Life is not in an equilibrium

Much of economics is built upon the idea of equilibrium: supply equals demand, and if there are opportunities to drive down price to equilibrium, they will immediately be taken.  Economists assume this because it is hard to predictions otherwise.  Important assumption though this is, this can be hard to empirically test; all such tests are necessarily indirect approximations.  So what happens when you have a virtual economy and can empirically test this?  Well, it turns out that the economy is frequently out of equilibrium:

The data makes it look like external shocks take you out of equilibrium (unsurprisingly) and that equilibrium returns fairly quickly.  But just look at how often the economy is out of equilibrium!  It’s all the bloody time!  It almost looks like instead of a fixed point we’ve got a limit cycle with varying amplitude.  This shouldn’t be a surprise, but it is nice to get real data.  Next the question is: which are the equilibrium and which are the non-equilibrium situations, how often do they each occur, and how big are the differences between markets?

The best defense is a good stick

Some typically great writing by Matt Soniak:

The screams were an alarm call indicating a predator on the ground. The group’s alpha male took off in the direction of the noise and was joined several other adult members of the group, and they all began making facial and vocal threats and shaking and throwing branches toward a dense tangle of liana vines…The mobbing of a predator by capuchins is a real sight. Led by adult and subadult males, the monkeys will make loud threat and alarm calls while breaking large branches from trees and dropping them on the predator like bombs.

…Some monkeys will get closer to predators if the aerial assault doesn’t work. In 1988, biological anthropologist Susan Boinski watched as a group of capuchins in Costa Rica killed a terciopeloor fer-de-lance, after pinning it to the ground with a heavy branch and then approaching on the ground to beat it with sticks. One monkey rained down a flurry of 55 strikes to the snake’s body and head with a stick that it clumsily wielded like a club. Boinsky later approached the snake and found a mangled mess of bleeding wounds, exposed tissue and broken bones.

Read more about monkeys banding together to fight predators (and use weapons!)

Soybeans all up in your brainz

It’s not exactly a secret that marijuana causes the munchies (see: Half Baked).  Marijuana aka cannabis contains what are called endocannabinoids (see the resemblance?), which are neuromodulators that your brain uses to regulate feeding.  But marijuana isn’t the only thing that contains endocannabinoids; vegetable oil does too:

If what happens in people mirrors what happens in animals, then the prevalence of soybean oil, corn oil and other polyunsaturated vegetable oils in today’s Western diet means your body is “dumping out a lot of these marijuana-like molecules into your brain,” explains Hibbeln, a nutritional neuroscientist. “You’re chronically a little bit stoned.”

Vegetable oil’s link to endocannabinoids is just one example of newfound and surprising ways that foods can confuse calorie-sensing networks and foster obesity — in some cases by damaging the brain. Especially troubling: Excess body weight itself can exaggerate the risk of the brain telling a well-fueled body that it is running on empty.

Deciding whether to eat or not eat seems like a pretty fundamental decision, no?  But decisions aren’t made in some platonic void, they’re contextual and dependent on our environment.  Hence, if your environment contains a lot of vegetable oils, well, you’re going to be making different decisions than if it didn’t.  What’s great about this research is that it explains how the neural pathway is modified by the environment, and which receptors are mediating this interaction with the environment.  But mostly, how the environment causes the munchies.

Round 1: FIGHT

I don’t know about you, but when I was in High School, I was treated to a close-up of more than a few fights (none including me, of course).  If you’d asked me, if those fights were totally random I probably would have said no: the two guys – and it was almost always guys – had something between them which festered for a while before they eventually went at it.

Just like humans, macaques live in extremely social environments.  Also just like people, macaques engage in fights which can be one-on-one or just be a straight-up gang war.  Since we can’t just sit the macaques down and ask them what it was that made them throw down, we can turn to statistics to figure out: what makes a monkey fight?

Jessica Flack studies the patterns and dynamics of social systems.  In a paper from her lab, Daniels et al. examined the statistics of monkey fights.  There are a few ways to analyze this, so the group examined three different possible strategies that the monkeys could be using.  First, they could just go at it willy-nilly; this was encoded in the form of a maximum entropy model – a model that basically assumes there are no correlations unless absolutely required.  This model assumed the only thing important to a fight was how often that particular individual fought.  On the other hand, a monkey could get in a fight because it hated the guts of some other guy, or because it had an ally it needed to defend; this was also examined with a maximum entropy model, albeit one that included the direct interactions between two individuals.  Finally, it’s possible that there are other more complex interactions – your buddy really wants you to go fight for that third guy, even though you don’t really know him.  This was tested with a ‘sparse coding’ model, the specifics of which aren’t actually important here.

What they find is that, just like people, it’s the direct connections that matter.  On virtually every metric, the model that includes the interactions between individuals is better than the one that just assumes random acts of violence.  But not only that, the direct interactions between individuals is mostly what’s important – when you include more than that, the only thing that you can do a better job of predicting is how many individuals there are in a fight in general, though not how big a fight is given a specific individual is in it.  In other words, you recruit your allies, they don’t do recruiting for you.

One of the advantages of using these models is that they can be used to estimate how complex the socialization is.  If one of these chimps wanted to remember the details of every fight with perfect fidelity, it would take 23,500 bits – roughly equivalent to a note written using only 3000 total letters (kind of; letters in words aren’t actually uncertain so it would probably take many more than this).  But if you only need to take into account these correlations, you can compress it to 1000 bits, or only 125 letters, and still do almost as well.  Which means that maybe social interactions aren’t as complicated as you might have thought – there is a lot of structure to them.

Of course, this raises the point that the ‘good’ predictions are only right 15% of the time.  Should we call that a good prediction?  For the complexity of what we’re trying to predict, maybe, but clearly it means that there is a lot more going on than the models let on.  Social interactions happen more than just because of general feelings between individuals; they are likely triggered by specific – or spontaneous – events.  But if a simple model can explain 15% of all of a social behavior in a large group of individuals?  And give an estimate of how complex those interactions actually are?  Well I’d say that’s pretty interesting.


Daniels BC, Krakauer DC, & Flack JC (2012). Sparse code of conflict in a primate society PNAS DOI: 10.1073/pnas.1203021109

Photo from

Individuals, groups, and decisions

Imagine buying something from a friend: do you think you’d give him a better or worse offer than you’d give a stranger? Would you buy something you might not normally want if pressured into it by a friend?  The thing is, our preferences and decisions aren’t consistent from moment to moment, they’re always changing.  One pet interest of mine has long been how decisions change when in groups than when alone.  Do we make the same decisions?

It turns out that we don’t.  People in groups are more akin to ruthless machines, making the economically “rational” self-interested decision even when the socially-optimal decision is something else.  Take, for example, the Trust and Ultimatum games.  Whereas people – westerners, at least – tend to make more pro-social offers, sharing and avoiding pure self-interest, when a group of individuals make a decision, they’re more likely to make the ‘rational’ decision and screw the other group.  Groups of people favor themselves, even when the individuals in the groups may have been willing to share if on their own.  When you consider that a lot of our economy is built on how much we trust each other, this could be a bad thing.

But it’s not all bad!  Groups of individuals may choose the economically optimal decision – but sometimes this decision is good for everyone.  Take games that have multiple equilibria, especially ones that require coordination.  When individuals are playing in groups for a coordination game, they are more likely to be able to coordinate with the other group in order to help everybody.  Even an individual that is simply on their own, but identified as part of some group, will be able to coordinate better.

Humans strongly identify with in- and against out-groups, and it shouldn’t be surprising to anyone with a knowledge of psychology that people in groups may be more self-interested.  But what is interesting is how often the self-interest helps everyone.  In general, in conditions where there is a unique pure-strategy equilibrium, the group will find this equilibrium and hurt everybody.  In conditions where there are multiple equilibria that require coordination, groups will be more efficient and help everybody.  Groups are better at strategizing, better at taking into account (so far) unchosen strategies, and better at anticipating the actions of other groups.

There are a few different possible explanations for this.  It could be – and is extremely likely – that there are strong in-group versus out-group effects.  It could be that being in a group increases the motivation of individuals.  But then there’s a new study in Nature that suggests a possible alternative: that the individuals are being given more time to think about their decision, and people who are given more time to think are more self-interested.  Not that this should be a surprise to those with beer-goggles.

If you just strap a single person down, and ask them to play a social dilemma game, GO NOW FAST!  They will make a choice and it will, more often than not, be somewhat fair.  If you strap that person down, and ask them to play, but hey, take your time?  They’ll think about it, maybe, who knows, nurse some grievances, and decide that they are more important and be less fair.  In fact – and I think this is way cool – if you look at their data, how much people are willing to give is almost perfectly negatively linearly correlated with the logarithm of the decision time.  I mean, look at this beauty:

And maybe this can partly explain what is happening with the groups: they’re simply given more time to rationalize their decisions.  I’m sure that’s not everything – as I said, in-group effects are strong in people – but it is a lot.  It turns out that the two great rationalizers are taking your time, and worrying about your group.

G Charness & M Sutter (2012). Groups make better self-interested decisions Journal of Economic Perspectives DOI: 10.1257/jep.26.3.157
Rand DG, Greene JD, & Nowak MA (2012). Spontaneous giving and calculated greed. Nature, 489 (7416), 427-30 PMID: 22996558

Culture and human evolution

Edge has an excellent interview with Joseph Henrich on cultural and biological evolution.  He argues that the distinction between the two is fuzzy; he says they are inseparable but I think what he really means is that we don’t know how to separate them yet.  Although they are distinct concepts, they have feedback on each other which makes the separability difficult-to-impossible (though does not mean they are not distinct!).  To get an example of what he’s saying here:

Another example here is fire and cooking. Richard Wrangham, for example, has argued that fire and cooking have been important selection pressures, but what often gets overlooked in understanding fire and cooking is that they’re culturally transmitted—we’re terrible at making fires actually. We have no innate fire-making ability. But once you got this idea for cooking and making fires to be culturally transmitted, then it created a whole new selection pressure that made our stomachs smaller, our teeth smaller, our gapes or holdings of our mouth smaller, it altered the length of our intestines. It had a whole bunch of downstream effects.

We did not evolve the ability to make fire.  But once we were able to make fire, biological evolution took hold.  Cultural evolution drove biological evolution.  An important point that he makes is that culture and technology can only reach a certain level of richness in any given population level.  More complex societies require larger – or more connected – populations:

I began this investigation by looking at a case study in Tasmania. Tasmania’s an island off the coast of Southern Victoria in Australia and the archeological record is really interesting in Tasmania. Up until about 10,000 years ago, 12,000 years ago, the archeology of Tasmania looks the same as Australia. It seems to be moving along together. It’s getting a bit more complex over time, and then suddenly after 10,000 years ago, it takes a downturn. It becomes less complex.

The ability to make fire is probably lost. Bone tools are lost. Fishing is lost. Boats are probably lost. Meanwhile, things move along just fine back on the continent, so there’s this kind of divergence, and one thing nice about this experiment is that there’s good reason to believe that peoples were genetically the same.

You start out with two genetically well-intermixed peoples. Tasmania’s actually connected to mainland Australia so it’s just a peninsula. Then about 10,000 years ago, the environment changes, it gets warmer and the Bass Strait floods, so this cuts off Tasmania from the rest of Australia, and it’s at that point that they begin to have this technological downturn. You can show that this is the kind of thing you’d expect if societies are like brains in the sense that they store information as a group and that when someone learns, they’re learning from the most successful member, and that information is being passed from different communities, and the larger the population, the more different minds you have working on the problem.

If your number of minds working on the problem gets small enough, you can actually begin to lose information. There’s a steady state level of information that depends on the size of your population and the interconnectedness. It also depends on the innovativeness of your individuals, but that has a relatively small effect compared to the effect of being well interconnected and having a large population.

The analogy between brains and population level is a good one: in the brain, it is not the individual neurons that give rise to complex behavior, but the interactions between them.  The number of neurons determines the complexity of patterns that can be extracted from the environment.  A simple example in computer science is the perceptron; if you have one neuron, you can make a linear decision between two choices.  As you connect more and more neurons, you’re able to increase the complexity of the decision by adding another linear filter; eventually you can be arbitrarily complex, but at low numbers of neurons you’re going to be really limited in the number of patterns that you can decode.

But the level of complexity also has an impact on how we interact with each other:

In the Ultimatum Game, two players are allotted a sum of money, say $100, and the first player can offer a portion of this $100 to the second player who can either accept or reject. If the second player accepts, they get the amount of the money, and the first player gets the remainder. If they reject, both players get zero. Just to give you an example, suppose the money is $100, and the first player offers $10 out of the $100 to the second player. If the second player accepts, he gets the $10 and the first player gets $90. If he rejects, both players go home with zero. If you place yourself in the shoes of the second player, then you should be inclined to accept any amount of money if you just care about making money.

Now, if he offers you zero, you have the choice between zero and zero, so it’s ambiguous what you should do. But assuming it’s a positive amount, so $10, you should accept the $10, go home with $10 and let the other guy go home with $90. But in experiments with undergraduates, Western undergraduates, going back to 1982, behavioral economists find that students give about half, sometimes a little bit less than half, and people are inclined to reject offers below about 30 percent.

…I was thinking that the Machiguenga would be a good test of this, because if they also showed this willingness to reject and to make equal offers, it would really demonstrate the innateness of this finding, because they don’t have any higher level institutions, and it would be hard to make a kind of cultural argument that they were bringing something into the experiment that was causing this behavior.  I went and I did it in 1995 and 1996 there, and what I found amongst the Machiguenga was that they were completely unwilling to reject, and they thought it was silly. Why would anyone ever reject? They would almost explain the subgame perfect equilibrium, the solution that the economists use, back to me by saying, “Well, why would anybody ever reject? You lose money then.” And they made low offers, the modal offer was 15 percent instead of 50, and the mean comes out to be about 25 percent.

We found we were able to explain a lot of the variation in these offers with two variables. One was the degree of market integration. More market-integrated societies offered more, and less market integrated societies offered less. But also, there seemed to be other institutions, institutions of cooperative hunting seemed to influence offers. Societies with more cooperative institutions offered more, and these were independent effects.

This creates a puzzle because typically people think of small-scale kinds of societies, where you study hunter-gatherers and horticultural scattered across the globe (ranging from New Guinea to Siberia to Africa) as being very pro social and cooperative. This is true, but the thing is those are based on local norms for cooperation with kin and local interactions in certain kinds of circumstances. Hunter-gatherers are famous for being great at food sharing, but these norms don’t extend beyond food sharing. They certainly don’t extend to ephemeral or strangers, and to make a large-scale society run you have to shift from investing in your local kin groups and your enduring relationships to being willing to pay to be fair to a stranger.

This is something that is subtle, and what people have trouble grasping is that if you’re going to be fair to a stranger, then you’re taking money away from your family. In the case of these dictator games, in order to give 50 percent to this other unknown person, it meant you were going home with less money, and that meant your family was going to have less money, and your kids would have less money. To observe modern institutions, to not hire your brother-in-law when you get a fancy job or you get elected to an office is to hurt your family. Your brother-in-law doesn’t have a job now. He has to have whatever other job he has, a less good job.

Laughter is life

I read somewhere that laughter is a social phenomena.  If you’re sitting at home alone, reading a funny book, a lot of times you smile and perhaps occasionally emit a giggle or two.  When a friend is making jokes?  You laugh.  Even just hearing laughter can cause you to laugh:

Psychology researchers jumped on the new phenomenon of “canned” laughter, confirming that laugh tracks do indeed increase audience laughter and the audience’s rating of the humorousness of the comedy material, attributing the effect to sometimes baroque mechanisms (deindividuation; release restraint mediated by imitation; social facilitation; emergence of social norms, etc). Decades later, we learned that the naked sound of laughter itself can evoke laughter – that you don’t need a joke.

Recorded laughter produced by a “laugh box”, a small, battery-operated record player from a novelty store, was sufficient to trigger real laughter among my undergraduate students in a classroom setting. On their first exposure to the laughter, nearly half of the students reported that they responded with laughter themselves. (More than 90% reported smiling on first exposure.) However, the effectiveness of the stimulus declined with repetition. By the 10th exposure, about 75% of the students rated the laugh stimulus as “obnoxious”, a reminder of the sometimes derisive nature of laughter, especially when repetitive and invariable.

What’s interesting here is that laughter is being used as a social stimulus, and has a very clear “stimulus – response” function: you hear the sound of laughter, it makes you laugh.  I would love to see a study of the network behavior of cascades of laughter.

Laughing with brings the pleasure of acceptance, in-group feeling, and bonding. But laughing at is jeering and ridicule, targeting outsiders who look or act differently, pounding down the nail that sticks up, shaping them up, or driving them away. Being laughed at can be a very serious, even dangerous business.

Laughter is a rich source of information about complex social relationships, if you know where to look. Learning to “read” laughter is particularly valuable because laughter is involuntary and hard to fake, providing an uncensored, honest account about what people really think about each other, and you.

What is the mechanism in the brain that translates this?  Do other animals laugh?  Sophie Scott suggests that we do.  If anyone knows of any studies of the mechanisms of translating laughter to social input, let me know!

(Another super interesting video here)

Plants are people too

Ever since I started studying neuroscience, plants have always fascinated me.  These guys don’t have a nervous system, really, but they are able to do a lot of things we would normally expect to require a nervous system.  A recent book – which I hope to read soon, by god I put it near the head of my 300+ goodreads “to read” list – has a lot to say on how plants experience the world:

When Chamovitz introduces the baffling way that irises appear to “remember” what color of light they last saw or how the parasitic plant dodder (Cuscuta pentagona) can “smell” whether it’s next to a tomato (one of its preferred hosts) or a stalk of wheat, it’s hard not to share his enthusiasm for unraveling these mysteries. He elaborates on elegant early experiments in plant biology as well as modern-day discoveries, providing a window on the work of the many scientists who clarified the mechanisms driving these perplexing phenomena. The latter include the use of genetic mutants of the botanical workhorse Arabidopsis to unveil 11 different photoreceptors that allow the plant to discern, among other things, whether it was last exposed to the red light present in the morning or the far-red light present in the evening. Finely tuned gas chromatography has revealed how dodder differentiates between the attractive chemicals in eau de tomato and the repulsive ones ineau de wheat.

…Consider proprioception, the sense of the relative position of our body parts in space that allows us to complete coordinated movements without tripping over our own feet. Do plants have something like proprioception? Certainly, says Chamovitz, but for plants, it’s about the position of their parts relative to gravity.

Photoreceptors?  Odor receptors?  Proprioception?  These all seem like fundamental attributes of our sensory nervous system.  And yet what use does a plant have for a nervous system?  It moves too slowly to really need one, I imagine.  They can also sense and communicate with each other in a way that seems similar to how bacteria sense and communicate with each other.  I imagine we have  a lot to learn from plants about basic principles for nervous system integration of social and sensory inputs.

PS. There’s more here!

You eat what you are

Following up on my recent post on scientists trawling the silk road in search of taste receptor variants, there’s serendipitous news out that a set of polymorphisms have been found that determines whether you think cilantro tastes like soap or like heaven!  Our good friends at 23andme analyzed their database of users who responded to the questions, “Does fresh cilantro taste like soap to you?” or “Do you like the taste of fresh (not dried) cilantro?” and found a SNP – a single letter of your DNA – that was associated with the soapiness.  But they’re not the only ones – two other unpublished studies have found other SNPs associated with the taste of soapiness.

The thing is, taste and smell perception are complicated things.  Every food – every ingredient – is a unique combination of a large set of flavors and odorants.  Turning an ingredient from godly to soapy might take the interaction of many different odorant receptors to tell your brain that what you’re smelling is more like soap than an herb.  And it’s not purely genetic, either; much of the preference (or perhaps hatred) for cilantro is learned.

A similarly noxious food is the (disgusting) brussel sprout; some people taste these as terribly bitter and disgusting – and others do not.  This is reliably linked to a single SNP and here, too, the connection is not perfect.  Still, 80% of the time, if you know your genetic variant, you’ll know what you think of brussel sprouts.  The ability to taste this bitterness isn’t just in brussel sprouts: it’s also in broccoli, coffee, and dark beers.

What these SNPs are probably doing is modifying the receptor that detects the bitter-tasting chemical in a such a way that it can no longer detect it at all.  The receptor itself isn’t what causes something to be ‘bitter’: that role is relegated to the neuron upon which the receptor is located.  A famous experiment by Cori Bargmann showed that if you have an odor receptor on one neuron, it can make smells attractive; move it to another neuron and that scent becomes repulsive.  The receptor only senses the chemical, it does not tell you how you feel about it.  Flavors are complex combinations of these chemicals, and it is the combination of neurons that are activated by these chemicals that let you know whether the flavor is bitter or sweet, delicious or disgusting.

This is why it will be so interesting to see what the results of the silk road expedition are.  Do these genetic variants arise because there is selective pressure to make these herbs as ‘attractive’ in people?  Will cultures with lower levels of the cilantro soap SNPs be more likely to use recipes that include cilantro – even if in the next valley over there are a group of people with very different sets of SNPs?  And it should make you wonder what it means to have ‘good taste’ – in food as well as everything else.


Nicholas Eriksson,, Shirley Wu,, Chuong B. Do,, Amy K. Kiefer,, Joyce Y. Tung,, Joanna L. Mountain,, David A. Hinds,, & Uta Francke (2012). A genetic variant near olfactory receptor genes influences cilantro preference arxiv

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The straw that broke the camel’s back

One of the most interesting things in neuroscience is that we find again and again that the different nervous systems come up with the same solutions to related problems.  Take the ability to make a decision – something that is about as basic and fundamental as you get, while needing to be applied to all sorts of situations.  In monkeys deciding whether to look at or away from an object, if you track neurons in one area of the brain (LIP), you see that the activity fluctuates up and down until it gets to some threshold and the decision is made.

This principle extends beyond simple decisions to include what may seem to be more complex decisions such as the decision to fight or (at a later time) flee.  Although it may not be the first example to leap to mind, cricket fighting can give us plenty of insight into how this decision might be represented in the brain.  Cricket fights have been a popular past time in China for over a millenia (though imho beetle fighting is much more entertaining).  Crickets make for great subjects for scientific study: they’re small, don’t take a lot of resources, don’t complain too much, and have highly stereotyped behaviors which make quantifiable analysis simple.  When two male crickets meet, they will often fence with their antennae (pictured above), and as fights become more intense will move to engaging with their mandibles and eventually some pretty intense wrestling-like grappling.  The winner will then sing the loser off to prove his might.

All cricket fights are required to start with antennal fencing.  If their antennae are removed, the poor little guys will not fight.  They still recognize each other – they can still court – but there is no fighting.  Of course, not every cricket will want to fight every other cricket.  They have some sense of hierarchy, so a highly dominant cricket will be more likely to run off a highly submissive cricket.  And if a cricket is placed in a tiny little home, as soon as 2 minutes later they will be more likely to get aggressive in order to defend their home.  There’s something very anthropomorphic and sweet about that, I think.

Aggressiveness is represented in the brain through the neuromodulator octopamine, and this can have surprising side effects.  See, octopamine is the insect equivalent of neuroadrenaline and it is released by physical movements.  The fans of Chinese Cricket Fighting will already know this; it has long been suggested that if your cricket isn’t being aggressive enough for your taste you should just chuck the guy in the air.  And what do you know?  He’ll be more likely to put up a fight now.  Even better is it to make him fly for a while in a wind tunnel.  So we see that the representation of aggression can have surprising side effects.

The flip side of fight is flight, and a cricket needs to know when in a fight to switch to flight.  One can begin to determine how a cricket knows when to flee by mangling the cricket.  Sorry guys, that’s science for you.  You can blacken their eyes so they cannot see, lame their mandibles so that they cannot bight, and clip their claws so that they cannot tear, then mamed crickets to fight and see how they do.

Blinded crickets who fight crickets with mamed mandibles will win 98% of the time.  That’s quite a lot!  These blinded animals will not feel much of a physical blow from their opponents, and will not be receiving any visual social input either.  Remove either of these conditions – make a nonblinded cricket fight a lamed one, or a blinded cricket fight a healthy one – and the healthy one will probably win.  So how do these crickets know when to flee?  By the steady accumulation of visual and physical input.  Once enough of this input is received – possibly represented in the form of some hormone or peptide – it’s time to fly for the cricket!

It wouldn’t be surprising if something like this happened in humans, too.  We already have a proverb for it after all: the straw that broke the camel’s back.  Crickets will continue to fight after a serious injury, only to retreat seconds later for no apparent reason.  So too are humans known to accept plenty of punishment and grit out, only to have something small cause them to cry and give up when their threshold is reached.  This is one of the the fundamental lessons of decision neuroscience so far: discrete decisions are made when information has accumulated up to some threshold.  It’s just not always easy to tell what our thresholds are.


Stevenson PA, & Rillich J (2012). The decision to fight or flee – insights into underlying mechanism in crickets. Frontiers in neuroscience, 6 PMID: 22936896

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