FIGURE 6.5 MIT’s Affective Computing program.
The diagram indicates the complexity of the human affective system and the challenges required to monitor affect properly. From the work of Prof. Rosalind Picard of MIT.
(Drawing courtesy of Roz Picard and Jonathan Klein.)
Professor Rosalind Picard at the MIT Media Laboratory leads a research effort entitled “Affective Computing,” an attempt to develop machines that can sense the emotions of the people with whom they are interacting, and then respond accordingly. Her research group has made considerable progress in developing measuring devices to sense fear and anxiety, unhappiness and distress. And, of course, satisfaction and happiness. Figure 6.5 is taken from their web site and demonstrates the variety of issues that must be addressed.
How are someone’s emotions sensed? The body displays its emotional state in a variety of ways. There are, of course, facial expressions and body language. Can people control their expressions? Well, yes, but the visceral layer works automatically, and although the behavioral and reflective levels can try to inhibit visceral reaction, complete suppression does not appear to be possible. Even the most controlled person, the so-called poker-face who keeps a neutral display of emotional responses no matter what the situation, still has micro-expressions—short, fleeting expressions that can be detected by trained observers.
In addition to the responses of one’s musculature, there are many physiological responses. For example, although the size of the eye’s pupil is affected by light intensity, it is also an indicator of emotional arousal. Become interested or emotionally aroused, and the pupil widens. Work hard on a problem, and it widens. These responses are involuntary, so it is difficult—probably impossible—for a person to control them. One reason professional gamblers sometimes wear tinted eyeglasses even in dark rooms is to prevent their opponents from detecting changes in the size of their pupils.
Heart rate, blood pressure, breathing rate, and sweating are common measures that are used to derive affective state. Even amounts of sweating so small that the person can be unaware of it can trigger a change in the skin’s electrical conductivity. All of these measures can readily be detected by the appropriate electronics.
The problem is that these simple physiological measures are indirect measures of affect. Each is affected by numerous things, not just by affect or emotion. As a result, although these measures are used in many clinical and applied settings, they must be interpreted with care. Thus, consider the workings of the so-called lie detector. A lie detector is, if anything, an emotion detector. The method is technically called “polygraph testing” because it works by simultaneously recording and graphing multiple physiological measures such as heart rate, breathing rate, and skin conductance. A lie detector does not detect falsehoods; it detects a person’s affective response to a series of questions being asked by the examiner, where some of the answers are assumed to be truthful (and thus show low affective responses) and some deceitful (and thus show high affective arousal). It is easy to see why lie detectors are so controversial. Innocent people might have large emotional responses to critical questions while guilty people might show no response to the same questions.
Skilled operators of lie detectors try to compensate for these difficulties by the use of control questions to calibrate a person’s responses. For example, by asking a question to which they expect a lie in response, but that is not relevant to the issue at hand, they can see what a lie response looks like in the person being tested. This is done by interviewing the suspect and then developing a series of questions designed to ferret out normal deviant behavior, behavior in which the examiner has no interest, but where the suspect is likely to lie. One question commonly used in the United States is “Did you ever steal something when you were a teenager?”
Because lie detectors record underlying physiological states associated with emotions rather than with lies, they are not very reliable, yielding both misses (when a lie is not detected because it produces no emotional response) and false alarms (when the nervous suspect produces emotional responses even though he or she is not guilty). Skilled operators of these machines are aware of the pitfalls, and some use the lie detector test as a means of eliciting a confession: people who truly believe the lie detector can “read minds” might confess just because of their fear of the test. I have spoken to skilled operators who readily agree to the critique I just provided, but are proud of their record of eliciting voluntary confessions. But even innocent people have sometimes confessed to crimes they did not commit, strange as this might seem. The record of accuracy is flawed enough that the National Research Council of the United States National Academies performed a lengthy, thorough study and concluded that polygraph testing is too flawed for security screening and legal use.
SUPPOSE WE could detect a person’s emotional state, then what? How should we respond? This is a major, unsolved problem. Consider the classroom situation. If a student is frustrated, should we try to remove the frustration, or is the frustration a necessary part of learning? If an automobile driver is tense and stressed, what is the appropriate response?
The proper response to an emotion clearly depends upon the situation. If a student is frustrated because the information provided is not clear or intelligible, then knowing about the frustration is important to the instructor, who presumably can correct the problem through further explanation. (In my experience, however, this often fails, because an instructor who causes such frustration in the first place is usually poorly equipped to understand how to remedy the problem.)
If the frustration is due to the complexity of the problem, then the proper response of a teacher might be to do nothing. It is normal and proper for students to become frustrated when attempting to solve problems slightly beyond their ability, or to do something that has never been done before. In fact, if students aren’t occasionally frustrated, it probably is a bad thing—it means they aren’t taking enough risks, they aren’t pushing themselves sufficiently.
Still, it probably is good to reassure frustrated students, to explain that some amount of frustration is appropriate and even necessary. This is a good kind of frustration that leads to improvement and learning. If it goes on too long, however, the frustration can lead students to give up, to decide that the problem is above their ability. Here is where it is necessary to offer advice, tutorial explanations, or other guidance.
What of frustrations shown by students that have nothing to do with the class, that might be the result of some personal experience, outside the classroom? Here it isn’t clear what to do. The instructor, whether person or machine, is not apt to be a good therapist. Expressing sympathy might or might not be the best or most appropriate response.
Machines that can sense emotions are an emerging new frontier of research, one that raises as many questions as it addresses, both in how machines might detect emotions and in how to determine the most appropriate way of responding. Note that while we struggle to determine how to make machines respond appropriately to signs of emotions, people aren’t particularly good at it either. Many people have great difficulty responding appropriately to others who are experiencing emotional distress: sometimes their attempts to be helpful make the problem worse. And many are surprisingly insensitive to the emotional states of others, even people whom they know well. It is natural for people under emotional strain to try to hide the fact, and most people are not experts in detecting emotional signs.
Still, this is an important research area. Even if we are never able to develop machines that can respond completely appropriately, the research should inform us both about human emotion and also about human-machine interaction.
Machines That Induce Emotion in People
It is surprisingly easy to get people to have an intense emotional experience with even the simplest of computer systems. Perhaps the earliest such experience was with Eliza, a computer program developed by the MIT computer scientist Joseph Weizenbaum. Eliza was a simp
le program that worked by following a small number of conversational scripts that had been prepared in advance by the programmer (originally, this was Weizenbaum). By following these scripts, Eliza could interact with a person on whatever subject the script had prepared it for. Here is an example. When you started the program, it would greet you by saying: “Hello. I am ELIZA. How can I help you?” If you responded by typing: “I am concerned about the increasing level of violence in the world,” Eliza would respond: “How long have you been concerned about the increasing level of violence in the world?” That’s a relevant question, so a natural reply would be something like, “Just the last few months,” to which Eliza would respond, “Please go on.”
You can see how you might get captured by the conversation: your concerns received sympathetic responses. But Eliza has no understanding of language. It simply finds patterns and responds appropriately (saying “Please go on” when it doesn’t recognize the pattern). Thus, it is easy to fool Eliza by typing: “I am concerned about abc, def, and for that matter, ghi,” to which Eliza would dutifully reply: “How long have you been concerned about abc, def, and for that matter, ghi?”
Eliza simply recognizes the phrase “I am concerned about X” and replies, “How long have you been concerned about X?” with absolutely no understanding of the words.
Because most people took Eliza seriously, they did not try to trick it. Instead, they took each reply seriously, pondering their hidden meanings. As a result, they would sometimes discover themselves in deep, philosophical debate or, in the most popular version of Eliza that was scripted to act like a psychotherapist, they would find themselves discussing their most intimate secrets.
In the mid-1960s, one of my long-term friends and research collaborators, Daniel Bobrow, was a research scientist at BBN, a Cambridge, Massachusetts company doing research in artificial intelligence, computer networks, and acoustics. Bobrow told me that a programmer, having worked hard to get Eliza working on a BBN computer, finally finished about 5:00 A.M. Saturday morning and then went home, leaving the program running on the computer. (In those days—this was 1966—personal computers were unheard of, and only advanced companies, such as BBN, even had computers for its employees to use, which is one reason programmers had to work at night to get computer time.)
The vice president of Bobrow’s division came in later that Saturday morning to demonstrate the computer to some customers. Seeing that the computer was running, he assumed that Bobrow was using it from home, and, according to Bobrow, “decided to use the ‘obvious’ communication link I must have left going” to chat with him. So the VP typed some questions for Bobrow. But Bobrow was at home, asleep, and what ensued was actually taking place between Eliza and the VP, who assumed these responses came from Bobrow:VP: Do you think that I can use the computer this morning?
Eliza: Why do you ask?
VP: I might be able to make some additional Telcom sales.
Eliza: Why aren’t you so sure?
VP: My prospective customers have never seen a demonstration of the system.
Eliza: Does this mean very much to you?
VP: Of course it does.
Eliza: You seem very positive about that.
VP: This is ridiculous.
Eliza: What do you mean, this is ridiculous?
VP: Is there any reason why I can’t use the computer now?
Eliza: Tell me more about your prospective customers.
VP: Please dial me up on 491-1850
In those days, computers were not very powerful, and the only way that the Eliza program could tell when a person had finished a sentence was by noticing a period. Up to that last line, the VP had finished each line with a period, but this time he neglected it. As a result, the computer kept waiting for more typing and, therefore, didn’t answer. According to Bobrow, “This so infuriated the VP, who thought I was playing games with him, that he called me up, woke me from a deep sleep, and said: ‘Why are you being so snotty to me?’ ‘What do you mean I am being snotty to you?,’ I answered.” Then, Bobrow told me, “The VP angrily read me the dialog that ‘we ’ had been having, and couldn’t get any response but laughter from me. It took a while to convince him it really was a computer.”
As Bobrow told me when I discussed this interaction with him, “You can see he cared a lot about the answers to his questions, and what he thought were my remarks had an emotional effect on him.” We are extremely trusting, which makes us very easy to fool, and very angry when we think we aren’t being taken seriously.
The reason Eliza had such a powerful impact is related to the discussions in chapter 5 on the human tendency to believe that any intelligent-seeming interaction must be due to a human or, at least, an intelligent presence: anthropomorphism. Moreover, because we are trusting, we tend to take these interactions seriously. Eliza was written a long time ago, but its creator, Joseph Weizenbaum, was horrified by the seriousness with which his simple system was taken by so many people who interacted with it. His concerns led him to write Computer Power and Human Reason, in which he argued most cogently that these shallow interactions were detrimental to human society.
We have come a long way since Eliza was written. Computers of today are thousands of times more powerful than they were in the 1960s and, more importantly, our knowledge of human behavior and psychology has improved dramatically. As a result, today we can write programs and build machines that, unlike Eliza, have some true understanding and can exhibit true emotions. However, this doesn’t mean that we have escaped from Weizenbaum’s concerns. Consider Kismet.
Kismet, whose photograph is shown in figure 6.6, was developed by a team of researchers at the MIT Artificial Intelligence Laboratory and reported upon in detail in Cynthia Breazeal’s Designing Sociable Robots.
Recall that the underlying emotions of speech can be detected without any language understanding. Angry, scolding, pleading, consoling, grateful, and praising voices all have distinctive pitch and loudness contours. We can tell which of these states someone is in even if they are speaking in a foreign language. Our pets can often detect our moods through both our body language and the emotional patterns within our voices.
Kismet uses these cues to detect the emotional state of the person with whom it is interacting. Kismet has video cameras for eyes and a microphone with which to listen. Kismet has a sophisticated structure for interpreting, evaluating, and responding to the world—shown in figure 6.7—that combines perception, emotion, and attention to control behavior. Walk up to Kismet, and it turns to face you, looking you straight in the eyes. But if you just stand there and do nothing else, Kismet gets bored and looks around. If you do speak, it is sensitive to the emotional tone of the voice, reacting with interest and pleasure to encouraging, rewarding praise and with shame and sorrow to scolding. Kismet’s emotional space is quite rich, and it can move its head, neck, eyes, ears, and mouth to express emotions. Make it sad, and its ears droop. Make it excited and it perks up. When unhappy, the head droops, ears sag, mouth turns down.
FIGURE 6.6 Kismet, a robot designed for social interactions, looking surprised.
(Image courtesy of Cynthia Breazeal.)
Interacting with Kismet is a rich, engaging experience. It is difficult to believe that Kismet is all emotion, with no understanding. But walk up to it, speak excitedly, show it your brand-new watch, and Kismet responds appropriately: it looks at your face, then at the watch, then back at your face again, all the time showing interest by raising its eyelids and ears, and exhibiting perky, lively behavior. Just the interested responses you want from your conversational partner, even though Kismet has absolutely no understanding of language or, for that matter, your watch. How does it know to look at the watch? It doesn’t, but it responds to movement so it looks at your rising hand. When the motion stops, it gets bored, and returns to look at your eyes. It shows excitement because it detected the tone of your voice.
FIGURE 6.7
Kismet’s emotional system.
The heart of Kismet’s operation is in the interaction of perception, emotion, and behavior.
(Figure redrawn, slightly modified with permission of Cynthia Breazeal, from http://www.ai.mit.edu/projects/sociable/emotions.html.)
Note that Kismet shares some characteristics of Eliza. Thus, although this is a complex system, with a body (well, a head and neck), multiple motors that serve as muscles, and a complex underlying model of attention and emotion, it still lacks any true understanding. Therefore, the interest and boredom that it shows toward people are simply programmed responses to changes—or the lack thereof—in the environment and responses to movement and physical aspects of speech. Although Kismet can sometimes keep people entranced for long periods, the enhancement is somewhat akin to that of Eliza: most of the sophistication is in the observer’s interpretations.
Aibo, the Sony robot dog, has a far less sophisticated emotional repertoire and intelligence than Kismet. Nonetheless, Aibo has also proven to be incredibly engaging to its owners. Many owners of the robot dog band together to form clubs: some own several robots. They trade stories about how they have trained Aibo to do various tricks. They share ideas and techniques. Some firmly believe that their personal Aibo recognizes them and obeys commands even though it is not capable of these deeds.
When machines display emotions, they provide a rich and satisfying interaction with people, even though most of the richness and satisfaction, most of the interpretation and understanding, comes from within the head of the person, not from the artificial system. Sherry Turkle, both an MIT professor and a psychoanalyst, has summarized these interactions by pointing out, “It tells you more about us as human beings than it does the robots.” Anthropomorphism again: we read emotions and intentions into all sorts of things. “These things push on our buttons whether or not they have consciousness or intelligence,” Turkle said. “They push on our buttons to recognize them as though they do. We are programmed to respond in a caring way to these new kinds of creatures. The key is these objects want you to nurture them and they thrive when you pay attention.”
Emotional Design Page 19
