Did I Do That? Distinguishing Real from Imagined Actions


If you’re like most people, you spend a great deal of your time remembering past events and planning or imagining events that may happen in the future. While these activities have their uses, they also make it terribly hard to keep track of what you have and haven’t actually seen, heard, or done. Distinguishing between memories of real experiences and memories of imagined or dreamt experiences is called reality monitoring and it’s something we do (or struggle to do) all of the time.

Why is reality monitoring a challenge? To illustrate, let’s say you’re at the Louvre standing before the Mona Lisa. As you look at the painting, visual areas of your brain are busy representing the image with specific patterns of activity. So far, so good. But problems emerge if we rewind to a time before you saw the Mona Lisa at the Louvre. Let’s say you were about to head over to the museum and you imagined the special moment when you would gaze upon Da Vinci’s masterwork. When you imagined seeing the picture, you were activating the same visual areas of the brain in a similar pattern to when you would look at the masterpiece itself.*

When you finally return home from Paris and try to remember that magical moment at the Louvre, how will you be able to distinguish your memories of seeing the Mona Lisa from imagining her? Reality monitoring studies have asked this very question (minus the Mona Lisa). Their findings suggest that you’ll probably use additional details associated with the memory to ferret out the mnemonic wheat from the chaff. You might use memory of perceptual details, like how the lights reflected off the brushstrokes, or you might use details of what you thought or felt, like your surprise at the painting’s actual size. Studies find that people activate both visual areas (like the fusiform gyrus) and self-monitoring regions of the brain (like the medial prefrontal cortex) when they are deciding whether they saw or just imagined seeing a picture.

It’s important to know what you did and didn’t see, but another crucial and arguably more important facet of reality monitoring involves determining what you did and didn’t do. How do you distinguish memories of things you’ve actually done from those you’ve planned to do or imagined doing? You have to do this every day and it isn’t a trivial task. Perhaps you’ve left the house and headed to work, only to wonder en route if you’d locked the door. Even if you thought you did, it can be hard to tell whether you remember actually doing it or just thinking about doing it. The distinction has consequences. Going home and checking could make you late for work, but leaving your door unlocked all day could mean losing your possessions. So how do we tell the possibilities apart?

Valerie Brandt, Jon Simons, and colleagues at the University of Cambridge looked into this question and published their findings last month in the journal Cognitive, Affective, and Behavioral Neuroscience. For the first part of the experiment (the study phase), they sat healthy adult participants down in front of two giant boxes – one red and one blue – that each contained 80 ordinary objects. The experimenter would draw each object out of one of the two boxes, place it in front of the participant, and tell him or her to either perform or to imagine performing a logical action with the object. For example, when the object was a book, participants were told to either open or imagine opening it.

After the study phase, the experiment moved to a scanner for fMRI. During these scans, participants were shown photographs of all 160 of the studied objects and, for each item, were asked to indicate either 1) whether they had performed or merely imagined performing an action on that object, or 2) which box the object had been drawn from.** When the scans were over, the participants saw the pictures of the objects again and were asked to rate how much specific detail they’d recalled about encountering each object and how hard it had been to bring that particular memory to mind.

The scientists compared fMRI measures of brain activation during the reality-monitoring task (Did I use or imagine using that object?) with activation during the location task (Which box did this object come from?). One of the areas they found to be more active during reality monitoring was the supplementary motor area, a region involved in planning and executing movements of the body. Just as visual areas are activated for reality monitoring of visual memories, motor areas are activated when people evaluate their action memories. In other words, when you ask yourself whether you locked the door or just imagined it, you may be using details of motor aspects of the memory (e.g., pronating your wrist to turn the key in the lock) to make your decision.

The study’s authors also found greater activation in the anterior medial prefrontal cortex when they compared reality monitoring for actions participants performed with those they only imagined performing. The medial prefrontal cortex encompasses a respectable swath of the brain with a variety of functions that appear to include making self-referential judgments, or evaluating how you feel or think about experiences, sensations, and the like. Other experiments have implicated a role for this or nearby areas in reality monitoring of visual memories. The study by Brandt and Simons also found that activation of this medial prefrontal region during reality-monitoring trials correlated with the number of internal details the participants said they’d recalled in those trials. In other words, the more details participants remembered about their thoughts and feelings during the past actions, the busier this area appeared to be. So when faced with uncertainty about a past action, the medial prefrontal cortex may be piping up about the internal details of the memory. I must have locked the door because I remember simultaneously wondering when my package would arrive from Amazon, or, because I was also feeling sad about leaving my dog alone at home.

As I read these results, I found myself thinking about the topic of my prior post on OCD. Pathological checking is a common and often disruptive symptom of the illness. Although it may seem like a failure of reality monitoring, several behavioral studies have shown that people with OCD have normal reality monitoring for past actions. The difference is that people with checking symptoms of OCD have much lower confidence in the quality of their memories than others. It seems to be this distrust of their own memories, along with relentless anxiety, that drives them to double-check over and over again.

So the next time you find yourself wondering whether you actually locked the door, cut yourself some slack. Reality monitoring ain’t easy. All you can do is trust your brain not to lead you astray. Make a call and stick with it. You’re better off being wrong than being anxious about it – that is, unless you have really nice stuff.


Photo credit: Liz (documentarist on Flickr), used via Creative Commons license

* Of course, the mental image you conjure of the painting is actually based on the memory of having seen it in ads, books, or posters before. In fact, a growing area of neuroscience research focuses on how imagining the future relies on the same brain areas involved in remembering the past. Imagination seems to be, in large part, a collage of old memories cut and pasted together to make something new.

**The study also had a baseline condition, used additional contrasts, and found additional activations that I didn’t mention for the sake of brevity. Check out the original article for full details.

Brandt, V., Bergström, Z., Buda, M., Henson, R., & Simons, J. (2014). Did I turn off the gas? Reality monitoring of everyday actions Cognitive, Affective, & Behavioral Neuroscience, 14 (1), 209-219 DOI: 10.3758/s13415-013-0189-z

fMR-Why? Bad Science Meets Chocolate and Body Envy


Imagine this: You have bulimia nervosa, a psychiatric condition that traps you in an unhealthy cycle of binge eating and purging. You’ve been recruited to participate in a functional MRI experiment on this devastating illness. As you lie in the scanner, you are shown pictures of pizza, chocolate and other high-calorie foods and you’re told to imagine eating them. You do this for 72 pictures of delicious, fatty foods. At other points in the experiment, you see pictures of bodies (sans heads) of models clipped from a women’s magazine. You are told to compare your body to each of the bodies in the pictures. You do this 72 times, once for each skinny (and probably retouched) model’s body. The experience would have been unsettling enough for normal women trying to eat healthier or feel happier with their not-so-super-model bodies. But for women with bulimia, it must have truly been a hoot and a half.

Luckily, the misery was worth it. When the researchers publish their findings, they claim to have shown that patients with bulimia process body images differently. In their conclusions, they say that their results can inform how psychotherapists should treat patients with the illness. They even suggest that it might someday lead to direct interventions, such as a targeted zap to the head using transcranial magnetic stimulation.

My recommendation? Cover your therapist’s ears and stay away from the head zapper. This study shows nothing of the sort.

Functional MRI is a widely used and quite powerful method of probing the brain, but it is only useful for experiments that are thoughtfully conceived and carefully interpreted. Unfortunately, many fMRI papers that make it to publication are neither.

One of the most common problems in fMRI is making bad comparisons. All fMRI studies rely on comparisons because brains are all different and scanners are all different. If you are going to say that Region X becomes active when you see a picture of chocolate, you first have to answer that crucial question: compared to what? If you’re interested in how the brain reacts to unhealthy food in particular, you might compare looking at pictures of chocolate with looking at pictures of raisins or eggplant. And if you’re comparing these comparisons across subject groups (such as patients versus non-patients), both groups had better have the same the control condition. Otherwise, you’re not even comparing apples to oranges. You’re comparing apples to gym socks.

Sadly, that is just what these experimenters did. They compared brain blood flow when the subjects looked either at junk food or skinny women with blood flow during 36-second stretches of time when subjects just stared at a small, white ‘+’ on the screen. The authors say that using a more similar control condition (say, imagining using non-food objects like a lamp or a door) would be bad because patients with bulimia might respond to these objects differently than healthy subjects. This argument is nonsensical. There’s no reason to believe that people with bulimia feel any differently about doors or lamps than anyone else, but there’s plenty of reason to believe that they would spend 36-second moments of downtime before or after comparing their bodies to those of models either obsessing or trying not to obsess about how their bodies ‘measure up.’

In fact, I suspect that could not help but wonder if the authors didn’t originally intend to use this ‘+’ as the control condition. They actually had less crappy control conditions built into the experiment. As a control for imagining eating pizza and chocolate, the participants were also shown non-food objects like tools and told to imagine using them. They also saw interior décor photos and had to compare the furniture to those in their own homes – a control for comparing each model’s body to one’s own.

When the authors did their analyses using these (better) control conditions, they found very few differences between patients and non-patients. None, in fact, for the imagine-eating-junk-food portion of the study. For the comparing-oneself-to-models portion, they only found that patients showed less activation than controls in two regions of visual cortex. These regions may correspond to areas that specifically process body images. But would less activation in these regions mean that patients with bulimia process body images differently than other people? Not at all. If the patients were not looking at the pictures as much as non-patients or were more distracted/less attentive to them, you would see the same pattern of results. In short, the authors had no story to tell when they used the better controls. They had a ‘null result’ that would not get published.


Based on the design of their experiment, I suspect that find myself wondering if this was how they originally intended to analyze their data.* And it’s really the only sensible way to analyze these data. Experiments like these include the ‘+’ condition to establish a baseline (essentially, what you’re going to call ‘zero’). These ‘+’ blocks also correct for an unfortunate phenomenon called scanner drift that adds noise to the data.

It’s possible that I have to wonder if the authors decided to use the ‘+’ for their comparisons because they didn’t get any exciting results with the actual control conditions. If so, it unfortunately worked. Using the baseline condition, they found two differences between patient and non-patient activations in the food task and even more differences between the groups in the body task. Ultimately, the authors got their significant results and they got them published.  But those differences have nothing to do with the causes of bulimia and everything to do with what flits through people’s minds while they stare at a plus sign.

Unfortunately, this is just one example from a growing sea of bad fMRI studies out there. And while many people do wonderful work with the technique and advance the field, others do it a disservice and set us all back. From researchers to reviewers, publishers, science writers and reporters, we all need to proceed with caution and evaluate papers with a critical eye. The participants in our experiments deserve it. The public deserves it. Most of all, patients deserve the best information we can give them. Science done well and served to them straight.

Update: I’ve made a few small changes to this post to clarify my intent. I don’t personally know the study’s authors and have no insight into their actions, intentions, or motivations. In writing the piece, I hoped to bring attention to a widespread problem in fMRI research. Of the study’s authors I can only say that they did some seriously flawed research. Why, when, or how is as much your guess as mine.

Since posting this piece, I’ve contacted the editor of BMC Psychiatry regarding my concerns with the paper. Not only have I received no reply from her, but this paper is still listed as one of the ‘Editor’s Picks’ on their website as of 1/5/14.


*For curious fMRI folk: each run contained 6 food/body blocks, 6 non-food/décor blocks, and only 3 baseline ‘+’ blocks. That means they collected twice the data for the control conditions that they supposedly didn’t intend to use than for the ones that they did.

Photo #1 credit: MRI scanner, photo by Matthias Weinberger (cszar on Flickr), used via Creative Commons license

Photo #2 credit: Structural MRI of kiwi fruit by Dom McIntyre (McBadger on Flickr), used via Creative Commons license

Van den Eynde F, Giampietro V, Simmons A, Uher R, Andrew CM, Harvey PO, Campbell IC, & Schmidt U (2013). Brain responses to body image stimuli but not food are altered in women with bulimia nervosa. BMC Psychiatry, 13 (1) PMID: 24238299


Looking Schizophrenia in the Eye

272994276_3c83654e97_bMore than a century ago, scientists discovered something usual about how people with schizophrenia move their eyes. The men, psychologist and inventor Raymond Dodge and psychiatrist Allen Diefendorf, were trying out one of Dodge’s inventions: an early incarnation of the modern eye tracker. When they used it on psychiatric patients, they found that most of their subjects with schizophrenia had a funny way of following a moving object with their eyes.

When a healthy person watches a smoothly moving object (say, an airplane crossing the sky), she tracks the plane with a smooth, continuous eye movement to match its displacement. This action is called smooth pursuit. But smooth pursuit isn’t smooth for most patients with schizophrenia. Their eyes often fall behind and they make a series of quick, tiny jerks to catch up or even dart ahead of their target. For the better part of a century, this movement pattern would remain a mystery. But in recent decades, scientific discoveries have led to a better understanding of smooth pursuit eye movements – both in health and in disease.

Scientists now know that smooth pursuit involves a lot more than simply moving your eyes. To illustrate, let’s say a sexy jogger catches your eye on the street. When you first see the runner, your eyes are stationary and his or her image is moving across your retinas at some relatively constant rate. Your visual system (in particular, your visual motion-processing area MT) must first determine this rate. Then your eyes can move to catch up with the target and match its speed. If you do this well, the jogger’s image will no longer be moving relative to your retinas. From your visual system’s perspective, the jogger is running in place and his or her surroundings are moving instead. From both visual cues and signals about your eye movements, your brain can predict where the jogger is headed and keep moving your eyes at just the right speed to keep pace.

Although the smooth pursuit abnormalities in schizophrenia may sound like a movement problem, they appear to reflect a problem with perception. Sensitive visual tests show that motion perception is disrupted in many patients. They can’t tell the difference between the speeds of two objects or integrate complex motion information as well as healthy controls. A functional MRI study helped explain why. The study found that people with schizophrenia activated their motion-processing area MT less than controls while doing motion-processing tasks. The next logical question – why MT doesn’t work as well for patients – remains unanswered for now.

In my last two posts I wrote about how delusions can develop in healthy people who don’t suffer from psychosis. The same is true of not-so-smooth pursuit. In particular, healthy relatives of patients with schizophrenia tend to have jerkier pursuit movements than subjects without a family history of the illness. They are also impaired at some of the same motion-processing tests that stymie patients. This pattern, along with the results of twin studies, suggests that smooth pursuit dysfunction is inherited. Following up on this idea, two studies have compared subjects’ genotypes with the inheritance patterns of smooth pursuit problems within families. While they couldn’t identify exactly which gene was involved (a limitation of the technique), they both tracked the culprit gene to the same genetic neighborhood on the sixth chromosome.

Despite this progress, the tale of smooth pursuit in schizophrenia is more complex than it appears. For one, there’s evidence that smooth pursuit problems differ for patients with different forms of the disorder. Patients with negative symptoms (like social withdrawal or no outward signs of emotion) may have problems with the first step of smooth pursuit: judging the target’s speed and moving their eyes to catch up. Meanwhile, those with more positive symptoms (like delusions or hallucinations) may have more trouble with the second step: predicting the future movement of the target and keeping pace with their eyes.

It’s also unclear exactly how common these problems are among patients; depending on the study, as many as 95% or as few as 12% of patients may have disrupted smooth pursuit. The studies that found the highest rates of smooth pursuit dysfunction in patients also found rates as high as 19% for the problems among healthy controls. These differences may boil down to the details of how the eye movements were measured in the different experiments. Still, the studies all agreed that people with schizophrenia are far more likely to have smooth pursuit problems than healthy controls. What the studies don’t agree on is how specific these problems are to schizophrenia compared with other psychiatric illnesses. Some studies have found smooth pursuit abnormalities in patients with bipolar disorder and major depression as well as in their close relatives; other studies have not.

Despite these messy issues, a group of scientists at the University of Aberdeen in Scotland recently tried to tell whether subjects had schizophrenia based on their eye movements alone. In addition to smooth pursuit, they used two other measures: the subject’s ability to fix her gaze on a stable target and how she looked at pictures of complex scenes. Most patients have trouble holding their eyes still in the presence of distractors and, when shown a meaningful picture, they tend to look at fewer objects or features in the scene.

Taking the results from all three measures into account, the group could distinguish between a new set of patients with schizophrenia and new healthy controls with an accuracy of 87.8%. While this rate is high, keep in mind that the scientists removed real-world messiness by selecting controls without other psychiatric illnesses or close relatives with psychosis. This makes their demonstration a lot less impressive – and a lot less useful in the real world. I don’t think this method will ever become a viable alternative to diagnosing schizophrenia based on their clinical symptoms, but the approach may hold promise in a similar vein: identifying young people who are at risk for developing the illness. Finding these individuals and helping them sooner could truly mean the difference between life and death.


Photo credit: Travis Nep Smith on Flickr, used via Creative Commons License

Benson PJ, Beedie SA, Shephard E, Giegling I, Rujescu D, & St Clair D (2012). Simple viewing tests can detect eye movement abnormalities that distinguish schizophrenia cases from controls with exceptional accuracy. Biological psychiatry, 72 (9), 716-24 PMID: 22621999

Neural Conspiracy Theories

140775790_e3e122cd65_bLast month, a paper quietly appeared in The Journal of Neuroscience to little fanfare and scant media attention (with these exceptions). The study revolved around a clever and almost diabolical premise: that using perceptual trickery and outright deception, its authors could plant a delusion-like belief in the heads of healthy subjects. Before you call the ethics police, I should mention that the belief wasn’t a delusion in the formal sense of the word. It didn’t cause the subjects any distress and was limited to the unique materials used in the study. Still, it provided a model delusion that scientists Katharina Schmack, Philipp Sterzer, and colleagues could study to investigate the interplay of perception and belief in healthy subjects. The experiment is quite involved, so I’ll stick to the coolest and most relevant details.

As I mentioned in my last post, delusions are not exclusive to people suffering from psychosis. Many people who are free of any diagnosable mental illness still have a tendency to develop them, although the frequency and severity of these delusions differ across individuals. There are some good reasons to conduct studies like this one on healthy people rather than psychiatric patients. Healthy subjects are a heck of a lot easier to recruit, easier to work with, and less affected by confounding factors like medication and stress.

Schmack, Sterzer, and colleagues designed their experiment to test the idea that delusions arise from two distinct but related processes. First, a person experiences perceptual disturbances. According to the group’s model, these disturbances actually reflect poor expectation signals as the brain processes information from the senses. In theory, these poor signals would make irrelevant or commonplace sights, sounds, and sensations seem surprising and important. Without an explanation for this unexpected weirdness, the individual comes up with a delusion to make sense of it all. Once the delusion is in place, so-called higher areas of the brain (those that do more complex things like ponder, theorize, and believe) generate new expectation signals based on the delusion. These signals feed back on so-called lower sensory areas and actually bias the person’s perception of the outside world based on the delusion. According to the authors, this would explain why people become so convinced of their delusions: they are constantly perceiving confirmatory evidence. Strangely enough, this model sounds like a paranoid delusion in its own right. Various regions of your brain may be colluding to fool your senses into making you believe a lie!

To test the idea, the experimenters first had to toy with their subjects’ senses. They did so by capitalizing on a quirk of the visual system: that when people are shown two conflicting images separately to their two eyes, they don’t perceive both images at once. Instead, perception alternates between the two. In the first part of this experiment, the two images were actually movies of moving dots that appeared to form a 3-D sphere spinning either to the left (for one eye) or to the right (for the other). For this ambiguous visual condition, subjects were equally likely to see a sphere spinning to the right or to the left at any given moment in time, with it switching direction periodically.

Now the experimenters went about planting the fake belief. They gave the subjects a pair of transparent glasses and told them that the lenses contained polarizing filters that would make the sphere appear to spin more in one of the two directions. In fact, the lenses were made of simple plastic and could do no such thing. Once the subjects had the glasses on, the experimenters began showing the same movie to both eyes. While this change allowed the scientists to control exactly what the subjects saw, the subjects had no idea that the visual setup had changed. In this unambiguous condition, all subjects saw a sphere that alternated direction (just as the ambiguous sphere had done), except that this sphere spun far more in one of the two directions. This visual trick, paired with the story about polarized lenses, was meant to make subjects believe that the glasses caused the change in perception.

After that clever setup, the scientists were ready to see how the model delusion would affect each subject’s actual perception. While the subject continued to wear the glasses, they were shown the two original, conflicting movies to their two separate eyes. In the first part of the experiment, this ambiguous condition caused subjects to see a rotating sphere that alternated equally between spinning to the left and right. But if their new belief about the glasses biased their perception of the spinning sphere, they would now report seeing the sphere spin more often in the belief-consistent direction.

What happened? Subjects did see the sphere spin more in the belief-consistent direction. While the effect was small, it was still impressive that they could bias perception at all, considering the simplicity of the images. They also found that each subject’s delusional conviction score (how convinced they were by their delusional thoughts in everyday life) correlated with this effect. The more the subject believed her real-life delusional thoughts, the more her belief about the glasses affected her perception of the ambiguous spinning sphere.

But there’s a hitch. What if subjects were reporting the motion bias because they thought that was what they were supposed to see and not because they actually saw it? To answer this question, they recruited a new batch of participants and ran the experiment again in a scanner using fMRI.

Since the subjects’ task hinged on motion perception, Sterzer and colleagues first looked at the activity in a brain area called MT that processes visual motion. By analyzing the patterns of fMRI activity in this area, the scientists confirmed that subjects were accurately reporting the motion they perceived. That may sound far-fetched, but this kind of ‘mind reading’ with fMRI  has been done quite successfully for basic visual properties like motion.

The group also studied activity throughout the brain while their glasses-wearing subjects learned the false belief (unambiguous condition) and allowed the false belief to more or less affect their perception (ambiguous condition). They found that belief-based perceptual bias correlated with activity in the left orbitofrontal cortex, a region just behind the eyes that is involved in decision-making and expectation. In essence, subjects with more activity in this region during both conditions tended to also report lopsided spin directions that confirmed their expectations during the ambiguous condition. And here’s the cherry on top: subjects with higher delusional conviction scores appeared to have greater communication between left orbitofrontal cortex and motion-processing area MT during the ambiguous visual condition. Although fMRI can’t directly measure communication between areas and can’t tell us the direction of communication, this pattern suggests that the left orbitofrontal cortex may be directly responsible for biasing motion perception in delusion-prone subjects.

All told, the results of the experiment seem to tell a neat story that fits the authors’ model about delusions. Yet there are a couple of caveats worth mentioning. First, the key finding of their study – that a person’s delusional conviction score correlates with his or her belief-based motion perception bias – is built upon a quirky and unnatural aspect of human vision that may or may not reflect more typical sensory processes. Second, it’s hard to say how clinically relevant the results are. No one knows for certain if delusions arise by the same neural mechanisms in the general population as they do in patients with illnesses like schizophrenia. It has been argued that they probably do because the same risk factors pop up for patients as for non-psychotic people with delusions: unemployment, social difficulties, urban surroundings, mood disturbances and drug or alcohol abuse. Then again, this group is probably also at the highest risk for getting hit by a bus, dying from an curable disease, or suffering any number of misfortunes that disproportionately affect people in vulnerable circumstances. So the jury is still about on the clinical applicability of these results.

Despite the study’s limitations, it was brilliantly designed and tells a compelling tale about how the brain conspires to manipulate perception based on beliefs. It also implicates a culprit in this neural conspiracy. Dare I say ringleader? Mastermind? Somebody cue the close up of orbitofrontal cortex cackling and stroking a cat.


Photo credit: Daniel Horacio Agostini (dhammza) on Flickr, used through Creative Commons license

Schmack K, Gòmez-Carrillo de Castro A, Rothkirch M, Sekutowicz M, Rössler H, Haynes JD, Heinz A, Petrovic P, & Sterzer P (2013). Delusions and the role of beliefs in perceptual inference. The Journal of Neuroscience, 33 (34), 13701-13712 PMID: 23966692

Why Bigger Isn’t Always Better

One of my entertainments this holiday season was following the online buzz over a recent article in Nature Neuroscience. The authors’ findings were covered by Wired, Time, Slate, U.S. News & World Report, and the BBC, to name a few. One headline read: Scientists Discover Facebook Center of the Brain. Another: How to Win Friends: Have a Big Amygdala?

The authors of the Nature Neuroscience article report a correlation between the size of a subcortical brain structure called the amygdala and the extent of a person’s social network. In effect, people with larger amygdalas tended to have more friends and close acquaintances than those with lesser-sized amygdalas. The popular press and the public leapt on this idea. We are predestined by our anatomy to be popular or not. If we were alone on New Years Eve, if our Facebook friend count is low, it’s not our fault. Chalk that one off to our brains, our genes, our parents.

All of this struck me as both amusing and sad because of a book I was reading at the time. The book, Postcards from the Brain Museum by Brian Burrell, chronicles the history of neuroscience in the context of our search for greatness (as well as criminality, idiocy, and inferiority.) It tells how scientists spent most of the 19th century collecting human brains from geniuses, criminals, and the poor to try to understand why some people demonstrate remarkable abilities while others flounder and fail.

It is a sad and sordid history. At first, some believed that the sheer size or weight of one’s brain predicted greatness, so that large brains were capable of better thinking. Since women’s brains (like the rest of their bodies) were on average smaller than those of their male counterparts, this provided a perfect explanation for their intellectual inferiority. Later, when the link between brain volume and intelligence was debunked, scientists suggested that the amount of folding on the brain’s surface was the marker of a brilliant brain. The more convolutions on the surface, the smarter the individual. Other scientists identified specific fissures that they deemed inferior, as they were supposedly found more often in apes and women. These lines of research would be used to justify racial and gender stereotypes and give rise to the practice of eugenics in the first half of the 20th century.

The peer review process and established statistical methods ensure that today’s science is more legitimate than it was in centuries past. But neuroimaging has allowed us to probe the living brain to a degree heretofore unimagined. With it, scientists amass enormous amounts of data that strain our standard statistical techniques and challenge our ability to distinguish between profound, universal discoveries and those idiosyncratic to our subject sample or functionally irrelevant. We still don’t know whether ‘bigger is better’ nor understand the source or functional consequences of individual differences in the size and shape of brain regions. Certainly we don’t know enough to look at a person’s brain and guess with accuracy how smart they are, how good they are, or, yes, even how many friends they have.

Just look at this graph from the Nature Neuroscience paper plotting amygdala volume on the horizontal axis and social network size on the vertical axis:

The figure above shows each subject as a black dot (for younger participants) or a gray triangle (for older ones). The diagonal line shows a mathematical correlation between amygdala volume and social network size, but look at how many dots and triangles lie away from the line. For the same amygdala volume (say, 3 cubic mm), there are dots that lie far above the line and others that lie far below it. No one looking at this figure can say that amygdala size determines one’s sociability. Perhaps it plays some small role, sure. But we are not slaves to our amygdala volumes, just like we’re not slaves to our overall brain size, our fissural patterns or cerebral convolutions. Our abilities and thoughts do come from our brains, but we have to keep in mind that those brains are far more complex than we can fathom. Who you are can never be reduced to a list of measured volumes. It’s important that we remember that, and that we never return to those days of ‘mine’s bigger than yours.’

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