The Pentagon of Neuroscience — An Infographic/Listicle for Understanding the Neuroculture

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brainzNeuroscience has hit the big time. Every day, popular newspapers, websites and blogs offer up a heady stew of brain-related self-help (neuro-snake oil?) and gee wiz science reporting (neuro-wow?). Some scientists and journalists — perhaps caught up in the neuro-fervor — throw caution to the wind, promising imminent brain-based answers to the kinds of questions that probably predate civilization itself: What is the nature of mind? Why do we feel the way we do? Does each person have a fundamental essence? How can we avoid pain and suffering, and discover joy, creativity, and interpersonal harmony?

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Why does dreaming sometimes produce Inception-style time distortions?

I answered the following question on Quora:

Last night I slept for 8.5 hours and had a dream that lasted for a month. It was full of incredible landscapes, animals, and interesting interactions with people. I woke to my alarm at 9:30, silenced it, then went back to my dream for a week before waking up at 10:30. What was happening in my brain?

Looks like no one has mentioned hippocampal replay yet!

I’m not a big fan of the movie Inception, but there is some tentative neuroscientific evidence that the time distortions experienced during dreaming have measurable neural correlates. (Experiential time distortions are of course purely subjective, so no one can tell you that you didn’t experience them. All experiences are real experiences.)

There are neurons in the hippocampus called place cells that tend to fire when an animal is in a particular location. (Incidentally, the discoverers of place cells and grid cells won the 2014 Nobel Prize in Physiology or Medicine.)

Let’s say a rat is navigating through a maze. When it reaches point A, a particular cell (or group of cells) fires. When it reaches point B, another cell fires. So there is a sequence of neuronal firing patterns that corresponds to the sequence of locations that the animal has experienced. In the picture above, each color represents the firing of one place cell. So each place cell covers a region of the maze/track.

So what does any of this have to do with dreaming? Well, when the animal is in REM (dreaming) sleep, or is quietly resting, the place cells that were recently active become reactivated. These reactivations are typically much faster than actual experience. They can also run backwards relative to prior waking experience, and can even be jumbled.

Of course, your experiences in dreams are more than a sequence of places. To extend the insights from rodent place cells into the study of actual human dreaming, we have to make a few speculative leaps. Perhaps in humans, there are ‘experience cells’ or ‘episode cells’ that encode broad categories of perception and cognition. Many neuroscientists refer to the set of cells that participate in such categorization as a cognitive map. Sleep seems to involve a free-form exploration of the cognitive map.

Hippocampal replay is widely seen as crucial for consolidating memories, and for learning. If you’ve been doing something during the day, when you sleep or rest, unconscious neural processes help you extract useful information, so that the next day your performance can improve.

Most of the data on hippocampal processing come from animals. It’s worth remembering that we can’t know if animals have the kinds of dreams that humans do. Nevertheless, the picture of sleep emerging from various lines of inquiry suggests that dreaming may be a subjective ‘side-effect’ of various sleep-related neural processes such as hippocampal replay. (Nowadays most neuroscientists would refrain from claiming that the dreaming experience per se is the purpose of dreaming.)

Many neuroscientists think that one of the purposes of sleep is to replay the past, and thereby discover new possibilities for the future. Perhaps speeding up the process is an efficient way to cycle through multiple ‘angles’ on the past, or on the wider space of possibilities. No one has any idea why this process should have subjective experiential correlates — and we are even more in the dark about why these experiences tend to be so vivid and bizarre.

Last night I slept for 8.5 hours and had a dream that lasted for a month. It was full of incredible landscapes, animals, and interesting …

Why can most people identify a color without a reference but not a musical note?

[I was asked this on Quora. Here’s a slightly modified version of my answer.]

This is an excellent question! I’m pretty sure there is not yet a definitive answer, but I suspect that the eventual answer will involve two factors:

  1. The visual system in humans is much more highly developed than the auditory system.
  2. Human cultures typically teach color words to all children, but formal musical training — complete with named notes — is relatively rare.

When you look at the brain’s cortical regions, you realize that the primary visual cortex has the most well-defined laminar structure in the whole brain. Primary auditory cortex is less structured. We still don’t know exactly how the brain’s layers contribute to sensory processing, but some theories suggest that the more well-defined cortices are capable of making more fine distinctions.

[See this blog post for more on cortical lamination:
How to navigate on Planet Brain]

However, I don’t think the explanation for the difference between music and color perception is purely neuroscientific. Culture may well play an important role. I think that with training, absolute pitch — the ability to identify the exact note rather than the interval between notes — could become more common. Speakers of tonal languages like Mandarin or Cantonese are more likely to have absolute pitch, especially if they’ve had early musical training. (More on this below.)

Also: when people with no musical training are exposed to tunes they are familiar with, many of them can tell if the absolute pitch is correct or not [1] Similarly, when asked to produce a familiar tune, many people can hit the right pitch. [2]. This suggests that at least some humans have the latent ability to use and/or recognize absolute pitch.

Perhaps with early training, note names will become as common as color words.

This article by a UCSD psychologist described the mystery quite well:

Diana Deutsch – Absolute Pitch.

As someone with absolute pitch, it has always seemed puzzling to me that  this ability should be so rare. When we name a color, for example as  green, we do not do this by viewing a different color, determining its  name, and comparing the relationship between the two colors. Instead,  the labeling process is direct and immediate.

She has some fascinating data on music training among tonal language speakers:

” Figure 2. Percentages of subjects who obtained a score of at least  85% correct on the test for absolute pitch. CCOM: students at the  Central Conservatory of Music, Beijing, China; all speakers of Mandarin.  ESM: students at Eastman School of Music, Rochester, New York; all  nontone language speakers.”

Looks like if you speak a tonal language and start learning music early, you are far more likely to have perfect pitch. (Separating causation from correlation may be tricky.)


References:

[1] Memory for the absolute pitch of familiar songs.
[2] Absolute memory for musical pitch: evidence from the production of learned melodies.

Quora: Why can most people identify a color without a reference but not a musical note?

What are the limits of neuroscience?

[My answer to a recent Quora question.]

There are two major problems with neuroscience:

  1. Weak philosophical foundations when dealing with mental concepts
  2. Questionable statistical analyses of experimental results

1. Neuroscience needs a bit of philosophy

Many neuroscientific results are presented without sufficiently nuanced  philosophical knowledge. This can lead to cartoonish and potentially harmful conceptions of the brain, and by extension, of human behavior, psychology, and culture. Concepts related to the mind are among the hardest to pin down, and yet some neuroscientists give the impression that there are no issues that require philosophical reflection.

Because of a certain disdain for philosophy (and sometimes even psychology!), some neuroscientists end up drawing inappropriate inferences from their research, or distorting the meaning of their results.

One particularly egregious example is the “double subject fallacy”, which was recently discussed in an important paper:

“Me & my brain”: exposing neuroscience’s closet dualism.

Here’s the abstract of the paper:

Our intuitive concept of the relations between brain and mind is  increasingly challenged by the scientific world view. Yet, although few  neuroscientists openly endorse Cartesian dualism, careful reading  reveals dualistic intuitions in prominent neuroscientific texts. Here,  we present the “double-subject fallacy”: treating the brain and the  entire person as two independent subjects who can simultaneously occupy  divergent psychological states and even have complex interactions with  each other-as in “my brain knew before I did.” Although at first, such  writing may appear like harmless, or even cute, shorthand, a closer look  suggests that it can be seriously misleading. Surprisingly, this  confused writing appears in various cognitive-neuroscience texts, from  prominent peer-reviewed articles to books intended for lay audience. Far  from being merely metaphorical or figurative, this type of writing  demonstrates that dualistic intuitions are still deeply rooted in  contemporary thought, affecting even the most rigorous practitioners of  the neuroscientific method. We discuss the origins of such writing and  its effects on the scientific arena as well as demonstrate its relevance  to the debate on legal and moral responsibility.

[My answer to the earlier question raises related issues: What are the limits of neuroscience with respect to subjectivity, identity, self-reflection, and choice?]

2. Neuroscience needs higher data analysis standards

On a more practical level, neuroscience is besieged by problems related to bad statistics. The data in neuroscience (and all “complex system” science) are extremely noisy, so increasingly sophisticated statistical techniques are deployed to extract meaning from them. This sophistication means that  fewer and fewer neuroscientists actually understand the math behind the statistical methods they employ. This can create a variety of problems, including incorrect inferences. Scientists looking for “sexy” results can use poorly understood methods to show ‘significant’ effects where there really is only a random fluke. (The more methods you use, the more chances you create for finding a random “statistically significant” effect. This kind of thing has been called “torturing the data until it confesses”.)

Chance effects are unreproducible, and this is a major problem for many branches of science. Replication is central to good science, so when it frequently fails to occur, then we know there are problems with research and with how it is reviewed and published. Many times there is a “flash in the pan” at a laboratory that turns out to be fool’s gold.

See these article for more:

Bad Stats Plague Neuroscience

Voodoo Correlations in Social Neuroscience

The Dangers of Double Dipping (Voodoo IV)

Erroneous analyses of interactions in neuroscience: a problem of significance.

Fixing Science, Not Just Psychology – Neuroskeptic

The Replication Problem in the Brain Sciences


Quora: What are the limits of neuroscience?

Is neuroscience really ruining the humanities?

For my latest 3QD post, I expanded on my answer to a Quora question: Is neuroscience ruining the humanities?


Here’s an excerpt:

“Neuroscience is ruining the humanities”. This was the provocative title of a recent article by Arthur Krystal in The Chronicle of Higher Education.  To me the question was pure clickbait [1], since I am both a  neuroscientist and an avid spectator of the drama and intrigue on the  other side of the Great Academic Divide [2]. Given the sensational  nature of many of the claims made on behalf of the cognitive and neural  sciences, I am inclined to assure people in the humanities that they  have little to fear. On close inspection, the bold pronouncements of  fields like neuro-psychology, neuro-economics and neuro-aesthetics — the  sorts of statements that mutate into TED talks and pop science books —  often turn out to be wild extrapolations from a limited (and internally  inconsistent) data set.

Unlike many of my fellow scientists, I have occasionally grappled  with the weighty ideas that emanate from the humanities, even coming to  appreciate elements of postmodern thinking. (Postmodern — aporic? — jargon is of course a different matter entirely.) I think the  tapestry that is human culture is enriched by the thoughts that emerge  from humanities departments, and so I hope the people in these  departments can exercise some constructive skepticism when confronted  with the latest trendy factoid from neuroscience or evolutionary  psychology. Some of my neuroscience-related essays here at 3QD were  written with this express purpose [3, 4].

The Chronicle article begins with a 1942 quote from New York intellectual Lionel  Trilling: “What gods were to the ancients at war, ideas are to us”.  This sets the tone for the mythic narrative that lurks beneath much of  the essay, a narrative that can be crudely caricatured as follows. Once  upon a time the University was a paradise of creative ferment. Ideas  were warring gods, and the sparks that flew off their clashing swords  kept the flames of wisdom and liberty alight. The faithful who erected  intellectual temples to bear witness to these clashes were granted the  boon of enlightened insight. But faith in the great ideas gradually  faded, and so the golden age came to an end. The temple-complex of ideas  began to decay from within, corroded by doubt. New prophets arose, who  claimed that ideas were mere idols to be smashed, and that the temples  were metanarrative prisons from which to escape. In this weak and bewildered state, the  intellectual paradise was invaded. The worshipers were herded into a  shining new temple built from the rubble of the old ones. And into this  temple the invaders’ idols were installed: the many-armed goddess of  instrumental rationality, the one-eyed god of essentialism, the cold  metallic god of materialism…

The over-the-top quality of my little academia myth might give the  impression that I think it is a tissue of lies. But perhaps more nuance  is called for. As with all myths, I think there are elements of truth in  this narrative.


Read the rest at 3 Quarks Daily: Is neuroscience really ruining the humanities?

How to navigate on Planet Brain

I was asked the following question on Quora: “How do you most easily memorize Brodmann’s areas?”. The question details added the following comment: “Brodmann area 7 is honestly where the numbering starts to seem really arbitrary.” Here’s how I responded:

Yup. The Brodmann numbering system for cortical areas is arbitrary. If you find a mnemonic, do let us know!

I’m a computational modeler working in an anatomy lab, so I confront the deficits in my anatomical knowledge on a daily basis! I can barely remember the handful of Brodmann areas relevant to my project, let alone the full list! I have a diagram of the areas taped up next to my monitor. :)

Neuroanatomists become familiar with the brain’s geography over years and years of “travel” through the brain. Think of it like this: what they’re doing is like navigating a city that doesn’t have a neat New York -style city block structure with sensibly numbered streets and avenues. Boston, where I live, is largely lacking in regularity, so one really has to use landmarks — like the Charles River, the Citgo sign, or the Prudential Center. The landmarks for neuroanatomists are sulci and gyri. Over time they learn the Brodmann area numbers. Only instead of a 2D city, neuroanatomists are mapping a 3D planet!


Over the years my lab — the Neural Systems Laboratory at Boston University — has developed a structural model that explains cortical areas and their interconnections in terms of cytoarchitectonic features. They don’t have a naming/addressing system, but at least they provide a way to make sense of the forest of areas!

Fig 1. Schematic representation of four broad cortical types. Agranular and dysgranular cortices are of the limbic type. Figure from [1].

The structural model [1,2] is based on the observation that the 6-layer nature of isocortex is not uniform, but varies systematically. The simplest parts of the cortex are the “limbic” cortices, which include posterior orbitofrontal and anterior cingulate cortices. Limbic cortices have around 4 distinct layers. The most differentiated parts of the cortex are the “eulaminate” cortices, which include primary sensory areas, and some (but not all!) parts of the prefrontal cortex, such as dorsolateral prefrontal cortex. Eulaminate cortices have 6 easily distinguished layers. [See Fig 1]. Interestingly, there is some evidence that the simplest cortices are phylogenetically oldest, and that the most differentiated are most recent.

Fig 2. Schematic representation of cortico-cortical projections. Figure from [2].

Every functional cortical hierarchy* consists of a spectrum of cortices from limbic to eulaminate areas. Areas which are similar tend to be more strongly connected to each other, with many layers linking to each other in a way that can be described as “columnar”, “lateral” or “symmetric”. Dissimilar areas are generally more weakly connected, and have an “asymmetric” laminar pattern of connections, in which projections from a less differentiated area to a more differentiated area originate in deep layers (5 and 6), and terminate in superficial layers (1,2 and 3). Projections from a more differentiated area to a less differentiated area have the opposite pattern: they originate in superficial layers (2 and 3), and terminate in deep layers (4,5 and 6). [See Fig 2.]

 For more on the details of the model, check out the references [1,2]. My boss, Helen Barbas, just submitted a short review about the structural model. When it is out I will append it to this answer.

To return to the city analogy, the structural model tells us that we can infer the (transportation/social/cultural?) links between pairs of neighborhoods based on what the two neighborhoods look like. If the structural model were true for cities, then neighborhoods that have similar houses and street layouts would be more closely linked that dissimilar neighborhoods. Similar neighborhoods would have one type of linkage (the “symmetric” type), whereas dissimilar neighborhoods would have another (the “asymmetric” type).

References

[1] Dombrowski SM, Hilgetag CC, Barbas H (2001) Quantitative architecture distinguishes prefrontal cortical systems in the rhesus monkey. Cereb Cortex 11: 975-988.

[2] Barbas H, Rempel-Clower N (1997) Cortical structure predicts the pattern of corticocortical connections. Cereb Cortex 7: 635-646.

Notes

* Heterarchy might be a better description than hierarchy.

Here’s a link to the Quora answer: How do you most easily memorize Brodmann’s areas?

Why we can’t anticipate what future science will look like

I was asked the following question on Quora:

What kind of information do we need to discover everything about memory in the brain and its mechanism?

I took the opportunity to recapitulate an excellent point made by Paul Feyerabend in his book Against Method, which I am currently reading.

Here’s my answer:

We’ll need to collect information at the genetic, synaptic, cellular, network, and behavioral levels (and perhaps even environmental and social levels), and integrate them into a single picture of memory in action. In other words neuroscientists are already more or less on the right track. Sometimes we know exactly what we’d like to study experimentally, but we lack the technical ability to do so. (For example, our non-invasive techniques for measuring human neural activity are extremely coarse-grained and indirect.)

But I don’t think it is possible to know in advance what specific kinds of data will prove decisive in the creation of a comprehensive theory of memory. Every new experiment can potentially throw up new theoretical questions. We can’t anticipate the evolution of a scientific research program, because we lack the very thing we are searching for: a theory that tells us what is important and what isn’t. If we already had a perfect theory, it wouldn’t be research.

We typically think of experiments and theories as completely separate entities. So we imagine that science involves a linear process like this:

observation -> theory -> new observation -> new theory ->

…and so on. But this doesn’t really capture how science actually proceeds. Think of it this way. Before we have a good theory, our observations may be contaminated by the old partially-successful theories. A theory — even a half-baked one — comes with its own ontology of what exists and what doesn’t. Experimentalists have their own working models and rules-of-thumb that tell them what is worth recording/analyzing and what isn’t. Some of these models and rules may prove wrong, once a good theory comes along. But before that theory comes along, we can’t say much about them. Theory and experiment are intertwined –each can reinforce (or refute) the other.

Philosophers have pointed out for a while now that experiments are not just true pictures of the world — they are intrinsically theory-laden. Theory goes into both the design and the analysis of experiments. This doesn’t mean they are not to be trusted. It only means that in the periods where there is no obviously successful theory, you cannot say which experiments will prove to be the building blocks of a future theory, and which will eventually prove to be wrong or in need of a fresh interpretation.

To sum this up: if we find ourselves in an unlit room we’ve never entered before, we have no choice but to fumble around in the dark until we find a light switch. We can’t anticipate our trajectory through the room — but after we find the light switch, every stumbling step and bruised toe can be restrospectively explained.

 

Does dopamine produce a feeling of bliss? On the chemical self, the social self, and reductionism.

Here’s the intro to my latest blog post at 3 Quarks Daily.


“The  osmosis of neuroscience into popular culture is neatly symbolized by a  phenomenon I recently chanced upon: neurochemical-inspired jewellery. It  appears there is a market for silvery pendants shaped like molecules of  dopamine, serotonin, acetylcholine, norepinephrine and other celebrity  neurotransmitters. Under pictures of dopamine necklaces, the  neuro-jewellers have placed words like “love”, “passion”, or “pleasure”.  Under serotonin they write “happiness” and “satisfaction”, and under  norepinephrine, “alertness” and “energy”. These associations presumably  stem from the view that the brain is a chemical soup in which each  ingredient generates a distinct emotion, mood, or feeling. Subjective  experience, according to this view, is the sum total of the  contributions of each “mood molecule”. If we strip away the modern  scientific veneer, the chemical soup idea evokes the four humors of  ancient Greek medicine: black bile to make you melancholic, yellow bile  to make you choleric, phlegm to make you phlegmatic, and blood to make  you sanguine.

“A dopamine pendant worn round the neck as a symbol for bliss is  emblematic of modern society’s attitude towards current scientific  research. A multifaceted and only partially understood set  of experiments is hastily distilled into an easily marketed molecule of  folk wisdom. Having filtered out the messy details, we are left with an  ornamental nugget of thought that appears both novel and reassuringly  commonsensical. But does neuroscience really support this reductionist  view of human subjectivity? Can our psychological states be understood  in terms of a handful of chemicals? Does neuroscience therefore pose a  problem for a more holistic view, in which humans are integrated in  social and environmental networks? In other words, are the “chemical  self” and the “social self” mutually exclusive concepts?”

– Read the rest at 3QD: The Chemical Self and the Social Self