Over the past several years, the momentum has shifted away from hard-core materialism. The brain seems less like a cold machine. It does not operate like a computer. Instead, meaning, belief and consciousness seem to emerge mysteriously from idiosyncratic networks of neural firings. Those squishy things called emotions play a gigantic role in all forms of thinking. Love is vital to brain development.

This paragraph is interesting, because it provides a nice summary of recent trends in neuroscience (everything but the first sentence is true) while simultaneously betraying a deep misunderstanding of the materialist worldview. Brooks holds up constructs like meaning, belief, and consciousness as if they were antithetical to the “hard-core” materialist worldview; but should it surprise anyone that meaning and belief emerge from “idiosyncratic networks of neural firings”? Do materialists quake in their boots at the thought that love plays a role in brain development? It shouldn’t, and they don’t. A good materialist (not just a ‘hard-core’ materialist, whatever that means, but any good one) takes these observations as self-evident. If you believe, as materialist neuroscientists do, that the brain is the proximal source all thought, feeling, and action, then you must believe that meaning and belief arise through the actions of neurons chattering with one another; you must believe that the nurturing effects on love on human development are mediated by changes in the brain. For Brooks, the notion that love might influence brain development appears to come as an epiphany; but really, what alternative is there? Does he suppose that the real materialists are the ones who would deny the existence of meanings, beliefs, consciousness, and love? If so, there aren’t any. Maybe there used to be, briefly, in the 1980s heyday of eliminative materialism; but those materialists were philosophers (e.g., the Churchlands), not neuroscientists, and it appears they’ve long seen the light and backed away from their stronger claims (e.g., that terms like “belief” are just conveniences of folk psychology, and don’t map onto anything real).

This fundamental misunderstanding of the central tenet of materialism gets played out repeatedly in Brooks’ op-ed (despite the fact that it’s only one page long). Consider the following assertion, which Brooks seems to take as evidence against ‘militant’ materialism:

First, the self is not a fixed entity but a dynamic process of relationships. Second, underneath the patina of different religions, people around the world have common moral intuitions.

These points are hard to dispute, but they certainly don’t constitute an argument against “militant atheism” or “hard-core materialism”, unless one takes these militant atheist materialists to be people who not only don’t believe in meaning, belief, consciousness, and love, but also think the self is a fixed entity and that there’s no such thing as a moral intuition. Now, I haven’t met any of these people, but they sound like fascinating individuals, if a bit odd.

Or take the following statement, which accurately describes ongoing research in certain areas of cognitive science, neuroscience, and genetics:

Researchers now spend a lot of time trying to understand universal moral intuitions. Genes are not merely selfish, it appears. Instead, people seem to have deep instincts for fairness, empathy and attachment.

Or this one:

Scientists have more respect for elevated spiritual states. Andrew Newberg of the University of Pennsylvania has shown that transcendent experiences can actually be identified and measured in the brain (people experience a decrease in activity in the parietal lobe, which orients us in space). The mind seems to have the ability to transcend itself and merge with a larger presence that feels more real.

From a descriptive standpoint, “Brooks gets the research essentially right,” as Kelly Bulkeley notes in a commentary over on the SSRC blogs. But why Brooks thinks such findings will soon lead to militant materialism falling by the wayside, I don’t know. I would have thought precisely the opposite, and so it seems, does Bulkeley:

To begin with, neuroscientist Andrew Newberg’s brain-imaging studies of meditation, highlighted by Brooks, can easily be used to confirm rather than disprove a materialist worldview. Newberg’s finding that people who are meditating have measurable decreases in parietal lobe activity fits perfectly with the idea advanced by Richard Dawkins and others that religious experience is a product of altered or abnormal brain functioning. Contrary to the popular view that Newberg’s research supports religion, it can readily be taken as supporting the “militant atheism” Brooks wants to reject. The mind may, as Brooks says, have “the ability to transcend itself,” but we didn’t need Newberg’s SPECT scanners to tell us that.

This conclusion seems exactly right to me. After all, it would surely be better for non-materialists if it turned out that religious experiences didn’t have some identifiable neural correlates. “Look,” one could imagine them saying then, “visual perception, motor control, and speech production… all of these things depend on the brain. But transcendental experiences don’t!” Unfortunately, it doesn’t work out that way. Religious experiences turn out to have underlying neural representations, just like every other psychological state or process that’s been investigated. That includes meaning, belief, consciousness, and yes, love. Such findings aren’t inconsistent with materialism; they’re necessary for materialism to hold. Why this simple observation baffles Brooks so, I don’t know.

Having said all that, I do think there’s one redeeming point to Brooks’ Op-Ed. I think he has it basically right when he suggests that “we’re in the middle of a scientific revolution” that’s going to have “big cultural effects”. But I suspect that he’s banking on the wrong revolution. Instead of modern neuroscience giving rise to “neural Buddhism”, what’s much more likely to happen is that, as our understanding of the brain increases and we learn more and more about precisely those aspects of human behavior and cognition that were once thought to be resistant to material explanation, it’ll become increasingly difficult for non-materialists to adhere to their dogmas in the face of reductive explanations. In a world where religious experiences are scientifically mysterious, a dualist worldview is defensible, because there’s no better explanation than “God did it”. In a world where such experiences unfold as, say, a sequence of attractor states in a temporoparietal network that mediates the experience of agency, one has a choice between “God did it” and “the brain did it”. My bet is that, for many (though certainly not all) people, the brain will beat God.

On its face, writing a book doesn’t seem to be a high priority in psychology. Very few psychologists can claim to have an authored book on their vita, and these select few individuals still typically list books under a separate heading well below the almighty “Peer-Reviewed Journal Articles” section. I can count on one hand (well, maybe two) the number of times another psychologist has said something to me along the lines of “you should really read so-and-so’s book”. Books don’t play much of a role in day-to-day psychological research, and if anything, many researchers seem to harbor a slight contempt for them. Psychologists typically write books for laypersons, so that research-wise, the level of detail often leaves much to be desired. When psychologists want to know about fancy new experiments, they read fancy new research articles in journals like JEP and Psych Science; when they want to get a bird’s-eye view of a field, they read review articles in journals like Psych Review and Psych Bulletin. Books like Stumbling on Happiness or The Blank Slate may make for great reading before bed, but they’re rarely cited as a primary source in research articles (with a few notable exceptions, e.g., Antonio Damasio’s “Descartes’ Error”, which everyone and their grandmother cites).

Now, given academic psychologists’ general apathy toward authored books, you might expect that the authors of popular books on psychology would tend to be writers first and foremost, and that well-known researchers would rarely if ever take time out of their schedule to write a 400-page volume. That would be my intuition, at least; but it turns out to be the wrong one. In fact, a disproportionate number of popsci psychology books are written by very eminent researchers. People like Dan Wegner (The Illusion of Conscious Will), Dan Gilbert (Stumbling on Happiness), Dan Schacter (Searching for Memory), Paul Bloom (Descartes’ Baby), Steven Pinker (a zillion bestselling books on language and/or evolution), Antonio Damasio (several books nominally about old dead white guys but really about emotion), Michael Gazzaniga (The Mind’s Past), and Joseph LeDoux (The Emotional Brain) have all had extremely productive, well-respected research careers independently of their popular output.

The interesting question, of course, is why. Why are popular psychology books more likely to be written by eminent researchers?  Broadly speaking, I think there are three classes of explanations (I’m sure I’m leaving many out, though). One possibility is that popular books aren’t actually more likely to be written by famous psychologists; rather, psychologists are more likely to become famous if they’re written popular books. This would be interesting if true inasmuch as it would suggest that the conventional wisdom is wrong: rather than focusing single-mindedly on publishing peer-reviewed journal articles, young academics might do better to divert at least some of their time to popularizing psychology by writing full-length books (then again, most of the aforementioned authors wrote their first book after receiving tenure). Of course, this still wouldn’t explain why psychologists become famous after writing popular books. Perhaps there’s a familiarity effect: psychologists who publish popular books are likely to have their names repeated widely and often, which might subsequently bias researchers to assign more weight to those authors’ empirical research, regardless of its actual merit. Or perhaps hiring committees at schmancy universities use popular fame as an explicit criterion when evaluating candidates (though that seems unlikely, because many of the people listed above–e.g., Wegner, Gilbert, and Schacter, all currently at Harvard–published their major popular works after moving to elite universities).

Another possibility is that there’s a kind of selection effect: lots of psychologists publish (or try to publish) popular books, but only books by famous psychologists are widely read. Other things being equal, one would expect that books by Harvard professors sell more copies than books by professors at third-tier schools, so a bias may emerge at either the publishing stage (famous psychologists are more likely to get book deals) or the consumer stage (people are more likely to buy books that say Harvard or Yale on the cover).

The final possibility, which I personally find most interesting, is that there’s something characterological about good researchers–or at least, a subset of good researchers–that makes them more likely to publish popular works. There are a number of traits that come to mind here. One is simply intelligence: while popular science books are often maligned for their lack of depth, synthesizing a broad research literature into a clear, readable package can be a considerable feat of intellect. Another relevant dimension is creativity and/or the ability to see the big picture. Researchers who are good at integrating diverse ideas maybe both more likely to produce good research and more motivated to paint a discipline with broad strokes in a popular book. Or, it could be a matter of drive: writing a book takes persistence and hard work, and persistent, hard-working people are likely to be more productive in general. Of course, one could also cast this all in a more negative light, as simply a result of egomania: if you’re highly driven to be respected and admired by your academic peers, you might also be driven to show the public at large how cleverly you can write a book.

If I had to put money on it, I’d guess the reason book authors tend to be respected researchers is a little of column B (selection) and a little of column C (character). In theory this is a pretty easily testable hypothesis (the question being, essentially, what factor(s) mediate the relationship between (a) book authorship and (b) research eminence)–in fact, there’s a sizeable literature on the personality of highly successful scientists (e.g., Dean Simonton’s work). In practice, you’d probably be hard-pressed to get Steven Pinker to sit down with you for two hours of psychological testing. Which is fine, because it’s really not that informative a question anyway. The bottom line is just that there’s a interesting discrepancy between what many academic psychologists think about popular psychology books (negatively, or not at all) and what they think about the people who tend to write those books (positively).

* * *

As always, there are too many posters to see. The CNS schedule of events doesn’t begin to approach SFN standards–the latter consisting of a CD’s worth of fully indexed and searchable abstracts, and five different books (one per day)–but if you do any sort of neuroimaging work, a much higher proportion of the abstracts are likely to interest you. I start every conference I go to by spending half an hour meticulously checking off all the posters I want to see at the next session. Then when that session rolls around I promptly discard my notes and drift aimlessly from aisle to aisle.

* * *

There are a lot of complaints this year about the quality of the poster halls here at the Sheraton New York. The halls are (a) maze-like; (b) dark; and (c) warm. It’s a safe bet that some small proportion of attendees enjoys this environment, but for those of us who (a) don’t have an exquisitely-tuned spatial navigation system, (b) aren’t vampires, or (c) don’t suffer from hyperthyroidism, it’s a little bit uncomfortable.

* * *

The drinks last night at the welcome reception started at $6.50 for a soft drink. $11.50 for a beer. When I asked the bartender why I couldn’t just have a cup of tap water for free, he shrugged in antipathy. I suppose it was more polite than saying “because it would undercut our bottom line, schmuck.” So I went down the street, bought a bucket, filled it with ice and water, and gave away free refreshments to all the thirsty neuroscientists. No, just kidding. I bent over and took it just like everyone else.

* * *

Memo to presenters: that signup sheet next to your poster isn’t real. Etiquette requires that after someone’s finished being bored by the intimate details of your presentation for fifteen minutes, they be provided with some way of expressing their joy and gratitude to you for furnishing them with a life-changing experience. They do this by signing up to receive a second iteration of your treatment in written form. Putting their name on your form completes all contractual obligations. There’s no requirement that you actually follow up and email them your poster. In fact, doing so only inconveniences your audience. Last time I came back from CNS I spent an entire morning hitting the ‘delete’ button. I could have been doing much more productive things, like brushing my teeth.

* * *

I’m slowly realizing that New York is an expensive place with terrible service. Take for instance this morning. I was standing on the corner outside the hotel when a nice man approached me and said he was an artist and that he could put a beautiful glossy sheen on my poster for just $80. So I gave him my poster and $80, and he said he’d be back in twenty minutes. Well it’s been 3 hours and I haven’t heard anything. When he comes back, I’m going to be very angry with him. Just wait till he sees what kind of customer evaluation I give him at the information desk.

* * *

Auditory perception, memory systems, emotion, and numerical processing. These are all important areas of research, and certainly worthy of inclusion in the poster sessions. But there’s no reason to be elitist. Cognitive neuroscience is a diverse field. I’ve been emailing the poster committee my suggestions for topics for several years, and I’ve yet to see any follow-through or receive a reply. What’s wrong, people? Too creative? Too novel? Don’t envy me just because I thought of having a symposium on brick-selective cortex and you didn’t. It’s not my fault you lack vision.

* * *

There’s a lot of talk at this conference about how the brain is this wonderfully clever device that lets us project ourselves effortlessly into the past and future, move forwards and backwards in time, etc. etc. It’s not entirely unlike that other device that smoothly whisks you from the seventeenth floor down to the atrium while you’re busy placing mental bets on the length of the coffee line. Between your brain and the elevator, there’s no dimension you can’t conquer! You’re a master of time and space! Then the doors open up and someone jams your shoulder into the wall as they rush by you. Looks like you’re a lowly grad student again, grasshopper.

* * *

After a long day spent milling around hundreds of posters made by hundreds of scientists, all as smart and creative as you, all working on equally interesting problems, it’s easy to get a little down on yourself. What’s the point, you might ask yourself. Why bother participating in science if the best any of us can ever hope for is to make a tiny, insignificant contribution to that great puzzle that is the human mind. And what’s so great about the human mind anyway, if it’s just the temporal analog of an elevator. You might as well be studying dirt. Dirt is less dynamic than the mind, but more tractorable.

* * *

If a neuroscientist gives a great keynote address at a conference and no one hears it because they’ve all skipped the morning session to go roam around lower manhattan, does she still get to put it on her vita?

For one thing, there’s a lot of empirical evidence that suggests I’m right. Virtually every psychological study that investigates expert “performers” – from chess grandmasters to concert pianists to brain surgeons – concludes that what separates these individuals from their peers is the amount of “deliberate practice” they are willing to endure. If there is an innate difference between Yo Yo Ma and a mediocre cellist, or between Tiger Woods and your golfing uncle, it is a willingness to practice, and not an innate aptitude for the cello or the 9 iron. As K. Anders Ericsson, a cognitive psychologist at Florida State University, wrote in his influential article “The Role of Deliberate Practice in the Acquisition of Expert Performance,” “The differences between expert performers and normal adults are not immutable, that is, due to genetically prescribed talent. Instead, these differences reflect a life-long period of deliberate effort to improve performance.”

I incline to disagree with this for several reasons. First, I think it mischaracterizes the notion of heredity. Saying that variance in a behavior is under genetic influence is patently not the same thing as saying it’s immutable. Consider that, in Western societies (where malnutrition isn’t an issue), height is almost entirely under genetic control. Does this mean height is immutable? Of course not. Take a child born to 6’6” parents and deprive it of its basic nutritional needs, and it’ll be lucky to reach average height. Saying that someone has ‘tall genes’ isn’t saying they have a fixed endowment that’ll express itself in the same way regardless of environment. It’s saying that, across a range of environments, a person born to tall parents is more likely to be tall than other people around them exposed to the same basic environment. It’s not clear why this should be at all controversial. Does anyone really believe that if 10 people sampled at random were each forced to practice the piano for exactly 10,000 hours, they’d all attain exactly the same level of skill?

A second problem relates to the false dichotomy between the effects of practice and genetically prescribed talent. These aren’t opposing factors; in fact, they’re completely orthogonal to one another. Saying that someone practices a lot isn’t actually saying anything about whether the contribution to the behavior is under genetic or environmental influence. The reason for this is simple: any number of genetic factors could drive a person to practice an ability to a greater or lesser degree. These include everything from very general factors such as fluid intelligence to specific cognitive abilities to personality factors to creativity to aesthetic sensibilities.

Take the case of prodigious musical ability. It may be comforting to think that we could all be Yo Yo Ma if we really wanted to. But it’s almost certainly not true: the vast majority of the population would never be able to play the cello like Yo Yo Ma no matter how much they practiced or how early they started. And the reason isn’t that there’s something magical about Yo Yo Ma’s brain—some single amorphous genetic talent he possesses that other people just don’t. It’s much more likely that he simply possesses lots of little genetic quirks that virtually no one else happens to have the right combination of (for playing the cello really well, at least).

What does it take to be Yo Yo Ma? Well here’s a very short list of just a few factors that are likely to be under considerable genetic control and undoubtedly make one more likely to be a good cello player: a certain amount of general intelligence; a certain amount of visuospatial ability; good sequencing skills; a liking of music; absolute pitch; long, slender fingers; agility; a tremendous degree of personal motivation; and high levels of conscientiousness. Of course, plenty of people (indeed, the vast majority) will be above average on one or more of these dimensions. But that’s not the point. The point is that being a great cellist isn’t about having a magically different brain. It’s about having a particular combination of abilities, many of which are genetically influenced, that just happens to make one well-suited to playing the cello. That’s not in any way denying that practicing thousands of hours is necessary in order to achieve Yo Yo Ma’s stature. It’s just saying that it’s not sufficient.

Lehrer actually seems to concede this point to some degree when it comes to personality factors:

If there is a genetic element linking Mozart and Jordan it is the talent for practice itself, a willingness to endure the endless hours of sweat and toil required of all great performers.

This is quite clearly true, since about half of the variance in personality traits like conscientiousness is sucked up by additive genetic influences in most twin studies. But there’s no reason to suppose the buck stops with personality. Why should Mozart and Jordan’s relevant genetic endowment differ from other people only when it comes to motivation? Isn’t Jordan’s height under genetic influence? Should we really believe that a 5’3” male with a 12” vertical leap could become the world’s greatest basketball player given enough effort? Or that someone who’s tone deaf and has a congenital hand tremor is as likely to produce virtuoso violin performances after 10,000 hours of practice as someone who has absolute pitch and excellent motor control? These are extreme examples, but they’re only quantitatively and not qualitatively different from the vast majority of individuals, who are likely to be somewhat taller than 5’3” and to be neither tone deaf nor have absolute pitch.

A third problem with the claim that expertise doesn’t depend on innate factors stems from the fact that none of the evidence Lehrer cites, including Ericsson’s line of research, really addresses the fundamental issue. Ericsson’s work, particularly the Psych Review article Lehrer cites, is a textbook case of attacking a straw man (just to be clear, I think it’s excellent research when framed as a study of expertise or of the structure of memory; it’s specifically the claim that expertise isn’t innate thats problematic). The argument has the following form: (a) in virtually all expert populations studied to date, long hours of practice are a defining feature; (b) the number of hours of practice is positively correlated with ability; (c) most people don’t practice a lot and aren’t very good; therefore we can conclude (d) that practice is responsible for expertise and innate factors have little or nothing to do with it.

What’s the problem with this reasoning? Well, consider the following analogue often found in the developmental psychology literature: (a) violent parents tend to abuse their children; (b) those children tend to perpetuate the ‘cycle of violence’ by abusing their own children when they grow up; (c) the amount of violent behavior displayed by children correlates with that displayed by their parents; so (d) we can conclude that parental behavior causes aggression in children.

The problem with the latter chain of reasoning should be obvious: one can’t conclude that parental environmental factors are the key contributors to children’s behaviors without explicitly modeling the shared genetic variance, because correlation doesn’t entail causation. And when you do model the genetic variance, parental influence almost invariably represents a negligible amount of the total variance, whereas additive genetic factors typically account for about half. This may be counterintuitive, but the explanation is simple enough: violent parents pass on violent genes to their kids, and the genetic contribution seems to dwarf the influence of parents’ overt behavior.

The exact same problem applies to correlational studies of expertise. It’s surely not remarkable to note that thousands of hours of practice are necessary to become a world-class expert in almost any field. The very notion of ‘expertise’ practically requires as much: if it could be acquired in a matter of hours, everyone would be an expert, and the term would lose all meaning. The issue isn’t whether or not practice is the proximal cause of expertise. It’s whether or not genetic factors underlie the drive and desire to practice itself. And that’s a question that simply cannot be addressed without explicitly modeling the genetic contribution to a behavior. You simply can’t tell just by studying an individual grandmaster who’s played 60,000 hours of chess whether they’re that good at chess because they’re naturally gifted, or because they were pushed to play chess in spite of a lack of natural ability.

Consider: if you had an IQ of 80 and had trouble learning the rules of chess, would you keep playing the game and making mistakes? Probably not. If you were merely mediocre, and lost to everyone else in your chess club, would you keep plugging away at it for thousands of hours? Possibly, but it’s unlikely. The people who end up practicing a single skill for thousands of hours are almost invariably those who (a) love what they do; (b) have an unusual level of drive; and (c) find it comes naturally to them. People tend not to practice at things they don’t like, can’t keep at, or don’t seem to be any good at. All of these dispositions are, of course, at least partially (and probably substantially) under genetic control.

But there is virtually no evidence that expert performers are born with extraordinary brains. In fact, the average IQ of people at the top of their field – whether they are surgeons or politicians, pianists or painters – equals that of the average college student. In other words, their expertise is very specific, confined to a particular “cognitive domain”.

Two problems here. First, as noted above, expert performers don’t have to differ in some general intellectual domain in order for their skills to be driven by genetic influences. Why shouldn’t we think that oratorical, artistic, or motoric skills are under some amount of genetic control independently of general factors such as fluid intelligence? To argue otherwise would be to disregard any amount of evidence from behavioral genetics. And secondly, the domain specificity argument is also a red herring. The fact that skills acquired in one domain don’t easily generalize to other domains doesn’t say anything about the role of genetics if one grants that genetic predispositions can be highly specialized, or that different domains rely on different combinations of abilities.

But practice doesn’t just change which brain areas are activated by a certain task. It also leads to anatomical changes within those same brain regions. For example, the brains of expert violin players have swollen representations of the fingers of their left hand in the somatosensory cortex. This increase in neural space makes Bach easier to play.

Again, this is true, but says nothing about the role of nature and nurture. The issues has never been whether plasticity occurs as a result of practice (it does!), it’s whether variations in the degree of plasticity across individuals are due to genetic or to environmental factors. Expert violin players are presumably individuals whose somatosensory cortex is capable of adapting to the demands of an instrument over time. It’s entirely possible that the vast majority of humanity doesn’t display the same range of plasticity. Without modeling the genetic variance, there’s no way to tell.

This leads to the gauntlet Lehrer throws down in a comment following the post:

In fact, the only innate talent that talented people seem to contain is a talent for practice. But if there are scientific studies that suggest otherwise, I’d love to hear about them.

Just to reiterate, let’s be clear that none of the data Lehrer discusses in his post offer any support for the notion that innate differences don’t contribute to expert performance and skill acquisition. In the absence of direct evidence, the reasonable position would be to default to estimates of heredity obtained in non-expert domains as an appropriate estimate of the genetic variance. For example, fluid intelligence and most major personality dimensions are around 50-60% heritable in most studies. Given such results, the default hypothesis should probably be that expertise is under substantial genetic control.

That said, there’s a big problem associated with the very notion of estimating heredity for expert populations, which is that the estimate obtained will be highly dependent on the type of sample used. If, for example, you were to recruit a genuinely random sample from the population of, say, American adults, the likely result would be massive overinflation of the environmental contribution to expertise. The reason is that, for any given domain of expertise, there are only a small number of experts, and the vast majority of the population has never so much as attempted to acquire the ability in question. By way of analogy, if you studied the genetic contribution to ice hockey-playing ability in 12 year-olds living in central Arkansas, virtually all of the variance would be environmental, simply because most kids in central Arkansas have probably never laced up skates in their lives. Conduct the same study in Quebec, and a large chunk of the variance will be genetic, because hockey is heavily emphasized in the culture and almost everyone gets the opportunity to try their skills out.

The opposite extreme isn’t very useful either: if you conducted a twin study that only sampled experts with more than, say, 10,000 hours of practice in a given domain, you’d inflate the genetic variance, since there’d be a severe reduction in environmental variance in the sample. Put differently, when everyone gets the same environmental treatment, any differences in behavior must be the result of genes (or just random noise).

What’s the happy medium? Well, there really isn’t one. But probably the best indirect evidence stems from studies that have looked at genetic and environmental contributions to skill learning on a compressed timeframe (i.e., across hours instead of thousands of hours). A study of this kind was reported by Fox et al. in Nature in 1996 (“Genetic and environmental contributions to the acquisition of a motor skill”). In it the authors demonstrated convincingly that a substantial portion of the variance in both levels of performance and rates of skill acquisition was under genetic control. The latter point is particularly compelling given the current context, because it’s easy to forget that people can differ not only in how good they are at something, but in how fast they pick it up. Saying that thousands of hours of practice are required to become an expert is misleading, in that some people might need 12,000 and others only 7,000. These aren’t trivial differences.

In sum, what can we conclude about the heredity of expertise, given available data? The answer is, unfortunately, not much. Virtually all the data promoted as evidence for a practice model is irrelevant, because it attacks a straw man. The issue isn’t whether practice leads to expertise, it’s what gives rise to the tendency to practice a particular skill in the first place. Conversely, there isn’t much hard data that does approach the issue from the appropriate perspective (i.e., that of behavioral genetics). So in the interim, the appropriate position is probably to default to existing estimates obtained in non-expert domains, and maintain that it’s a bit of both: genetic and environmental contributions both influence expert performance. It’s not clear that we’re going to get a more meaningful answer.

The posts up at Mixing Memory, Frontal Cortex, and Neurotopia express several different concerns about mirror neurons. One is the standard “they’re getting a disproportionate amount of attention” complaint. I agree with this worry to an extent–so far mirror neurons haven’t miraculously solved any of cognitive science’s biggest problems–but excitement of this sort accompanies quite a few major discoveries in the cognitive neurosciences. And the existence and localization of mirror neurons is without a doubt a major discovery. For one thing, mirror neurons offer what is clearly the most plausible current model of imitative behavior, which isn’t a trivial matter, since imitation turns out to be pretty rare in the animal kingdom. For another, it wasn’t all that long ago that people were pretty skeptical about the prospect of knowledge being grounded in perceptual-motor processing. While we’re nowhere near grounding concepts like justice and mercy in monkeys’ left elbow joints, these days no one seriously doubts that at least some kinds of concepts are likely to be pretty intimately related to perceptual and motor representations. Mirror neurons provide an elegant way to study those kinds of representations.

Another kind of worry about mirror neurons is the potential lack of generalizability of monkey findings to humans. Well, it’s true that there haven’t been any electrophysiological studies of the human mirror system to date. But there’s any amount of indirect evidence supporting a system in humans that’s (a) located in brain regions homologous with regions F4 and F5 in monkeys (i.e., Broca’s area and adjoining premotor regions), (b) displays virtually all of the functional characteristics displayed in monkeys, and (c) displays additional functionality that appears to be unique to humans and can plausibly be related to the more basic functionality already present in monkeys. Given the existing evidence, it’s vastly more parsimonious to suppose that what fMRI and EEG studies are picking up on is something very much like what’s been observed electrophysiologically in monkeys, and not some highly dissimilar system.

A third concern is that it’s a leap to go from “mirror neurons represent actions” to “mirror neurons represent meaning”. I’m not sure this is the case; I personally find the evidence that mirror neurons in monkeys specifically code for the meaning of actions pretty compelling. Mirror neurons are often described as being sensitive to both the visual presentation of an action and the commission of that same action by the monkey itself. That’s true, but it’s only part of the story. It turns out that a large minority of mirror neurons respond not only to visual observation of an action, but also to associated sounds, even when the monkey can’t see the action being performed. Now I don’t know what definition of ‘meaning’ mirror neurons need to satisfy, but I feel pretty comfortable with one that accords it to neurons that respond selectively to polymodal presentation of a specific action.

Along the same lines, what’s equally striking is that a given visual stimulus can elicit very different patterns of activation in mirror neurons depending on the context it’s presented in. For example, ahand reaching behind a screen for an object that the monkey knows is there (but can’t directly see) might increase firing in a given neuron, but the same neuron may fail to respond at all when the hand reaches behind the screen and there’s no object present. Is the neuron representing the meaning of the action? Well, why not? It’s clearly tracking something like grasping, as opposed to something like hand moving in particular direction at particular orientation.

Finally, there seems to be some pretty serious skepticism about the mirror neuron-language evolution theory proposed by Rizzolatti and colleagues. I don’t have much to say here, because (a) I don’t know very much about this debate, and don’t feel qualified to evaluate Rizzolatti’s claims; and (b) based on what little I do know, it does seem like the least convincing aspect of Rizzolatti’s research program. That said, I’m not sure it’s fair to subject Rizzolatti and Arbib’s (1998) paper to scrutiny in 2006 without considering the body of work accumulated since then. A more recent and comprehensive review of the mirror neuron system (including relation to language) can be found in Rizzolatti’s recent (2004) Annual Review of Neuroscience chapter. While it certainly doesn’t come close to providing a fully-worked out model of either the neuron system or its connection specifically to language, the views Rizzolatti’s been proposing most recently at least don’t strike me as being absurd on their face. And the payoff will be very large if he’s even partly right. These are still early days, but I think it’s already pretty clear that there’s a large and potentially very important ongoing research program into the structure and function of the mirror neuron system.

The “cognitive neuroscience of religion” phrase is used early on in the piece:

“This is a new science that’s emerging,” says Patrick McNamara, an assistant professor of neurology at Boston University School of Medicine. “You might call it the cognitive neuroscience of religion. This is definitely a new discipline, and it’s poised to make some major new discoveries.”

The article itself alternates haphazardly between covering genuinely interesting developments in the neuroscience of belief and spirituality (e.g., the recent focus on Buddhist meditation) and focusing on the beliefs of neuroscientists who happen to harbor religious or mystic worldviews themselves. The author, Richard Monastersky, doesn’t acknowledge anywhere in the piece that there’s a big difference between trying to understand the neural bases of religious belief and trying to justify one’s beliefs via neuroscience. Plenty of people are interested in the former; relatively few are interested in the latter. Yet Monastersky allocates the majority of time to the latter group. Some choice quotes:

Mr. Price also questions the reigning materialist concept of the mind, asking, “Why say that consciousness exists only inside a body?” Regarding subjective experience, he wonders, “Are we talking about some organ inside our skull, or are we talking about our connection with something outside ourselves? That connection outside ourselves can include a spiritual connection.”

…

Other scientists are asking similarly heretical questions. Jeffrey M. Schwartz, a research professor of psychiatry at the University of California at Los Angeles, has been treating people with obsessive-compulsive disorders to counter their urges through focused attention of the mind. Scans of his patients’ brains reveal that such mental therapy can alter the behavior of their brains, something that could not happen if the mind emerged entirely from the brain, he says.

“It is a tragedy of history that materialism became the regnant paradigm,” says Dr. Schwartz, who rails against the contemporary norms that divide science and religion (see related story, Page A18). There are a growing number of scientists, he says, who “believe that this separation of science from religion is a cultural artifice.”

In Monastersky’s defense, he does give some airtime to opposing (and sensible) views from some of the field’s luminaries:

Stephen F. Heinemann, president of the Society for Neuroscience and a professor in the molecular-neurobiology lab at the Salk Institute for Biological Studies, in La Jolla, Calif., echoed many scientists’ reactions when he said in an e-mail message, “I think the concept of the mind outside the brain is absurd.”

…

Michael S. Gazzaniga, a professor of psychology at the University of California at Santa Barbara and a leading neuroscientist who serves on President Bush’s Council on Bioethics, says that essentially all brain biologists accept the materialist view of the mind. “I would say that 98 or 99 percent of people in the business think that,” he says.

You’d think 98 or 99 percent would come across as a pretty big number, yet somehow the article still left me with the same uncomfortable sense of ambiguity found in much of the pro-ID literature (though I’m certainly not suggesting Monastersky, a widely-acclaimed science writer, is pro-ID): the sense that somehow, somewhere, there’s a genuine controversy, and researchers need to give equal time to both sides. Which of course they don’t, as the Gazzaniga quote makes abundantly clear.

One would expect that the brain sciences would be among the last places deeply religious people would venture. To my mind it seems it would be very difficult for most people to continue to hold non-materialist views of the mind after a year or two of staring at images of localized activation increases or dealing with patients with focal lesions and equally selective behavioral deficits. When I look around at my colleagues, I don’t see the “growing numbers of religious and nonreligious researchers who support” the view of a non-material mind. What I do see is a growing number of researchers who want to understand why it is that so many people around them maintain religious beliefs, and for the first time feel they have the tools to do it at the level of the brain.

On the other side of the ball, I think there are also a number of reasonable criticisms of fMRI that haven’t been highlighted yet. Some of these highlight important problems with current practices (e.g., limitations on power, overly liberal statistical analyses, or rampant overinterpretation) or interpretative (drawing rather speculative conclusions from thin results). These are the types of criticism non-cognitive neuroscientists tend to focus on, and they’re certainly worth paying attention to. Still, I think the largest and most important class of criticisms of fMRI are essentially constructive ones that take the form of “well, why not do it this way?” There’s just so much you can do with fMRI that relatively few people are doing because of a lack of awareness or an absence of consensus procedures and corresponding tools.

The more quantitatively sophisticated groups (e.g., the folks at UCL, who’ve pioneered most of the statistical approaches currently in use) regularly publish all sorts of papers detailing clever approaches to data that everyone concludes sounds really neat in principle but that most people (myself included) don’t really understand. Collectively, neuroimaging researchers are only just starting to talk about things like functional connectivity and parametric analyses; there are all sorts of places people want to go, but the methods haven’t caught up to the ideas yet.

Part of the problem is that functional neuroimaging datasets are much more flexible than behavioral datasets. A single subject’s brain might contain 100,000 voxels (each an interesting measurement in its own right) per image, with several hundred images per scan. And these observations aren’t independent of one another; what one part of your brain is doing at a given point in time depends not just on the experimental manipulation, but on interactions with other regions (David Dobbs provided an excellent overview of he problem in his Scientific American article). Given that level of complexity, it’s not surprising that people have generally stuck with familiar methods imported from other areas of behavioral research. T-tests on subtractive contrasts aren’t necessarily the most natural way to explore brain activity, but they’re what’s closest to standard analyses of experimental treatments in other fields. As time goes on, we’ll probably see a shift toward more sophisticated multivariate modeling. Hopefully, such studies will be less susceptible to many of the common criticisms.

Remember the Sokal affair? Back in 1996, physicist Alan Sokal submitted a paper full of clever-sounding gibberish to Social Text, a leading postmodern journal. Shortly after the paper was accepted, Sokal revealed it was a hoax. He pointed out that even a cursory familiarity with physics should have tipped the editors off, since the paper was riddled with ludicrous (and humorous) statements that any qualified reviewer would have been able to pick out. The fact that no physicists were asked to review the paper (on the relationship between quantum gravity and hermeneutics!) was taken to show just how low standards have fallen in some humanities fields.

This week’s Nature has what at first glance appears to be a counter-point: a news article entitled “Sociologist fools physics judges.” Could it be? Have social scientists finally cracked the physical code? Should we celebrate the champions of a new quantum sociology?

Well, not quite. The sociologist in question, Harry Collins, hasn’t actually managed to get a physics article through peer review. Nor has he tried to. What he has done is provide sensible answers (in English, not math) to seven questions related to gravity waves. At least, sensible enough to render a panel of 9 physicists unable to distinguish his answers from those of a real physicists. Based on this result (and a paper to be published later this year in Studies in History and Philosophy of Science–preprint available here), Collins proposes the concept of ‘interactional expertise’: the idea that non-experts can acquire sufficient knowledge of a technical field to converse intelligently.

Personally, I think this idea is something of a stretch, at least with respect to quantitative disciplines like physics. Being able to understand the meaning of physical equations in general terms (and Collins certainly has the background: he’s been studying physicists for 30 years!) doesn’t mean you can pass for a physicist, unless you think being a physicist consists in writing prose answers to canned questions on a relatively circumscribed topic. The Nature article has a nice quote from Sokal in this regard:

Sokal says he is struck by Collins’s skills in physics, but notes that such understanding would not be enough for more ambitious sociology research that attempts to probe how cultural and scientific factors shape science. “If that’s your goal you need a knowledge of the field that is virtually, if not fully, at the level of researchers in the field,” says Sokal. “Unless you understand the science you can’t get into the theories.”

Now it’s probably a testament to Collins’ versatile intellect that he can answer questions on gravity waves at all; but as a demonstration of ‘interactional expertise’ it’s on roughly the same level as early chatbot attempts to pass the Turing Test. The fact that a very simple program like Eliza can fool some people into thinking they’re communicating with a real individual is amusing, but it hasn’t helped us create anything like an AI that can pass for a human under normal circumstances. Similarly, physicists don’t normally display their knowledge in a forum as constrained as Collins’ series of questions. They can use their acquired knowledge and formal skills to generate novel theorems and predictions in a way that a sociologist of physics presumably can’t. So unless one weakens the notion of interactional expertise to the point where it’s almost vacuous, it’s not clear how much interaction can really take place. You simply can’t do physics without being able to do math, and hanging out with physicists for 30 years probably isn’t a good substitute for calculus textbooks–though it may make for interesting dinner conversations.

Here’s how this question came up. In response to my last post, in which I argued that the BOLD signal has been shown to correlate very highly with neural activity, Jonah Lehrer wrote:

But this is what I want fMRI researchers to grapple with. Instead of searching for the neural correlates of romantic love, why not grapple with the fascinating anomalies the technology actually illuminates. In my earlier post, I said that I wasn’t entirely convinced that fMRI has earned its reductionist conclusions. This is why. I’m fascinated and bewildered by the Logothetis data. In the five years since his paper was published, there have been thousands upon thousands of fMRI papers documenting all sorts of really interesting things. But we still don’t understand a significant part of the “nuanced” relationship between neural activity and the flow of oxygenated blood. The devil is always in the details.

I don’t want to rehash the technical debate about BOLD in this post. Instead, what I want to highlight is that last sentence about the devil (who could resist the devil?). What I want to know is: is it true? Is the devil always in the details?

Let’s suppose so. And let’s work through the implications of a failure of the “BOLD reflects neural activity” assumption. There are basically two ways an assumption like this could hurt us. They correspond roughly to the issues of measurement reliability and construct validity. Measurement reliability has to do with whether a given instrument consistently measures the same thing under similar conditions. This is pretty similar to the common-sense notion of reliability: if you have a reliable friend, you can trust that they’ll behave in a consistent manner when you need something from them; conversely, an unreliable car might crap out on you the next time you drive it, even if you don’t do anything radically different. An often overlooked but important point about reliability is that it isn’t a single value–technically, there’s no such thing as a reliable measure, only a measure that’s reliable when tested on a particular sample in a particular context. When you go to Walgreens to buy a thermometer, you’re banking on the fact that it’ll measure body temperature ‘reliably’. And it will; but that’s only because the temperatures you subject it to fall within the range it’s attuned to. Stick a $10 thermometer in the oven and it’s no longer reliable.

In theory, you could have the same problem with fMRI. Like any other signal, the BOLD signal only indexes neural activity reliably under a particular range of circumstances. Most fMRI researchers assume that the kind of research they do is always going to fall within that range. But that assumption could fail; it could be that there are nuances we don’t appreciate, and if we don’t study those nuances, we’ll end up applying fMRI in situations where it’s worthless. So that’s one potential reason for worrying about the devil in the details.

What’s the other reason? Well, ensuring that a measure is reliable doesn’t necessarily mean it’s valid. Validity, and specifically construct validity, is the degree to which your measure gets at what you think it does. Construct validity is a huge bugbear in research, because there’s no way to quantify it. You can calculate a reliability coefficient pretty easily in most cases, but determining whether a measure accurately reflects a construct of interest is a qualitative matter. Suppose you have a 30-item self-report questionnaire you think assesses people’s generosity. You publish an article showing that people’s score on the measure predicts how much money they report giving to charity last year. What’s the problem? Well, your measure could have very high reliability and still be a bad measure of generosity. Why? For one thing, it’s a self-report measure. Who says people are the best judges as to their own generosity? Maybe what you’re really measuring is some aspect of social desirability. Most people want to see themselves in a positive light; it’s not inconceivable people who say they’re more generous also overreport how much money they donate (this kind of thing is a huge problem in personality psychology research, which is why researchers often include ‘lie’ scales in their experiments, or go to great lengths to get more objective corroborating data).

Along the same lines, an fMRI study reporting on the neural correlates of romantic love isn’t really reporting on the neural correlates of love unless you think showing someone photographs of loved ones is the same thing as inducing a strong feeling of romantic love. The behavioral responses induced by such photographs could be perfectly reliable, but we don’t know if that’s because they’re accurately tapping people’s deep romantic feelings or just accessing aspects of familiarity, social norms, etc. The same thing could be true with respect to the BOLD signal: maybe our failure to understand all of the subtleties of the relationship between blood flow and neural activity prevents us from understanding what fMRI is really measuring.

So what does this have to do with the original question I posed? Well, admittedly, not much (I thought a tangential discussion of reliability and validity was worthwhile), except that time is finite and all risks are not equal. In order to get any research done at all, researchers have to make assumptions about all kinds of things. We assume that our subjects will show up at 11 am like they promised (and we mumble vague threats into the air when they don’t materialize); we assume that our measures are reliable and do what we think they do; we assume that we’re applying certain statistical procedures correctly even though we don’t really understand what all those equations mean; and so on. Most of the time, we’re not even aware we’re making these assumptions. They only become apparent when we make a mistake and have to go back and fix or re-learn something.

Should we make people question all of their assumptions before getting down to actual research? Maybe. But who’s going to make the list of Assumptions that Need to be Questioned? And what should we put on it? Should we emphasize methodological issues, statistical issues, or conceptual issues? Do we want scientists who ably conduct sophisticated laboratory experiments costing tens of thousands of dollars and then inadvertently throw half their study’s power out the window because they never learned that Median Splits Are Bad and cheerfully dichotomize continuous variables? (This happens all the time.) Or do we want scientists who deftly manipulate matrices, can write three volumes on the merits of canonical correlation, but have no interest in collecting empirical data?

The correct answer is ‘all of the above’. We want scientists with every possible combination of strengths and weaknesses. There are people who are interested in developing new research methods but don’t have the faintest interest in using them to study anything substantive, and there are people who are brilliant at asking probing questions and designing clever experiments but don’t know how to test their hypotheses quantitatively. These aren’t interchangeable populations: the kind of person who really really wants to understand how music perception works isn’t necessarily the kind of person who really really wants to develop a reliable and valid measure of neural activity.

Let’s come back to the pitfalls of fMRI. Suppose you’re the former kind of person, and one day someone tells you about a nifty new technology called fMRI that you can use to study music perception. You get really excited, take a couple of classes, and manage to con a lab head into funding your experiment by whispering sweet nothings in their ear about multiple publications in Science and Nature. On your way to the scanner on the first day, a famous biophysicist pulls you aside and says: “you probably shouldn’t do that experiment yet; we know there’s a strong correlation between neural activity and the signal that machine measures, but we’re not absolutely positively 100% sure it’s always going to measure how much involvement a given brain region has in a cognitive process. Maybe you should hold off.”

What are you going to do? My guess is you don’t care. You go in and do the experiment anyway. My guess is if you ask most cognitive neuroscientists working in the field today, they do the experiment anyway, regardless of how much they know about the BOLD signal. That’s not blind faith in the machine or an irrational devotion to pretty pictures; it simply reflects the fact that (a) you have to take risks to do science (which are pretty minimal in this case), and (b) studying the neural basis of the BOLD signal is (for most cognitive neuroscientists) incredibly boring. And it’s not like this relative disregard is unique to imaging. Consider any of the following potential criticisms:

1. You’re a cognitive psychologist? Don’t you think you should do some work on the brain before you develop any complex abstract models? After all, we don’t yet fully understand the brain, and you wouldn’t want to develop theories that aren’t compatible with the way the brain works…
2. You’re a philosopher? Don’t you think you should do a bunch of psychology research before you start formulating weird metaphysical constructs? How do you think you’re going to relate your referential semantics program to information processing terminology? You are a materialist, right?
3. You’re a systems neuroscientist? Listen, your theories about rate and place codes are very nice, but they’re really simple. You should probably do a Ph.D. in cellular neuroscience so you can understand how synchrony in neural assemblies develops before you go chasing after complex interactions between them.

All of these are pretty good criticisms, I think. But they’re beside the point. We don’t do experiments thinking we’ve got all the assumptions covered; we do them in spite of the fact that we know we’ll be wrong a good deal of the time. Because that’s the only way science can work. If we had to worry about every little assumption before making a start, we’d be paralyzed by doubt and never get off the ground. Now, I’m not saying scientists should be reckless. There’s no question it makes sense to worry about issues that could present major obstacles to research. But for most cognitive neuroscientists, the fidelity of the BOLD response to the underlying neural activity just isn’t one of them. So, is the devil in the details? Well, sure. But that’s right where we want him: he only hides in the details after we’ve evicted him from all of the more important places.