How is that Psychology? - Rat Pup Huddling

In a past-life I was going to be an agent based modeler, working with Jeff Schank at UC Davis. He spent many years modeling rat pup huddling... in a psychology department. My main interest in the work was that it showed how a group of organisms could perform very complex behavior, even when no individual organism knew what it was doing, or had access to sufficient information to coordinate what it was doing. As I'll talk about below, this is a special case of the phenomenon where groups of simple and dumb systems can produce intelligent actions.



What are we modeling?
Rat pups cannot thermo-regulate successfully on their own. Each rat pup can raise its temperature a little bit, but they cannot maintain temperature for long periods of time if the environment is even a little bit chilly (like it is in any underground nest-cave). Groups of rat pup, huddled together, can keep themselves warm enough, and can even overheat. Thus a nest of rat pups maintains the temperature needed to stay alive by continuously forming different sized huddles. The individual pups can feel when they are touching things, they can swivel their torsos back and forth, and they can push forward with their legs, oh, and they are sensitive to their own internal temperature.  But that is about it... the pups are blind, and they also lack any knowledge of how warm the group is as a whole, or how many pups are in any given huddle - and they certainly don't 'know' that they are trying to form ideal-sized dynamic group distributions - and even if they did, there is nothing they can do to coordinate the movements of other pups. Instead, each pup must make a simple decision: Stay where I am, or move.

For a little more context: Schank built a special device while post-docing with Jeff Alberts at Indiana University, that kept the pups on a flat surface of uniform temperature, and set about observing what the pups did. Many things were measured, including direct the pup was oriented and whether it was moving (change between frames of videos), but of particular interest were the distribution of huddles - e.g., out of ten pups, at this moment, there are three groups of 3, 3, and 4 pups. (Incidentally, this also leads into cool investigation of how you measure synchrony, which is not at all intuitive, and to proof that menstrual synchrony does not exist, such as the human data presented here, but that is a story for another time.)

Schank then built probabilistic models of the simple stay or move decision rule, deployed the models in digital rat pups in a digital apparatus, and tuned them through Darwinian algorithms. He knew what the distribution of huddles looked like in real rat-pup huddles, and he ran tens of thousands of simulations with different values of the model variables to see how well they matched the desired pattern. Those values that produced better matches were allowed to 'reproduce' with minor variation, and tens of thousands of generations later you end up with a set of variables (or a few sets of variables) that produce very good matches.

The equations are pretty nasty, for example, the probability of a 10 day old pup being active are predicted by the following equations (taken from here):




What do we find?
The most basic finding is in terms of which potential variables are significant in the final model. Schank looked at 7-day old and 10-day old rat pups. There is significant developmental change in those three days, and the behavior of the rat pups reflects that. The models show that the behavior of the 7-day old pups is not coupled with the behavior of other members of the group. The behavior of 10-day old pups is coupled, it a function of the activity of those pups it is in contact with. The equations change tremendously, meaning that the underlying dynamics that maintain system stability are changing dramatically. This is amazing, not because there are dynamics in development, but because the system is so dynamic without failure. Recall, that if the system malfunctions for even a small amount of time, the whole group of pups is dead. (Not in the lab, the lab pups are safe, but in the wild the whole group would be dead.) If the individual pups fail to join and leave huddles at the right time, all pups relying on the huddle for heat will be out of luck. This is an excellent example of a situation in which natural selection must favor developmental systems that maintain functionality.

Why is this psychology?
There are two important reasons that this work is "psychology":
The first is that it is basic work towards better understanding behavioral development. It not only tells us about the dynamics of individual behaviors, but also about the dynamics of group coordination. It demonstrates that complex, intelligent, adaptive behaviors are possible within individuals and within groups with no 'knowledge' of what is being done, and it investigates a mechanism that could account for such adaptive behavior (probabilistic behavior deployment).

The second is that this work has important implications for how we understand the interaction of living systems. I mentioned that this work was a special case of distributed 'dumb' systems that do intelligent things. It is likely that the way the human body and the brain function can be usefully thought of in this way as well. How do a bunch of neural systems that have no idea what they are trying to do, and what the systems near them are trying to do, work in conjunction with other bodily systems such that the organism behaves in an intelligent manner? In a recent post, Andrew Wilson covered the case of the lobster that had had over 400,000 neural states that resulted in exactly the same action sequence. Just like the rat pups, none of the Lobster's ganglions 'know' what behavior they are trying to do, and they do not 'know' what the other ganglion are doing (though like the 10-day old rat pups, they are not behaviorally independent of each other). As we try to think about better ways to talk about how the mind works, we might keep in mind the dynamics of agent based modeling in situations like these.

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Also of Note
Schank has also worked with robotic 'rat pups' (good picture here), to better understand how real-world constraints effect pup behavior. The idea was to explore the additional insights that can be gained by making physical instantiations of agent based models.

P.S. Oh, and a new reason I just found for appreciating Schank: He wrote a blog post about agent based models as scaffolds that actually started by explaining what 'scaffolding' was, and proceeded to make sense of the metaphor! (Most uses of the term 'scaffolding' in psychology lead me to think the user has no idea what scaffolding is or how it works, especially those uses in mainstream developmental psychology.)