The parable of the three districts: A projective test for psychologists

A political candidate running for regional public office asked a famous political psychologist what kind of television ads she should air in three heavily contested districts: positive ones emphasizing her own record, or negative ones attacking her opponent’s record.

“You’re in luck,“ said the psychologist. “I have a new theory of persuasion that addresses exactly this question. I just published a paper containing four large studies that all strongly support the theory and show that participants are on average more persuaded by attack ads than by positive ones.“

Convinced by the psychologist’s arguments and his confident demeanor, the candidate’s campaign ran carefully tailored attack ads in all three districts. She proceeded to lose the race by a landslide, with exit surveys placing much of the blame on the negative tone of her ads.

As part of the campaign post-mortem, the candidate asked the psychologist what he thought had gone wrong.

“Oh, different things,“ said the psychologist. “In hindsight, the first district was probably too educated; I could see how attack ads might turn off highly educated voters. In the second district““and I’m not going to tiptoe around the issue here—I think the problem was sexism. You have a lot of low-SES working-class men in that district who probably didn’t respond well to a female candidate publicly criticizing a male opponent. And in the third district, I think the ads you aired were just too over the top. You want to highlight your opponent’s flaws subtly, not make him sound like a cartoon villain.“

“That all sounds reasonable enough,“ said the candidate. “But I’m a bit perplexed that you didn’t mention any of these subtleties ahead of time, when they might have been more helpful.“

“Well,“ said the psychologist. “That would have been very hard to do. The theory is true in general, you see. But every situation is different.“

brain-based prediction of ADHD–now with 100% fewer brains!

UPDATE 10/13: a number of commenters left interesting comments below addressing some of the issues raised in this post. I expand on some of them here.

The ADHD-200 Global Competition, announced earlier this year, was designed to encourage researchers to develop better tools for diagnosing mental health disorders on the basis of neuroimaging data:

The competition invited participants to develop diagnostic classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging (MRI) of the brain. Applying their tools, participants provided diagnostic labels for previously unlabeled datasets. The competition assessed diagnostic accuracy of each submission and invited research papers describing novel, neuroscientific ideas related to ADHD diagnosis. Twenty-one international teams, from a mix of disciplines, including statistics, mathematics, and computer science, submitted diagnostic labels, with some trying their hand at imaging analysis and psychiatric diagnosis for the first time.

Data for the contest came from several research labs around the world, who donated brain scans from participants with ADHD (both inattentive and hyperactive subtypes) as well as healthy controls. The data were made openly available through the International Neuroimaging Data-sharing Initiative, and nicely illustrate the growing movement towards openly sharing large neuroimaging datasets and promoting their use in applied settings. It is, in virtually every respect, a commendable project.

Well, the results of the contest are now in–and they’re quite interesting. The winning team, from Johns Hopkins, came up with a method that performed substantially above chance and showed particularly high specificity (i.e., it made few false diagnoses, though it missed a lot of true ADHD cases). And all but one team performed above chance, demonstrating that the imaging data has at least some (though currently not a huge amount) of utility in diagnosing ADHD and ADHD subtype. There are some other interesting results on the page worth checking out.

But here’s hands-down the most entertaining part of the results, culled from the “Interesting Observations” section:

The team from the University of Alberta did not use imaging data for their prediction model. This was not consistent with intent of the competition. Instead they used only age, sex, handedness, and IQ. However, in doing so they obtained the most points, outscoring the team from Johns Hopkins University by 5 points, as well as obtaining the highest prediction accuracy (62.52%).

…or to put it differently, if you want to predict ADHD status using the ADHD-200 data, your best bet is to not really use the ADHD-200 data! At least, not the brain part of it.

I say this with tongue embedded firmly in cheek, of course; the fact that the Alberta team didn’t use the imaging data doesn’t mean imaging data won’t ultimately be useful for diagnosing mental health disorders. It remains quite plausible that ten or twenty years from now, structural or functional MRI scans (or some successor technology) will be the primary modality used to make such diagnoses. And the way we get from here to there is precisely by releasing these kinds of datasets and promoting this type of competition. So on the whole, I think this should actually be seen as a success story for the field of human neuroimaging–especially since virtually all of the teams performed above chance using the imaging data.

That said, there’s no question this result also serves as an important and timely reminder that we’re still in the very early days of brain-based prediction. Right now anyone who claims they can predict complex real-world behaviors better using brain imaging data than using (much cheaper) behavioral data has a lot of ‘splainin to do. And there’s a good chance that they’re trying to sell you something (like, cough, neuromarketing ‘technology’).

fMRI: coming soon to a courtroom near you?

Science magazine has a series of three (1, 2, 3) articles by Greg Miller over the past few days covering an interesting trial in Tennessee. The case itself seems like garden variety fraud, but the novel twist is that the defense is trying to introduce fMRI scans into the courtroom in order to establish the defendant’s innocent. As far as I can tell from Miller’s articles, the only scientists defending the use of fMRI as a lie detector are those employed by Cephos (the company that provides the scanning service); the other expert witnesses (including Marc Raichle!) seem pretty adamant that admitting fMRI scans as evidence would be a colossal mistake. Personally, I think there are several good reasons why it’d be a terrible, terrible, idea to let fMRI scans into the courtroom. In one way or another, they all boil down to the fact that just  isn’t any shred of evidence to support the use of fMRI as a lie detector in real-world (i.e, non-contrived) situations. Greg Miller has a quote from Martha Farah (who’s a spectator at the trial) that sums it up eloquently:

Farah sounds like she would have liked to chime in at this point about some things that weren’t getting enough attention. “No one asked me, but the thing we have not a drop of data on is [the situation] where people have their liberty at stake and have been living with a lie for a long time,” she says. She notes that the only published studies on fMRI lie detection involve people telling trivial lies with no threat of consequences. No peer-reviewed studies exist on real world situations like the case before the Tennessee court. Moreover, subjects in the published studies typically had their brains scanned within a few days of lying about a fake crime, whereas Semrau’s alleged crimes began nearly 10 years before he was scanned.

I’d go even further than this, and point out that even if there were studies that looked at ecologically valid lying, it’s unlikely that we’d be able to make any reasonable determination as to whether or not a particular individual was lying about a particular event. For one thing, most studies deal with group averages and not single-subject prediction; you might think that a highly statistically significant difference between two conditions (e.g., lying and not lying) necessarily implies a reasonable ability to make predictions at the single-subject level, but you’d be surprised. Prediction intervals for individual observations are typically extremely wide even when there’s a clear pattern at the group level. It’s just easier to make general statements about differences between conditions or groups than it is about what state a particular person is likely to be in given a certain set of conditions.

There is, admittedly, an emerging body of literature that uses pattern classification to make predictions about mental states at the level of individual subjects, and accuracy in these types of application can sometimes be quite high. But these studies invariably operate on relatively restrictive sets of stimuli within well-characterized domains (e.g., predicting which word out of a set of 60 subjects are looking at). This really isn’t “mind reading” in the sense that most people (including most judges and jurors) tend to think of it. And of course, even if you could make individual-level predictions reasonably accurately, it’s not clear that that’s good enough for the courtroom. As a scientist, I might be thrilled if I could predict which of 10 words you’re looking at with 80% accuracy (which, to be clear, is currently a pipe dream in the context of studies of ecologically valid lying). But as a lawyer, I’d probably be very skeptical of another lawyer who claimed my predictions vindicated their client. The fact that increased anterior cingulate activation tends to accompany lying on average isn’t a good reason to convict someone unless you can be reasonably certain that increased ACC activation accompanies lying for that person in that context when presented with that bit of information. At the moment, that’s a pretty hard sell.

As an aside, the thing I find perhaps most curious about the whole movement to use fMRI scanners as lie detectors is that there are very few studies that directly pit fMRI against more conventional lie detection techniques–namely, the polygraph. You can say what you like about the polygraph–and many people don’t think polygraph evidence should be admissible in court either–but at least it’s been around for a long time, and people know more or less what to expect from it. It’s easy to forget that it only makes sense to introduce fMRI scans (which are decidedly costly) as evidence if they do substantially better than polygraphs. Otherwise you’re just wasting a lot of money for a fancy brain image, and you could have gotten just as much information by simply measuring someone’s arousal level as you yell at them about that bloodstained Cadillac that was found parked in their driveway on the night of January 7th. But then, maybe that’s the whole point of trying to introduce fMRI to the courtroom; maybe lawyers know that the polygraph has a tainted reputation, and are hoping that fancy new brain scanning techniques that come with pretty pictures don’t carry the same baggage. I hope that’s not true, but I’ve learned to be cynical about these things.

At any rate, the Science articles are well worth a read, and since the judge hasn’t yet decided whether or not to allow fMRI or not, the next couple of weeks should be interesting…

[hat-tip: Thomas Nadelhoffer]