Transmitting behavior between groups

Crowds chanting in unison, wolves hunting in a pack, the superorganism that is the ant colony: these are all things that require the coordination of many individuals to accomplish something that they could not on their own.  And yet, replace any individual with another and the behavior will turn out pretty much the same.  Right?

Let’s look at the example of colonies of harvester ants that forage in the desert for seeds.  These ants adjust their collective foraging behavior through small interactions between individuals: ants decide whether to leave the colony to search for food if they sense other successfully returning foragers.  This way, if a lot of ants are returning with food, more ants will leave because the world is feeling bountiful.  But if few ants are returning with food, fewer new ants will leave to search; it’s just not worth it when there’s not a lot of food out there.  After all, leaving the colony carries a cost.  Every moment in the desert desiccates the poor ant foragers, and if they stay out too long they’ll up and die.

Screen shot 2013-07-17 at 10.30.46 AMAnt colonies don’t forage every day.  Their foraging depends not just on the abundance of food, but on environmental conditions such as heat and humidity.  Beyond this, there are colony-specific traits.  Some colonies will forage every day, some will just forage some days, and this trait persists across years.  This is trait is somewhat transmissible as colonies that reduce their forage on an uncommon day also have daughter colonies that are likely to reduce their foraging on uncommon days. This transmission of collective behavior suggests that responses to environmental conditions can be transmitted from one colony to the next.  This is the human equivalent of a teenager from Scandinavia founding a new town in the midwest and recapitulating parts of his culture there…

It’s not clear what the mechanism here is.  Since daughters of a queen continue to forage in a colony-specific manner, the transmitted component must be unrelated to the genetic contribution of the father.  So is it genetic, and linked to the X chromosome?  Or is it in some sense cultural, learning from the behavior of the greater colony it was raised in?  Hopefully someone who knows more about young ant behavior can enlighten us here…

Either way is interesting.  I can certainly imagine that a dynamic, collective behavior is controlled genetically.  Dopamine receptor expression is linked to foraging behavior, so genetic differences here could easily transmit motivation to forage.  And yet – cultural transmission would be pretty exciting, too.  This would indicate there is some sort of learned component and makes me wonder: if we can measure all the movement of an animal throughout its life, how well could we predict the behavior of a whole group?

References

Gordon DM (2013). The rewards of restraint in the collective regulation of foraging by harvester ant colonies. Nature, 498 (7452), 91-3 PMID: 23676676

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.

Cosyne: Foraging!

I think I have found my people.  The workshops after the main Cosyne meeting were smaller and more focused, and really allowed you to delve into a topic.  I spent the first day at the Neural Mechanisms of Foraging workshop and found myself a bunch of neuroecologists!

I think I’m just going to summarize a bunch of talks instead of any one individually.  I missed the first few minutes of introduction, but I got the impression that this was the first meeting of ‘neuroforagers’ to ever actually take place; Michael Platt called it a “coming out party for foraging”.  Foraging – to define it briefly – is the decision to leave a reward source to explore new options.  It’s apparently a great task for monkeys too – many basic behaviors that we train monkeys to perform can take a long time to train; teaching them to do foraging happens in a single session.  It’s totally natural which is itself a reason for why we should be studying it!

There were two recurring themes at the talks – the anterior cingulate cortex (ACC) is the foraging center and that economics approaches aren’t doing much good.  Talk after talk recorded from the ACC or studied how ACC activity is shaped.  Just like the Basal Ganglia meeting that The Cellular Scale attended, every talk included The Dopamine Slide.  Michael Platt suggested at the end that he hoped at foraging meetings every talk would include a figure from one of his papers that I have now forgotten!  Well, I don’t do ACC so probably not for me anyway.

The other theme was the failure of economic models to explain behavior.  Talk after talk included some variant of, “we tried fitting this to a [temporal discounting/risk-preference/reinforcement-learning/optimal foraging]  model but it didn’t account for the data”.  Almost all of them said that!  The naive assumption that we should move to optimize immediate reward is, somehow, failing.  Some kind of new principle (or perhaps better model-fitting) will be needed to consistently explain actual behavior.

Old bees get a new lease on life (through glutamate!)

Have you ever heard a story about an elderly person who seems surprisingly fine and with it in the outside world, but is then transferred to a nursing home where they quickly slide from their mental peak?  Have you ever stayed at home all day, playing video games (ahem) and feeling a bit sluggish only to go back to mentally stimulating work and feel more alert?  No matter what people say, our work is our life.

Honeybees spend the first two or three weeks of their life as nurses, taking care of the young, tending to the queen, building out and cleaning the hive.  When they get older, they get reassigned to a job outside the hive as a forager.  Now they have to search out nectar and pollen and live in the dangerous outside world.  They are quick to die off as the stress of the outside environment and downright intense physical work causes them to age.  Not only are there physical effects, but mental ones, too: their ability to learn and associate is impaired.

But not all is lost for these bees!  Sometimes disaster falls a hive and more nurses are needed.  When this happens, some forager bees return to become nurses.  Baker et al studied these bees to see how returning to the hive affected them.  Although in some ways the returned bees looked like their foraging compatriots, in terms of learning and memory they were identical to their younger nursing brethren.  They had a new lease on life!

Some of these returned nurses did better than others.  Baker et al looked at what proteins were differentially expressed between these two groups, and the data pointed to proteins that affected physical structure (alpha-tubulins), stress and cell maintenance, and neuronal functioning and signaling.  One of the most abundantly different proteins was the glutamate transporter homologous to EAAT2.  Glutamate is the primary neurotransmitter in the brain, and is the basis for the most common form of long-term learning.  The glutamate transporter will remove glutamate from the extracellular space, so different amounts of glutamate transporter will change the concentration.  This means that cells will be generally more or less excitable and will have different levels of plasticity.

There are clearly a couple of problems with this study which can basically be labeled statistics.  Do the bees learn better because they have returned to nursing?  Or do they return to nursing because they are the better bees?  This is selection bias.  Also, if the bees are learning better, is it because of this change in proteins?  Or were those differences in proteins there before they returned, and something totally different has changed?

What the paper may provide evidence for, though, is the social brain hypothesis.  This hypothesis suggests that the reasons humans got smarter is because we lived in social groups, and the fittest individual was the one that was smartest at dealing with the social group.  Perhaps the bees that return need to be the smartest because they have to return and deal with a social environment, a possibly more intellectually demanding environment.  These bees have more to keep track of, a variety of other bees to placate.  Not only does your job affect you, but so does your social environment.

Well, it’s something to think about at least.

References

Baker et al (2012). Age-related learning deficits can be reversible in honeybees Apis mellifera
Experimental Gerontology DOI: 10.1016/j.exger.2012.05.011

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Papers of the week, 6/10 – 6/17

Joint attention, social-cognition, and recognition memory in adults

“The early emerging capacity for Joint Attention (JA), or socially coordinated visual attention, is thought to be integral to the development of social-cognition in childhood…We tested the validity of the differentiation of IJA [initiating joint attention] and RJA [responding to joint attention] in our paradigm in two studies of picture recognition memory in undergraduate students. Study 1 indicated that young adults correctly identified more pictures they had previously viewed in an IJA condition (67%) than in a RJA (58%) condition, η2 = 0.57. Study 2 controlled for IJA and RJA stimulus viewing time differences, and replicated the findings of Study 1.”

The biological bases of conformity

“We review the relevant literature considering the causation, function, history, and ontogeny of conformity, and describe a computer-based experiment on human subjects that we carried out in order to resolve ambiguities. We found that only when many demonstrators were available and subjects were uncertain was subject behavior conformist. A further analysis found that the underlying response to social information alone was generally conformist. Thus, our data are consistent with a conformist use of social information, but as subjects’ behavior is the result of both social and asocial influences, the resultant behavior may not be conformist.”

Effects of age, sex, and  neuropsychological performance on financial decision-making

“Results indicated that Older participants significantly outperformed Younger participants on a multiple-choice test of acquired financial knowledge. However, after controlling for such pre-existing knowledge, several age effects were observed. For example, Older participants were more likely to make immediate investment decisions, whereas Younger participants exhibited a preference for delaying decision-making pending additional information…In terms of sex differences, Older Males were more likely to pay credit card bills and utilize savings accounts than were Older Females. Multiple positive correlations were observed between Older participants’ financial decision-making ability and performance on neuropsychological measures of non-verbal intellect and executive functioning. Lastly, the ability to justify one’s financial decisions declined with age, among the Older participants.”

Efficient coding and the neural representation of value

“Although normative theories of choice have outlined the theoretical structure of these valuations, recent experiments have begun to reveal how value is instantiated in the activity of neurons and neural circuits. Here, we review the various forms of value coding that have been observed in different brain systems and examine the implications of these value representations for both neural circuits and behavior. In particular, we focus on emerging evidence that value coding in a number of brain areas is context dependent, varying as a function of both the current choice set and previously experienced values. Similar contextual modulation occurs widely in the sensory system, and efficient coding principles derived in the sensory domain suggest a new framework for understanding the neural coding of value.”  By Paul Glimcher, so of course you should read this.

Orbitofrontal cortical activity during repeated free choice

“OFC neurons encode important features of the choice behavior. These features include activity selective for exceptionally long runs of a given choice (stay selectivity) as well as activity selective for switches between choices (switch selectivity). These results suggest that OFC neural activity, in addition to encoding subjective values on a long timescale that is sensitive to satiety, also encodes a signal that fluctuates on a shorter timescale and thereby reflects some of the statistically improbable aspects of free-choice behavior.”

Physical competition increases testosterone among Amazonian forager-horticulturalists: a test of the ‘challenge-hypothesis’

“We tested whether the Tsimane, pathogenically stressed forager-horticulturalists of the Bolivian Amazon, would express acute T increases in response to physical competition…Linear mixed-effects models were used to establish that T increased significantly immediately following competition (β = 0.23, p < 0.001), remaining high 1 h later (β = 0.09, p = 0.007); equivalent to 30.1 and 15.5 per cent increases in T, respectively. We did not find larger increases in T among winners (p = 0.412), although T increases were positively associated with self-rated performance (β = 9.07, p = 0.004). These results suggest that despite lower levels of T than US males, Tsimane males exhibit acute increases in T at the same relative magnitude reported by studies in industrialized settings, with larger increases in T for those who report better individual performance.”  I covered this partly in my introduction to testosterone earlier in the week.

Individual plastic responses by males to rivals reveal mismatches between behavior and fitness outcomes

“Behaviour (mating duration) was remarkably sensitive to the level of competition and fully reversible, suggesting that substantial costs arise from the incorrect expression of even highly flexible behaviour. However, changes in mating duration matched fitness outcomes (offspring number) only in scenarios in which males experienced zero then high competition. Following the removal of competition, mating duration, but not offspring production, decreased to below control levels. This indicates that the benefit of increasing reproductive investment when encountering rivals may exceed that of decreasing investment when rivals disappear.”

The dynamics of coordinated group hunting and collective information transfer among schooling prey

“Predators were found to frequently form coordinated hunting groups, with up to five individuals attacking in line formation. Attacks were associated with increased fragmentation and irregularities in the spatial structure of prey groups, features that inhibit collective information transfer among prey. Prey group fragmentation, likely facilitated by predator line formation, increased (estimated) per capita risk of prey, provided prey schools were maintained below a threshold size of approximately 2 m2.”

Aging-related increases in behavioral variability: relations to losses of dopamine D1 receptors

“Increasing ISDs [intraindividual standard deviation] were associated with increasing age and diminished D1 binding in several brain regions (anterior cingulate gyrus, dorsolateral prefrontal cortex, and parietal cortex) for the interference, but not control, condition. Analyses of partial associations indicate that the association between age and IIV in the interference condition was linked to D1 receptor losses in task-relevant brain regions. These findings suggest that dysfunctional DA modulation may contribute to increased variability in cognitive performance among older adults.”

Posts this week

Type of species and type of social network determine how parasites infect a population as it gets larger

Hermit crab social networks!

Social needs sculpt primate faces

Remember: song learning is social learning

Social decision-making across species …and more, but older

Economic Sociology newsletter on New Institutional Economics

A bit late on this one but genoeconomics is an important trend

A really great writeup about serotonin and anorexia

Elephant seal foraging!

When grasshoppers are afraid of spiders, plants decay more slowly.  This is the whole point of this blog: the environment and our interactions with our environment shapes our bodies (and minds!), and the two really cannot be disentangled.

Deciding about deciding

In the field of decision-making, a typical laboratory experiment goes something like this: give a subject an option between two choices, let them decide, force them to do it again.  Put a novel variation on the way the decision is made and BAM, you’ve got yourself a little paper!  Mostly the decisions are something akin to choosing between a picture of a cake and a picture of a moldy cheese.  But a more realistic decision process might involve choosing whether the cake and moldy cheese are the best one can get; maybe you should look for something better!  One (might) call this a foraging decision, something that has been studied extensively in other contexts.  Let’s look at how the brain represents this decision.

The setup of the first experiment is a bit tricky.  Subjects were shown a choice of two rewards that they could choose between, or a set of other rewards that could be selected from randomly.  In the initial ‘foraging’ round, they got to decide whether to keep the two rewards, or get two new (random) ones for a small price.  This was repeated until they were satisfied with the two options, at which point they moved to a ‘decision’ round where they chose between the two rewards.  It is a bit unsurprising that subjects required a higher expected value from the ‘foraging’ option in order to choose it.  The authors call this their ‘foraging readiness’ but it would be more accurate to call it their level of risk-aversion.  It has been known for a long time that people prefer more sure options than more risky options, even if the economically rational man would have no preference.  I guess that’s a less sexy phrase, though.

The authors zeroed in on the anterior cingulate cortex (ACC).  Like pretty much everything that comes out of fMRI and cognitive studies, there is a lot of controversy about what exactly the ACC is doing (this isn’t a ding on fMRI or cognitive studies, it’s just really hard).  Here, researchers find that activity in ACC was positively correlated with the expected value of the foraging and negatively correlated with the expected value of the binary decision.  The BOLD signal in ACC was able to predict the number of times a subject would repeatedly search, as well as how the subject weighted the expected value of the foraging option.  And that last point is important!  Even though the researchers knew that the two new options would be chosen with equal probability, the subjects did not know that.  Or, they at least did not know that they could trust that information from the researchers who are notoriously unreliable in what they tell their subjects.   So the signal probably represents some measure of what their posterior probability distribution was, as well as how much they valued risky gains and losses, all convolved with the expected reward of each option.

Another recent paper looked at a visual task in monkeys and skipped the whole fMRI step, just putting electrodes directly into the dorsal region of ACC (dACC).  Monkeys were allowed to saccade between patches that would give a continual reward that decreased with time.  They then faced a real foraging decision: when do you leave a depleted patch to find a new source of reward?  Neurons in dACC seemed to increase their firing rate when the monkeys were making this decision.  The speed with which the firing rate increased was related to the travel time to a new patch (the cost of going to that patch of reward).  This increase continued until it reached a threshold related to the relative value of leaving the patch.

The authors are clear that the dACC signal itself is not sufficient for a leaving-decision; an observer would have to get information from other regions to determine what the threshold for leaving is.  But the data strongly suggests that dACC is coding the value of relative value of leaving a patch.

So what do the two studies together tell us about how ACC helps us make a decision?  The first paper tells us that ACC is representing the predicted cost of finding new options.  This calculation probably includes the predicted probability distribution of all available options, and will also include how many times (how long) someone is willing to go searching for a better option.  The second paper is in broad agreement, and claims that dACC represents the relative expected value but is an insufficient signal to tell the brain when to make that decision; it just encodes that signal.  It does however represent the maximum cost the brain is currently willing to bear to find a new option, just like the fMRI study shows.

These two papers are great together as they really show how (1) fMRI can be useful and (2) the differences in how the same question is framed in different subfields of neuroscience.

References

Neural mechanisms of foraging.  Kolling, Behrens, Mars, Rushworth.  Science (2012).  DOI: 10.1126/science.1216930

Neuronal basis of sequential foraging decisions in a patchy environment.  Hayden, Pearson, Platt.  Nature Neuroscience (2011).  DOI: 10.1038/nn.2856

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