Why is happiness so hard to detect in the brain

Original author: Dean Burnett
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Talk about spotting with a visualization researcher

I arrived at a meeting with Professor Chambers at the pretty Cardiff Pub, located next to his office, to have lunch there, as agreed. He was already waiting for me in the back of the room, and waved his greeting when I entered.

Professor Chris Chambers is a discouraging calm Australian under the age of 40. Fully falling under the cultural stereotype, at the meeting he was wearing a T-shirt and spacious shorts, despite the rain outside. He also turned out to be completely bald, right to the brilliance. I have already met with several younger professors, who also had poor vegetation on their heads. It seems to me that their large and powerful brain produces so much heat that the hair follicles simply burn out.

I decided to go straight to the point, and simply asked what I wanted: “Can I use your MRI device, scan me at the moment when I’m happy, and find out where the happiness comes from in my brain?”

After five minutes he stopped laugh. Even a very optimistic person would come to the conclusion that such a start to the conversation was unsuccessful. The next hour, Chambers explained in detail to me why my plan was ridiculous.

Functional magnetic resonance imaging (fMRI) does not work, or should work not so. When it was still being developed, in the 90s, at the time that we call the “evil old time” of neuroimaging , what we call “blobology” was practiced: people were put into scanners and hunted for “spots” of activity in the brain .

One of my favorite examples I met at one of the first conferences I visited: they presented the study “fMRI chess compared to leisure”. People lay in the scanner, and either played chess or did nothing. With different types of activity, the brain was activated entirely, but in different ways, and in the case of chess, some parts of the brain were "more" active. Based on this study, scientists said that these areas are responsible for the process of playing chess. However, in this case, the cause and effect were confused: such and such a part of the brain is active, we do such things in chess, therefore, probably, this part of the brain is needed just for such activity. In fact, the opposite is true. This approach is akin to likening the brain to the motor of a car; as if each part of the brain should perform one and only one function.

This approach leads to incorrect results; we see the activity of a particular area of ​​the brain and assign it a specific function. But this is completely untrue. Many functions are performed by many parts of the brain, and cognitive networks control all of this. It is very difficult. This is the general problem of neuroimaging, and it goes to an even higher level when trying to work with such subjective things as happiness.

Despite the fact that I myself joined in ridiculing naive fools who believe that fMRI can be used to figure out where the ability to play chess comes from in the brain, inside I was burned with shame. I was hoping to do something similar on my own. That is, using the term I recently met, I exposed myself as a full pyatnologist.

It turns out that one thing is to use the visualization of a function such as vision; here it is possible to reliably control what exactly the subjects see, to ensure that each of them is given the same picture so that the experiment is consistent, and in this way to find and study the visual cortex. It is much more difficult to study what Chambers calls "interesting things" - higher-order functions, emotions, or self-control.

“The question is not,“ Where is happiness in the brain? ”It's like asking“ Where is the sensation of the sound of a dog barking in the brain? ”The better question is,“ How does the brain support happiness? What networks and processes are used to generate it? ”

Chambers also mentioned another problem: what is happiness in a technical sense? “What time period are we talking about? Short-term happiness, like “This pint went well!”? Or a longer and more general one, such as happiness being a parent, or working to achieve a goal, getting satisfaction from life, feeling calm and relaxing - like that? "There are several levels of functionality in the brain that support such things - and how to unpack them all?"

At this point, I had already thrown aside all hopes of conducting my ill-conceived experiment, which I admitted. Chambers, despite my fear of the ferocity of professors who met a lower intellect, dealt with this question very nicely, and said that in principle he would allow me to do this, even just for a useful demonstration of technology. Unfortunately, the use of fMRI is very expensive, and several research teams compete over its time. If he had spent the precious time of the scanner for some jester to take a picture of his bark in search of happiness, this would upset many people.

I considered a proposal to pay for the use of equipment from my pocket, but the prices were too high. Even generous publishers like mine would have succumbed to such expenses. £ 48 for a train ticket, £ 5 for a sandwich, £ 3 for coffee, £ 13,000 for a day using fMRI. I do not think that such figures would pass by the attention of bookkeeping.

But instead of declaring the meeting unsuccessful, I decided to ask Chambers if using fMRI has any other problems that I need to know before converting my ideas into a more practical form.

It turned out that Chambers very willingly and actively able to highlight the problems that stand in the way of modern research on neuroimaging and psychology in general. He even wrote a book about this, The Seven Deadly Sins of Psychology ( 1 ), which talks about how modern psychology can and should be improved.

There are several important issues associated with fMRI that make it clear how difficult it would be for me to use this technology to search for happiness. Firstly, as already mentioned, it is expensive. Studies using it are usually quite small and cost a limited number of subjects. And this is a problem - the less research objects you have, the less confidence in the significance of the results. The larger the number of objects, the higher the statistical significance ( 2 ) of the results, and the stronger your confidence in their correctness can be.

Imagine that you are throwing a dice. You threw it 20 times, and 25% of them threw a six. That is, only five times. You might think that this is unlikely, but it is quite real. There is no special significance here. Let's say that now you threw it 20,000 times, and 25% of them threw six. This is 5,000 times. Now it already seems strange. You will most likely decide that something is wrong with the cube, that it is somehow changed. With psychological experiments the same story: to get the same effect in five people will be interesting, but in 5000 - this is more like a serious discovery.

To experiment with one person, as I wanted to do, is scientifically pointless. It's good that I found out about this before I started.

Chambers then explained to me that such expenses ensure that few experiments are repeated. Scientists are being pressured terribly, demanding the publication of positive results (that is, “We found something!” Rather than “We tried to find something, but did not find”). Such results are more likely to be published in magazines, read by reviewers, improve career prospects and the likelihood of receiving grants, and so on. But it is also very good to repeat experiments whenever possible in order to show that the result was not random. Unfortunately, scientists are pressured to quickly move on to the next research, make the next big discovery, so nobody often checks interesting results ( 3 ), especially in the case of fMRI.

So if I could even conduct my experiment, I would need to conduct it again and again, regardless of the result. Even if he did not give me the data that I need. And this is a completely different situation.

The data obtained from fMRI are not as clear as they are described in mainstream reports. First, it talks about which parts of the brain are “active” during the study, but Chambers pointed out that “This is essentially nonsense. All parts of the brain are constantly active. The brain works like that. The question is, how much more active are these specific areas, and are they much more active than usual? ”

To get at least to the standards of "spotting", you need to determine which spots on the scanner are relevant to your experiment. And this is a rather complicated question in such a painstaking task as tracking the activity of certain areas of the brain. To begin with, what is considered a “significant” change in activity? If the activity of each part of the brain constantly fluctuates over time, how much activity should increase so that we can consider it significant? What threshold should she overcome? These values ​​vary from research to research. It is like trying at a pop star's concert to identify her biggest fan by listening to who screams the loudest; this is probably possible, but not at all simple and requires a lot of work.

As a result, Chambers explained, this leads to another obvious problem.

"The fMRI has a large, as we call it, the problem of degrees of freedom of the researcher." People often don’t think about how they will analyze the data, or even what questions they will ask before they do the research. They do this, study the issue, get a "garden of diverging paths" when even in the simplest studies with fMRI there are thousands of opportunities to make an analytical decision, each of which will slightly change the final result. Therefore, the researchers process all their data to find some useful result. "

This happens because complex data can be analyzed by many different methods, and one combination of approaches can choose a useful result, while others will not. This may seem like a dishonest approach, such as firing a machine gun at a wall, and then drawing a target where the most bullet holes have accumulated, and declaring it a good hit on the target. In fact, everything is not so bad, but it is moving in this direction. But if your career and success depends on hitting the target, and this option is possible - why give it up?

However, this is only the tip of the iceberg of problems related to experiments with fMRI. Chambers has options for answers and solutions to all these problems: report on analysis methods before embarking on them; share data and subjects between groups in order to increase reliability and reduce cost; change how scientists evaluate and judge when giving out grants and opportunities.

All these are wonderful and suitable solutions. But they did not help me. I arrived at the meeting, hoping that high-tech magic will help me understand where happiness comes from in my brain. Instead, there were thousands of advanced science issues in my brain, which made me definitely miserable.

Chambers eventually went back to work, and I, upset, drove home with my head buzzing not only because of a couple of beers I had drunk during the conversation. At first, I thought it would be quite simple to determine what makes us happy and where happiness comes from. It turned out that even if the scientific technologies that I tried to use were straightforward (and this is not so), then the happiness that everyone experiences, which everyone aspires to, and everyone considers understandable, is a much more complicated thing than I thought .

I imagine it as a burger. Everyone knows what a burger is. Everyone understands burgers. But where do the burgers come from? The obvious answer would be McDonald's, or Burger King, or some other commonplace you’re familiar with. Everything is simple.

But the burgers do not appear from the void fully prepared in the fast food kitchen. There is chopped meat that is formed into cutlets by a supplier who receives meat from a slaughterhouse, which receives meat from cattle farmers, who grows livestock on the ground, grows it and feeds it, which absorbs quite a lot of resources.

There are buns in burgers. They come from another supplier, a certain baker who needs flour, yeast, and many other ingredients (maybe even sesame) to mix them together and put in an oven that constantly needs fuel to create heat. Do not forget about the sauce (a large number of tomatoes, spices, sugar, packaging, and industrial production for all this) and the side dish (fields where vegetables grow, which must be collected, transported, stored with the help of complex infrastructure).

And all these things give us only the basic ingredients. Still need someone who will collect and cook it. This is done by people who need to be fed, watered, taught and paid. The restaurant needs electricity, water, heat, service to work. All this, an endless stream of resources and labor that an ordinary person does not even think about, is invested in giving you a burger on a silver platter that you can chew abstractly while staring at the phone.

This may be a confusing and complex metaphor, but that’s the point. If you think about it, the burger and happiness are the familiar but pleasant results of the incredibly complex network of resources, processes and actions. If you want to understand the whole, you must consider parts of it.

Therefore, if I want to know how happiness works, I need to explore the various things that make us happy and understand how they do it. And I decided to do just that. After eating a burger. I don’t know why, but I suddenly wanted to eat it,

Dean Burnett is a neurobiologist who teaches at the Center for Medical Education at Cardiff University, and the author of the popular science column in The Guardian, Brain Flapping.


Chambers, C. The Seven Deadly Sins of Psychology: A Manifesto for Reforming the Culture of Scientific Practice Princeton University Press, Princeton NJ (2017).

Cohen, J. The statistical power of abnormal-social psychological research: a review. Journal of Abnormal and Social Psychology 65, 145-153 (1962).

Engber, D., Sad face: Another classic psychology finding — that you can smile your way to happiness — just blew up. slate.com (2016).

Excerpt from Happy Brain: Where Happiness Comes From, and Why], Dean Burnett, 2018

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