AI Magazine Volume 20 Number 1 (1999) (© AAAI) Book Review machines. Although Picard warns Review of Affective about ethical issues, she apparently welcomes the use of emotion detec- tors in a wide range of contexts and Computing relationships (for example, teacher and pupil). The final chapter, in par- ticular, suggests that computing devices will help us choose our mood Aaron Sloman music; decide which scenes to record for our photo albums; and find out about exhibitions, plays, and other events likely to suit our tastes. Reac- tions to this prospect will differ wide- riting a multidisciplinary implants; so, it will be possible to have ly. Many will dislike the idea of using book is a risky business. a wide range of sensors, processors, remote devices to tell them which WSome experts are likely to and transmitters constantly monitor- mood to expect in their spouse: It will be fiercely critical because of omis- ing, analyzing, recording, and trans- strike them as an improper intrusion. sions or errors. Others with tunnel mitting information about one’s However, there probably are some vision are likely to miss the point. blood pressure, temperature, blood- couples who, having found bedroom Rosalind Picard, with considerable sugar level, muscular tension, and ceiling mirrors tame, will relish mutu- courage, addresses a broad collection many other physiological states. Some ally informative intimate sensors. of themes, including the nature of of these devices, suitably hidden, If I were a pilot or a bus driver I motivation, emotions, and feeling; could also monitor various aspects of might accept the right of my passen- the detection of emotional and other the environment, including other gers to insist on my being wired up to affective states and processes; the people. Thus, even your friends and minimize the risk of disasters if I fall nature of intelligence and the rela- colleagues will easily be able to record asleep at the controls or have a heart tionships between intelligence and your conversation; your facial expres- attack while in control. However, I emotions; the physiology of the brain sions; and, perhaps with remote sen- would not want a computer linked to and other aspects of human physiolo- sors, your muscular tension, tempera- such sensors to select music for me, gy relevant to affective states; require- ture, sweating, and so on. Picard and I have no wish to use such ments for effective human-computer believes that such machines can learn devices, especially hidden devices, to interfaces in a wide range of situa- to predict our reactions and use such tell me what my wife, my colleagues, tions; wearable devices with a range of predictions to inform us of risks and or my students are feeling or to sensing and communication func- inform them of what I am feeling. I’d tions; philosophical and ethical issues prefer us all to become more sensitive. relating to computers of the future; Others might have a different view. and a brief encounter with theology. Affective Computing, All this discussion begs the deeper This is a book with a bold vision. Rosalind Picard, question, Can computational devices Some readers will find it inspiring and really be used to detect emotions, as mind stretching. Some will find it irri- The MIT Press, Cam- claimed? Yes, a subset, emotions that tating. Some will have both reactions. bridge, Massachusetts, produce characteristic patterns in It gives many pointers to the vast lit- measurable physiological states, can erature on emotions, including useful 1997, 292 pp., detect emotion. How such sentic mod- recent material, for example, books by ISBN 0-262-16170-2 ulation (defined on p. 25) might be LeDoux, Goleman, and Damasio. produced and detected is discussed at The book ranges over themes of length in chapters 5 and 6. I argue varying depth. The main theme con- below that this will not work for most cerns the nature of intelligence and opportunities (“You’d really like that socially important emotions. the role of emotions in intelligence, film ...”). She argues that such devices which I discuss later after comment- will increasingly be able to measure Communication ing on some of the simpler themes. and categorize emotional and other affective states to help us comprehend and Affect Ubiquitous Computing what is going on in ourselves and oth- The book makes an important claim and Sensing ers. (The New Scientist [1998] reports that is relevant to AI research on com- on a Japanese device that purports to munication and teaching, namely, It will increasingly be feasible to tell you what a pet or infant is trying that the ability to detect and influence install sensors and computing devices to communicate!) affective states in others is important in furniture, walls, car seats, driving Some people might be alarmed by in human communication and will be controls, clothing, jewelry, and even the prospect of being “spied on” by necessary for machines to interact Copyright © 1999, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1999 / $2.00 SPRING 1999 127 Book Review effectively with humans. I agree with technically defined terms and use matic evaluations to be performed. this statement and made similar these, which is best done using archi- Possible effects include (1) reactions in claims in Sloman (1992). However, I tecture-based concepts: Starting from the primary emotion system, includ- am not sure the methods proposed by an architecture, we can derive the ing physiological changes, for exam- Picard will be very effective. types of state and process that the ple, muscular tension, weeping, flush- Can emotions be recognized on the architecture can support (Sloman ing, and smiling, which can produce a basis of physiological changes? Chap- 1998, 1992; Wright, Sloman, and Beau- characteristic “feel,” for example, “a ter 6 describes affect recognition “as a doin 1996; Simon 1979). Animals or flush of embarrassment” and “grow- pattern recognition problem and affect machines with different architectures ing tension” (try imagining a surgical expression as pattern synthesis” (p. will not be describable in these terms. operation on your eyeball), and (2) 165). Such patterns can be measured by On this basis, we can distinguish pri- rapid involuntary redirection of devices such as physiological sensors mary and secondary emotions. thought processes (compare Sloman and cameras recording facial expres- and Croucher [1981] and Simon sion or posture. “Despite its immense Primary Emotions [1979]). It is not always appreciated difficulty, emotion recognition is easier Human brains have many compo- that effects of type 2 can occur with- than thought recognition.” It is easier nents that are evolutionarily old. out effects of type 1. “largely because there are not as many Some are responsible for “animal” emotions as thoughts” (p. 167). emotions, for example, being startled, Two Types of Picard’s discussion may give some frozen with terror, sexually aroused, Secondary Emotion readers the impression that all emo- or nauseated. Information from per- Damasio conjectures that triggering tions essentially involve measurable ceptual systems fed to a fast pattern- by thought contents depends on physiological responses, that is, sentic recognition mechanism can rapidly somatic markers that link patterns of modulation. However, that impres- trigger massive global changes. Such thought contents with previously sion would be accurate for only a sub- mechanisms apparently include the experienced pleasures or pains or oth- set of emotions, as explained later. brain stem and the limbic system er strong feelings. Such triggering Moreover, many emotions involve (Goleman 1996; LeDoux 1996). Engi- enables secondary emotions to play an thought contents, and they are no less neers will appreciate the need for fast- important role by directing and redi- diverse than thoughts. You can, in acting pattern-based global “alarm” recting attention in dealing with com- principle, be afraid of, hope for, be mechanisms to ensure that an agent plex decisions (Damasio 1994). Picard pleased about almost anything that reacts appropriately to important also believes that secondary emotions you can believe or expect. Most of the risks and opportunities (Sloman always trigger primary mechanisms, emotions people are interested in are 1998). producing sentic modulation. Howev- constituted largely by their contents, Damasio (1994) calls these primary er, I think we need a distinction and the contents of emotions are also emotions (pp. 131–134), as does Picard. between two subclasses: (1) central the contents of thoughts. Even if there These products of our evolutionary secondary emotions and (2) peripheral are a few physiologically detectable history are still often useful. Because secondary emotions. universal categories covering all emo- they involve physiological reactions Central secondary emotions involve tions (happy, sad, angry, and so on), relevant to attacking, fleeing, freezing, involuntary redirection of ongoing which I doubt, it won’t be much use and so on, sensors measuring physio- cognitive processes such as planning, knowing that your spouse is happy logical changes (including posture and reasoning, reminiscing, and self-mon- without knowing whether it is happi- facial expression) can detect such pri- itoring. Such shifts of attention can ness about your promotion or about a mary emotions. occur entirely at the cognitive level message from a secret lover. Measuring without involving sentic modulation. sentic modulation will not, in general, Secondary Emotions An example might be guilt, which answer such crucial questions about Primary emotions can be less impor- involves negative assessment of one’s human emotions. tant for civilized social animals than own motives, decisions, or thoughts certain semantically rich affective and can produce thoughts about Ambiguity in the states generated by cognitive processes whether detection will occur, whether involving appraisal of perceived, or to confess, what the likely punish- Concept of Emotion imagined, situations. These are re- ment is, how to atone, how to avoid It has been clear for a long time ferred to by Damasio as secondary emo- detection, and so on.