Obesity, health, and Gary Taubes

I recently posted a link on Facebook to Gary Taubes' article about why the campaign to stop America's obesity crisis keeps failing, and my friend Scott raised the following issue:

Taubes may or may not be on to something. But, he comes off like an Intelligent Design guy, "The experts are wrong, and they can't handle the Truth that I'm bringing."

I agree that Taubes' writings sometimes have the feel of a crazy outsider fighting against the establishment.  However, both my personal experience (as well as those of a number of friends) and my (non-expert) reading of the literature both suggest that Taubes is largely right on in terms of his critique of the standard dogma regarding weight loss, food, and health.

First, the testimonial.  As I noted in a previous post, it was Taubes' writings that were largely responsible for pushing me towards the low-carb way of eating that I have followed for more than a year now.  After cutting carbs way down (except for my daily dose of dark chocolate, which is non-negotiable), I lost 20 pounds of fat and have kept it off, without any sense that I am being deprived; I basically eat whatever I want whenever I want, as long as it's real food and paleo-friendly (i.e., avoiding refined sugar, grains, and seed oils).  Most important, I feel great eating this way; in particular, whereas I used to get serious hunger pangs and energy dips 3-4 hours after eating, I can now easily fast for 24 hours without feeling particularly hungry.  My wife Jen has also had an interesting experience on this diet.  She has been able to maintain her weight or lose weight while never feeling hungry, whereas on our old carb-heavy vegetarian diet she was only able to lose weight through radical caloric restriction that left her constantly famished.  Similarly, a number of friends and family members have found that they were able to lose a substantial amount of weight after reducing carbs, while still feeling like they were able to eat to satiety.  I have *never* heard anyone say that they went on a low-carb diet and gained weight; more often, I have heard from people that they went on a low-carb diet and lost weight but then were afraid that all of the saturated fat was going to cause them to have a heart attack any day.

This brings us to one of the central points of Taubes' writings, which is that the standard story about what comprises a healthy diet, namely the link between heart disease, cholesterol, and saturated fat, is just plain wrong.  If you want a good overview of his general narrative without reading the books, I would suggest three NY Times pieces: one on the relation between dietary fat and disease from 2002, a blistering critique of epidemiological research from 2007, and his piece "Is Sugar Toxic" from 2011.

There are a lot of claims in the Taubes books, and I have not looked into all of them.  However, to the degree that I have looked into the claims that I found most important and relevant to my own diet, I have found them to all have fairly compelling scientific bases. The most important regards the relation between heart disease, cholesterol, and saturated fat.  It is amazing how the supposed unhealthiness of dietary cholesterol and saturated fat has become a "fact" that is repeated almost reflexively (e.g., most recently I encountered it in Tyler Cowen's "An Economist Gets Lunch").  I think that in part it is due to the visual similarity of saturated fat in meat and the plaques that are seen in atherosclerosis; it's just too easy to believe that the saturated fat that we eat is "clogging our arteries."  The data appear to say otherwise.  First, it has been known since 1950 that serum cholesterol bears little relation to dietary cholesterol; the mechanisms behind this are laid out nicely in Peter Attia's recent series on cholesterol.  Second, a large recent meta-analysis (including data from more than 347,000 individuals across 21 published studies) found no relation between saturated fat intake and heart disease or stroke.  Similarly, a recent Cochrane Collaborative meta-analysis of intervention studies showed that there was no significant reduction of total or cardiovascular mortality due to changes in dietary fat.  Although I think Taubes is correct in his arguments that epidemiological studies are hugely problematic (which I will discuss some other time), I trust these large meta-analyses much more than I trust any individual study (e.g., the China Study), especially when they show no effect (given all of the biases towards publishing positive effects).  I have also put my money where my mouth is: I now eat a high-fat diet including full-fat yogurt, eggs, and bacon almost every morning. It look me several months to stop craving sugar, but I'm now perfectly happy to a dinner without dessert, and in fact I no longer have a taste for foods that are extremely sugary.

Another of Taubes' main assertions is that obesity is caused primarily by carbohydrates (fleshed out in his book Why We Get Fat).  My feeling here is that obesity is an incredibly complex problem that involves both the body and the brain, and any story that tries to simplify it to a single component of our environment is bound to be wrong.  That said, it's clear to me from the person experience described above that the "calories in, calories out" story is wrong, and that it does matter a lot what one eats, not just how much.  There has recently been a big argument in the blogosphere recently between Stephan Guyenet and Taubes over the relative importance of peripheral factors (e.g., insulin's effects on fat storage) versus neural factors (e.g., the role of satiety hormones and reward pathways); both of them have staked out strong positions, and I think that the truth is likely to fall somewhere in the middle (as usual).  As a neuroscientist I clearly think that the brain plays an important role; I won't talk more about that here, maybe some time soon.  I have not dug very deeply into the science of feeding trials in animals, in part because my reading of several summaries of this work suggests that the details can be very tricky but very important. In particular, without a great deal of control over exactly what kinds of nutrients are in the food, it can be very difficult to make any conclusions from the data. There are however some individual studies in humans that do provide support for Taubes' claims.  For example, the A to Z Weight Loss Study showed that subjects assigned to the Atkins diet lost more weight and had better metabolic outcomes than people assigned to low-fat/high-carb diets like the Ornish Diet.   It's just one study, and it would be good to see more, but this along with my personal experience is enough to convince me.

Later in the discussion that I mentioned above, Scott made the following additional observation:

But the question remains, why doesn't the science on the cellular level ever reach the public health scientific community? I'm usually very skeptical when some sort of conspiracy is trotted out to explain the lack of uptake.

There are a lot of answers to this, which Taubes goes into great detail to discuss in his books.  But the general problem is that science itself can be a slow-moving ship; when it gets drawn well off course (as it appears to have been by the anti-fat arguments of Keys and others), it can take a long time to get back, and even longer for that new scientific knowledge to get translated into medical education and practice.  What is most striking to me is how studies whose data seem clearly inconsistent with the standard view are often presented in a way that suggests that they support the view, often by picking and choosing specific conditions.  Taubes gives numerous examples of this, as does Denise Minger's detailed analysis of the China Study.  I've also noticed it on a number of occasions when reading papers in this literature.  Thus, unless one is reading the papers closely (or following others who do this), it can be easy to continue to think that the standard model remains valid; you just can't take the abstract (or even the results section) at face value.  However, the fact that the rise in obesity has occurred alongside declining fat intake (coupled with increasing carb intake) over the last 40 years makes it pretty clear to me that the standard theory is just plain wrong, and that the carb theory is a viable alternative that needs to be studied more intently.  Unfortunately, many of the thought leaders in this area continue to expound solutions based on the "calories-in/calories-out" and low-fat ideas that got us here in the first place.

Things I like to do in Beijing

Several friends have asked for suggestions about things to do while in Beijing for the OHBM meeting this June.  I don't have any particular wisdom, but having visited several times I thought that I would share a few of my favorite things (along with photos from some of our past trips).  I'm not including many of the obvious attractions (Forbidden City, Summer Palace, Olympic Park) because I figure those will be in every guide book.

798 Art Complex: an amazing art complex created from an old factory complex.  If you like modern art you can easily spend more than 1/2 a day there. There is a great noodle shop tucked away in the middle of the complex, and also at least one really nice cafe.

 

Hutongs: These are the old neighborhoods in the center of the city.  Some of them are very touristy, but if you walk a few blocks off the main street you can find some streets that feel pretty far from touristy. I would particularly recommend the area around Heizhima Hutong, where these photos were taken.

 
 

Drum Tower: This was built in 1272 and served as the official timepiece of the Chinese government until 1924.  If you show up at the right time, you can see an awesome drumming show. 

Great Wall:  We visited the Great Wall at Badaling in 2005, which is apparently the most touristy place to go but also relatively close to Beijing.  Go early in the day, to avoid both crowds and heat.  Perhaps the best part of visiting at Badaling is that there is a roller coaster that can take you down from the top.  

 

Eating

Roasted duck hearts at Quanjude

We have had a lot of wonderful meals in Beijing, both as fish-etarians on our first two visits and as omnivores on our most recent visit. Prepare to eat well, but also be prepared to have your sensibilities challenged.  A few highlights are:

Roast duck:  As recently reformed vegetarians, we spent our first two visits to China without trying "Peking Duck" (or, as they call it in Beijing, "roast duck").  On our last visit we had it at Quanjude and it was pretty awesome.  We had the full on roast duck experience, including "duck breast" (which is basically just fat and skin) and roasted duck hearts.  A must-have. (NB: If you order the duck hearts, they come with a bowl of flaming liquid.  Apparently you are not supposed to actually dip the heart into the liquid, as I did.)

Spicy snails at Spicy Grandma restaurant

Sichuan food: Our friends in China are largely from Sichuan province, and thus we often end up eating at Sichuan restaurants.  The Sichuan peppercorn has an amazing numbing quality.  Also be sure to try the Sichuan hot pot, which is like a very spicy version of shabu shabu.  I would suggest bringing a significant ration of Pepto Bismol, as the western gut starts to ache after a few days of this kind of spicy food.  But it is so worth the burn. 

Yunnan food:  One of the  most amazing meals we had was at the Rainbow Restaurant in the Beijing Sun Palace Hotel. The greeters are dressed in traditional Yunnan dress, and the food is absolutely amazing with a heavy focus on mushrooms.  

A dish that contained "smelly tofu" - actually really tasty

Grilled matsutake musrooms at Rainbow

Skeletons in the closet

 As someone who has thrown lots of stones in recent years, it's easy to forget that anyone who publishes enough will end up with some skeletons in their closet.  I was reminded of that fact today, when Dorothy Bishop posted a detailed analysis of a paper that was published in 2003 on which I am a coauthor.

This paper studied a set of children diagnosed with dyslexia who were scanned before and after treatment with the Fast ForWord training program.  The results showed improved language and reading function, which were associated with changes in brain activation. 

Dorothy notes four major problems with the study:

• There was no dyslexic control group; thus, we don't know whether any improvements over time were specific to the treatment, or would have occurred with a control treatment or even without any treatment.
• The brain imaging data were thresholded using an uncorrected threshold.
• One of the main conclusions (the "normalization" of activation following training") is not supported by the necessary interaction statistic, but rather by a visual comparison of maps.
• The correlation between changes in language scores and activation was reported for only one of the many measures, and it appeared to have been driven by outliers.

Looking back at the paper, I see that Dorothy is absolutely right on each of these points.  In defense of my coauthors, I would note that points 2-4 were basically standard practice in fMRI analysis 10 years ago (and still crop up fairly often today).  Ironically,  I raised two of of these issues in my recent paper for the special issue of Neuroimage celebrating the 20th anniversary of fMRI, in talking about the need for increased methodological rigor:

Foremost, I hope that in the next 20 years the field of cognitive neuroscience will increase the rigor with which it applies neuroimaging methods. The recent debates about circularity and “voodoo correlations” ( [Kriegeskorte et al., 2009] and [Vul et al., 2009]) have highlighted the need for increased care regarding analytic methods. Consideration of similar debates in genetics and clinical trials led (Ioannidis, 2005) to outline a number of factors that may contribute to increased levels of spurious results in any scientific field, and the degree to which many of these apply to fMRI research is rather sobering:

•small sample sizes

•small effect sizes

•large number of tested effects

•flexibilty in designs, definitions, outcomes, and analysis methods

•being a “hot” scientific field

Some simple methodological improvements could make a big difference. First, the field needs to agree that inference based on uncorrected statistical results is not acceptable (cf. Bennett et al., 2009). Many researchers have digested this important fact, but it is still common to see results presented at thresholds such as uncorrected p < .005. Because such uncorrected thresholds do not adapt to the data (e.g., the number of voxels tests or their spatial smoothness), they are certain to be invalid in almost every situation (potentially being either overly liberal or overly conservative). As an example, I took the fMRI data from Tom et al. (2007), and created a random “individual difference” variable. Thus, there should be no correlations observed other than Type I errors. However, thresholding at uncorrected p < .001 and a minimum cluster size of 25 voxels (a common heuristic threshold) showed a significant region near the amygdala; Fig. 1 shows this region along with a plot of the “beautiful” (but artifactual) correlation between activation and the random behavioral variable. This activation was not present when using a corrected statistic. A similar point was made in a more humorous way by Bennett et al. (2010), who scanned a dead salmon being presented with a social cognition task and found activation when using an uncorrected threshold. There are now a number of well-established methods for multiple comparisons correction (Poldrack et al., 2011), such that there is absolutely no excuse to present results at uncorrected thresholds. The most common reason for failing to use rigorous corrections for multiple tests is that with smaller samples these methods are highly conservative, and thus result in a high rate of false negatives. This is certainly a problem, but I don't think that the answer is to present uncorrected results; rather, the answer is to ensure that one's sample is large enough to provide sufficient statistical power to find the effects of interest.

Second, I have become increasingly concerned about the use of “small volume corrections” to address the multiple testing problem. The use of a priori masks to constrain statistical testing is perfectly legitimate, but one often gets the feeling that the masks used for small volume correction were chosen after seeing the initial results (perhaps after a whole-brain corrected analysis was not significant). In such a case, any inferences based on these corrections are circular and the statistics are useless. Researchers who plan to use small volume corrections in their analysis should formulate a specific analysis plan prior to any analyses, and only use small volume corrections that were explicitly planned a priori. This sounds like a remedial lesson in basic statistics, but unfortunately it seems to be regularly forgotten by researchers in the field.

Third, the field needs to move toward the use of more robust methods for statistical inference (e.g., Huber, 2004). In particular, analyses of correlations between activation and behavior across subjects are highly susceptible to the influence of outlier subjects, especially with small sample sizes. Robust statistical methods can ensure that the results are not overly influenced by these outliers, either by reducing the effect of outlier datapoints (e.g., robust regression using iteratively reweighted least squares) or by separately modeling data points that fall too far outside of the rest of the sample (e.g., mixture modeling). Robust tools for fMRI group analysis are increasingly available, both as part of standard software packages (such as the “outlier detection” technique implemented in FSL: Woolrich, 2008) and as add-on toolboxes (Wager et al., 2005). Given the frequency with which outliers are observed in group fMRI data, these methods should become standard in the field. However, it's also important to remember that they are not a panacea, and that it remains important to apply sufficient quality control to statistical results, in order to understand the degree to which one's results reflect generalizeable patterns versus statistical figments.

It should be clear from these comments that my faith in the results of any study that uses such problematic methods (as the Temple et al. study did) is relatively weak.  I personally have learned my lesson and our lab now does its best to adhere to these more rigorous standards, even when they mean that a study sometimes ends up being unpublishable.   I can only hope that others will join me.

Quitting cable

I was inspired by Nathan's post at Flowing Data to say a bit about how our experiment with giving up cable TV is going. Back in September, we turned off our U-Verse subscription, sent back the DVR, and started getting our TV solely from the computer (we use a Mac Mini as our media center PC).  Here is our experience so far:

We watch a lot less TV.  Our TV routine has now morphed from watching 2-3 hours per night into watching a single show every night (recent favorites are The Layover and Top Chef, with Colbert Report as our fallback).  In its place we are reading a lot more; in fact, much of the money that we are saving on cable is probably flowing to the Kindle store at Amazon.  However, we have also recently started using the Austin Public Library's ebook lending service which is a great way to save on ebooks.  I've also been playing the guitar more often.

Sometimes you really want live TV.  The one problem with getting everything from the web is that it's often hard to find a good live stream; we had this problem on new year's eve.  To solve this, I recently installed a solution to allow us to view live broadcast TV from the computer, using an
Elgato EyeTV One Computer TV Tuner with a Mohu Leaf HDTV antenna.  With this slick combination we are able to get 13 channels of over-the-air HDTV for free.  The EyeTV software is really nice; it has good DVR functionality and an integrated TV Guide.  It's very much like having cable with 13 channels, except that the DVR functions are much better than any set-top DVR we ever had.

Hulu Plus  > Netflix.  We have found that Hulu Plus meets our TV viewing needs quite well.  Sure we have to watch some commercials, but we are usually able to get new shows the next day after they air, and the selection is pretty good.  I tried a free trial of Netflix online, but we have not found that it has much to offer us, except for an occasional movie.  However, we watch movies pretty rarely, and so it probably makes more sense for us to just buy them from iTunes.  For shows that are not available on the web or via Hulu (e.g., The Layover), we buy them from iTunes as well.  It's not cheap but we still come out ahead in the long run.

Media center software sucks.  We tried using both Plex and Boxee on the mac mini, but gave up on both after too many things just didn't work; in particular, the Hulu integration on Plex was really frustrating, as it seems like it should work but then it never quite does.  Now we just watch Hulu content through a web browser, live/recorded TV through EyeTV, and iTunes content through iTunes.  The main drawback of this setup is that we can't get remote functionality that works seamlessly across all these different interfaces, but that's not been a problem.

Overall I would rate this experiment as a success and would definitely recommend giving up cable.

2011 in review

In the spirit of Chris Guillebeau's Annual Review, I decided to take a few minutes this morning to review what worked well and what didn't 2011 and look forward to 2012.

Goals:

Last year I set three goals for 2011.  Here's how they fared.

FAIL: Work toward a travel moratorium for 2012.  This started with me responding to all requests with "I'm sorry, but I'm not traveling at all for work in 2012."  At some point that became untenable, and the floodgates opened.  At this point, I am still planning to travel less in 2012 than in 2011, but will still probably take 8-10 trips.

SUCCESS: Improve climbing skills well enough to lead climb. My climbing skills have improved enormously over the last year, and in the summer I began lead climbing at the rock gym, where I can now lead a number of routes in the 5.9 range.  I was not able to lead outside, mostly because the weather in December did not cooperate, but I plan to do so very soon.

SUCCESS: No new web projects. I only purchased one new domain name this year, and that was for a project that had been hatched in 2010.  We have instead focused heavily on our existing projects, particularly openfmri.org.

Here are my goals for 2012:

Improve my posture.  Some nagging neck and back issues this year have highlighted the need to improve my posture.  Who knows, I might even get some mental benefits from it as well.

Improve my code management.  In the last year I have started integrating source code management (using git) into my workflow (see my github repo for a tour of some of my adventures during the last year).  However, it still has not become a habit for me during everyday coding.

Exercise on every trip.  One of the reasons that I find travel so disruptive is that it interferes with my fitness routine.  I carry my yoga mat on nearly every trip, and this year I did a fairly good job of exercising while on the road, but I was not very consistent.  Next year I plan to make sure that I get some exercise on every trip, even if it's just some burpees and squats in the hotel room.

I hope it's not true that making these goals public will make them harder to achieve!

Stats

Countries visited: 7
Miles flown: 76,162
Talks given: 12
Papers published: 14
Grants funded: 2

Property crimes (committed against me, not by me): 2

Best meals:
1. Tasting menu at Congress
2. Lunch at Les Arcenuax, Marseille
3. Tie between Franklin BBQ and JMueller BBQ
4. Tasting menu at Uchi (the meal that sealed our transition to full-blown carnivores)

NYT Letter to the Editor: The uncut version

The NY Times has now printed our letter to the editor regarding the Lindstrom article.  However, the published version is an edited and shortened version of our original letter, which I am posting here for the record.


Dear Editor,
The Op-Ed “You Love Your iPhone, Literally” by Martin Lindstrom purports to show, using brain imaging, that our attachment to digital devices, reflects not addiction but instead the same kind of emotion that we feel for human loved ones. However, the evidence the author presents does not show this.  The region that he points to as being “associated with feelings of love and compassion” (the insular cortex) is a brain region that is active in as many as one third of all brain imaging studies.  Further, in studies of decision making the insula is more often associated with negative than positive emotions.  The kind of reasoning that Lindstrom uses is well known to be flawed, because there is rarely a one-to-one mapping between any brain region and a single mental state; insula activity could reflect one or more of several psychological processes. This same point was made by some of us regarding a similar Op-Ed piece in 2007.
We are disappointed that the Times has published extravagant claims based on scientific data that have not been subjected to the standard scientific review process, especially considering how often its pages exhort policy makers to pay more attention to peer-reviewed scientific evidence and disregard specious claims.
Sincerely,

Russell Poldrack, Ph.D., University of Texas at Austin
Geoffrey K Aguirre, M.D., Ph.D., University of Pennsylvania
Adam Aron, Ph.D., University of California at San Diego
Lisa Feldman Barrett, Ph.D., Northeastern University
Mark G. Baxter, Ph.D., Mount Sinai School of Medicine
Susan Bookheimer, Ph.D., University of California at Los Angeles
Colin Camerer, Ph.D., California Institute of Technology
McKell Carter, Ph.D., Duke University
Christopher Chabris, Ph.D., Union College
Molly Crockett, Ph.D., University of Zurich, Switzerland
Nathaniel Daw, Ph.D., New York University
Paul Downing, Ph.D., University of Bangor, Wales, UK
Russell Epstein, Ph.D., University of Pennsylvania
Michael Frank, Ph.D., Brown University
Janet Frick, Ph.D., University of Georgia
Paul Glimcher, Ph.D., New York University
Tom Hartley, Ph.D., University of York, UK
Benjamin Hayden, Ph.D., University of Rochester
Hauke R. Heekeren, M.D., Freie Universität Berlin, Germany
Simon Hjerrild, M.D., University of Aarhus, Denmark
Scott Huettel, Ph.D., Duke University
Nancy Kanwisher, Ph.D., Massachusetts Institute of Technology
Brian Knutson, Ph.D., Stanford University
John Kubie, Ph.D., SUNY Downstate Medical Center
Michael V. Lombardo, Ph.D., University of Cambridge, UK
Ken Norman, Ph.D., Princeton University
Olivier Oullier, Ph.D., Aix-Marseille University, France
Steven Petersen, Ph.D., Washington University
Elizabeth Phelps, Ph.D., New York University
Rajeev Raizada, Ph.D., Cornell University
Antonio Rangel, Ph.D., California Institute of Technology
Peter B. Reiner, Ph.D., University of British Columbia, Canada
Gregory Samanez-Larkin, Ph.D., Vanderbilt University
Geoff Schoenbaum, M.D., Ph.D., University of Maryland
Daphna Shohamy, Ph.D., Columbia University
Jon Simons, Ph.D., University of Cambridge, UK
Peter Sokol-Hessner, Ph.D., California Institute of Technology
David Somers, Ph.D., Boston University
Damian Stanley, Ph.D., California Institute of Technology
John Van Horn, Ph.D., University of California at Los Angeles
Bradley Voytek, Ph.D., University of California, San Francisco
Anthony Wagner, PhD, Stanford University.
Daniel Willingham, Ph.D., University of Virginia
Tal Yarkoni, Ph.D., University of Colorado Boulder
Jeff Zacks, Ph.D., Washington University
Jamil Zaki, Ph.D., Harvard University

Signers of letter to the editor of the New York Times

A letter has been submitted to the editor of the NY Times regarding the outrageous Op-Ed piece by Martin Lindstrom.  (Once it is published I will add a link here.)  Because the NY Times will not allow a long list of signers on a letter, I am attaching here a list of all of the individuals who contributed to and signed the letter.  If you would like to add your name in support of the letter, please do so in the comments section.

Russell Poldrack, Ph.D., University of Texas at Austin

Geoffrey K Aguirre, M.D., Ph.D., University of Pennsylvania

Adam Aron, Ph.D., University of California at San Diego

Lisa Feldman Barrett, Ph.D., Northeastern University

Mark G. Baxter, Ph.D., Mount Sinai School of Medicine

Susan Bookheimer, Ph.D., University of California at Los Angeles

Colin Camerer, Ph.D., California Institute of Technology

McKell Carter, Ph.D., Duke University

Christopher Chabris, Ph.D., Union College

Molly Crockett, Ph.D., University of Zurich, Switzerland

Nathaniel Daw, Ph.D., New York University

Paul Downing, Ph.D., University of Bangor, Wales, UK

Russell Epstein, Ph.D., University of Pennsylvania

Michael Frank, Ph.D., Brown University

Janet Frick, Ph.D., University of Georgia

Paul Glimcher, Ph.D., New York University

Tom Hartley, Ph.D., University of York, UK

Benjamin Hayden, Ph.D., University of Rochester

Hauke R. Heekeren, M.D., Freie Universität Berlin, Germany

Simon Hjerrild, M.D., University of Aarhus, Denmark

Scott Huettel, Ph.D., Duke University

Nancy Kanwisher, Ph.D., Massachusetts Institute of Technology

Brian Knutson, Ph.D., Stanford University

John Kubie, Ph.D., SUNY Downstate Medical Center

Michael V. Lombardo, Ph.D., University of Cambridge, UK

Ken Norman, Ph.D., Princeton University

Olivier Oullier, Ph.D., Aix-Marseille University, France

Steven Petersen, Ph.D., Washington University

Elizabeth Phelps, Ph.D., New York University

Rajeev Raizada, Ph.D., Cornell University

Antonio Rangel, Ph.D., California Institute of Technology

Peter B. Reiner, Ph.D., University of British Columbia, Canada

Gregory Samanez-Larkin, Ph.D., Vanderbilt University

Geoff Schoenbaum, M.D., Ph.D., University of Maryland

Daphna Shohamy, Ph.D., Columbia University

Jon Simons, Ph.D., University of Cambridge, UK

Peter Sokol-Hessner, Ph.D., California Institute of Technology

David Somers, Ph.D., Boston University

Damian Stanley, Ph.D., California Institute of Technology

John Van Horn, Ph.D., University of California at Los Angeles

Bradley Voytek, Ph.D., University of California, San Francisco

Anthony Wagner, PhD, Stanford University.

Daniel Willingham, Ph.D., University of Virginia

Tal Yarkoni, Ph.D., University of Colorado Boulder

Jeff Zacks, Ph.D., Washington University

Jamil Zaki, Ph.D., Harvard University