This Between Friends comic from July 5, 2015 provides a nice example of the spotlight effect. The protagonist is convinced that the fast-food restaurant employee is noticing everything that she doesn’t like about her appearance, everything from her hair color to the stain on her shirt. In the comic, the employee’s expression doesn’t change leading the reader to conclude that the employee notices nothing. Ask students to think about a recent spotlight effect experience they had. Was there something about their appearance that they were certain everyone would notice but likely no one or very few did? After students share their experiences with one or two people near them, ask for volunteers to share a few examples with the class. Conclude this exercise by inviting students to yell out ideas about what the fast food employee is thinking about what she is certain others are noticing about her. ... View more

Before talking about conformity, show students this 24-second video.  Five little boys, one-by-one, are exiting a tent. The first four all trip and fall on their way out. Boys two, three, and four even manage cringe-worthy face plants. Boy number five is the only one to successfully navigate the tent opening, but rather than bask in the glory of his success, he falls to the ground, landing nicely between boys three and four. Ask students to take a minute to jot down why they think boy number five chose to fall. Next, ask students to share their ideas with one or two students around them. Ask the class, “Why do you think he chose to fall?” Many will say he did it to be liked or to fit in (conformity). Some may say that if it were his first time in a tent, his choice to fall may be due to observational learning. “That’s just how you exit a tent,” he may have concluded. Or perhaps he didn’t want the other boys to feel bad for falling. “See? It’s not easy to get out. All of us fell!” Ask students to take a minute to think on their own how they could test whether such behavior is likely due to conformity or, say, observational learning. In his particular case, we could ask Boy Five to exit a tent with no one present. If he purposefully falls on his way out, the evidence leans toward an observational learning explanation for his intentional fall. Working with the assumption that his behavior was due to conformity, ask students to take a minute to write down what factors may have contributed to his choice. To help students think about this question, ask what could have been different so that boy number five may have made the choice to remain upright. After thinking quietly on their own, give students a couple minutes to share their factors with one or two other students. Gather responses by going around the room asking each group in turn to name one factor that has not already been mentioned that may have contributed to his decision to fall. Possible responses include the presence of others, particularly boys his age, his behavior was observable, and the group was unanimous (they all fell). With that as an introduction to conformity, your students are prepped to hear about Solomon Asch’s classic conformity studies. ... View more

After covering operant conditioning, ask students to silently identify a specific behavior they would like to change. Help students understand the difference between an outcome, e.g., lose 10 pounds, get an A in this course, and a behavior, e.g., walk 30 minutes a day five days a week, study psychology one hour a day six days a week. Ask students to raise their hands if they’ve tried to change a behavior only to have the effort peter out. All or almost all hands will go up. “If I were to pay you each week for engaging in your behavioral change, how much money would it take for you to stick with it?” By a show of hands, “At least $25?” With their hands still up, ask “At least $50?”  With their hands still up, “At least $75?” Keep going until all hands are up. In a recent experiment (Halpern, et.al, 2015), researchers randomly assigned participants to an incentive-based smoking cessation program. There were a few different ways they structured the incentive, but for all of them participants could earn up to $800 for being smoke-free after six months. How many were smoke-free after six months? The four incentive programs resulted in a 10% to 15% success rate. That may not sound like much, but the authors reports that “usual care” results in 6% smoke-free at six months. Don’t be surprised if students express dismay at such an incentive program. Providing positive reinforcement for doing things that we should do anyway makes some people uncomfortable. What’s the alternative? We know that healthcare costs will be lower, overall, for people who do not smoke. The higher someone else’s healthcare costs, the higher the cost of health insurance for all of us. Framed in that light, $800 per person seems like a reasonable investment. I don’t have someone else paying me, but I do have my own personal incentive program. When my pedometer tells me that I have reached 90,000 steps, I put $25 into a special account. It is out of this account that I pay for my Starbucks coffee, most restaurant meals, and anything else that’s considered a non-essential expense. Not only am I encouraged to walk more, but I have also reduced my spending. Health, however, is much broader than not smoking and walking. It also includes not shooting people. In Richmond, VA, the city council created a program designed to reduce violence. When they learned that 17 people, mostly young men, were responsible for 70% of the shootings, they knew who they needed to contact. They sent “street-savvy staff members” into the community to build relationships with these folks. With some trust established, the program coordinators invited the men to a meeting and made them an incredible offer. To paraphrase, if you stay out of trouble and attend meetings with the program’s mentors, we’ll pay you up to $1,000 per month for up to nine months. Is it working? Homicides and firearms assaults dropped by about half in just the first year of the program. Drug use among the program participants is down, employment is up, school enrollment is up. It took cash to get them started down a more productive path, but once they got going, the reinforcement came from other places. Historically we have relied on fines and jail time to try to change bad behavior. We know punishment, on the whole, is not as effective as reinforcement, so to change bad behavior why not reinforce good behavior? Ask students to think of behaviors that are typically punished, and then in pairs or small groups, ask students to generate some ways that alternative, good behaviors could be reinforced. Halpern, S. D., French, B., Small, D. S., Saulsgiver, K., Harhay, M. O., Audrain-Mcgovern, J., . . . Volpp, K. G. (2015). Randomized trial of four financial-incentive programs for smoking cessation. New England Journal of Medicine N Engl J Med, 372(22), 2108-2117. doi:10.1056/nejmoa1414293 ... View more

I enjoyed reading this piece out of the Harvard Business Review (just showing up also in Psychology Today), especially as it relates to trying to help students think through issues about motivation, especially with school work and job. Given how much technology and the expectation one will "respond right away" often competes with slow, deliberate and thoughtful processes, it is good to be prepared not to get pulled in. Planning, especially starting list words with a verb, can be a nice way to make goals specific and action-oriented. http://blogs.hbr.org/2014/06/how-to-spend-the-first-10-minutes-of-your-day/ ... View more

This piece was originally published on April 17th, 2008   Throughout the world, boys and girls prefer to play with different types of toys. Boys typically like to play with cars and trucks, while girls typically choose to play with dolls. Why is this? A traditional sociological explanation is that boys and girls are socialized and encouraged to play with different types of toys by theirparents, peers, and the “society.” Growing scientific evidence suggests, however, that boys’ and girls’ toy preferences may have a biological origin. In 2002, Gerianne M. Alexander of Texas A&M University and Melissa Hines of City University in London stunned the scientific world by showing that vervet monkeys showed the same sex-typical toy preferences as humans. In an incredibly ingenious study, published in Evolution and Human Behavior, Alexander and Hines gave two stereotypically masculine toys (a ball and a police car), two stereotypically feminine toys (a soft doll and a cooking pot), and two neutral toys (a picture book and a stuffed dog) to 44 male and 44 female vervet monkeys. They then assessed the monkeys’ preference for each toy by measuring how much time they spent with each. Their data demonstrated that male vervet monkeys showed significantly greater interest in the masculine toys, and the female vervet monkeys showed significantly greater interest in the feminine toys. The two sexes did not differ in their preference for the neutral toys. Alexander and Hines’s article contains a wonderful picture (reproduced here in full living color, courtesy of Gerianne M. Alexander) of a female vervet monkey conducting an anogenital inspection (examining the genital area of the doll in an attempt to determine whether it is male or female), as a girl might, and a male vervet monkey pushing the police car back and forth, as a boy might. If children’s toy preferences were largely formed by gender socialization, as traditional sociologists claim, in which their parents give “gender-appropriate” toys to boys and girls, how can these male and female vervet monkeys have the same preferences as boys and girls? They were never socialized by humans, and they had never seen these toys before in their lives. Yet, not only did male and female vervet monkeys show the identical sex preference for toys, but how they played with these toys was also identical to how boys and girls might. As stunningly ingenious and spectacular Alexander and Hines's initial study was, it stood alone in the scientific literature for a while. All new scientific discoveries must be replicated to make sure that the findings are both genuine and generalizable. Well, it took the field six years, but the original findings have now been replicated. In a forthcoming article in Hormones and Behavior, Janice M. Hassett, Erin R. Siebert, and Kim Wallen, of Emory University, replicate the sex preferences in toys among members of another primate species (rhesus monkeys). Their study shows that, when given a choice between stereotypically male “wheeled toys” (such as a wagon, a truck, and a car) and stereotypically female “plush toys” (such as Winnie the Pooh, Raggedy Ann, and a koala bear hand puppet), male rhesus monkeys show strong and significant preference for the masculine toys. Female rhesus monkeys show preference for the feminine toys, but the difference in their preference is not statistically significant. We do not yet know exactly why males of different primate species prefer wheeled toys and other vehicles, or why females of different primate species prefer plush toys and other dolls (except for their vague resemblance to babies, for which females are evolutionarily designed to care). However, it is becoming less and less likely that “gender socialization” is the reason why boys and girls prefer different toys, and more and more likely that there are some genetic, hormonal, and other biological reasons for the observed sex differences in toy preference. ... View more

This piece was originally published on April 28, 2014 How well can computers interact with humans? Certainly computers play a mean game of chess, which requires strategy and logic, and “Jeopardy!,” in which they must process language to understand the clues read by Alex Trebek (and buzz in with the correct question). But in recent years, scientists have striven for an even more complex goal: programming computers to read human facial expressions. The practical applications could be profound. Computers could supplement or even replace lie detectors. They could be installed at border crossings and airport security checks. They could serve as diagnostic aids for doctors. Researchers at the University of California, San Diego, have written software that not only detected whether a person’s face revealed genuine or faked pain, but did so far more accurately than human observers. While other scientists have already refined a computer’s ability to identify nuances of smiles and grimaces, this may be the first time a computer has triumphed over humans at reading their own species. “A particular success like this has been elusive,” said Matthew A. Turk, a professor of computer science at the University of California, Santa Barbara. “It’s one of several recent examples of how the field is now producing useful technologies rather than research that only stays in the lab. We’re affecting the real world.” People generally excel at using nonverbal cues, including facial expressions, to deceive others (hence the poker face). They are good at mimicking pain, instinctively knowing how to contort their features to convey physical discomfort. And other people, studies show, typically do poorly at detecting those deceptions. In a new study, in Current Biology, by researchers at San Diego, the University of Toronto and the State University of New York at Buffalo, humans and a computer were shown videos of people in real pain or pretending. The computer differentiated suffering from faking with greater accuracy by tracking subtle muscle movement patterns in the subjects’ faces. “We have a fair amount of evidence to show that humans are paying attention to the wrong cues,” said Marian S. Bartlett, a research professor at the Institute for Neural Computation at San Diego and the lead author of the study. For the study, researchers used a standard protocol to produce pain, with individuals plunging an arm in ice water for a minute (the pain is immediate and genuine but neither harmful nor protracted). Researchers also asked the subjects to dip an arm in warm water for a moment and to fake an expression of pain. Observers watched one-minute silent videos of those faces, trying to identify who was in pain and who was pretending. Only about half the answers were correct, a rate comparable to guessing. Then researchers provided an hour of training to a new group of observers. They were shown videos, asked to guess who was really in pain, and told immediately whom they had identified correctly. Then the observers were shown more videos and again asked to judge. But the training made little difference: The rate of accuracy scarcely improved, to 55 percent. Then a computer took on the challenge. Using a program that the San Diego researchers have named CERT, for computer expression recognition toolbox, it measured the presence, absence and frequency of 20 facial muscle movements in each of the 1,800 frames of one-minute videos. The computer assessed the same 50 videos that had been shown to the original, untrained human observers. The computer learned to identify cues that were so small and swift that they eluded the human eye. Although the same muscles were often engaged by fakers and those in real pain, the computer could detect speed, smoothness and duration of the muscle contractions that pointed toward or away from deception. When the person was experiencing real pain, for instance, the length of time the mouth was open varied; when the person faked pain, the time the mouth opened was regular and consistent. Other combinations of muscle movements were the furrowing between eyebrows, the tightening of the orbital muscles around the eyes, and the deepening of the furrows on either side of the nose. The computer’s accuracy: about 85 percent. Jeffrey Cohn, a University of Pittsburgh professor of psychology who also conducts research on computers and facial expressions, said the CERT study addressed “an important problem, medically and socially,” referring to the difficulty of assessing patients who claim to be in pain. But he noted that the study’s observers were university students, not pain specialists. Dr. Bartlett said she didn’t mean to imply that doctors or nurses do not perceive pain accurately. But “we shouldn’t assume human perception is better than it is,” she said. “There are signals in nonverbal behavior that our perceptual system may not detect or we don’t attend to them.” Dr. Turk said that among the study’s limitations were that all the faces had the same frontal view and lighting. “No one is wearing sunglasses or hasn’t shaved for five days,” he said. Dr. Bartlett and Dr. Cohn are working on applying facial expression technology to health care. Dr. Bartlett is working with a San Diego hospital to refine a program that will detect pain intensity in children. “Kids don’t realize they can ask for pain medication, and the younger ones can’t communicate,” she said. A child could sit in front of a computer camera, she said, referring to a current project, and “the computer could sample the child’s facial expression and get estimates of pain. The prognosis is better for the patient if the pain is managed well and early.” Dr. Cohn noted that his colleagues have been working with the University of Pittsburgh Medical Center’s psychiatry department, focusing on severe depression. One project is for a computer to identify changing patterns in vocal sounds and facial expressionsthroughout a patient’s therapy as an objective aid to the therapist. “We have found that depression in the facial muscles serves the function of keeping others away, of signaling, ‘Leave me alone,’ ” Dr. Cohn said. The tight-lipped smiles of the severely depressed, he said, were tinged with contempt or disgust, keeping others at bay. “As they become less depressed, their faces show more sadness,” he said. Those expressions reveal that the patient is implicitly asking for solace and help, he added. That is one way the computer can signal to the therapist that the patient is getting better. ... View more