All fMRI experiments measure relative changes in brain activity between different conditions. With only two conditions—the signal for “hot dog” and the signal for “no hot dog”—all we had to do was subtract the brain activity in one condition from the other. The difference would show us which parts of the dogs’ brains processed the meaning of the signals.
To do this, we usually calculate the difference in activity at every location in the brain and perform a statistical test to determine whether the results are real or simply random fluctuations in the fMRI signal. We then create a map from this analysis and overlay it on the structural image. By convention, neuroscientists use a color scheme that ranges from yellow for weak activations to bright red for strong ones. Andrew did the subtraction for Callie.
Everyone in the lab had been waiting for this moment and gathered around the computer screen.
There, overlaid on Callie’s pyramidal-shaped brain, were several hot spots of yellow and red. We still didn’t know what most of the brain was doing. It was important to stay focused on the one region that we knew a lot about.
“Zoom in on the caudate,” I said.
Sure enough, an orangish blob sat squarely on top of the right caudate. There was no doubt. The lab stared in amazement and let out a collective gasp.
McKenzie’s activation map was even stronger. Both dogs showed unmistakable proof of caudate activation to the signal for “hot dogs” but not the signal for “no hot dogs.”
Had only one of the dogs shown caudate activation, it would be easy to dismiss as a fluke. But we were looking at caudate activation in both dogs. The odds of that happening by chance we calculated to be 1 in 100.
“Caudate activation in both dogs?” I said. “That it is no accident. That is real.”
At dinner that night, I broke the good news to the girls.
“The Dog Project worked,” I announced.
“What do you mean?” Kat asked.
“We found reward-system activation in both dogs.”
“So,” Kat said skeptically, “you discovered that dogs like hot dogs?”
“No,” I replied. “We discovered that they understand the meaning of hand signals.”
This was a crucial distinction. In fact, Andrew and I did observe caudate activation to the hot dogs. But because the delivery of the hot dogs also caused Callie and McKenzie to move their heads as they swallowed and licked their lips, we had to discard a high proportion of those scans. Even so, the caudate activation was still plainly evident. But for the reasons Kat implied, such a finding would not be very surprising. Everyone knows that dogs like food.
No, the big result was caudate activation to the hand signal for “hot dog” but not “no hot dog.”
The Pavlovian behaviorists would say, “Ah, the dogs learned the association between a neutral stimulus—the hand signal—and an unconditioned response from the food. Nothing in the brain implies an understanding of meaning.” Had we done the experiment like Pavlov, using the ringing of a bell, for example, or the turning on of a light in place of the hand signal, this would certainly be true. But we used hand gestures. Humans take it for granted that hand gestures convey a great deal of information, almost as much as the eyes. Is it possible that dogs place as much importance on hand movements as we do?
A growing body of evidence suggests that they do.
Brian Hare, an anthropologist at Duke University, has pioneered the study of social cognition in dogs, especially the extent to which they understand human social signals. In his initial experiments, Hare hid food in one of several possible locations in a room. A human would stand in the room and point to the correct location. When a dog entered the room, it was able to use the pointing cue to more quickly find the food. Often, the dogs did this on the first try, indicating that simple associative learning, like the behaviorists believed, could not explain dogs’ ability to intuit the meaning of human social signals. Dogs seem to be particularly skilled at reading human signals. Hare later tested wolves and chimpanzees, and neither did as well as dogs.
Even at the dinner table, I could see that Callie was exquisitely attuned to our human social interaction. Lyra—not so much. But Callie sat in relaxed attention. Her head would swivel to whoever was speaking. Although she couldn’t understand all the words, if someone said one of the words she did know, like walk, she would run to that person and start wagging her tail vigorously. More than speech, I knew she understood hand signals, because all I had to do was point at the MRI tube, and she would go in.
Now we were faced with the conundrum of reverse inference.
If we had been studying humans, the interpretation of the caudate activation would be pretty simple. In fact my colleagues and I had done exactly this kind of experiment ten years earlier. Instead of hot dogs, we used Kool-Aid. In that experiment, our human subjects lay in the scanner with a tube snaking into their mouths. When a green light appeared on a computer screen, the subjects would have to press a button, and then, a few seconds later, they would get a squirt of Kool-Aid on their tongues. Just like Callie and McKenzie, the humans’ caudates activated to the signal indicating impending Kool-Aid. Since then, this result has been replicated dozens of times by us and other researchers. The advantage with humans, of course, is you can ask them what they thought and felt in response to the signals.
Inevitably, people attributed meaning to the signals. For some people, signals set up a state of anticipation. Indeed, a state of heightened anticipation, especially of something good, is probably the most universally experienced emotion associated with caudate activation. This state of anticipation drives people to get what they desire. In the extreme, we call it craving, and dysfunctional caudate activity is generally believed to be associated with addictions. Now, if simple computer cues are replaced with more humanlike cues, then caudate activity is even greater. For humans, there appears to be a bonus effect in the caudate to social cues, even if they convey the same information as nonsocial ones.
Why should dogs be any different? If anything, the research was showing that dogs care intensely about the meaning of human signals. In light of Hare’s findings, it seemed likely that Callie looked at my hand signals and constructed a dog theory of what I was thinking or at least intending. Dog theory of mind.
And if Callie was trying to intuit what I was thinking, it was inevitable that I would do the same and try to intuit what she was thinking. Locked in our MRI pas de deux, staring into each other’s eyes, I had had the overwhelming sense that we were directly communicating our intentions to each other. Callie’s caudate activation was just the first piece of evidence that my intentions had been received, and understood, in her brain.
Dogs, like humans, just want to be understood. Proof of actual mentalizing, though, would take some further examination of the brain activation in regions outside the caudate.
In fact, Callie showed evidence of more than reading our human intentions. She indicated her intentions. At dinner, she stood in front of the glass door leading from the kitchen to the back porch. She turned her head and looked at me. Then she turned back to gaze longingly outside. Back to me. Come on, I want to go outside.
She didn’t bark. She didn’t scratch at the door. Callie clearly communicated her intentions with her eyes. Just like humans.
I let her out, and she went racing through the ivy after some animal.
Callie’s behavior may seem unremarkable. She had probably been doing things like this for as long as she lived with us, but I had never had reason to pay much attention to the nuances of what she was doing until now. But with the results from the Dog Project, it now became a matter of scientific interpretation. Either she was a Pavlovian learning machine—great at making associations between events but without interpreting them—or Callie was a sentient being who understood, at some level, what I was thinking and reciprocated by communicating her thoughts within her behavioral repertoire.
I suspected the latter, but the proof was still hidden i
n the fMRI data.
Callie gave up whatever she was hunting. Long ago, she had quickly learned how to work door latches. Whether it was from luck or from watching humans, I don’t know, but now, she ran full tilt and jumped to push open the porch door, precisely timing her leap to hit the handle. She blasted into the kitchen with a burst of energy.
She immediately went over to Helen and rested her head on Helen’s thigh.
“Look!” Helen said. “She’s doing the ‘touch’ command.”
“She’s telling you something,” I said.
“She wants food?”
“Yup.”
Helen laughed and gave Callie a morsel from her plate. I am not sure who was more satisfied: Helen for understanding Callie’s intent, or Callie for making Helen do what she wanted.
“I have good news too,” Helen said.
“Really?”
Helen paused for dramatic effect.
“Come on,” Kat said. “Don’t keep us hanging.”
“I got an A on my science test.”
“Yay!”
“That’s awesome,” I said. “I’m very proud of you. You had to work really hard to do that.”
Helen beamed.
Sometimes playing hooky really does pay off.
20
Does My Dog Love Me?
THE FIRST PHASES OF THE Dog Project were coming to an end. Callie had been to the scanner four times and McKenzie three. We had proved that the dogs could hold still enough to obtain high-quality images of their brains. And even more impressively, we had shown that the reward systems of their brains activated in response to the appropriate hand signals. We had finished the first scientific paper and sent it off for publication, which meant that we had some time to reflect on what we found and what we wanted to do next. As far as we knew, we had the only two dogs in the world trained to go into an MRI scanner, and we had proven that the whole crazy idea wasn’t so crazy after all.
The excitement in the lab was electrifying. I had been scanning human brains since fMRI was discovered, but nothing I had experienced in my career matched this intensity. Even the dawn of human brain imaging didn’t match. Perhaps because scientists had been studying the human brain in various ways for over a century, we already knew a lot about how it worked. More often than not, brain imaging tended to confirm what we knew about the human brain and rarely resulted in a sea change in our understanding of the human mind.
But the Dog Project was entirely different.
I felt like Christopher Columbus discovering the New World. The dog’s brain was a great, unexplored continent. We had no idea how the canine brain worked, but we had the tools to figure it out and two subjects ready to assist. All we had to do was step into the unknown and start exploring.
The screensaver on Lisa’s computer was displaying a montage of Sheriff. Sheriff was almost two years old. Lisa had acquired him as a puppy when she graduated from Emory and started working in the lab. He was the first dog she could truly call her own, and she absolutely adored him.
“You really love Sheriff, don’t you?” I commented.
“Of course,” Lisa replied, “and he loves me too.”
Gavin, who had been observing with bemusement, couldn’t resist teasing Lisa about this.
“That depends on what you mean by love.”
Lisa, ever the pragmatist, replied, “Love? I would accept codependency.” She was dead serious. “Look, I think the best you can hope for with humans is to eventually have a relationship where both people are mutually dependent on each other. What’s wrong with that?”
She had caught Gavin uncharacteristically off guard and he had no response. Lisa continued. “So what if Sheriff’s love for me is based on food and belly rubs? He gives back affection and companionship. If most human relationships were that simple, more people would probably be happier.”
“What if we could prove that Sheriff loved you?” I asked.
“You mean more than food and belly rubs?”
Gavin rolled his eyes and said, “That’s impossible.”
Andrew, who had refrained from wading into the debate on love, had been staring intently at his computer screen. “Check this out.”
On the screen was the structural image of Callie’s brain. I had now seen this image a hundred times and knew it better than my own brain. Overlaid was an activation map. We had been looking at pictures like this for weeks and I had become accustomed to seeing the red, orange, and yellow hot spots superimposed on the caudate nucleus—the center of the reward system. But this image was different.
Andrew had digitally warped McKenzie’s brain to match Callie’s. This is a normal step in the analysis of human fMRI data. When we collect data on a large number of subjects, we need a way to compare activation in everyone’s brains. But because every person’s brain is physically different, we use a digital method that morphs each brain into the same size and shape. This allows scientists to average the activation patterns of many individuals and determine the commonalities of brain function.
In humans, brain sizes tend to vary by about only 1 or 2 percent. Some people have round heads while others are more oval-shaped. Even so, the basic anatomy is pretty much the same, and we need to stretch and twist the brains only a little bit to make them all match up.
Dogs are different. Of all the species on the planet, dogs have the largest variations in size. What other species can range in size from a 4-pound Chihuahua to a 150-pound Great Dane and still be considered the same animal? As you might expect, their brain sizes have a similarly large variation.
When we started analyzing the data from the Dog Project, we did it separately for Callie and McKenzie. McKenzie was about 50 percent larger than Callie, so we knew their brains were going to be different. Because of this large variation in size, we didn’t think the usual computer algorithms would work, so we hadn’t even attempted to digitally combine their brains.
Until now.
By carefully identifying key landmarks in the dogs’ brains, Andrew had been able to get them to line up. Once aligned, he was able to perform an analysis on the combined dataset. They say that two heads are better than one, and in this case that was absolutely true. Although both Callie and McKenzie had performed beyond our expectations, they still had their limits. They had each stayed in the MRI for ten minutes of continuous scanning. But ultimately, the noise and confinement wore them down, and they got tired of the task. In the end, Callie had sat through almost forty repetitions of the task and McKenzie about thirty. This was good enough to prove the feasibility of canine fMRI. But to go to the next step, and really start figuring out how the dog brain worked, we needed a lot more repetitions and, ideally, a lot more dogs. Combining the results from Callie and McKenzie was a first step in this direction.
More observations meant more power to detect faint signals in the brain. By merging the datasets of the two dogs, we were now staring at a result on the computer screen that we hadn’t seen when looking at the dogs individually.
Andrew pointed to an area of activation on the side of the brain. This region was about a centimeter higher than the reward system, and it was located in the middle of the cortex. Since the usual landmarks of the human brain didn’t apply, we were left guessing what part of the dog brain we were looking at.
Cross-referencing an atlas of dog brain anatomy, I asked, “Is that the motor cortex?”
Andrew shrugged and said, “It’s in the middle of the cortex, about where the human central sulcus would be.”
But the dogs weren’t moving in our experiment. Why would we see activity in the motor area?
“Mirror neurons,” I said.
Mirror neurons are a specific type of neuron in the brain that fires both when an animal initiates a movement and when it observes the same type of movement in another animal. They were originally discovered in the early 1990s by researchers recording the brains of monkeys. The scientists were primarily interested in how the motor system functioned, especially
when the monkey decided to reach for an object. They implanted electrodes to record from the area of the brain just in front of the central sulcus, called the premotor area. These neurons did, in fact, begin firing just before the monkey moved its hand. Somewhat accidentally, though, the scientists also noticed that these neurons fired when the researchers reached into the cage to replace the object that the monkey was trying to get, even though the monkey wasn’t moving at that moment. They were dubbed mirror neurons because they seemed to mirror both observation and action. They fired when the animal initiated a motor act as well as when somebody else performed a similar action, and it didn’t seem to matter whether it was a monkey or human hand that was doing the reaching.
It wasn’t long before researchers began searching for mirror neurons in humans. Using fMRI, several experiments found evidence for the same mechanism operating in the premotor area of the human brain, as well as a number of other areas. Rather than controlling the movement of a particular part of the body, these mirror neurons seemed to control action goals. For example, a baseball pitcher tries to throw the ball in the strike zone. The mirror neurons in a pitcher’s brain don’t control the muscles of the arm directly. Instead, they act like a guidance system so that all the muscles of the body act together to reach the ultimate goal of depositing the baseball in the catcher’s mitt at the desired location. And if a pitcher watched someone else doing the same thing, the pitcher’s mirror neurons would fire while he observed—as if his brain were simulating the act of pitching.
How Dogs Love Us: A Neuroscientist and His Adopted Dog Decode the Canine Brain Page 17