got two interacting forces or socio-cultural dynamics, which I called community orientation (CO) and reality orientation (RO). At this point those are little more than names for things I’ve not specified in any (empirical) detail. That’s not going to change anytime soon.

What I achieve with that crude conceptual hack is a way of thinking about the dynamics of a group of people having a common interest. At some point those dynamics undergo a change with the result being an intellectually closed community organized around fear of x-risk posed by AI. In fact, I’m tempted to say that these people didn’t come to function AS a community until their attention was focused on AI x-risk. Prior to that they were just a bunch of people interested in AI, AGI in particular, who may also have been interested in what would happen if, upon their emergence, those engines went rogue. Once THAT had their attention, they ‘closed ranks’ with one another and became a community.

More later.

Tugboat at night on the Hudson

As the era of globalization ends, China has become obsessed with food security

N.S. Lyons, Why is China so Obsessed With Food Security? The Upheaval, September 1, 2022:

The reality, which China’s leaders appear to have grasped at least since the outbreak of the U.S.-China trade war in 2018, is that the golden age of globalization is now over. That era, defined by a truly global marketplace, and by globe-spanning supply chains, was built on the transient peace of a global order produced by hegemonic post-Cold War American power. Now that global order is falling apart, and the world and its once-global market is fragmenting into blocs and spheres of influence. Despite the ambitious efforts of the globalists, it is nationalism and regionalism that is everywhere succeeding in reasserting itself. In this environment, complex, world-spanning supply chains may soon no longer be sustainable given the growing (and already demonstrable) risks involved. We instead seem to be moving quickly toward a future of regional and separated supply chains, shorter shipping routes, built-in redundancies, and greater national protectionism and self-sufficiency. Necessary as this may be, it will have serious consequences for a world economy based on shipping everything from one corner of the planet to another.

In 2017, Xi Jinping traveled to Davos and delivered a speech portraying China as the world’s new champion of globalization. This wasn’t a completely implausible claim. No country in the world has benefitted more from globalization than China. Globalization is the machine that allowed it to implement the most rapid and wide-scale industrialization in history, lift hundreds of millions of its people out of poverty, and become the near-superpower it is today. But, since 2020, Xi appears to have largely given up on trying to save globalization and accepted the inevitable – the foreseeable future will be defined by a global struggle between China and the United States, along with the actions of every enterprising opportunist on the margins. China’s new overriding goal is therefore to achieve “self-reliance” amid what Xi has described repeatedly as a period of “changes in the world unseen in a century.”

As Xi put it in an article published in May, “the international situation is complicated and severe,” and in this context, “In the future, the demand for food will continue to increase, and the balance between supply and demand will become tighter and tighter.” Which is why, as he put it in his speech in March, China “cannot rely solely on the international market to solve” its food security problem anymore.

There's more at the link.

H/t Tyler Cowen.

Traceable and interpretable reasoning using large language models

Antonia Creswell, Murray Shanahan, Faithful Reasoning Using Large Language Models, arXiv:2208.14271v1 [cs.AI], https://doi.org/10.48550/arXiv.2208.14271

Abstract: Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. This entails an unwelcome degree of opacity and compromises performance, especially on problems that are inherently multi-step. To address these limitations, we show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem. Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned LMs, one for selection and one for inference, to produce a valid reasoning trace. Our method carries out a beam search through the space of reasoning traces to improve reasoning quality. We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy, and generates humanly interpretable reasoning traces whose validity can be checked by the user.

Friday Fotos: It's a purple world out there

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A note about the middle image: That's a photo of objects on my dining table one of the evenings during Hurricane Sandy. The power was out so I was seeing by candle-light. To the right and a bit behind the candle are salt and pepper shakers. To the left and behind is a 2-liter bottle of Johnny Walker Red. That's a bottle of silver polish at the far left. I use it to polish my silver-plated trumpets and mouthpieces.

On Revising Prospero Yet Again [and again]

I thought I'd bump this to the top more or less on general principle. I've done another version of Prospero since the version posted below. This one dates from 2018 and incorporates ideas based computational techniques that didn't exist in 2014, of that just barely existed. It's called Virtual Reading: The Prospero Project Redux, where the idea of virtual reading replaces the type of computational reading I'd imagined in the old Prospero.

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By Prospero I mean a thought experiment that David Hays and I proposed back in 1976 in a review article, Computational Linguistics and the Humanist, we published in Computers and the Humanities. Sometime in the last decade or so, when I began thinking about doing a book on naturalist criticism, I took that old idea, of a computer program capable of a non-trivial simulation of a reader of Shakespeare, and elaborated it more or less as a stand-alone piece. In particular, I contextualized it with some remarks Stanley Fish had made about stylistic analysis in Is There as Text in This Class? I posted that piece about half a year ago, then revised and reposted it about a month ago.

I’ve now done yet another revision, this time incorporating the notion of a tabula rasa interpretation from Alan Liu’s article, “The Meaning of the Digital Humanities” (PMLA 128, 2013, 409-423). I’ve posted that version at my Academia.edu page. I’ve appended the abstract to this post.

Why yet another revision?

It would seem that this notion, this Prospero, has become a touchstone, something through which I gauge the state of my thinking on certain possibilities of literary analysis. But that’s not quite what it was when Hays and I advanced it almost 40 decades ago. Then it was a way of conveying something to an audience we presumed to be unfamiliar with current ideas in computational linguistics.

At that time, the mid-1970s, semantics was the Big New Thing. Computational linguistics was born in the early 1950s under the rubric of machine translation. The US Federal Government needed to translate a lot of Russian documents into English. Perhaps that could be done with computers?

And so a variety of investigators went to work recasting phonology, morphology, and syntax into computational form appropriate to machine translation. By the late 1960s it became apparent, both in computational linguistics and artificial intelligence, that it would be necessary to tackle meaning. Thus was born the computational semantics of natural language.

 

THAT’s what Hays was interested in when I began working with him in the spring of 1974. That’s what everyone was interested in. By the time we wrote that essay I’d completed and published preliminary work on Shakespeare’s sonnet “The Expense of Spirit”, which we discussed in the article. But we couldn’t go into any of the details in that article. So, to give our readers some sense of what that work portended we concocted Prospero.

The idea was straightforward: Code the Elizabethan worldview into a computer using formalisms then being developed, have a computer “read” a Shakespeare play, and then examine what it did in the process. For bonus points you could also program the computer with the knowledge needed to crank out Freudian, Marxist, feminist, and other interpretations. Who wouldn’t be excited at the prospect of working on such a project, even if it were a long-term (decades) project?

I don’t know what Hays thought about the real possibility of such a thing – he’d already lived through the institutional collapse of machine translation when it failed to deliver on some rather extravagant promises – but I figured that I’d be working on Prospero in my lifetime. Certainly not in the near-term future, but 20, 30 years out...?

It didn’t happen. Nor is there any immediate prospect of such a thing. IBM’s Watson is the state of the art, and it’s nowhere near the capability needed to implement Prospero.

What would it take to implement Prospero? I don’t know.

Oh, sure, it’s easy enough to say that it would require a good model of how the human brain operates, including conversation with others. But what would THAT require? We don’t know.

By way of comparison, the folks who want to send a manned mission to Mars know a great deal about what that would require. After all, we’ve already sent humans to the moon and brought them back. And we’ve sent probes that have landed on Mars and beamed back information about what’s there. All that’s directly relevant to the task of a manned mission to Mars. It may not be sufficient – it doesn’t tell us how the human body will adapt to months of weightlessness in transit, nor the mind to those months being bound to a very small group – but it IS a lot.

It is much more than we know about simulating the human brain or coding up the Elizabethan worldview.

So, if Prospero is not possible, then why think about it at all?

Let me put that more personally. What can you learn from me by reading about Prospero? To some extent that depends on what you already know about things such as cognitive science, AI, and computational linguistics, and how much you are willing to trust my knowledge of such things. And what could I learn from you through conversing about Prospero? And that depends on what you already know and on my willingness to trust in your knowledge.

That is to say, Prospero is a set of ideas for organizing a conversation, a conversation about what computers bring to the study of literature.

For example, in one of the essays in Is There a Text in This Class? (1980) Stanley Fish asserts that Michael Halliday, a linguist, is one of many lured on by “the promise of an automatic interpretive procedure” (p. 78). Though I can’t be sure of this, I rather doubt that Halliday himself had any such idea, certainly not as Fish attributes it to him. The idea’s a straw man. Thinking about Prospero is a way of thinking about why the idea IS a straw man. It may even get you to the edge of thinking about why Stanley Fish posits such a thing.

I suppose what Fish had in mind was that you “feed” your text into “the automatic interpretive procedure” and it “spits out an interpretation.” Given such a device, why should you trust the interpretation? Wouldn’t you have to know what it does? And if you know that, do you need the device?

Prospero is useful for thinking about that. So, we’ve got Prospero and we feed it, say, Much Ado About Nothing. How do we know that Prospero understood the play in some meaningful way? Of course we could ask: Did you understand it? But what would we know once Prospero answers Yes? Not much. And if Prospero were to tell us that, no, he didn’t understanding the play, would THAT tell us anything useful? We could ask Prospero questions about the play: Who is Hero? What’s her relationship to Beatrice? And when Prospero answers correctly (or not) then what do we know?

As I say in my various versions of this little thought experiment, what’s important is that knowledge that goes into building Prospero. But Prospero could give reasonable answers to those questions without understanding much about the play, no? After all, aren’t there students like that?

I’ve also said that, given that Prospero is a reasonable simulation (as determined by some as yet unspecified set of procedures), what we really want to do is examine what Prospero does internally in the process of reading a play. That, surely, would tell us a lot.

And now we’ve got a problem, one I’ve been aware of but chose not to bring up. Assume that Prospero IS a fairly robust simulation of a human mind. Isn’t there an ethical problem in opening it/her/him up and examining what happens while reading? Don’t we have to ask permission and, if permission is not granted, go no farther?

If I was aware of this issue, why didn’t I bring it up? I don’t quite know. The matter seems both obvious and beside the point. It exists in some other thought-world, one that impinges on the Prospero world, but that’s outside the actual business of creating a Prospero machine.

Perhaps that’s what Prospero is, a boundary marker, or a guardian spirit.

❖ ❖ ❖

The Prospero Project: A Thought Experiment

Revision 3, May 2014

Abstract: Prospero is a thought experiment, a computer program powerful enough to simulate, in an interesting way, the reading of a literary text. To do that it must simulate a reader. Which reader? Prospero would also simulate literary criticism, and controversies among critics. The point of Prospero, if we could build it, is the knowledge required to build it. If we had it, we could examine its activities as it reads and comments on texts. But our knowledge of Prospero is of a different kind and order from our knowledge of the world and of life, though those things are central to literary texts. The point of this thought experiment is to clarify that difference, for that is what we will have to do to build a naturalist literary criticism grounded in the neuro-, cognitive, and evolutionary psychologies. Though contemplation of this experiment we can see that, whatever computing promises literary study, it will not yield automatic interpretive procedures.

I don’t give a crap about science

Time for another bump to the top of the queue, on principle. (9.2.2022)

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I originally published this in The Valve, 25 March 2010. I republished it once before at New Savanna and I think it's worth republishing again, this time in the context of my extensive posting on digital humanities, which is sometimes subject to an unproductive discussion of humanistic vs. scientific method. In these discussions the opposed methods are ideological formations more than actual procedures real people use to arrive at more or less reliable knowledge.

Let me repeat that: I don’t give a crap about science. And again, just so you know I mean business: I don’t give a crap about science. Though just what kind of business I mean, well...

From which it follows, as the night the day, that I’m not interested in making the study of literature more scientific. I certainly want to see literary studies change in certain ways, use new ways of thinking—and of organizing and publishing our work. But making it “more scientific” is not part of the program.

Still, when I say “I don’t give a crap about science,” what do I mean? Anyone who has more than a casual acquaintance with my work knows that I frequently cite work in, e.g. cognitive science, neurobiology, and actually use those ideas in the body of my text. Until few years ago I only got three academic journals, PMLA, Science, and Nature. Then I dropped PMLA (not all that interesting), a year later I dropped Nature (too expensive), and finally, alas, Science (not as expensive as Nature, but still too much). It thus seems unlikely that I intend “I don’t give a crap about science” to have its most obvious meaning, that is: I don’t give a crap about science.

I mean something else.

 

Most intimately, most closely at hand, I mean that I don’t worry about whether or not my work is sufficiently scientific. I worry about whether or not it is interesting, about rigor and coherence, about whether the prose is clear and, as appropriate, elegant. But is it scientific? Not an issue. Nor is it an issue I worry about in the work of others.

Another thing I worry about is objectivity. Something I find deeply obscure.

As I walk about my apartment there are all these things that clearly are objects, existing apart from me. Much philosophical ink has been spilled on that issue, but it is not that ink and its intended meanings that I mean to evoke here and now. Just the ordinary experience and those real objects out there in the world. So that is one thing.

Then there is scientific objectivity, over which much philosophical ink has also been spilled. I believe such objectivity is real, though our understanding of it is obscure and contested.

What about the objectivity of journalists? That is different from the objectivity of scientists. How does it work? Some might wonder whether it is possible at all. Can the notion of “objectivity” be given useful meaning with respect to the situations on which journalists must report?

I also recognize that some matters are ineluctably subjective. Among those, some may be relentlessly idiosyncratic and specific to individuals. But I’m not sure that beauty is among those. Nor the good. It is often possible to reach substantial intersubjective agreement on matters that are subjective. Society would be impossible without such agreement. And we may well mistake widespread intersubjective agreement on subjective matters for objectivity. Or is that a mistake? Sometimes, often, always, never?

Objectivity, how to achieve it, that has perhaps been my main methodological concern in literary studies. These days I am particularly interested in the ways in which we can extend the reach of objective analysis and description of literary works (cf. this post on how "Kubla Khan" kicked my intellectual training into oblivion, of this article on literary morphology).

What about intersubjective agreement on subjective matters? What role does it play in literary studies? Note that I believe genuine critical activity, aesthetic or ethical evaluation of texts, is subjective and so criticism in this sense is about securing intersubjective agreement.

I further suspect that in many cases where we talk of humanistic knowledge being subjective we really mean something more like informal or unformalized.

(What about objectivity and intersubjective agreement?)

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Ergo, I tend to regard much-most humanities vs. science discussion as ideologically-driven wanking and I regard Snow’s Two Cultures and its spawn as children of the Devil.

Fred Rogers was a Mensch, who faced down a skeptical senator

Here's his 1969 testimony before the US Senate Subcommittee on Communications:

Mr. Rogers on the Tonight Show in 1983, with Joan Rivers as guest host. Watch Rivers' reaction as he sings "I Like You" to her (c. 6:16):

Fred Rogers accepts the Lifetime Achievement Award for Daytime Emmys in 1997:

The day that Fred Rogers washed the feet of a blackman on TV (c. 1:24):

Fred Rogers delivers the commencement address at Dartmouth, his alma mater, in 2002:

Prediction mania as a magical attempt to foreclose on the future

I argue that notions like prediction and optimisation work like what religious studies calls cosmograms: supersized, whole-world models for how things relate to each other & how History works. They provide a sense of progress and even teleology. 2/n pic.twitter.com/ZglcVb6XTl

— Sun-ha Hong (@sunhahong) August 29, 2022

Meanwhile, the present and its teething problems are somewhat diluted of reality: there is less need to worry so much about actually existing inequality or inefficiency, the idea goes, since technological breakthroughs will soon render them irrelevant. 4/n pic.twitter.com/OOefXKtmxv

— Sun-ha Hong (@sunhahong) August 29, 2022

So cars that can really drive themselves, facial recognition that can really detect liars - the final proof, the real test of viability, is always deferred to tomorrow. Technological futures are fantasies of coherence wrapped around perpetually broken product. 6/n pic.twitter.com/D6nuCEzw4U

— Sun-ha Hong (@sunhahong) August 29, 2022

The paper talks cosmograms, predictive tech, & even the 10,000 Year Clock (longtermism before it was cool?). And across it all, our technologies insist that we only dream of their future - in which nothing important need really change. /end

Open access @https://t.co/j9J5nZUpdn pic.twitter.com/2PGUdT5yTi

— Sun-ha Hong (@sunhahong) August 29, 2022

Abstract of the linked article:

Modernity held sacred the aspirational formula of the open future: a promise of human determination that doubles as an injunction to control. Today, the banner of this plannable future is borne by technology. Allegedly impersonal, neutral, and exempt from disillusionment with ideology, belief in technological change saturates the present horizon of historical futures. Yet I argue that this is exactly how today’s technofutures enact a hegemony of closure and sameness. In particular, the growing emphasis on prediction as AI’s skeleton key to all social problems constitutes what religious studies calls cosmograms: universalizing models that govern how facts and values relate to each other, providing a common and normative point of reference. In a predictive paradigm, social problems are made conceivable only as objects of calculative control—control that can never be fulfilled but that persists as an eternally deferred and recycled horizon. I show how this technofuture is maintained not so much by producing literally accurate predictions of future events but through ritualized demonstrations of predictive time.

Economist Bryan Caplan says that primary and secondary school are a waste of time, sorta’– nor does he have the foggiest idea about how to properly furnish a human mind

The New York Times is running a set of guest op-eds about education. Bryan Caplan, an economist at George Mason University has one under the title, “School Is for Wasting Time and Money,” a topic on which he has written a book, The Case Against Education. His opening paragraph:

I have deep doubts about the intellectual and social value of schooling. My argument in a nutshell: First, everyone leaves school eventually. Second, most of what you learn in school doesn’t matter after graduation. Third, human beings soon forget knowledge they rarely use.

I’ve got doubts about Caplan’s argument, and those doubts cluster around that third assertion, but also the second. But I’ll present his argument before presenting my reservations.

Caplan asserts:

My work focuses on tests of adult knowledge — what adults retain after graduation. The general pattern is that grown-ups have shockingly little academic knowledge. College graduates know about what you’d expect high school graduates to know; high school graduates know about what you’d expect dropouts to know; dropouts know next to nothing. This doesn’t mean that these students never knew more; it just means that only a tiny fraction of what they learn durably stays in their heads.

This is especially clear for subjects beyond the three R’s of reading, writing and arithmetic. Less than 1 percent of American adults even claim to have learned to speak a foreign language very well in school, even when two years of coursework is standard. Adults’ knowledge of history and civics is negligible. If you test the most elementary facts — like naming the three branches of government — they get about half right. The same goes for questions of basic science, like “Are electrons smaller than atoms?” or “Do antibiotics kill viruses as well as bacteria?”

That’s his third point, and I have little reason to contest it. I took two years of Latin in high school and two years of German in college, and, since I didn’t use either language later in life, I’ve forgotten them. I’m willing to believe that I’ve forgotten much/most of what I learned in high school and college.

He admits, however, that there is some value there:

The payoff for teaching basic literacy and numeracy is admittedly much larger. Since adults regularly use reading, writing and math, they retain much of what they learn. Even here, though, schools’ performance is mediocre and unlikely to meaningfully improve. Schools have been trying to overcome reading, writing and math deficits in underperforming students for decades. Boosting their performance in the short run is quite doable. The recurring problem is fade-out — the effects of interventions diminish or disappear completely over time.

He goes on to assert:

I freely admit that my dim assessment of American education is a minority view among my fellow economists, who offer piles of evidence that education has a big effect on what adults earn. They’re basically right about that, but that’s no excuse for ignoring the piles of evidence that education has little effect on what adults know.

This blind spot is especially odd because there’s a clean explanation for both piles of evidence. Namely: School is lucrative primarily because it certifies, or signals, employability. Most education isn’t job training; it’s a passport to the real training, which happens on the job. That’s why graduation pays individuals so well. You don’t learn much in your last few weeks of school, but completion convinces employers to trust you.

That makes sense to me as well. There’s more to his argument, but the rest is secondary to my objection, though it is worth your time.

What then is my objection to Caplan’s argument? He seems to think that the content education is mostly about conveying facts of one sort or another, plus literacy and numeracy, and those facts are promptly forgotten. So why bother?

But there’s more going on than piling facts upon facts. Students are being taught how to think. That’s not something you can do in the abstract. You teach them how to think by giving them something to think about, all those facts. It seems quite possible to me that they will have developed basic thinking skills even though they forget most of the facts on which those skills are based. Basic literacy and numeracy, they are fundamental systemic thinking skills.

I make many posts about the theory of cultural ranks that David Hays and I developed over the years. Though we never wrote about it, we did think a bit about how those ranks mapped on to the educational system. In private conversation we suggested that a Rank 1 system of thought required a grade 8 education, Rank 2 required a high school education, and Rank 3 required a college education. We never pursued that, so I don’t have much argumentation on the issue, but you might want to test that assertion against our core article, The Evolution of Cognition (1990). Our argument would be that high school students think about the world – all of it, the physical world, the biological world, history and society, the arts, etc. – in a more sophisticated way than 8th grade graduates, and that college graduates are more sophisticated than high school graduates. Just how one would measure that directly, rather than by testing for remembered facts, I don’t know. But it should be possible.

Having said that, it’s not at all obvious that our educational system would come out fine on such measures. I’m sure there’s much room for improvement, MUCH. What bothers me is that Caplan doesn’t seem to recognize that there’s an issue here. His views on this matter imply, to me at least, that his conception of the mind and of education is (very) superficial. I suspect that at least some of the value his fellow economists find in education resides in these less tangible matters and not in the repertoire of facts remembered.

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I wonder how students would fare if education were more project based? Here’s the opening paragraph of the Wikipedia article on Project-Based Learning:

Project-based learning (PBL) is a student-centered pedagogy that involves a dynamic classroom approach in which it is believed that students acquire a deeper knowledge through active exploration of real-world challenges and problems. Students learn about a subject by working for an extended period of time to investigate and respond to a complex question, challenge, or problem. It is a style of active learning and inquiry-based learning. PBL contrasts with paper-based, rote memorization, or teacher-led instruction that presents established facts or portrays a smooth path to knowledge by instead posing questions, problems or scenarios.

Hoboken peek-a-boo with hydrangeas

A critical period for acquiring rhythmic feel?

Steven Pinker recently posted a short video about a critical period for learning language. He reports a recent study he participated in that found that, yes, there apparently is a critical period for syntax and semantics, which ends at about 17 (c. 7:43). He went on to note, however, that their online survey could not test for pronunciation and accent. where “we have some reason to believe that the critical window might end earlier” (c. 9:12).

That got me to thinking and I put those thoughts into a note I sent to Pinker.

Hi Steve,

I just watched your little video on critical periods for language acquisition. The bit at the end, about the possibility that the critical period for acquiring accent may end earlier than that for syntax and semantics interests me.

I’ve got a bit of anecdotal evidence that musical rhythm may be subject to a similar critical period. I became interested in jazz at about the onset of puberty. Fortunately my trumpet teacher just happened to be a jazz musician and so he worked with me on jazz. In particular, he wrote out exercises in swing interpretation.

He told me that the hardest thing he ever had to do as a musician was when he was in the service working with musicians who were trained as ‘legit’ musicians – where ‘legit’ was an informal term of art and, for all I know, may still be in use. They simply could not get the hang of jazz rhythm. It wasn’t merely that jazz rhythms cannot be readily notated, but the musicians simply could not get the feel of the rhythm. In particular, swing eluded them.

That problem certainly wasn’t confined to the particular musicians he encountered in the service. It seemed to be a widely recognized problem. It showed up in high school concert band when we had pieces that were supposed to swing. It was difficult for the band to do.

I later found out, through practical experience, that proficiency in jazz did not automatically translate into proficiency in Latin rhythms. And a musician I played with in a rhythm and blues band maintained that RnB musicians could not play rock rhythms idiomatically. My sense is that jazz, rock, RnB, and Latin music are rhythmically closer to one another than any of them is to ‘legit’ music (that is, European concert and military music).

I have no idea whether or not this has been studied. If not, it would be worth looking into.

Best,

Bill B

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On the general relationship between music and language, see this recent review, David Temperley, Music and Language, Annu. Rev. Linguist. 2022. 8:153–70, https://doi.org/10.1146/annurev-linguistics-031220-121126

Abstract: This review presents a highly selective survey of connections between music and language. I begin by considering some fundamental differences between music and language and some nonspecific similarities that may arise out of more general characteristics of human cognition and communication. I then discuss an important, specific interaction between music and language: the connection between linguistic stress and musical meter. Next, I consider several possible connections that have been widely studied but remain controversial: cross-cultural correlations between linguistic and musical rhythm, effects of musical training on linguistic abilities, and connections in cogni- tive processing between music and linguistic syntax. Finally, I discuss some parallels regarding the use of repetition in music and language, which until now has been a little-explored topic.

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The first four minutes of the following video are about the difference between the time concepts of classica and jazz musicians:

The music on Mr. Rogers’ Neighborhood was good jazz [Media Notes 79]

Mr. Rogers’ Neighborhood was a children’s TV program that ran from 1968 to 2001. Thus my own childhood was well behind me by the time it got started. But I certainly knew about it. Other shows would occasionally reference it and I’d see part of an episode now and then on PBS. But, for whatever reason, I didn’t pick up on the fact that the music was more sophisticated than what you’d expect for children’s programming. It was uncompromising jazz played live by pianist Johnny Costa and a small ensemble. From the Wikipedia article about Costa:

Costa served as musical director, arranger, and keyboardist for the children's television series Mister Rogers' Neighborhood from the show's debut in 1968 until his death in 1996. The show's creator and host, Fred Rogers, regarded Costa as one of the most gifted musicians he had ever met. Rogers' choice was surprising because Costa's style was regarded as too complicated and sophisticated for a children's show. Costa accepted the job without hesitation because it wouldn't require him to travel away from Pittsburgh, and because Rogers offered him the same amount he needed to pay his son's college tuition ($5,000). Although Mister Rogers' Neighborhood was a children's show, Costa made it a point not to play "baby" music. He believed children understood good music[3] and that he could experiment with his own musical styles and techniques, even for a kids' show. Each day, Costa and his trio (Carl McVicker Jr. on bass, Bobby Rawsthorne on percussion) played live in the studio for the filming.

I learned about Costa from two YouTube videos that came across my feed on Tuesday, August 30. Here they are: