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?


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

What is the question about your field that you dread being asked? (Human collective behavior)

At Edge:

And with this hurricane of digital records, carried along in its wake, comes a simple question: How can we have this much data and still not understand collective human behavior?

There are several issues implicit in a question like this. To begin with, it’s not about having the data, but about the ideas and computational follow-through needed to make use of it—a distinction that seems particularly acute with massive digital records of human behavior. When you personally embed yourself in a group of people to study them, much of your data-collection there will be guided by higher-level structures: hypotheses and theoretical frameworks that suggest which observations are important. When you collect raw digital traces, on the other hand, you enter a world where you’re observing both much more and much less—you see many things that would have escaped your detection in person, but you have much less idea what the individual events mean, and have no a priori framework to guide their interpretation. How do we reconcile such radically different approaches to these questions?

In other words, this strategy of recording everything is conceptually very simple in one sense, but it relies on a complex premise: that we must be able to take the resulting datasets and define richer, higher-level structures that we can build on top of them.

What could a higher-level structure look like? Consider one more example—suppose you have a passion for studying the history of the Battle of Gettysburg, and I offer to provide you with a dataset containing the trajectory of every bullet fired during that engagement, and all the movements and words uttered by every soldier on the battlefield. What would you do with this resource? For example, if you processed the final day of the data, here are three distinct possibilities. First, maybe you would find a cluster of actions, movements, and words that corresponded closely to what we think of as Pickett’s Charge, the ill-fated Confederate assault near the close of the action. Second, maybe you would discover that Pickett’s Charge was too coarse a description of what happened—that there is a more complex but ultimately more useful way to organize what took place on the final day at Gettysburg. Or third, maybe you wouldn’t find anything interesting at all; your analysis might spin its wheels but remain mired in a swamp of data that was recorded at the wrong granularity.

We don’t have that dataset for the Battle of Gettysburg, but for public reaction to the 2012 U.S. Presidential Election, or the 2012 U.S. Christmas shopping season, we have a remarkable level of action-by-action detail. And in such settings, there is an effort underway to try defining what the consequential structures might be, and what the current datasets are missing—for even with their scale, they are missing many important things. It’s a convergence of researchers with backgrounds in computation, applied mathematics, and the social and behavioral sciences, at the start of what is by every indication a very hard problem. We see glimpses of the structures that can be found—Trending Topics on Twitter, for example, is in effect a collection of summary news events induced by computational means from the sheer volume of raw tweets—but a general attack on this question is still in its very early stages.

What is the question about your field that you dread being asked?

(In neuroscience?  Anything.)


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.”