Tagcognitive science

Review: Hurts So Good: The Science and Culture of Pain on Purpose, by Leigh Cowart

In academic circles, we have a half-joking-but-not-really saying: “All Research Is Me-Search,” and Leigh Cowart’s new book has taken that dictum to titanic new heights and visceral, evocative depths.

Cowart is a former ballet dancer, a biologist who researched Pteronotus bats in the sweltering jungles of Costa Rica, and a self-described “high-sensation-seeking masochist.” They wrote this book to explore why they were like this, and whether their reasons matched up with those of so many other people who engage is painful activities of their own volition, whether for the pain itself, or the reward afterward. Full disclosure: Leigh is also my friend, but even if they weren’t, this book would have fascinated and engrossed me.

Hurts So Good is science journalism from a scientist-who-is-also-a-journalist, which means that the text is very careful in who and what it sources, citing its references, and indexing terms to be easily found and cross-referenced, while also bringing that data into clear, accessible focus. In that way, it has something for specialists and non-specialists, alike. But this book is also a memoir, and an interior exploration of one person’s relationship to pain, pleasure, and— not to sound too lofty about it— the whole human race.

The extraordinarily personal grounding of Hurts So Good is what allows this text to be more than merely exploitative voyeurism— though as the text describes, exploitative voyeurism might not necessarily be a deal-breaker for many of its subjects; just so long as they had control over when and how it proceeds and ends. And that is something Cowart makes sure to return to, again and again and again, turning it around to examine its nuances and infinitely fuzzy fractaled edges: The difference between pain that we instigate, pain that we can control, pain we know will end, pain that will have a reward, pain we can stop when and how we want… And pain that is enforced on us.

Cowart writes again and again that if BDSM is not consensual then it is abuse, that the forms of training done in ballet have a direct effect on disordered eating and body image issues, and that the kinds of pain which are not in our control can contribute to lasting trauma. And they also discuss how healing it can be to take back the control of pain in consensual BDSM, or the story of a ballet dancer who found themselves drawn to mixed martial arts as a way to process what was done to them in the ballet studio, and how using therapy to recognize and grapple with what has been done to us can sometimes allow us to differently understand what we want for ourselves.

There are no firm answers, in Cowart’s book. There are multiple perspectives, from neuroscientists, to the aforementioned ballerinas-turned-MMA-fighters, to ultra-marathoners, to people who compete to eat the hottest chilies in the world, to people who pierce the skin of their backs to hang suspended from hooks and bars, in public, to polar bear plungers, and more. There are overlapping feelings and descriptions in all of these people who seek to experience pain on purpose, but there is also a stunning multiplicity of backgrounds, of beliefs, of reasons to seek these avenues out, each one helping the reader to understand something more about both individual psychology and whole cultures’ relationships to pain.

Hurts So Good

[Cover image for Hurts So Good, featuring various hot peppers, handcuffs, a  sword, a whip, a snake, a hook on the end of a rope, and pointe shoes, all arranged around the words “Leigh Cowart— Hurts So Good— The Science & Culture of Pain on Purpose]

As someone who studies, among other things, the intersections of religious belief, ritual, and social life, I was absolutely fascinated by Cowart’s discussion of the role of ritual in how we experience pain and pleasure, both in the context of the preparations for dance or other sport or how competitors psych themselves up before a chili eating match or the constant call-and-response and check-in and aftercare process of a BDSM scene, but also in terms of literal religious ceremonies. Cowart discusses the flagellants of the Black Plague era, and mystics who fasted and meditated to achieve oneness with the Divine (a practice that also had clear gender-political valences, which Cowart also gets into). So one thing I’d’ve loved more of from Cowart is what other religious groups they found also use pain in a spiritual context. I know of a few, myself, including schools of Zen Buddhism, and would have loved to have them alongside Cowart’s examples, as well.

Similarly, I greatly appreciated Cowarat’s exploration of the link between the psychological, emotional, and the physical, in categorizing and inscribing pain in the bodymind. I know firsthand that graduate school and academic research is often about enduring the emotional and psychological kinds of pain, for the sake of something more coming out of it, as well as for what we believe we can achieve in that moment, and so I would have loved even a bit more in psychological and emotional veins, too.

But, of course, when writing a book, we have the time we have, and the time that Leigh Cowart took to research and write their book was well worth it.

Hurts So Good: The Science and Culture of Pain on Purpose is an illuminating, joyous, deeply emotional examination of what makes pain what it is, what makes pain mean what means, and why. And what could be more fitting for one of the most intimate, personal, and universal experiences of the human species.

Hurts So Good is out now from Public Affairs and Hachette, and if you want more from Leigh, you can check out their appearance on NPr’s The 1a.

N-Back Training Exercise Still Holding Up in Tests

soakyourhead screenshot
Above: the Soak Your Head Dual N-Back Application

I’ve covered research on how most brain training exercises don’t actually hold-up in tests. The good news is that dual n-back training, also covered here previously, is continuing to hold up in tests:

Jonides, who is the Daniel J. Weintraub Collegiate Professor of Psychology and Neuroscience, collaborated with colleagues at U-M, the University of Bern and the University of Tapei on a series of studies with more than 200 young adults and children, demonstrating the effects of various kinds of n-back mental training exercises. The research was supported by the National Science Foundation and by the Office of Naval Research.

According to Jonides, the n-back task taps into a crucial brain function known as working memory—the ability to maintain information in an active, easily retrieved state, especially under conditions of distraction or interference. Working memory goes beyond mere storage to include processing information.

Medical Express: A Brain Training Exercise That Really Does Work

(Thanks Bill!)

Soak Your Head offers a free Web-based n-back training program, but it requires Microsoft Silverlight. You can find a list of other applications here.

Another way to boost your mental capabilities? Play first person shooters. This NPR story provides an overview of the research. You can also find a research paper that looks at multiple studies here (PDF).

The best way to stave off cognitive decline, however, may be to spend time socializing with friends.

Bees Can Solve the “‘Travelling Salesman Problem”

bees-complex-math

What’s interesting is that this doesn’t seem to be a result of “swarm intelligence” – individual bees can somehow make these calculations:

Scientists at Queen Mary, University of London and Royal Holloway, University of London have discovered that bees learn to fly the shortest possible route between flowers even if they discover the flowers in a different order. Bees are effectively solving the ‘Travelling Salesman Problem’, and these are the first animals found to do this.

The Travelling Salesman must find the shortest route that allows him to visit all locations on his route. Computers solve it by comparing the length of all possible routes and choosing the shortest. However, bees solve it without computer assistance using a brain the size of grass seed. […]

Co-author and Queen Mary colleague, Dr. Mathieu Lihoreau adds: “There is a common perception that smaller brains constrain animals to be simple reflex machines. But our work with bees shows advanced cognitive capacities with very limited neuron numbers. There is an urgent need to understand the neuronal hardware underpinning animal intelligence, and relatively simple nervous systems such as those of insects make this mystery more tractable.”

PhysOrg: – Bumblebees can find the solution to a complex mathematical problem which keeps computers busy for days

(via Fadereu)

A Grand Unified Theory of Artificial Intelligence

the thinker

Early AI researchers saw thinking as logical inference: if you know that birds can fly and are told that the waxwing is a bird, you can infer that waxwings can fly. One of AI’s first projects was the development of a mathematical language — much like a computer language — in which researchers could encode assertions like “birds can fly” and “waxwings are birds.” If the language was rigorous enough, computer algorithms would be able to comb through assertions written in it and calculate all the logically valid inferences. Once they’d developed such languages, AI researchers started using them to encode lots of commonsense assertions, which they stored in huge databases.

The problem with this approach is, roughly speaking, that not all birds can fly. And among birds that can’t fly, there’s a distinction between a robin in a cage and a robin with a broken wing, and another distinction between any kind of robin and a penguin. The mathematical languages that the early AI researchers developed were flexible enough to represent such conceptual distinctions, but writing down all the distinctions necessary for even the most rudimentary cognitive tasks proved much harder than anticipated.

Embracing uncertainty

In probabilistic AI, by contrast, a computer is fed lots of examples of something — like pictures of birds — and is left to infer, on its own, what those examples have in common. This approach works fairly well with concrete concepts like “bird,” but it has trouble with more abstract concepts — for example, flight, a capacity shared by birds, helicopters, kites and superheroes. You could show a probabilistic system lots of pictures of things in flight, but even if it figured out what they all had in common, it would be very likely to misidentify clouds, or the sun, or the antennas on top of buildings as instances of flight. And even flight is a concrete concept compared to, say, “grammar,” or “motherhood.”

As a research tool, Goodman has developed a computer programming language called Church — after the great American logician Alonzo Church — that, like the early AI languages, includes rules of inference. But those rules are probabilistic. Told that the cassowary is a bird, a program written in Church might conclude that cassowaries can probably fly. But if the program was then told that cassowaries can weigh almost 200 pounds, it might revise its initial probability estimate, concluding that, actually, cassowaries probably can’t fly.

PhysOrg: A Grand Unified Theory of Artificial Intelligence

(Thanks Josh!)

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