The Mysterious Power of Naming in Human Cognition

I’ve written a long-form essay for the blog/aggregator site 3 Quarks Daily:

Boundaries and Subtleties: the Mysterious Power of Naming in Human Cognition

Here’s a taster:

I’ve divided up the essay into four parts. Here’s the plan:

  1. We’ll introduce two key motifs — the named and the nameless — with a little help from the Tao Te Ching.
  2. We’ll examine a research problem that crops up in cognitive  psychology, neuroscience and artificial intelligence, and link it with  more Taoist motifs.
  3. We’ll look at how naming might give us power over animals, other people, and even mathematical objects.
  4. We’ll explore the power of names in computer science, which will facilitate some wild cosmic speculation.

How is depression (and other mental disorders) addressed with computational neuroscience?

Nowadays there are broadly two sorts of computational neuroscientist: those who analyze experimental data using statistical methods, and those who propose computational models aimed at a theoretical understanding. I belong to the latter category, so what I say doesn’t really apply to data analysis issues.

Computational or mathematical modeling/theory is still at a very rudimentary stage when it comes to addressing clinical neuroscientific questions. The sheer complexity of the brain’s neurons, connections, and dynamics prevents a direct “brute force” modeling of how the cells interact to generate behaviors and disorders.

But highly simplified models can be used to explore hypotheses, or the implications of experimental findings. So a schematic computational model can test out how a particular brain circuit might work, and how “breaking” the circuit in various ways could correspond with particular disorders.

For instance, depression could be modeled as a weakening of cells in the hippocampus that help a person discover new possibilities in life. Depression is often described as an inability to move out of the apathetic “place” you find yourself in. Metaphorically speaking, discovering new “places” to go to may involve the formation of new neurons (neurogenesis) in the hippocampal circuit — a process that may be disrupted in some depressed patients. (For more on the evidence read this excellent New York Times article on depression The Science and History of Treating Depression)

The (ideal!) steps involved in computational modeling of disorders might go something like this:

  1. A network of artificial neurons can be arranged in a circuit inspired by anatomical data.
  2. The dynamic properties can then be derived from physiological data. (Cell firing, fMRI, EEG etc.)
  3. The global activity or output of the network is related to some behavior, such as goal-directed decision making
  4. Changing parameters in the network or in the artificial neurons can be investigated. Weakening some parameters — such as inhibitory or excitory strength — may make the system resemble a “depressed” person or animal who is incapable of making a decision. Enhancing some other parameter may appear equivalent to the action of a drug.
  5. The modeling results can be discussed with experimentalists, and ways to verify or falsify the results can be invented.
  6. In the event of a falsification (which is pretty much all the time!) you return to step 1!
  7. If all goes well, a well-tested model can be introduced to clinicians and the general public, and can inform treatment strategies. As far as I know this has yet to happen! 🙂

A computational model I have developed in conjunction with anatomists and another modeler looks at a circuit centering on the amygdala, which is believed to play a major role in emotion, and in emotional disorders. The model suggests that prefrontal cortical modulation of the amygdala can push the system into a “cautious” state, or a more “reckless” state. And this modulation may be related to pathological brain states. We also show that weak attention could relate to overgeneralization, a problem that seems to occur in phobic patients.

Our model is highly simplified, but hopefully it will throw up some ideas for experimentalists to take further.

You can read more about our model here: Anatomy and computational modeling of networks underlying cognitive-emotional interaction | Frontiers in Human Neuroscience

(Pardon the self-promotion!)

View Answer on Quora

What do neuroscientists think of depression?

(Trying out cross-posting an answer I wrote on Quora to this blog.)

This is a very long and complicated story, and the science is at a rudimentary stage. I think the best thing you can do is read Siddhartha Mukherjee’s masterful piece on depression in the New York Times:

The Science and History of Treating Depression

Here’s a highly simplified take on some implications of this article (a must-read!) combined with some of the academic literature I have surveyed:

Some forms of depression may be equivalent to an inability to discover, create and/or access new possibilities in life. So if one gets stuck in a rut of apathy, ennui and/or sadness, one cannot feel motivated to try out or seek alternative behaviors, thoughts, and goals.

The hippocampus region of the brain may be crucial to the ability to recognize new contexts and situations. A region that is part of the hippocampal circuit — the dentate gyrus — is one of the only parts of the adult brain that produces new neurons, and this ability may be related to the brain’s ability to recognize new situations and opportunities.

There is some evidence that in depressed patients this ability to produce new neurons has been weakened. Electrical stimulation of the brain sometimes helps depressed patients, and this may be because neurogenesis in the dentate gyrus has been restored, at least partially. Chemical treatments such as SSRIs work in some cases, but this too appears to be related to the process of generating new neurons, rather than a chemical imbalance per se.

In other words, serotonin-based treatments, and deep brain stimulation may both assist the hippocampal circuit in its job of recognizing new contexts and opportunities.

And perhaps talk therapies and cognitive behavioral therapy also work to rejuvenate the brain’s ability to recognize and value new opportunities and goal-oriented behavior. But far more work is needed before all this can be considered solid science.

View Answer on Quora

Do mirror neurons explain understanding, or is it the other way round?

(Alternate title: In Soviet Russia, Mirror Neurons Explain YOU!)mirrorneurons1

A draft of this post has been sitting around for a few weeks, and while I’m happy with today’s sanity check, I still can’t help but suspect that I am missing something in the debate on “action understanding”. So I am happy to be convinced that I have completely misunderstood some key aspect of the mirror neuron story.

Mirror neuron “theory” strikes me as an odd mix of interesting experimental results and ambiguous reasoning. Nevertheless, I think the popularity of the mirror neuron idea serves as an opportunity to talk about some ideas that are hard to work into neuroscience writing aimed at a general, non-mathematical audience. People interested in mirror neurons may have intuitively found their way to a problem that computational neuroscientists consider one of the central challenges in our quest to understand biological intelligence: how does the brain detect and create invariants out of the formless and ever-changing sensory raw material available to it? As we shall see, the activity of a canonical mirror neuron displays a kind of invariance  — its firing reflects the recognition of an action irrespective of the agent performing the action.

First of all, what exactly are mirror neurons? They are supposedly a “class” of neurons that fire when a monkey is performing a goal-oriented action, and also when another individual (human or monkey) is performing the same action. A group of Italian researchers discovered them in the premotor cortex of rhesus macaques [1]. Since these neurons were found in premotor cortex the researchers inferred that they were instrumental in “understanding” the goal-directed action, irrespective of the agent. Some researchers link this “embodied” understanding with imitation, since it appears to be based in the brain areas that trigger motor responses.

People often refer to mirror neurons as the basis of empathy, based on the idea that our understanding of other people’s actions and feelings depends on our ability to imitate or emulate the feelings of others. There are two independent lines of research being merged here: (i) psychological studies in humans linking imitation with empathy, and (ii) mirror neuron studies in monkeys linking brain activity with imitation or action understanding. But there are many more connections that need to be made before even hinting at a neural theory of imitation-based empathy. First of all, the evidence for mirror neurons in humans is still being debated. And even if a consensus emerges that they exist as described, we still don’t have a very clear picture of the neural mechanisms linking the processes that are presumably involved in empathy: sensation, perception, pattern recognition, motor control and subjective feeling. Ignoring these and other major holes in the story, VS Ramachandran felt confident enough to assert that mirror neurons were “the driving force behind the great leap forward in human evolution”. I have cited some work that points out flaws in the way the experimental results have been interpreted [2], but for the purpose of this post, let’s just take the results as given.

I don’t deny that mirror neurons seem to be linked to fascinating phenomena, but we should not be surprised at all to find these phenomena reflected in the brain. Where else could they be? So let’s investigate what we already know — but rarely state explicitly — about the human mind-brain-behavior continuum. This will allow us to put the mirror neuron hype in perspective, and perhaps find out where the real scientific puzzles are lurking.

Let’s think about a typical goal-directed action — aiming at a target with a bow and arrow. When you see Robin Hood aiming at the target, you can say, “Robin Hood is aiming at the target”. When you see yourself aiming at the target, you can say, “I am aiming at the target.” This is beyond obvious. Now, unless you suspect that language is controlled by the stomach or the pancreas, you already knew — and I am just reminding you — that the concept of “aiming” that you are able to identify in other people and in yourself is somewhere in your brain.

I see what you're doing there!

I see what you’re doing there!

Convinced that concepts are represented somewhere in the brain (either in a localized or distributed manner), we can be reasonably confident that  the concept of “aiming” can affect the brain processes that control your mouth, lungs and vocal chords, allowing you to say the word “aiming”, or mirror the action. And it is clear that the concept must be independent of who is holding the bow and arrow. Imagine the alternative. You might call Robin Hood’s act of aiming “trapooling” and you might call your own act of aiming “caduffing”. If you were unable to see the similarity between what you do and what anyone else does, you would be unable to replace “trapooling” and “caduffing” with a single word that describe the action you and Robin Hood were both performing. If we were really plagued by a blindness to similarity between different experiences, we would be unable to communicate about them at all, because our perceptions would be unique and unrepeatable, and therefore private and inaccessible.

Language depends on the ability to recognize and categorize things, processes, and abstract concepts. So we can safely assume that some neurons or groups of neurons or patterns of neural activity will appear to be “mirroring”, because they can access your muscles and trigger aiming when you see someone (anyone!) engaged in aiming at a target. And since concepts must have access the motor system, it’s no surprise to find neurons that correlate with them in motor areas of the brain. They would have to affect the motor system at some stage, right?

It’s not hard to see why people often associate “understanding” with motor responses. How do we tell if someone has understood what we are saying? We can ask them to imitate the action we’re talking about. Or we can ask them to point to what we are talking about. Either way, to say that someone else understands a concept is to say the he or she can act on it appropriately. [See note 2 for some comments on why this kind of understanding is not merely a feature of language.]

All this becomes apparent when we witness a child learning the meaning of a word. When my cousin’s son was 2 years old he was still slowly figuring out how to use colour words. He would pick up a coloured Duplo block and ask us, “Blue?” If the block was in fact blue one of us would nod and smile, and he would be very pleased with himself. But sometimes he picked up a red block and asked “Blue?” And we would gently shake our heads and correct him. Clearly it took some trial-and-error for him to grasp the meaning of the word. Now how do we know he has understood? We don’t typically use brain scans or mind reading. We infer his understanding from his ability to use the word “blue” in the appropriate contexts. If he can use the word blue only when it’s appropriate, and pick out blue things when they are pointed out to him, most people will happily admit that he has understood the concept of blue. And the same goes for action words like running, jumping and aiming. Even for someone who is paralyzed or in a vegetative state, we infer understanding from the ability to respond to stimuli– to move an eyelid or finger, or to modulate internal brain activity that is detected by a scanner.

Very little neuroscience is needed to support the assertion that the brain is central to these phenomena. Neuroscience is really needed to explain how the brain does all this. We still have no idea. If we did, you would be able to teach your smartphone a new board game by just having it listen in while you read out the rules.

So what then is the radiant how question obscured by the mirror neuron hype cloud? I think it might be about invariance. In physics and mathematics, invariance (or symmetry) means insensitivity to a change or transformation [3]. For example, a perfect uniform square is invariant with respect to rotations of 90 degrees. If you rotate a square by 90 degrees, it looks the same. Let’s say you’re playing that toddler’s puzzle of slotting wooden blocks of various shapes into a board with corresponding holes. There’s a hole for each block. You pick up a square block and try to place it in the square hole. Even if you rotate it by 90 degrees, it will fit in the hole. An equilateral triangle, on the other hand, cannot be rotated by 90 degrees and still fit in its designated hole. But it does have an invariance — you can rotate it by 120 degrees and it will fit. “Rotation by 90 degrees” and “Rotation by 120 degrees” are examples of transformations. So a square is invariant to rotation by 90 degrees, and an equilateral triangle is invariant to rotation by 120 degrees. The board with the holes in it is like a rudimentary pattern recognition system. It recognizes shapes of particular sizes, for a handful of orientations. You could imagine a high-tech version of this puzzle, in which successfully fitting a square block in the corresponding hole triggers a computerized voice that says “You have found a square!”

Humans can make use of far more invariants that the board with the holes. When it comes to shape recognition, the average humans is better than most advanced computer programs. Our ability to detect squares, for instance, is invariant or near-invariant to changes in size, orientation, viewing angle, texture, and so on. And consider how well we recognize other people! You can often spot a person you know even if he or she is far away, in disguise, in an unexpected place, wearing different make-up or sporting a new hairstyle. So your internal representation of that person is invariant with respect to a variety of transformations.

Similarly, your internal representation of an action is, to varying degrees, invariant to a change in the agent performing the action, the nature of the goal or target, and the context in which the action is taking place. Thus you can recognize “running”, whether it’s a human running on a track, a chicken running in a field, or a bull running through the streets of Pamplona. And you can recognize “aiming” whether you are doing it or Robin Hood is doing it, so your internal representation of “aiming” is invariant with respect to the change of agent. How the brain facilitates finding and creating this sort of invariance is a major open problem, and is being investigated from various angles by psychologists, cognitive scientists, artificial intelligence researchers, and neuroscientists.

Mirror neuron activity appears to be invariant with respect to a change of agent. This is clearly an interesting experimental finding, and gives us possible physical correlates of a complex psychological phenomenon. But does it deserve all the recent hype? After all, (a) we know from human behavior that the brain must show invariants, and (b) we don’t know how particular invariants arise.

Both the hype and the ambiguous reasoning I mentioned before seems to stem from using mirror neurons as an explanation for understanding, rather than a consequence of brain processes that remain to be explained.  In popular articles, and even in the technical literature, mirror neurons are starting to sound like magical antennas that pick up what is happening, and somehow “know” that the same goal-oriented action is being performed irrespective of who is doing it. And if you lack these magical antennas, so the story goes, you may become autistic! Neuroscientists often make fun of the idea of the homunculus — a little man inside your brain that sits in a control room and “sees” what you see, “hears” what you hear and so on. The homunculus was dismissed because it simply passes the explanatory buck down one level, forcing us to ask how the homunculus sees and hears in the first place. But the mirror neuron “system” somewhat resembles a homunculus — it does all the hard work of recognizing goal-oriented actions and determining that they are similar in the first place.

So the real question to be asked is: how are mirror neurons able to do what they do? Mirror neurons are not magical antennas. The invariants they are able to create or detect must therefore be a product of their intrinsic properties, their inputs, and their interactions with other cells. If they seem like antennas then we must understand how they can “tune-in” to particular aspects of stimuli and not others. It is the goal of computational neuroscience to understand how the great radio that is the brain helps an organism tune-in to various “frequencies” in the world in order to play the music that is its behavior. To do so we investigate the biophysical mechanisms by which the neural networks act together to control the state of the whole body. Simply saying that the mirror neurons “do empathy” doesn’t  help much.



Blogospheric critiques of mirror neuron theory

Mirror Neurons — The unfalsifiable theory — Talking Brains

What’s So Special About Mirror Neurons? — SciAm

Mirror Neurons: The Most Hyped Concept in Neuroscience? — Psychology Today


  1. Here’s a somewhat unfair and exaggerated analogy that gets at the inadequacy of mirror neurons as an explanation of anything. Imagine if physicists explained the elliptical orbits of planets by simply stating that the planets contain innate “ellipse particles” that are predisposed to making elliptical paths. So ellipses are caused by ellipse particles. This would be a useless explanation. On the one hand, we already knew about the ellipses. On the other, the explanation doesn’t tell us anything about how the ellipse particles work. Solving the real puzzle requires understanding how the planets interact with the sun and each other, and how these interactions result in elliptical motion. The task of neuroscience is to understand how interactions between neurons in neural networks allows for mirroring behavior to emerge. And if we want to talk about empathy, we will have to do even more work, connecting sensory signals with perception, pattern recognition and subjective feeling. In the interim we are saying not much more that this: “Mirroring is caused by mirror neurons.” We could easily reverse this, and say “The faculty of mirroring results in mirror-neuron-like firing patterns.” Rather than an explanation of empathy or understanding, mirror neuron theory thus far seems to be little more than a re-description of the phenomena with no added explanatory power.
  2. In order to learn the names of new concepts, you must be able to recognize phenomena in the world in agent-independent ways. For example, let’s say you recently learned the name of a particular yoga position — padmasana, for instance. Before discovering the name, you must be able to recognize the position as something that multiple people, including yourself, might be capable of performing. Otherwise when someone points to a person performing padmasana, you will not know what is being pointed to, and therefore will not be able to recognize the concept in other people and yourself. Without this recognition, naming would be impossible. So even though I use language to illustrate this process of mirroring or invariance-finding, the process is “prelinguistic” and seems to form part of the scaffolding necessary for language development.

[1] Rizzolatti G, Fogassi L, & Gallese V (2001). Neurophysiological mechanisms underlying the understanding and imitation of action. Nature reviews. Neuroscience, 2 (9), 661-70 PMID: 11533734 [pdf]

This is the group that discovered mirror neurons. This paper reviews the idea of mirror neuron theory as an account of “action understanding”. They say that “By action understanding, we mean the capacity to achieve the internal description of an action and to use it to organize appropriate future behaviour”. They claim that mirror neuron theory supports the idea that “an action is understood when its observation causes the motor system of the observer to ‘resonate’.”

[2] Hickok, G. (2009). Eight problems for the mirror neuron theory of action understanding in monkeys and humans. Journal of Cognitive Neuroscience,21(7), 1229-1243. doi:10.1162/jocn.2009.21189 [PubMed]

Here are some key problems listed by Greg Hickok (who also wrote the Talking Brains blog post I linked to). As you can see, they are much more specific to the experimental methodology, the specific location where the purported mirror neurons were found, and the relevance of mirror neurons to “action understanding”.

  • There Is No Evidence in Monkeys That Mirror Neurons Support Action Understanding
  • Action Understanding Can Be Achieved via Nonmirror Neuron Mechanisms
  • The Relation between Macaque Mirror Neurons and the ‘‘Mirror System’’ in Humans Is Either Nonparallel or Undetermined
  • Action Understanding in Humans Dissociates from Neurophysiological Indices of the Human ‘‘Mirror System’’
  • Damage to the Inferior Frontal Gyrus Is Not Correlated with Action Understanding Deficits

[3] Rosen, J. (1995). Symmetry in Science: An Introduction to the General Theory. [goodreads link]

This is a highly recommended textbook explaining the concept of mathematical symmetry, and its relevance to science. In fact, Rosen argues very convincingly that science is the quest to uncover symmetry in nature.

Star Trek and Tin Man pics courtesy Wikipedia.

Robin Hood Clipart courtesy FCIT. [Source: Louis Rhead. Bold Robin Hood and His Outlaw Band (New York and London: Harper & Brothers, 1912) 29. Retrieved March 13, 2013, from]

What does fMRI measure, anyway?

In the previous post, I began discussing functional magnetic resonance imaging (fMRI), a popular but controversial experimental technique that allows researchers to investigate brain activity in humans and animals in a relatively safe and  non-invasive way. But I found myself commenting more on the problems associated with statistical methods. While these problems are important to acknowledge and deal with, they are not specific to fMRI. In this post I hope to leave stats aside, and examine some of the biophysical assumptions underlying interpretations of fMRI studies. For some years I had been ignoring fMRI papers because of the myriad problems of interpretation, but a recent paper from Aniruddha Das’s group at Columbia University (Cardoso et al., 2012) rekindled my curiosity, and spurred me to survey the literature on the links between neural activity, blood flow, and metabolism in the brain.

“It’s a long way from behaviour to BOLD.” From Singh (2012). Click to view full size.

Despite its popularity, it is still not entirely clear what fMRI is measuring. Many people know that the technique measures a signal called BOLD: the blood-oxygen-level-dependent contrast. But I imagine fewer people know that the  relationship between blood flow and neural activity is still under active investigation. This is vividly illustrated in the figure above, taken from Singh (2012).

When people point to an fMRI heatmap and say that some brain region or the other “lights up”, what they mean is that oxygenated blood is flowing into this region at a rate that is higher than some baseline. (See my previous post for a discussion of baselines and subtractions.) Interpreting blood flow as neural activity rests on a key assumption: that neural activity is coupled with increased blood flow. Blood flow, in turn, is assumed to correlate with metabolic activity.  The idea rests on a line of thinking that is intuitively straightforward [but see notes]. Neural firing activity and synaptic change both require energy in the form of glucose. The brain does not store glucose –it produces it locally as needed, with the help of oxygen delivered through haemoglobin in the blood. So we would expect increased neural firing to necessitate increased flow of oxygenated blood. In summary, the simplest description of fMRI is that it measures oxygenated blood flow, which changes as a result of oxidative metabolic processes that drive neural firing. But none of the steps in this purported chain of causation (Neural Activity > Metabolism > Blood Flow) has been fully understood, so as usual, the devil is in the details.

To understand the typical view of fMRI, an analogy might help. Let us imagine a modern city, most of whose inhabitants require electricity to do their work. Imagine that the city planners wish to know how many of the inhabitants work at night. They can’t go from house to house to check, so they decide to monitor electricity usage, for which they have accurate meter readings. They assume that no one works in the dark, no one sleeps with the lights on, and that lights and machines cannot be turned on automatically. They also assume that the engineers in charge of the power plants do not actively change or redirect electricity flows. If these assumptions are correct, then the meter readings can be aggregated to create a heatmap of the city’s nocturnal work habits. The more people work at night, the more electricity they draw from the grid. Thus electrical power usage becomes a useful stand-in for human activity.

Researchers who use blood flow as a proxy for brain activity are like the city planners who use electricity usage as a proxy for nocturnal work activity. But in the labyrinthine metropolis that is the brain, perhaps some inhabitants don’t need that much power? Perhaps some need a lot? Perhaps the engineers in charge of the power plants, by virtue of a holistic, long-term, and wide-angled view of the needs of the city, can anticipate which neighborhoods will need the most power at any given time, and preemptively allocate it?

A recent study from Aniruddha Das’s group (Cardoso et al., 2012) adds to a growing body of work that should encourage us to take that last possibility seriously. They looked at the correlation between blood flow and neural activity, and found that the blood flow signal was not just coupled with neural firing — it had more to say! The research team examined neural activity and blood flow simultaneously — but instead of using fMRI, which is a coarse measure, they used a technique called optical imaging. Since the technique is invasive, it cannot be done in humans, so it was conducted with rhesus macaques. They investigated the responses of cells in primary visual cortex (V1) to periodic sensory tasks. The monkeys had to fixate on a visual stimulus, and if they succeeded, they were rewarded with juice. Monkeys love juice.

Cardoso, Das and colleagues found that the blood flow signal could be decomposed into two separate signals — one that was linearly correlated with stimulus-triggered local neural firing, and another than had no connection with local electrical activity or the visual stimuli being presented, but instead was related to the task the monkey was performingPrior work (Sirotin and Das, 2009) from the same group showed that this non-sensory component of the blood flow signal is anticipatory, and can preempt the behavior of the monkey. Cardoso et al. suggest that the non-sensory component of the BOLD signal might reflect neuronal processes that have nothing to do with stimulus-driven spiking activity. The BOLD signal might reflect neural modulation of blood flow via top-down executive control or via a timing signal from the brain stem.

This line of research is exciting to me for two reasons. Firstly, it points to a potential source of ambiguity in interpreting fMRI studies that assume the BOLD signal is a perfect stand-in for local firing rate or local field potentials. If these results hold up, it means that the BOLD signal carries at least two pieces of information — one about local neuronal activity, and the other about anticipatory or preparatory task-related activity. Researchers must be careful about interpreting a BOLD signal as neural firing — perhaps in some situations only task-related blood flow is being modulated. In such situations a brain region might “light up” even when its neurons are not electrically active. If researchers are interested in localizing neural firing, they must be sure to subtract out all irrelevant aspects of the signal [but see note 5].

The second source of excitement relates to the anticipatory component of blood flow. Most of us are not interested in the electrical activity of the brain for its own sake — we assume it is the most important physiological phenomenon in the brain, underlying adaptive human and animal behavior. We typically assume that  cognition, emotion, memory and control over the body are subserved primarily by neurons. But what if anticipatory blood flow is itself an adaptive process? If this implies too radical a paradigm shift, consider this possibility: neural activity in some brain regions may play a role in modulating the blood flow elsewhere. This could be an extension of the brain’s role in monitoring and controlling bodily states. Perhaps there are metabolic coordination centers in the brain that can influence blood flow? Perhaps active resource allocation can improve speed and efficiency? Perhaps some parts of the brain assess contexts and goal states to infer which brain regions will require additional metabolic resources in the future. There is evidence that the locus coeruleus may participate in the active modulation of blood flow (Bekar et al, 2010).

To return to the city analogy, perhaps the engineers running the power stations can anticipate when and where power usage will be particularly high — say, during wartime, or in periods of creative expansion and neighborhood renewal — and redirect power actively, as peers of the city planners, rather than as passive subordinates.

Studies such as these serve as an important reminder that the brain is not simply a computer manipulating abstract information — it is also like a city with a bustling economy that requires energy and raw materials for its growth and maintenance. Resource allocation via blood flow and other mechanisms may therefore be an active, adaptive process, and therefore as important to neuroscientists as neuronal signaling.


If you spot errors, desire clarification, or have comments / additional citations, feel free to let me know in the comments section!



  1. The concept of neural activity can be a bit fuzzy in these contexts. As KD Singh (2012) says: “the very phrase neural activity is in itself a rather poorly specified and ultimately meaningless term. In most people’s minds the term is probably a surrogate for the firing of action potentials. However, within the cortex there are multiple neural signals, at different oscillatory frequencies, that might all contribute to the metabolic demand that then drives the BOLD signal. Furthermore, it’s not clear which of these neural signatures are most relevant to each aspect of perception and cognition.”
  2. “[F]ocal, stimulus-induced increases in brain blood flow are driven by local metabolic demand“. This hypothesis has been around since at least 1890, and has since been almost universally accepted, despite some important critiques by researchers such as Peter T. Fox, whose group has carried out experiments that indicate that blood flow and metabolism are not very closely coupled. For example, in a study from 2010, his group concluded that the cerebral blood flow response is “mediated by factors other than oxygen demand”.
  3. A complete picture how neural activity, blood oxygen, and cellular metabolism depend upon each other must also incorporate astrocytes and other non-neuronal brain cells — cells that were until recently assumed to serve custodial and infrastructural purposes, but are now believed to play active roles in signal transduction and memory.
  4. The review by Heeger and Ress (2002) is also worth looking at. The topic of neural activity and metabolism is far too complicated for a single blog post — maybe to fully understand we must “solve the brain” (!?) — so it’s important to glance at the primary literature from time to time.
  5. A somewhat bipolar commentary on this paper by Karl Friston (2012)  seems to suggest the fMRI community was well aware of the task-related component of the BOLD signal. He says “they have chosen to use their set-up to largely confirm things that have been known for over a decade”. And yet he goes on to say that the “implications of their findings are far-reaching”. Not sure what to make of this.


Cardoso MM, Sirotin YB, Lima B, Glushenkova E, & Das A (2012). The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components. Nature neuroscience, 15 (9), 1298-306 PMID: 22842146

Singh, K. D.  (2012). Which “neural activity” do you mean? fMRI, MEG, oscillations and neurotransmitters. NeuroImage Volume 62, Issue 2, 15 August 2012, Pages 1121–1130.

Sirotin, Y. B. & Das, A. (2009). Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity Nature, 457 (7228), 475-479 DOI: 10.1038/nature07664

Bekar, L. K., Wei, H. S., & Nedergaard, M. (2012). The locus coeruleus-norepinephrine network optimizes coupling of cerebral blood volume with oxygen demand. Journal of Cerebral Blood Flow & Metabolism. doi:10.1038/jcbfm.2012.115

Fox, P. T. (2012). The coupling controversy. NeuroImage Volume 62, Issue 2, 15 August 2012, Pages 594–601.

Heeger, D. J., & Ress, D. (2002). What does fMRI tell us about neuronal activity?. Nature Reviews Neuroscience, 3(2), 142-151. doi:10.1038/nrn730

Friston, K. J. (2012). What does functional MRI measure? Two complementary perspectives. Trends in Cognitive Sciences.

Brains, Boats & Baseball bats — some thoughts on fMRI

I wanted to write a post on a new fMRI paper that looks really interesting. But in attempting to do so I felt the need to condense some of my own cloudy thoughts on fMRI. Think of this as one part explanation, one part rant, and one part thinking aloud.

Functional magnetic resonance imaging (fMRI) has become a popular tool for human neuroscience and experimental psychology. But its popularity masks several major issues of interpretation that call into question many of the generalizations fMRI researchers would like to make. These generalizations often lead to overzealous and premature brain-centric redefinitions of high-level concepts such as thinking, emotion, love, and pleasure.

Sensationalist elements in the media run with these redefinitions, taking advantage of secular society’s respect for science in order to promote an unfounded reductionist attitude towards psychological and cultural phenomena. A reductionist attitude feeds off two often contradictory human desires: one for simple, intuitive explanations, and the other for controversial, novel solutions. This biased transfer of ideas from the laboratory to the newspaper, the blog and the self-help book is responsible for a rash of fallacious oversimplifications, such as the use of dopamine as a synonym for “pleasure”. (It correlates with non-so-pleasurable events too.)

Neuroscientific redefinitions of high-level phenomena, even when inspired by an accurate scientific picture, often fall prey to the “mereological fallacy“, i.e.,  the conflating of parts of a phenomenon for the whole. Bits of muscle don’t dance, people do. And a free-floating brain doesn’t think or feel, a whole organism does.

Wikipedia saves lives.

what is this i dont even

But before dealing with the complex philosophical, sociological and semantic issues posed by neuroscience, we must be sure that we understand what the experimental techniques are actually telling us. fMRI experiments are usually interpreted as indicating which parts of the brain “light up” during a particular, task, event, or subjective experience. For instance, a recent news headline informs us that “Speed-Dating Lights Up Key Brain Areas“. The intuitive simplicity of such statements masks a hornets’ nest of interpretation problems.

What does an fMRI picture represent? fMRI results only reach statistical significance if the studies are carried out in a group of at least 5-10 people, so the “lighting up” is reliable only after pooling data [But see the edit below]. And in addition to averaging the data from multiple subjects, usually the experimenter must also average multiple trials for each subject. So  the fMRI heat-map is an average of averages. Far from being a snapshot of the brain activity as it evolves through time, an fMRI heat-map is like a blurry composite photograph produced by superimposing several long-exposures of similar, non-identical things.

As if this were not problematic enough, the results of an fMRI scan of a single person must be subtracted from a baseline before further analysis. The brain is never quiescent, so to find out about activity in a particular region during a particular event or task, the experimenter must design a control that is identical to the task except with respect to the phenomenon of interest. Imagine a boat with three people on it, sailing on choppy seas. If an observer watching calmly from the shore wants to understand how each person is moving on the boat, she must first factor out the overall motion of the boat caused by the waves. Only then can the observer determine, say,  that one person on the boat is jumping up and down, the other is swaying from side to side, and the third is trying to be as still as possible. The subtractions used in fMRI studies are like the factoring out of the motion of the boat — they allow the experimenter to zoom in on the activities of interest and ignore the choppy sea that is the brain’s baseline activity.

Here’s another analogy that might help (me) understand what’s happening with fMRI averaging and subtracting. Let’s say you want to understand the technique of baseball batters that allows them to successfully hit a particular kind of pitch. You take videos of 20 batters hitting, say, a fastball. The fastball is the “task”, and each attempt to hit the ball is a “trial”. Suppose each batter makes 100 swings, and around 50 connect with the ball. The rest are strikes. So there are 50 hits and 50 misses. You take the videos for the 50 hits and average them, so you get a composite or superimposed video for each batter. Then you take the videos of the 50 strikes, and average those. This is the control. Now you “subtract” these two averaged videos, for each batter, getting a video that would presumably show a series of ghostly images of floating, morphing body parts — highlighting only what was different about the batter’s technique when he made contact with the ball versus when he didn’t.  In other words, if the batter’s torso moves in exactly the same way whether he hits or misses, then in the video the torso will be subtracted out, and only the head, arms and legs will be visible. Finally, you pool together the subtracted videos for all 20 batters and average them. Now you have a single video that shows the average difference in batting technique between successful hits and misses. If you’ve done everything right, you have some idea of which batting techniques tend on average to work against fastballs.

But consider what may be misleading about this video. Perhaps there are two different techniques or strategies for hitting a fastball. The averaged video will only show a kind of midway point between them. Basically, individual differences can get blurred out by averaging. So sometimes the batting technique that seems to be suggested by the average doesn’t really exist — it  can be an artifact of averaging, rather than a picture of an actual trend. Good statistical practices help experimenters avoid artifacts, but as the task and the stats become more complicated, the scope for misunderstanding and misuse expand. In other words, every mathematical angel is shadowed by its very own demon.

Another interpretation issue has to do with what the subtraction means. In the case of the missing torso, you can assert that the difference between success and failure at hitting a fastball does not depend on the torso’s movement, since it’s the same regardless of what the batter does. But this does not mean, however, that the torso has nothing to do with batting. After all, we know the torso is what links up everything else and provides the crucial central services to the arms and legs. So if a brain region doesn’t light up in an fMRI study, this doesn’t mean that it has no role to play in the task being studied. It may in fact be as central to the task as the torso is!

But the problems associated with averaging and subtraction crop up in all forms of data analysis, so they’re among the inevitable hazards that go with  experimental science. The central question that plagues fMRI interpretation is not mathematical or philosophical, it’s physiological. What neural phenomenon does fMRI measure, exactly? It seems a partial answer may have been found, which I’ll touch on, hopefully, in the next post.



  • William Uttal’s book The New Phrenology (which I have only read a chapter or so of) describes how the “localization of function” thread that runs through fMRI and other neuroscientific approaches may be a misguided return to that notorious Victorian pseudoscience. Here is a precis of the book.
  • This New Yorker piece deals with many of the issues with fMRI, and also links to related resources including a New York Times article.
  • Neuroscience may not be as misleading to the public as was originally thought. Adding fMRI pictures or neurobabble was thought to make people surrender their logical faculties, but a recent study suggests that the earlier one may have been flawed.
  • For a vivid illustration of the potential effects of averaging, check out this art project. The artist averaged every Playboy centerfold, grouping them by decade, producing one blurry image each for the 60, 70s, 80s, and 90s. Don’t worry, it’s very SFW.
  • You can also play around with averaging faces here.


EDIT: In the comment section Kayle made some very important corrections and clarifications:

“You say that fMRI results only reach statistical significance if the studies are carried out in groups. This is not quite right. Almost all fMRI studies begin with a “first-level analysis,” which are single-subject statistics. This way you can contrast different conditions for a single subject. With large differences, small variablity, and enough trials, robust maps can be created. This is done for surgical planning when doctors are considering how much brain they can resect surrounding a tumor without endangering someone’s ability to move or talk. When examining mean differnces between groups, however, you need to examine results from multiple people (“second-level analysis”). Again, this is not specific to fMRI. The rule of thumb goes something like this: Most people are interested in being able to detect differences with effect sizes of about 1 SD or above. To do this with some confidence (Type II p < 0.20) you need about 10 to 30 observations per group.”


” Every fMRI result you’ve seen includes single-person single-session results that generally are not reported because most people aren’t interested.”