Dogs are celebrated everywhere these days for the clever things they and their brains can do, and the science of dog cognition continues to soar in popularity.
As a cat person, I can't help but add that cats, too, show off their savviness for science.
Now, some cognitive scientists are asking about another domesticated animal companion that's been comparatively neglected: horses.
Japanese scientists Monamie Ringhofer and Shinya Yamamoto of Kobe University have published online in the journal Animal Cognition the results of the first research to investigate how horses respond to the state of knowledge or ignorance of their human companions. The results are impressive.
Ringhofer and Yamamoto designed research to test eight thoroughbred horses in a paddock at Kobe University's equestrian club. The horses watched as a research assistant put a carrot in a food bucket. The bucket wasn't accessible to the horses, only to a human caretaker. In one experimental condition, the human caretaker witnessed the food going into the bucket (knowledge state). In a second condition, the caretaker did not watch as the carrot was placed into the bucket (uninformed state). The horses' responses were videotaped and compared between the two conditions.
The horses used more visual and tactile signals with the uninformed than the informed caretaker. The horsesincreased how much they looked at, touched and/or lightly pushed the ignorant caretaker (compared to the caretaker in the know) to get them to realize where food was hidden.
The authors acknowledge that follow-up studies are needed. It's an important result, though, because it points not only to advanced cognition but also to flexible cognition, with the horses adjusting their communicative behavior to the humans' knowledge state.
"This study is the first to show that horses possess some cognitive basis for this ability of understanding others' knowledge state in social communication with humans," Ringhofer and Yamamoto write.
Some non-human primates do this but, of course, horses are evolutionarily far more distant relatives of ours than chimpanzees. So what about dogs: How do they respond?
Ringhofer and Yamomoto write that in a similar experiment carried out by other researchers, dogs didn't do what the horses did — they didn't look at, touch or push their caretakers. Instead, the dogs alternated their gaze between the uninformed human experimenter and the hidden food's location.
In other words, the dogs directed the humans' attention also — just in a different way. It could be that it's, perhaps, in keeping with their different evolutionary history as herding, hunting, service and rescue animals. Each species has in its own way fine-tuned a skill leading to effective communication with humans.
Science journalist and equestrian Wendy Williams, author of The Horse: The Epic History of Our Noble Companion, told me via email that: "This break-through study has been a long time coming."
"For most of the history of horse domestication, we've assumed that communications between humans and horses was unidirectional. Humans order. Horses obey. But in this study, we see that communication could be a two-way street. Horses do try to communicate with humans. Most of us just don't try to learn their language."
Williams pointed out that social signaling is important among horses in a herd:
"Horses are highly social animals. In a natural state, they depend on each other for information that provides for the survival of the whole band. If a predator, for example, appears on the horizon, one horse immediately alerts the others through a wide variety of signals. Snorting, pricked ears and stamping are only a few of these signals. There's no reason why they wouldn't try to communicate with humans as well."
Lead researcher Ringhofer said, via email, that not all the horses responded during the experiment in the same way. This is interesting and also expected: Animals' behavioral tendencies and personalities vary.
"Most horses used visual and tactile signals to request the [attention of the] caretakers. However, two horses seemed to use extra behavior. They stood near the caretaker and located their face in front of the caretaker (very close to the caretaker's face). Then, both of them finally hit the caretakers' face with their face," Ringhofer said.
Ringhofer couldn't determine if the face-hitting was accidental or purposeful on the horses' part, and so didn't include it in her analyses. But she does wonder if those two horses might have come up with quite a startling way of social signaling!
Direct comparison of intelligence across species doesn't work well, because there is no single standard of what "smart" means across differently evolved animals. Asking if horses and dogs are equally smart, then, doesn't really make much sense.
The bottom line here is all about the horses themselves.
Together with other recent research showing that horses can use symbols to communicate with humans, this new study tells us that horses think carefully about what's going on around them.
Barbara J. King is an anthropology professor emerita at the College of William and Mary. She often writes about the cognition, emotion and welfare of animals, and about biological anthropology, human evolution and gender issues. Barbara's most recent book on animals is titled How Animals Grieve, and her forthcoming book, Personalities on the Plate: The Lives and Minds of Animals We Eat, will be published in March. You can keep up with what she is thinking on Twitter: @bjkingape
In a column posted a few days ago (November 1) I mentioned that my friend John Evans, a Cambridge (England) mathematician, has developed a general formula for estimating biocomplexity. It is quite simple, using only two variables: the number of units in a system, and the number of connections (interactions) each unit has with other units in the system. Today, in fact, biologists publish ‘interactomes” with furry ball figures that illustrate the number of proteins in a given cell and the number of interactions each protein has with other proteins. The concept of complex interactomes has become embedded in systems biology.
John’s formula is simple: C (complexity) = logN * (1 + 2logZ) where N is the number of units and Z is the average number of interactions. (If you would like to see his logic, you can read the paper cited below.) In the paper, we tested the formula by calculating C for the nervous systems of animals ranging from nematodes (N = 302 neurons, Z ~ 10) through insects and frogs and finally a series of mammalian brains.
What I proposed is that it might be an interesting exercise to see how well the complexity calculation predicts our perception of animal intelligence, so I challenged readers to use their intuition to put a set of mammalian species into order from most to least intelligent. Ten readers immediately replied, which was sufficient for our purposes. (If you would like to see all ten lists, they are in the Comments at the end of the earlier column.) I put together a consensus list simply by adding up the value of the placements, then putting the animals in order from lowest to highest totals. For instance, if all ten readers put humans first, the total would be 10, if chimps were number 2 in everyone’s lists, their total would be 20, and so on. Here is the consensus list and totals for each animal:
1. Human 10
2. Chimpanzee 24
3. Dolphin 33
4. Gorilla 34
5. Elephant 51
6. Horse 62
7. Dog 69
8. Cat 75
9. Rat 92
10. Opossum 100
11. Mouse 107
Now I will present several other rankings that are based on the variables we need to use in the calculation. This is followed by a ranked list calculated from the complexity formula itself, and finally a list which was normalized to take into account a third variable called encephalization quotient which I will explain later. We will compare that list to the consensus list to see how well the formula fits our expectations, and then pose a question for discussion.
One thing to make clear is that we will take Z to be a constant for all the mammalian species. It is estimated that each cortical neuron connects with around 1000 other neurons, so the second term in the formula is 1 + 2log1000) or 7. (In simpler organisms Z is much smaller. For instance, in nematodes Z ~ 10.)
The first list is ranked according to brain weight, and of course the elephant comes out on top, with humans and dolphins tied for second. (This list includes a rhesus monkey which I forget to include in my earlier column):Brain weight (grams)
The next list ranks the animals according to the number of cortical neurons estimated to be present in the brain of each species. In this list, humans and elephants are in a virtual tie for first place, with ~11 billion cortical neurons, followed by chimps, dolphins and gorillas:
Cortical neurons (millions)
The next list shows the order given by John’s formula. Again, humans and elephants are close due to the fact that they have the same number of neurons:
Ranking according to calculated complexity
Chimp/dolphin 68 (tied)
It doesn’t seem reasonable that humans and elephants are so close in the rankings, and in fact in the consensus list elephants are ranked fifth, below gorillas. Are we missing something? Maybe we can do better by incorporating the encephalization quotient (EQ). When the amount of brain tissue in a series of animals is plotted against size, from mice to elephants, there is a roughly linear relationship. However, the value for some animals lies significantly above the line, while others are well below the line. Humans come out on top of the EQ ranking, followed by dolphins, chimps and gorillas. Here is our list according to EQ:
Ranked by EQ
The way I think about EQ is that an animal like an elephant, with an EQ of 1.3, needs a greater absolute number of neurons to serve the much larger number of cells in their bodies, but these neurons are not necessarily given over to intelligence. Humans, with the highest EQ of all (7.6) have developed larger brains in relation to body size because our evolutionary pathway happened to select for whatever it is that we call intelligence, which apparently requires more brain tissue devoted to that function.
We can use relative EQ to correct for the effect of body size by normalizing against the human EQ. The complexity equation then becomes:
C = log(N*EQa/EQh) * (1 + 2logZ), where EQa is the animal EQ and EQh is the human EQ, taken to be 7.6.
Normalized complexity compared to the consensus list
Human 70 Human
Dolphin 67 Chimpanzee
Chimp 65 Dolphin
Elephant 64 Gorilla
Gorilla 63 Elephant
Monkey/Horse 57 (tied) Horse
Cat 53 Dog
Dog 52 Cat
Opossum/rat 41 (tied) Rat
Mouse 38 Opossum
Well, that’s pretty amazing! The consensus list and the calculated list are very similar, with no animal farther away than a single rank inversion between the two lists.
What does it all mean? I have a couple of suggestions. The first is that a certain level of complexity is required for higher nervous functions, just as my Mac iBook is much more complex than the Apple IIe I purchased back in 1981. It is interesting that all five animals with complexity values of 63 and above are self-aware, at least according to the following test. If you glue a round red dot to the forehead of a chimpanzee (or a dolphin, as demonstrated by Lori Marino) and let the animal see itself in a mirror, it will react to the presence of the dot. All animals with complexity of 57 and below are unable to understand that the image in the mirror is in fact themselves, and they pay no attention to the dot.
The second point is that a chimpanzee, although self-aware, cannot come close to what we recognize as human intelligence. It seems that a complexity value of 70 or above is essential, that is, 11 billion neurons, each with 1000 connections to other neurons, and an EQ of 7.6.
A caveat: I am not a neurobiologist, and am uncritically taking all of the parameters used in the calculation out of the literature. If you read the literature, you can probably find more sophisticated theories of the relationship between neuroanatomy and self-awareness, intelligence and the conscious state.
Now, for those readers who enjoy Scientific Blogging, here is the question: Is there a minimal complexity required for the phenomena of self-awareness and consciousness? If so, how can we arrive at a quantitative estimate of that complexity?Reference: Deamer DW, Evans J. 2006. Numerical analysis of biocomplexity. In Life As We Know It. J Seckbach, ed. p 201 - 12. New York: Springer.