Crown Shyness

The fact that trees do not rub on each other is known as "crown shyness." Google Scholar lists about 200 scientific journal articles using the term "crown shyness":

http://scholar.google.com/scholar?as_sdt=1,49&q=%22crown+shyness%22&hl=en&as_vis=1

A nice little literature. One abstract says the issue was first identified in the 1920s, making it almost 100 years old. Cool! The prevailing theory seems to be that the trees touch when the wind blows so stop growing, although this is questioned and crown shyness is often termed a mystery. 

So this is a known issue. However, what I observe is much more complex than a "crown shyness" where the branches simply stop growing. They change direction and keep growing, forming intricate avoidance structures. 

Trees avoid rubbing on other trees

If you go into a deciduous forest and look up you will see what appears at first glance to be a complex tangle of of branches. This is the forest canopy, where the trees compete for light.

On closer inspection, however, one finds a remarkable feature. This is that very few of the branches from any given tree rub on the branches from its neighbors.

In fact it appears to me that much of the complexity in the way the branches have grown is specifically to avoid rubbing on a neighbor. Rubbing is dangerous for a tree, because it creates the equivalent of an open wound. in many cases a branch will actually change direction, in a way that seems designed specifically to avoid a neighboring branch. Or so it seems to me.

Of course there are exception and some rubbing does occur. I think this is analogous to people accidentally bumping into things or other people. But if you took two open grown trees and moved them together there would be a tremendous amount of contact. That this does not occur when the trees grow close together is thus quite remarkable. If they grew as though their neighbors were not there, there would be a lot of rubbing.

If the trees actually grow so as to avoid rubbing on their neighbor's branches, then they must know where those branches are, without touching them. In the last post I discussed the idea that trees know where their parts are and grow them so as to maintain their balance. Now it seems that the deciduous also know where their neighbor's parts are, and they grow so as to avoid rubbing on them.

This all sounds rather farfetched, but I have difficulty coming up with any other explanation for this apparent growth behavior. Perhaps it is a function of the way the neighboring trees affect the light. In any case it is certainly a challenging research question.

Seeing Tree Growth As Behavior (part 1)

Over the years I have observed some interesting forms of growth in trees, which have the aspect of behavior, as opposed to simple growth.

First is what is called an oxbow. Here the tree's leader is damaged, so another branch curves upward to become the new leader. What is remarkable is that the new leader first curves backward, then straightens upward, so that it is positioned directly above the lower trunk. Somehow the new growing leader knows where the lower trunk is. I once had a large collection of oxbows from downed trees in a small patch of boreal forest, indicating they are not a rare or chance occurrence.

Note that there is a good reason for this behavior, which is balance. If the upper trunk grew some distance out from the lower trunk then the tree would be unbalanced. Wind is a great threat to trees, so balance is important. This is probably why open grown trees brow symmetrically, even at mid-latitudes where the sun is always on just one side. My conjecture is that the trees growth in general is a tradeoff between balance and efficient solar collection.

I observed another case of apparent balance when I began building a log cabin on a remote island in Northern Ontario, in the Boreal forest. I started with several spruce trees that grew along the shore, on the edge of the island's forest. Because of their location, their branches on the open or water side were much larger than those branches on the forest side, probably because the open side is where the sunshine was.

When I limbed them I found that they were unusable because the trunks were curved. The trees had actually grown so as to lean backward, toward the forest and away from their heavy side, just a a person would do if holding a weight out in front of them. By doing this they were more balanced.

What this suggests is that trees somehow know where their various parts are and can control their growth in order to achieve overall balance. If so then their growth is something like behavior.

Behaviorism versus Artificial Intelligence

Never having taken a psych course, I know very little about behaviorism, except that it stresses observable behavior. As a scientific method it is almost a hundred years old.

However, the rise of artificial intelligence provides us with a new set of tools and approaches. These include expert systems, knowledge engineering, decision modeling and robotics. Given these tools we can ask questions like what does a horse or other animal have to understand or decide in order to do what it does?

I am particularly interested in the instinctive understanding that leads to complex behavior, many cases of which we have already discussed. My impression is that behaviorism tends to focus on relatively simple behaviors, looking for things like stimulus-response, conditioning, etc. If so then I am looking at something different.

In any case my understanding is that the behaviorist study of animals was a reaction to, and a rejection of, what was deemed anthropomorphism. This is the attribution of human characteristics to animals (among other things).

My argument here is quite the opposite, in its way. That is, I think that animals understand a great deal more than humans do, in those instinctive areas that they specialize in. That is, we humans do not understand what these animals understand.

For example, I have no idea how to build a bird nest and certainly could not find and pick the right materials to do so. I actually tried to build a beaver dam once, at a time when I was an expert on designing earth dams. It was an abject failure, because a beaver dam is much more complex structurally than a human earth dam is. Yet the beaver does it with ease and without learning from others.

Moreover, I have seen beaver dams built from a wide variety of materials, depending on what was locally available. In short the beaver knows instinctively what to do, with what it has. That is expertise.

So what I am proposing is possibly a new approach to understanding animal behavior. It is an approach that sees animals as instinctive experts in those things that mean the most to them. Things that humans do not understand.

Complex instincts require thinking

Looking at the standard definitions of instinct reveals a deep conceptual confusion. Here is a simple example.

"An instinct is something you don't need to learn; it happens naturally, without you even thinking about it."
http://www.vocabulary.com/dictionary/instinct

This definition is a good example of the mistaken idea that instinctive behavior does not require thinking. The many cases discussed here at Horse Cognition show that complex instinctive behaviors require a lot of decision making and that means thinking.

A complex instinct is a body of expert knowledge that is not learned. Nest building by birds, dam building by beavers and grazing by horses are examples of complex instinctive behaviors.

Part of the confusion is that there are also simple instinctive behaviors, which do not involve thinking. Fear of snakes may be an example.

Many definitions also include the notion of environmental stimulus. I imagine this stimulus-response model is the influence of behaviorism. But while the arrival of spring may stimulate a pair of phoebes to build a nest, it does not tell them where to build it, nor which specific materials to use, nor how to find them. These are all complex expert decision making processes. If anyone doubts this I invite them to try to build a nest.

Crows can count

Crows are fascinating because they are collectively very active and vocal. It is often unclear what they are doing but there seems to be a lot of communication involved. In particular, they have many different calls.

An important feature of their calls is that they often involve a specific number of unit calls, which I call caws. For example there is a three caw call that may be repeated a number of times. There is also a four caw call that includes a slight pause between the second and third caws, sort of caw-caw caw-caw.

In order to make these calls the crows have to count the caws. This is not counting in the sense of naming the numbers. It is not like saying or thinking one-two-three, etc. Rather it is counting in the sense of knowing how many caws have been made. The hearers must also count the caws if communication is to occur.

This is really a case of the distinction between verbal thinking and non-verbal thinking, which I discuss early on in this blog. Because so much human thinking is verbal, it can be hard to see what non-verbal thinking looks like. But I can see that there are, say, three horses in view without going through the numbers one, two, three mentally.

As with other cases we have discussed, it is useful to think about building robots that do what the animals do. In order for robotic crows to make or respond to these various calls, there would have to be some sort of counting mechanism. In short, crows can count.

The amazing cow circle

When a small herd of cows, with calves, is properly threatened, it may form a circle. Each cow facing outward, with the calves in the middle, protect by the herd. I have seen this done, when a herd of about a dozen herefords, with several calves, was threatened by several dogs.

The circle was amazing. Consider the complex decision making and group coordination required to execute this behavior.

First the herd has to collectively decide that the proper threat exists. How this is done is of course a deep mystery.

Then they have to form up into a good circle. This is far more sophisticated that simple grazing together. The Roman battle square comes to mind. How does each cow decide where to go, or who to get next to?

To see the complexity, consider this question. If you had a dozen robotic cows, how would you program them to quickly form a good cow circle? This is clearly a grand challenge. Until this question can be answered I would say that we do not understand the behavior.

Then the calves have to get into the center. A cattleman friend tells me that there is a call that mother cows can make that brings their calf running. Perhaps this plays a role in the protective circle behavior. There may even be a specific "get into the center" call. While perhaps not language it would certainly be auditory communication. (The crows seem to do a great deal of this.)

Then when the threat has passed, as it did when I chased the dogs away, they have to collectively decide that it has passed and go back to grazing.

Instinct tells them how to do all this, but the decision making still has to be done in each specific instance. The cow circle is an amazing case of collective action.

Herd behavior and cascades (in humans)

There is a lot of research on what is called "herd behavior" in humans, which might be useful when applied to herd animals like horses. The basic idea is that in some cases people do what they do because they see others doing it. There are also some specialized models of human herd behavior, which are called "cascades."

In the last two decades several distinct sorts of cascading behavior have emerged as topics of research. Some of this work involves the development of mathematical models which might be useful in modeling animal group behavior.

These include the informational cascade, the information cascade, the reputational cascade, the availability cascade and herd behavior. The relative size of these various research efforts can be gauged from the number of documents found by Google Scholar, when one searches on the name of the behavior. As of this writing these results are as follows:
 "Herd behavior" gives over 15,000 hits.
"Informational cascade" gives about 1700 hits.
"Information cascade" gives about 3000 hits.
"Availability cascade" gives about 200 hits.
"Reputational cascade" gives about 100 hits.

Taken together this amounts to a great deal of research into herd-like behavior.

Collective animal behavior

http://en.m.wikipedia.org/wiki/Collective_animal_behavior has a nice summary of some of the research. Clearly there has been quite a lot.

While some of it is in the form of decision rules, these seem to be too simple for the kind of decision making we have been discussing. Our view is that animals do complex reasoning using instinctive expert knowledge.

There is nothing anthropomorphic about this, except that we are forced to use our own concepts in order to try to understand the concepts that the animals are using. But we are not attributing these human concepts to the animals. If anything, our concepts are something of an impediment.

On the other hand some of the conjectures regarding animal behavior seem to be too complex. For example, the Wikipedia article suggests that group decision making may be based on the Condorcet method of voting, but this seems entirely too anthropomorphic. See http://en.m.wikipedia.org/wiki/Condorcet_method This method involves not merely voting but a ranking of alternatives by each voter. These ranking votes are then tallied by someone. It is an extremely complex procedure.

Horses grazing in motion are not repeatedly voting on the direction they will move. I suspect that something very different from voting is going on. For example, something that has degrees of importance, which voting does not have. Invoking the human concept of voting makes it harder to see this, not easier.

Representing instinctive knowledge

Given that an instinct is an inherited body of expert knowledge, we should be able to apply the methods of artificial intelligence to build a model of an instinct. Such a model might tell us interesting and useful things about the behavior of the critters in question, such as horses.

For those who are not familiar with these methods, here are some short, non-technical introductory readings. The field itself involves a lot of math and many of the articles linked to from these Wikipedia articles are much more technical. 

Knowledge representation is the core field.

http://en.m.wikipedia.org/wiki/Knowledge_representation_and_reasoning

Knowledge engineering is the method for uncovering the knowledge.

http://en.wikipedia.org/wiki/Knowledge_engineering

Expert systems are examples of how knowledge based decision making can be modeled.

http://en.wikipedia.org/wiki/Expert_system

I have been involved with this stuff since I was on the faculty of Carnegie Mellon University in the 1970's. In fact I worked with some of the pioneers, especially Herb Simon, who got a Nobel prize in 1978.

Dogs and Cats (and skillful affection)

Dogs and cats seem to be a difficult case and I have been wondering why. There seem to be at least two reasons. First, their behavior is so tied up with ours that it is hard to see the instincts at work. Second, a lot of what is instinctive is not thought of as knowledge based.

The whole point of domestication is to work well with humans, so this is the focus of the instincts. For dogs and cats these instincts include affection (giving and seeking), obedience, trust, loyalty, companionship, etc. There are others, like play, warning and defense with dogs, or hunting with cats.

In humans the traits of affection, obedience, trust, loyalty, etc., are not thought of as knowledge based, so we also do not see them that way in dogs and cats. This is probably because they are instincts in humans.

But in fact each of these activities involves a great deal of decision making and therefore requires considerable skill, which means expertise. The same is true of horses and other domestic animals, but perhaps to a somewhat lesser degree.

For example, it is not enough to want to be affectionate (whatever that might mean), one also has to know how to do it. When it comes to dealing with humans, the differences between wild and domestic animals are dramatic. Domestic behavior is highly complex, requiring a lot of skill.

As my wife puts it, dogs are professional people pleasers. Most do it well. The research question is what do they have to know in order to do this well? It is not a simple question, by any means.

Basic versus applied and "The Horse's Manifesto"

The distinction between basic and applied science is worth considering here. What we are doing is basic science, which means looking for understanding without regard for how useful it might be.

It is not that we are indifferent to the potential application of what we figure out and that is a common misconception about basic science. It is just that the focus has to be on what can in fact be understood, because that is a hard question in itself.

Science is based on the fact that there are cases where simple rules can explain complex behavior. This is true for physics, chemistry, biology and behavior, and all the varied sciences. Thus the first challenge in doing science is to identify specific cases where important rules can actually be discovered. The art of science is to identify important answerable questions.

But there is often an overlap between important basic science and importantly useful applied science. To make this point in our case of critter cognition, consider three elegant essays called "The Horse's Manifesto" by Lauren Fraser.

Fraser is the Chair of the Horse Division of the International Association of Animal Behavior Consultants. The focus of IAABC and its member consultants is the scientific treatment of behavioral problems, many of which are cognitive in nature.

The connection between IAABC's applied cognitive science and our basic cognitive science can be seen in the fact that Fraser's three essays touch on many of the topics we have been exploring. She talks about the three "F"s of Friends, Forage and Freedom, while we talk about herd decision making, grazing, play, movement, etc. In some cases even the details overlap, such as with what we call grazing in motion, which she also discusses.

Here is the difference. Fraser points out that these behaviors are deeply based on instincts, such that interfering with these instincts can lead to common behavioral problems. This is applied science. What we are trying to do is figure out how to understand these instincts much more clearly than can presently be done. This is basic science. Everyone talks about instincts but how do we describe one scientifically? Hopefully if we are successful the results will flow into the applied arena, to help solve the kind of behavioral problems that Fraser describes, even though that is not our primary concern. This is how basic science feeds applied science.

Changing fields

In addition to grazing in motion, which we have discussed previously, our horses sometimes do something which I call changing fields. This means that they move, as a group and relatively quickly, from one place to another that is a significant distance away. In some cases this means actually leaving one field and going to another, which involves considerable effort. typically there is little, if any, grazing done during the move.

The research questions here are things like this;
1. How do they decide, as a group, to make this change?
2. How do they decide where to go?
3. Do they know where they are going when they start out?
4. Why do they go where they go?

In some cases this change involves a food change. For example, they may go from eating pasture grass to eating bushes along a fence, or weeds in a wetland. On the other hand, what amount to field changes often happen within a single field. It would take good data on the location of various food types, as well as the horses' behavior, to explore this food aspect of field changes.

There is no single horse that always leads a field change. However, good data might reveal that some horses lead more often than others. This may be due to the fact that our group appears to have no leader. For example, it appears that every horse in the herd can be pushed off of a hay pile by some other horse. I call this a circular pecking order because horse A can push horse B, while horse B can push horse C, but horse C can push horse A. Thus there is no top horse, one which cannot be pushed. This behavior in itself is worth studying, especially in the context of group decision making.

On the other hand this herd has had top horses in the past. Whether having a top horse in the herd changes who leads field changes is another interesting question.

The basic point is that changing fields is an easily recognizable behavior that raises specific questions about the decision making involved.