Unrelated to all that, 5/31 edition

frogging in the rain

Dictators are only nightmares, they don’t exist in real life.  How much are the results of dictator games laboratory artifacts?

Also, because it was bad.  Seven reasons why journals reject papers.

The origin of outsight, I’d say!  Book review on foraging with prefrontal cortex.  Not that humans have particularly large frontal lobes anyway.

But look at that author list!  GWAS identifies genetic variants associated with educational attainment, but might it be a bit underpowered?  The variants only explain 2% of variation which is…not a lot.

Australia is surprisingly recognizable.  But then, broad stretches of Norway look like my homeland in the Pacific Northwest.  Geoguessr.

In the “things you should know about” department.  The world’s bloodiest civil war: China, 1850-1864.

Well there go all my passwords.  Why your password sucks.

It’s been a musical week.  Boards of Canada are back, and just as awesome as I had hoped.  Daft Punk and 2001 were made for each other.  And I really can’t get this Daft Punk/Mad Men combo out of my head.

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Mechanisms of collective decision-making in bees

Thomas Seeley gave a talk yesterday about how honeybees collectively decide on a new home, and how they use the evidence accumulation/drift-diffusion model to make decisions!  When bees are ready to start a new colony, they’ll find somewhere to hang out and swarm.  Foragers will then periodically wander off to try to find a good home: they like spacious holes high up on tall trees.  When a forager finds a home that it likes, it will report back to the swarm what its found in the form of a waggle dance.   Bees will shake their little bottoms as they walk in the direction of the potential home, and the longer they shake the further away their new home is.  But the job of the bee is more of that of a politician or proselytizer trying to get other bees to follow their lead to the new nest site that they found.  By dancing more and more, a scout bee will impress other watchful bees to go check that site out; generally, the more the scout likes the nest, the more time and energy its willing to invest in its wagglin’.

Waggle danceBut politics is rough and tumble.  Scout bees don’t only advertise nest sites they like, they’ll actively go find the other scouts advertising other nests sites and headbutt and buzz at them.  Needless to say, the more an Opposition bee gets headbutted, the less likely it is to continue advertising its own preferred site.

What this gives us, though, is a feedback loop where better nests cause more waggles and more inhibition of other nest sites which recruits even more scouts to check out the nest to do more waggles and more headbutts.  In this way, bees will essentially always find the best nest.

This resembles nothing more than the noisy evidence accumulation that is used to explain human and other animal behavior, but on a large scale; now it is not neurons or brain regions accumulating evidence, but a society.  Every time a scout brings back its opinion on the nest quality, it will recruit more bees (evidence accumulation in favor) as well as inhibit other scouts (evidence accumulation against).  When enough evidence has been accumulated, the swarm reaches a threshold and off they all go!  Interestingly, the threshold is always when 15 bees have reached the potential nest site.

Bees drift and diffuse

I had a couple of questions for Thomas Seeley which he unfortunately did not know the answer to.  I was curious whether the bees that waggle were the same ones that headbutt.  That is, are are bees just ferociously in favor of their personal site and will do what they can to promote it?  Or are some bees bullies and some bees charismatic politicians?  Each has a different set of implications.  I also wondered whether the threshold changes with swarm size.  Too many bees in a swarm and you can reach the tipping point too soon; too few and it can take too long.  Seely didn’t have evidence on that either and seemed to misunderstand the decision-making model, unfortunately (he kept explaing, “physicists say this is how it is done.”).

References

Griffin, S., Smith, M., & Seeley, T. (2012). Do honeybees use the directional information in round dances to find nearby food sources? Animal Behaviour, 83 (6), 1319-1324 DOI: 10.1016/j.anbehav.2012.03.003

Seeley, T., Visscher, P., Schlegel, T., Hogan, P., Franks, N., & Marshall, J. (2011). Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms Science, 335 (6064), 108-111 DOI: 10.1126/science.1210361

Decision Theory Journal Club: The failure of rationality when foraging

When an animal forages for food, it leaves it current location for what it hopes is a better locale.  We like to believe that this foraging decision is made when the animal expects to get more food if it leaves than if it stays.  Simple and obvious, right?  Unfortunately for our intuition, this doesn’t seem to be the case.  When I was at the Foraging workshop at Cosyne, one of the main themes of the day seemed to be that animals aren’t optimal foragers and they don’t look like our idealized rational economic agents, either.

Let’s consider an animal that is forced to choose between different sources of food.  Life isn’t easy, so it takes some time to gather the food from each source and after gathering it must leave the area before its allowed to gather food there again.  The way this particular experiment happens to be set up, if our animal was an Optimal Forager that was trying to get as much food as possible, it would apportion its time solely on the easiest food source, ignoring the rest.  Anything else is a waste of time and energy.  And yet – and yet! – the animal doesn’t do this; it still will go gather food at one location, move to the next and gather food there, and move on again.  Look at how often (pWait) an animal will wait at a food source versus how often they should wait.  It doesn’t exactly look rational to me.

Rats aren't rational economic actors

Okay, so maybe Optimal Foraging isn’t the way to go; maybe an animal doesn’t apportion its time based on maximizing food rate but uses some other economic criterion.  Maybe it matches its investment and reward so that the more reward it gets from a food source, the more time it spends there?  Data says nope.  Maybe it is a temporal discounter and prefers rewards that happen sooner over rewards that happen later?  Nope again.

Let’s consider another option: maybe the animals get attached to the different options, or just like sampling different opportunities.  What if we introduce a parameter into these different theories that represents an unwillingness to reject potential food options?  This helps a little with the temporal discounting model but where it really shines is when added to the Optimal Foraging model.   But foragers aren’t just always opposed to rejecting a food option.  Rather, as the environment becomes more resource-rich, they are more willing to reject a food option when the environment is resource poor (low opportunity cost of time) than when it is resource rich (high opportunity cost of time).

Rats don't like to give up an opportunity

But does this tell us what we think it does?  It seems like much of the effect is explained by the length of time the animal must wait for the longest food source; the length of the other options don’t seem to affect whether an animal will reject those options.  This gives us another possible option: the task was just too easy for the animals.  I would like to see whether the usefulness of the discounting model changes as everything gets harder and the environment becomes sufficiently ‘resource poor’, and also how animals behave when the basic optimal foraging model predicts something other than “always go to the easiest option”.

References

Wikenheiser, A., Stephens, D., & Redish, A. (2013). Subjective costs drive overly patient foraging strategies in rats on an intertemporal foraging task Proceedings of the National Academy of Sciences, 110 (20), 8308-8313 DOI: 10.1073/pnas.1220738110

Why Upstream Color is the best film about ecology that you’ll see

I bet when you ask a person on the street what they first think of when someone mentions ecology they will tell you something like wildlifeenvironment, or hippies.  While accurate, the world of ecology is so much more complicated and interesting!  Perhaps it’s a recency effect (okay, okay) but if I were to nominate one movie that best represents ecology it would be Upstream Color.

The new movie by the director of Primer – which, if you haven’t seen it, you better go watch it directly after you read this post – Upstream Color is visually, aurally, and in terms of story an absolutely gorgeous movie.  Science fiction, it tells the story of the life cycle of a worm, from infection of its human host to transferral to a new porcine host to release into orchids from where it will be harvested to infect a new human host, whose excretions are able to induce a psychic linking between beings.

Not only does it have a perfectly-illustrated lifecycle, it shows the effect of it all on other who are caught up in its environment: the Thief that uses it to rob people, the Sampler that takes advantage of it to sample the lives of strangers as inspiration for his music, the infected who are now linked.

The majority of the story is two infected humans who meet and fall in love, only to find that their lives become more intertwined than they expected; soon they are confusing their memories and feelings, and are pushed around by the emotions and circumstances of the pigs that had in turn been infected by their worms.  This illustration of how the broader environment beyond their control (the “upstream color”, as it were) really reinforces the “everything is connected” ethos in a visceral way.

Go watch it.

Decision Theory Journal Club: Our brains are perfect machines

A few of us have started a Decision Theory journal club where we plan on reading papers from a variety of fields that examine how decisions are made.  We have people from neuroscience, economics, and cognitive science participating (so far), including people participating through Google+ hangouts!, which will hopefully make lead to some productive discussions.  I’m a couple papers behind, but I hope to post summaries of what we have been reading.

Our first paper follows an idea that is common in the psychological literature concerning how someone gains evidence, the noisy evidence accumulator (diffusion to a boundary).  Let’s say you hear a loud noise and have to decide whether to look to your left or your right.  If the noise is almost directly behind you, it can be difficult to tell which way to look.  Both of your ears are going to be hearing something loud, and as the sound waves crash about the room it will make the sound even noisier: sometimes one ear will be louder than the other.  But one ear is usually louder than the other and when you’ve received enough evidence that one ear is hearing something louder than the other, your head will swivel and your decision is made.

We can do essentially the same thing with rats.  They can be put into a chamber where clicks will randomly come from speakers to their left and to their right, and if they turn in the direction with the most clicks, they get a reward.  Rats are fairly good at this – as are humans.  One interesting difference, though, is that when humans are certain, they will always go in the direction of the most clicks.  Rats, on the other hand, peak out at about 90% certainty; I guess they don’t trust the experiment as much as people do and want to explore their environment more!

But we’re interested in how this decision is made, so we can go back to our noisy evidence accumulator and see if that can explain how well the decision is made.  We can also through in all sorts of options: is the memory of the rats a bit forgetful? Is there all sorts of internal noise in the brain?  Is there noise in the environment?  And so on.  It turns out that the headline of the paper tells it all: rats and humans are optimal evidence accumulators.  There is no internal noise.  There is no forgetting.  Every bit of evidence that is given to the animals is in there, waiting to be used.

Fortunately, results from a different paper can explain to us what might be happening.  There are direct connections between the cortical auditory neurons and neurons in striatum – an area of the brain that receives dopamine and is involved in selecting the best action to take.  Activating these auditory neurons signals the striatum and makes the animal more likely to go in whichever direction the experimenter wants.  Inhibiting these neurons has the opposite effect.  It’s quite possible that the auditory input is interacting with this dopamine system to keep track of where an animal wants to go – and what decision it wants to make.

As for the optimality of the animals, well, that’s at least the headline, and it would be great if that were always true.  In actuality, there’s a large population of rats which show sub-optimal evidence accumulators.  Although they don’t discuss this in the paper, to me this is the most exciting result (although the lack of neural noise ranks up there, too.  Our brains are machines.).  Of course you’d expect that evolution would evolve animals that, well, make good decisions.  So why are there any animals that do show significant neural noise? Why is there such large variability in forgetfulness?  Although the majority of animals are almost perfect, many are not.  Hopefully in the future, we will be able to explain why it’s good to not always be perfect.

References
Brunton, B., Botvinick, M., & Brody, C. (2013). Rats and Humans Can Optimally Accumulate Evidence for Decision-Making Science, 340 (6128), 95-98 DOI: 10.1126/science.1233912

Znamenskiy, P., & Zador, A. (2013). Corticostriatal neurons in auditory cortex drive decisions during auditory discrimination Nature, 497 (7450), 482-485 DOI: 10.1038/nature12077

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Unrelated to all that, 05/21 edition

But I thought the second mouse got the cheese?  The loudest mouse pup gets the most attention, thanks to FOXP2.

Why are unproductive firms still around? Worth it mostly for how hard it is to get people to boil water; the diffusion of cultural innovation is harder than you’d think.

Under da sea sign language.  These fish communicating to coordinate their hunting sounds like we live in a horror movie.

Unrelated, but important!  How to be gracious.

The lethality of loneliness

Emotional isolation is ranked as high a risk factor for mortality as smoking. A partial list of the physical diseases thought to be caused or exacerbated by loneliness would include Alzheimer’s, obesity, diabetes, high blood pressure, heart disease, neurodegenerative diseases, and even cancer—tumors can metastasize faster in lonely people.

Loneliness, she said—and this will surprise no one—is the want of intimacy…They insist that loneliness must be seen as an interior, subjective experience, not an external, objective condition. Loneliness “is not synonymous with being alone, nor does being with others guarantee protection from feelings of loneliness,” writes John Cacioppo, the leading psychologist on the subject. Cacioppo privileges the emotion over the social fact because—remarkably—he’s sure that it’s the feeling that wreaks havoc on the body and brain…Another school of thought insists that loneliness is a failure of social networks. The lonely get sicker than the non-lonely, because they don’t have people to take care of them; they don’t have social support.

A key part of feeling lonely is feeling rejected, and that, it turns out, is the most damaging part.

As expected, he found the students with bodily symptoms of distress (poor sleep, high cortisol) were not the ones with too few acquaintances, but the ones who were unhappy about not having made close friends. These students also had higher than normal vascular resistance, which is caused by the arteries narrowing as their tissue becomes inflamed. High vascular resistance contributes to high blood pressure; it makes the heart work harder to pump blood and wears out the blood vessels. If it goes on for a long time, it can morph into heart disease. While Cole discovered that loneliness could hasten death in sick people, Cacioppo showed that it could make well people sick—and through the same method: by putting the body in fight-or-flight mode.

 The lethality of loneliness.

You are what you eat – wait, no, you eat what you are. Wait, that’s not it…

Mouse Mansion

The public will never tire of the nature versus nurture debate but here’s a hint: the answer in biology is always both.  But if you’ve ever known any twins, you know they can have quite different personalities which, you would think, are attributable to differences in nurture of one sort or another.  To understand this better, some scientists did what scientists like to do which is trap some mice in a little mouse palace and watched how they behaved.  These mice were isogenic so there were no genetic differences (excepting, of course, what are probably trivial mutations and some hopefully minimal epigenetic influences).

Now the mouse palace is a wonderful place but there’s not really a lot to do there beyond roaming about, exploring their environment.  But not every mouse explores their environment in the same way: some mice like to explore the whole thing, some like to stay in just a few places where they are comfortable.  This alone suggests that the environment has a strong impact on behavior, over and above genetics.  But they also point to two other facts that they find: first, that over time the variance across the population in this exploratory difference increases.  Second, more neurons are born in the hippocampus, the area related to spatial maps and learning, in the animals that roam more than in the animals that stay put.

Now although this paper is pretty cool just for Mouse Mansion (it’s Big Brother: Mice!), there’s a lot to quibble with.  They never normalize the roaming variance by the roaming mean so we don’t really know that the variability is increasing.  We don’t know whether neurogenesis is increasing more in the animals that increase their roaming more.  And even if they did, it’s totally unsurprising that there would be more neurogenesis in the animals that explored more: because that’s just what we think neurogenesis is for!  Remembering more locations!  Further, from the first moment that they are recording from – 20 days (after birth ?) – the animals that explore the least continue to explore the least, and the animals that explore the most continue to explore the most, but everyone explores more as they get older.  So whatever induced most of the variability happened before the behavior was recorded.

We already know a lot about how exploratory behavior arises, and my guess is if you assayed the dopamine receptor expression level, you’d find the differences that you’re looking for to explain the behavior.  My naive guess as to what explains the difference is that it is mostly social – the authors don’t really demonstrate any effects of exploratory learning.  We know that mice have social structure, and social structure affects serotonin and dopamine levels which in turn affect exploratory behavior.  Now I don’t know if they looked at any type of social information in the Mouse Mansion, but I’d bet that the results of social play and social behavior prior to the start of the study are what creates the difference.  The fact that a few weeks of social play can change your behavior for the rest of your life?  Now that would be interesting.

But then, you don’t have to take my word for it.

References

Freund, J., Brandmaier, A., Lewejohann, L., Kirste, I., Kritzler, M., Kruger, A., Sachser, N., Lindenberger, U., & Kempermann, G. (2013). Emergence of Individuality in Genetically Identical Mice Science, 340 (6133), 756-759 DOI: 10.1126/science.1235294

The cosmopolitan ape

Peony had arthritis and was very old, so she could barely move. She would try to climb into a climbing frame where a bunch of chimps were sitting and grooming each other. She wanted to join them, but she could barely get in there. The younger females would walk up to her, put their hands on her behind, and start pushing until she was up there with the rest. We’ve also seen cases where she started walking towards a water faucet, but, since it’s a very large enclosure and she walked with so much difficulty, a younger female would run ahead of her, take water in her own mouth, walk back to Peony, and then spit the water into her mouth so she wouldn’t need to walk all the way to the faucet. The acts of kindness made us interested in testing for altruistic behavior more systematically because the literature has claimed only humans care about others, that ifprimates are altruistic, it’s only to get favors in return.

…I don’t think primates have religions, but they may have certain superstitions. For example, if a thunderstorm comes through with an enormous amount of noise and rain, male chimpanzees will put their hands up and start walking around bipedally, in a dancing sort of fashion. It’s called a rain dance and it has been observed with chimpanzees approaching a waterfall. We really don’t know why they do it. Are they impressed by what happens? Do they think they can stop it? Of course, that would be superstition. Are they somehow in awe of nature? They also react to death. We see that primates are very strongly affected by the death of others. They will not eat for days after one of their group members has died.

Go read about the cosmopolitan ape.

The young and the restless

Elderly chinese men playing chess

It struck me recently that one of the key differences between economists and neuroscientists studying decision-making is their interest in dynamics.  Economists seem more interested in explaining how behavior operates (or should operate) on average whereas neuroscientists would like to explain trial-to-trial variability.  Decisions are rarely made just once in a lifetime, but are instead made repeatedly.  Any behaviorist would instantly tell you that this means that there will be a learning component, something that I hardly see in the economic decision-making literature (feel free to correct me if this is wrong).

In many of these repeated decisions, people are not simply making a decision in a vacuum but are responding to the actions of others.  The decision must then be balanced by their prior beliefs, the results of recent decisions, and their predictions of how other people will act.  All of this can be incorporated into a reinforcement learning (RL) paradigm, where the expected value of any action is a combination of classical RL – where every payoff suggests future payoffs, and every loss suggests future losses – as well as a ‘mentalizing’ component that predicts how the opponent is likely to act, and how the opponent will react.  By fitting the responses of different brain regions to this type of model, one can get a sense of what each region is (kind of) doing.  One region that instantly pops out is the medial prefrontal cortex (mPFC): this region is highly correlated with the prediction of other people’s behavior.

I once took a behavioral economics class in which the professor pointed out that deviations from rational behavior are only important if they translate to something in aggregate.  In other words, who cares if just a few people have abnormal mPFC function.  In a large population you won’t notice them.  But in fact there is a very large group of people with degraded mPFC: the elderly.  13 percent of the US is over the age of 65, and this group is known to have significant loss of volume in mPFC.  The prediction, then, would be that older individuals would be less inclined to take into account the behavior of other individuals when making decisions.

To test how they will act, we can take the experimental game the “Patent Race”.  In this game, two players are selected from a pool to compete for a prize.  They are each given either a large five credit or a small four credit endowment, and are asked to “invest” some portion of that.  They then get to keep whatever is left over, and the person who “invested” the most wins ten extra credits.

Cumulative distribution plots of how influential other individual's behavior is in determining one's own behavior.  Blue represents young adults and purple-dashed represents the elderly.

Cumulative distribution plots of how influential other individual’s behavior is in determining one’s own behavior. Blue represents young adults and purple-dashed represents the elderly.

There does exist a Nash equilibria to this game, and young adults will play the Nash equilibria exactly.  Old adults, on the other hand, play a significantly different strategy.  What is more interesting, though, is half of elderly adults behave as if they did not care at all about the strategy of the other player.  In other words, they are making decisions using a pure reinforcement learning strategy where they only cared about payoffs, not about how the other player was going to act.  In contrast, no young adults played like this: they all took into account the strategy that the other player would use.

References

Hampton, A., Bossaerts, P., & O’Doherty, J. (2008). Neural correlates of mentalizing-related computations during strategic interactions in humans Proceedings of the National Academy of Sciences, 105 (18), 6741-6746 DOI: 10.1073/pnas.0711099105

Zhu, L., Walsh, D., & Hsu, M. (2012). Neuroeconomic Measures of Social Decision-Making Across the Lifespan Frontiers in Neuroscience, 6 DOI: 10.3389/fnins.2012.00128

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