Looking for a great summer read? If you like Nate Silver’s quantitative assessments of politics and sports, you will love Seth Stephens-Davidowitz’s new book on big data revelations about our human interests, traits, and behaviors. By drilling down through millions of data points, often from people’s anonymous Google searches, he offers insights into racial prejudice, sexual orientation, child abuse, and even the age at which people’s long-term sports loyalties crystallize.   With data science he can also test popular ideas. Was Freud right to suppose that phallic symbols in dreams, and innuendos in word slips, reveal our unconscious sexuality? Is the man who dreamed of eating a banana on his wedding day “secretly thinking of a penis”? Is typing “lipsdick” when you meant “lipstick” an eruption of your hidden desire?   In search of answers, Stephens-Davidowitz analyzed whether phallic-shaped foods “sneak into our dreams with unexpected frequency.” His answer: They do not. In dreams, bananas are the second most common fruit . . . and they also are the second most consumed fruit. Cucumbers are the seventh most dreamt vegetable, and the seventh most consumed vegetable.   In search of Freudian slips, he analyzed 40,000 typing errors collected by Microsoft. A few were sexually tinged—“sexurity” instead of “security,” and “cocks” instead of “rocks.” But then there also were innocent slips such as “pindows,” “fegetables,” and “aftermoons.” After analyzing the frequency of various errors in random typos, Stephens-Davidowitz concludes that “People make lots of mistakes.” And when you make enough, you can expect an occasional and statistically predictable miscue. Searching the quarter million e-mails I’ve received since 2000, for example, I see that friends have written me about their experiences with “Wisconsin Pubic Radio,” with hearing access in “pubic venues” and with “pubic access,” and in their work as a national organization’s “Director of Pubic Policy.”   Thus, “Freud’s theory that errors reveal our subconscious wants is indeed falsifiable—and, according to my analysis of the data, false.” ... View more

On rare occasion, I have reported startling findings that challenge current wisdom: Brain training games do NOT boost intelligence. Traumatic experiences are NOT often repressed. Seasonal affective disorder (wintertime blues) is NOT widespread.   The just-arrived lectures from the 2016 Bial Symposium on Placebo Effects, Healing and Meditation, offers another shocker: In an update on his meta-analyses, Irving Kirsch concludes that antidepressant drug effects are close to nil.   Here’s Kirsch’s gist: Many, many studies, including unpublished drug trials made available by the FDA, consistently show that Antidepressants work. They produce clinically significant benefits (using a standard depression scale). Placebos work, too. In two large meta-analyses, placebos produced 82 percent of the antidepressant effect. Moreover, “the difference between drug and placebo is . . . so small that clinicians cannot detect it.” Side effects can “unblind” a drug. The statistically (but not clinically) detectable drug effect may be attributable to antidepressants’ detectable side effects. The FDA only counts “successful trials.” Kirsch reports that despite meager evidence of antidepressant efficacy, the drugs gain approval because of a stunning FDA policy—which ignores trials that find no drug effect and reports only successful trials. “All antidepressant drugs seem to be equally effective.” As one would expect from a placebo effect, the benefits of various antidepressant drugs are “exactly the same regardless of type of drug.” Various serotonin-increasing drugs relieve depression, but so does a drug that decreases serotonin! “What do you call pills, the effects of which are independent of their chemical composition?,” asks Kirsch. “I call them ‘placebos.’”   Given that antidepressants work, even if they are hardly more than active placebos, what’s a clinician to recommend? Kirsch notes three considerations: Antidepressants have side effects, which can include sexual dysfunction, weight gain, insomnia, and diarrhea. Antidepressant use increases the risk of relapse after recovery. Cognitive behavioral therapy, acupuncture, and physical exercise also effectively treat depression. Ergo, “When different treatments are equally effective, choice should be based on risk and harm, and of all of these treatments, antidepressant drugs are the riskiest and most harmful. If they are to be used at all, it should be as a last resort.” But surely this is not the last word. Stay tuned for more findings and debate. ... View more

The Lancet reports that 15.3 percent of all humans are daily smokers. Yet smoking varies enormously, from: 25 percent among men to 5 percent among women, and from 43 percent among Greenlanders to 1 percent among Sudanese. Even the gender difference varies dramatically, from: nonexistent among Icelanders, where 14.4 percent of women and 14.5 percent of men smoke, to huge among Armenians, where nearly half (43.5 percent) of men and virtually no women (1.5 percent) are smokers. A question: What else do we know about all humanity (apart from our shared physiology)? Here is my short list. Do you know of more? If so I’d love to hear from you.  Human life expectancy: 71 years but with huge variation—from 39 years in Sierra Leone to 84 years in Japan. Humans overweight: 37 percent of men and 38 percent of women but with huge variation—from 3 percent in Timor-Leste to 85 percent in Tonga. Human religiosity: 68 percent say “Religion is important in my daily life” but with huge variation—from 16 percent in Estonia to 100 percent in Niger. Humans employed full time by an employer: 26 percent  but with huge variation—from 19 percent of women to 33 percent of men, and with child-free women varying from 11 percent employed in North Africa to 67 percent in Russia. The bottom line: We humans are kin. But how we differ!   Caiaimage/Robert Daly/OJO+/Getty Images ... View more

After covering experiments or as a research methods boost when covering attractiveness, pose this hypothesis to your students: Tattoos on men influence how others perceive the men’s health and attractiveness. Ask students to design an experiment to test this hypothesis, identifying the independent variable (including experimental and control conditions) and the dependent variables. In the design of the experiment, how would students eliminate any potential confounding variables? Circulate among groups as students work through the design. As discussion dies down, ask volunteers to share their experimental designs. Now share with students the experiment conducted by Andrzej Galbarczyk and Anna Ziomkiewicz (2017) using over 2,500 Polish participants recruited through Facebook; all participants self-identified as heterosexual. Researchers used nine non-tattooed male models, photographed from the waist up and without shirts for the control condition. “A professional photographer digitally modified the pictures by adding a black arm tattoo with an abstract, neutral design” for the experimental condition. This means that the only difference in the conditions was the tattoo. Participants were randomly assigned to see one photo for each model pair, and in the nine photos seen, each participant saw at least one tattooed model and one non-tattooed model. The dependent variables were ratings of attractiveness, health, dominance, aggression, fitness as a partner, and fitness as a father. Data were analyzed separately for male and female research participants. Before revealing the results, ask students to predict how the participants responded. Using clickers or a show of hands, ask students: Who did women rate as healthier? Tattooed men Non-tattooed men No difference [Women rated the tattooed men as healthier] Who did men rate as healthier? Tattooed men Non-tattooed men No difference [Men didn’t see a health difference between tattooed and non-tattooed men.] Who did women rate as more attractive? Tattooed men Non-tattooed men No difference [Women didn’t see a difference in attractiveness between tattooed and non-tattooed men.] Who did men rate as more attractive? Tattooed men Non-tattooed men No difference [Men rated the tattooed men as more attractive.] Who did men and women rate as more masculine, dominant, and aggressive? Tattooed men Non-tattooed men No difference [Tattooed men.] Who did women rate “as worse potential partners and parents”? Tattooed men Non-tattooed men No difference [Tattooed men.] Who did men rate “as worse potential partners and parents”? Tattooed men Non-tattooed men No difference [No difference.] Ask students to volunteer guesses as to why women would see tattooed men as healthier than non-tattooed men. And why men would see tattooed men as more attractive than non-tattooed men. The article’s authors offer a number of possible explanations, all worthy of further research.    REFERENCE Galbarczyk, A., & Ziomkiewicz, A. (2017). Tattooed men: Healthy bad boys and good-looking competitors. Personality and Individual Differences, 106, 122-125. doi:10.1016/j.paid.2016.10.051 ... View more

After introducing correlations, I share a number of examples with students. Here are couple new ones that going into my pool. Young adults (age 19-32) who spend their time on a lot (7 to 11) of different social media platforms are more likely (than those who spend their time on 0 to 2 different social media platforms) to report symptoms of depression. That same relationship exists between the number of different social media platforms and anxiety (Primack, et.al., 2017). Ask students if these represent a positive, negative, or zero correlation (clicker question, show of hands, shout out). (Both correlations are positive.) If students say negative, they may be caught in the trap of thinking that this is a “bad thing,” so it’s negative. Remind students that that is not what positive and negative mean in this context. It can help students in identifying correlations to first note what the two variables are (“number of social media platforms used” and “depression”; “number of social media platforms used” and “anxiety”) and then sort out whether those variables are moving in the same direction (positive) or opposite directions (negative). Next, to help students see that correlations don’t mean causation, ask students to consider the causes, why anxiety and depression may be related to the number of social media platforms used. In think-pair-share, give students a couple minutes to jot down their thoughts, then give students a couple minutes to share their ideas with one or two classroom neighbors, then ask for volunteers to share their thoughts. Students may surmise that jumping from one social media platform to another may cause depression or anxiety. The authors note three possibilities here: “participation in many different social media platforms may lead to multitasking between platforms, which is known to be related to poor cognitive and mental health outcomes;” since each platform has its own rules and customs “as the number of platforms used increases, individuals may experience difficulty navigating these multiple different worlds successfully, leading to potentially negative mood and emotions;” and the more social media platforms you are on you run an “increased risk of damaging gaffes.” Students may also surmise that those who are depressed or anxious may choose to jump from one social media platform to another. The authors suggest “[t]his may be because these individuals tend to search multiple avenues for a setting that feels most comfortable and in which they feel most accepted.”  A third possibility is that something else, a third variable, could be affecting both social media use and depression/anxiety, perhaps loneliness. Those who are more isolated may be more likely to seek out community in social media and being more isolated may contribute to depression/anxiety. Remind students that the value in correlations comes from revealing a relationship between two variables. Students identified a number of possible reasons as to why there is a relationship between those variables. The next step is to do more research on which of those possibilities are right -- and it may be one, some, or all of them.  Side note. Even though their research is clearly correlational, the article's authors are comfortable suggesting that it’s the social media use that’s causing or at least contributing to depression/anxiety. They write “it may not be too soon to suggest that individuals with depressive and/or anxiety symptoms, and who use a high number of different social media platforms may wish to decrease the number of platforms used.” Ask students if this is a fair statement for the authors to make.    Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among U.S. young adults. Computers in Human Behavior, 69, 1-9. doi:10.1016/j.chb.2016.11.013 ... View more

A political science grad student, Kevin Munger (2016a, b), decided to conduct an experiment on Twitter. His goal was to reduce the amount of hate speech posted to that media platform. To that end, he searched Twitter for a particular racial slur and sent every writer of such a tweet the same message: “@[subject] Hey man, just remember that there are real people who are hurt when you harass them with that kind of language.” But that’s not all. Munger reasoned that the impact of the message on future use of racial slurs likely depends on the communicator of that message, such as the perceived race and status of the person who is sending the reminder of the impact of language. Munger created four Twitter accounts: “High Follower/White; Low Follower/White; High Follower/Black; and Low Follower/Black.” The high followers had over 500 followers whereas the low followers had two followers. The White accounts displayed a White avatar and had a more stereotypically White-sounding name, Greg. The Black accounts displayed a Black avatar and had a more stereotypically Black-sounding name, Rasheed. Following Munger’s be-nice tweet, Munger tracked daily use of the racial slur over the next two months. The results. When Munger’s twitter account was High Follower/White, the number of racial slurs dropped .3 per day, the highest of any group. High status ingroup members do have power to influence. With about an equal, but smaller, decrease in daily use of racial slurs where the High Follower/Black and Low Follower/White avatars, although these differences disappeared within a few weeks (Munger, 2016b). Ingroup membership and high status both have an immediate, but not lasting impact, all on their own. What about Low Follower/Black? No Impact. In Intro Psych, you can give your students a little practice with experimental design by using this article at the beginning of the course when you cover research methods or as a research methods refresher later in the course when you cover social psychology. Ask students to identify the independent variables and dependent variable. The response to Munger’s missive wasn’t uniform; reponses differered by the anonymity of the Twitter user who received Munger’s message. Munger looked at the profiles of the Twitter users in his experiment. Of those users who were not anonymous, Munger reports that the High Follower/White Twitter account had no impact on the ensuing use of racial slurs. But the Low Follower/Black Twitter account “actually caused an increase (emphasis in original) in the use of racist slurs.” These were largely directed at Munger’s Twitter account that sent the be-nice message. Shout out to Danae Hudson (Missouri State University) for finding this article and suggesting this activity!   References Munger, K. (2016a, November 17). This researcher programmed bots to fight racism on Twitter. It worked. Retrieved from https://www.washingtonpost.com/news/monkey-cage/wp/2016/11/17/this-researcher-programmed-bots-to-fight-racism-on-twitter-it-worked Munger, K. (2016b). Tweetment effects on the tweeted: Experimentally reducing racist harassment. Political Behavior. doi:10.1007/s11109-016-9373-5 ... View more

Authors of Discovering the Scientist Within: Research Methods in Psychology, Gary Lewandowski, Natalie Ciarocco and David Strohmetz are all active researchers and committed teachers at Monmouth University. They’re excited to engage in a conversation about the Research Methods course, why it is so important, and talk about how they solve challenges in their own classrooms. Check out their Facebook live video from 11/4! Please feel free to leave any questions below or directly on the Facebook video and the authors will respond! ... View more

Originally posted on October 18, 2016.  For both graduate students and instructors alike, there are many reasons to teach a research methods course. From demand for professors to the ability to harbor student skills, these pragmatic approaches to teaching an engaging course are beneficial for students and instructors. Read more about my approach to teaching research methods on TeachPsych: http://teachpsych.org/page-1784686/4311450 ... View more

When covering research methods or as a research methods boost in health psych in Intro, ask your students, “Do fitness trackers, like Fitbit, work? If you were a psychological scientist trying to answer this question, how would you do it?” Give students a couple minutes to think about this on their own. Next, ask students to work in pairs or small groups to design an experiment that would help answer this question. As you circulate among the groups, make sure the groups are answering the question “Do fitness trackers work… at doing what?” As discussion is winding down, bring the class back together and ask for groups willing to volunteer their experimental designs. There should be some sort of random assignment to wearing a fitness tracker or not. Ask students why? [Because if you compared existing users with existing non-users, the users may already be more motivated to engage in physical activity.] Ask students to identify the independent variable [fitness tracker usage] and the experimental condition [fitness tracker] and control condition [no fitness tracker]. How long did students think participants should use/not use a fitness tracker to ensure a fair test? Why that amount of time? Ask students to identify the dependent variable(s) [perhaps weight loss]. With class discussion on the design wrapping up, share with students the results of such a study (Jakicic, et.al., 2016). Participants (470 of them) were six months into a 2-year weight-loss study when they were randomly assigned to either wear a fitness tracker that included a website for monitoring diet or self-monitor exercise and diet via a website (74.5% completed the study; every six months, participants were given $100). Note that this study did not have a no-treatment control group. Ask students to predict the results by a show of hands or via an audience response system. A. Fitness-tracker users lost the most weight B. Self-monitors lost the most weight C. Fitness-tracker users and self-monitors lost about the same amount of weight. Ready for the results? Those assigned to wear fitness trackers lost 3.5 kg (7.7 lbs). Those assigned to self-monitor lost 5.9 kg (13 lbs). There were no differences in the groups at the 6-month mark, the point in the study where they were randomly assigned to wear the fitness tracker or self-monitor. But at the next three check-ins (12 months, 18 months, and 24 months), the self-monitoring group had always lost more weight. Did your students guess right? Were they surprised by the results? Some explanations for these results are offered in this NPR story. But before you share these with students, ask students to generate some hypotheses as to why the self-monitoring group lost more weight than the fitness-trackers. If time allows, give students a couple minutes to think on their own before sharing in pairs or small groups.  Ask student volunteers to report out their hypotheses. Write the hypotheses where students can see them. If you’d like to send students off with a take-home assignment, assign students to design an experiment (but not conduct it!) that would test one of the student-generated hypotheses. Students should identify their independent and dependent variables and anything else they would do that would eliminate confounding variables. You can either let students choose the hypothesis, or assign hypotheses by last name, e.g., “If you’re last name begins with A through F, you have hypothesis 1.” Students can submit as a written assignment, or if you have time at the beginning of the next class, give students an opportunity to share their designs with each other, and then take a few minutes to ask volunteers to share their designs.   Reference Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., . . . Belle, S. H. (2016, September 20). Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. Retrieved from http://jama.jamanetwork.com/article.aspx?articleid=2553448 ... View more

Years ago, as a young instructor, I didn’t have the tools to help my struggling students who came to my office for studying advice. I had a clear idea of what “studying” was since I had done it for so long, but I don’t know if it’s that I couldn’t put words to what I did to study or if I assumed that everyone did the same thing when “studying,” and that if that didn’t work, I didn’t know what would. In any case, over time, I got better at my advice – test yourself, space out your studying, for example. One term I had a student who earned a perfect score on an exam. Students in the class knew somebody did, and they asked who it was. I said I couldn’t reveal the person’s name, but that person can if so desired. My perfect-score student immediately said, “I did! And I’m proud of it.” The other students began peppering her with some version of “How did you do it!” She explained that she spent time every day on the class. She read the textbook, took notes, merged her textbook notes with her class notes, thought of examples, tested herself over what she was learning. Students started exclaiming, “Oh! I don’t want to do all of that!” Her response? “Then you don’t get an A.” Learning is hard work. There’s no way around it. For students who are willing and able to put in the hard work, I want them to use effective, research-based study techniques. Unfortunately, students may not know what those research-based study techniques are. Some of the techniques students use may be a complete waste of time. Gurung, Weidert, and Jeske (2010) asked 120 students to complete a questionnaire on 35 different study behaviors. The behaviors that correlated positively with the students’ final exam scores: “attended every class,” “answered every question in the study guide,” “used practice exams to study,” and “was able to explain a problem or phenomenon using the material.” Behaviors that correlated negatively with exam scores: “after class, I looked over my notes to check for and fill in missing information,” “highlighted the most important information in each chapter to review later,” “reviewed the chapter after the lecture on that topic,” “asked… a classmate/friend to explain material I didn’t understand,” and “asked the professor or TAs for additional materials.” Interestingly, when they looked at just the top half of exam performers, only one correlation remained. Those who reported highlighting as a study strategy scored lower than those who did not. Highlighting is easy to do – it’s too easy to do. It doesn’t require deep processing; it’s a very shallow process. But at the end, with words highlighted, it’s easy to fool oneself into thinking studying was accomplished. Highlighting is really just coloring – and there are reasons coloring is relaxing: it takes little to no cognitive effort. Now, how about some advice on how to study? Yana Weinstein (UMass Lowell) and Megan Smith (Rhode Island College) of the LearningScientists.org blog have created a wonderful set of posters (slides and sticker templates) to help students learn how to study better. The strategies: spaced practice, retrieval practice, elaboration, interleaving, concrete examples, and dual coding. Side note: I love the use of the very specific word practice instead of the fuzzier word study. Elliott Hammer (2016) reports that “I’m also trying as of late to drop the word ‘study’ from my vocabulary in favor of ‘practice. It’s difficult to get students to be more active in their approach, and I want them to get beyond simply trying to read and call that studying. I don’t have data showing that the switch is working, but it feels more genuine.” To get your students to dive into these learning strategies, after covering the memory chapter, ask students to explain why each strategy is effective based on the concepts and research covered in their reading. This makes a nice out-of-class assignment, but it would also work well done in class with small groups. Give each small group the set of posters to explain the effectiveness of each. After discussion wanes, ask a group to report out on one of the posters; give other groups an opportunity to add to the conversation. Move onto another group, and ask them to report out on a different poster. Continue until all of the posters have been covered. If you use a classroom response system, ask students if they currently use the study strategy and whether they plan to use it in the future. Or you could do a jigsaw classroom. Divide the class into 6 groups of at least 6 members each (or 12 groups of at least 6 members or some other multiple of 6, depending on your class size) and give each group a different poster. (For smaller classes, use multiples of 3 and give each group 2 posters). After each group identifies why the strategy is effective (using the concepts learned in the memory chapter), break apart the groups so that at least one person from each group now forms a new group. Ask each new group member to share the strategy on their poster and explain why the strategy is effective. Bonus: Use the Gurung, et.al. (2010) study as examples when you cover correlations in the research methods chapter. Or use it here in the memory chapter to reinforce correlations. And then ask students which learning strategy is being used in the practice of learning correlations.   References Gurung, R. A., Weidert, J., & Jeske, A. (2010). Focusing on how students study. Journal of the Scholarship of Teaching and Learning, 10(1), 28-35. Hammer, E. (2016, July 31). I’m a member of STP and this is how I teach. Retrieved from http://teachpsych.org/page-1703896/4186852 ... View more

Did you ever wish you had access to a searchable database of twin correlations and trait heritability statistics? If not, once you see this, you will wonder why you hadn’t been looking for this kind of resource. Shout out to David Myers (Hope College) for pointing me toward MaTCH. Let’s take height as an example. From the first drop-down menu, select “ICF/ICD10 Subch” and then from the second drop-down menu, select “Height (297). The number in parentheses refers to the number of studies included in the displayed data. This is the first chart that is generated. If one identical (mz = monozygotic) twin is tall, there is a very good chance the other will be as well. If one is short, there is a very good chance the other will be as well. The correlation between being a twin and height is .91. The chart also gives correlations for just male identical twins (mzm = monozygotic male) and female identical twins (mzf = monozygotic female). If one fraternal (dz = dizygotic) twin is tall, there is a smaller chance the other will be as well – correlation of .54. Correlations are also given for all same-sex fraternal twins (dzss), just male fraternal twins (dzm), just female fraternal twins (dzf), and all other-sex fraternal twins (dos). Below the chart is this table. “Est.” is the estimated correlation based on the data from all of the studies included in the dataset. These are the correlations reported in the bar chart. “SE” is the standard error – the smaller the number, the more confident we are that the data reflect what’s true in the population. “Ntraits” are the number of studies in the dataset. “Npairs” are how many pairs of twins were included. While the correlations are interesting – and can certainly provide you with some interesting correlations when covering research methods – the real interesting stuff in this website comes from the last chart. This is where we get the “Reported ACE” – the heritability data. ACE is a model used among heritability researchers. A is additive genetics (the contribution of genes), C is common environment (the contribution of experiencing a shared environment), and E is [unique] environment (the contribution of our own, individual experiences). Before we get into the data, let’s a do a quick refresher of what heritability – and the ACE model – is. Within a population, people vary, say, in height. In the United States, the average height for adult females is about 5’ 4” (Onion, 2016). Some women are taller than that average, while others are shorter. It’s that difference between the shortest and the tallest – the variance – that ACE addresses. Let’s look at the “Reported ACE” chart for height. Picture this. Let’s say that we got all of the women in the United States together in one space. We measured each of their heights. A few would be less than 3 feet tall and a few would be more than 8 feet tall. Most would probably fall between 4’ 6” and 6’ 3 inches. The ACE model addresses where those differences in height come from. We are all going to be of some height just by virtue of being born. But what explains the differences in height among us? This article provides a nice explanation of heritability (Adam, 2012). “h2_all” is the heritability estimate for everybody based on the twin data. This means that 63% of the difference (the variability) in the height among all of us is due to genetics. “c2_all” is the estimate of the role played by a shared, common environment. This means that 30% of the difference in the height among all of us is due to a shared environment. Those two variables, genetics and common environment, together account for 93% (63% plus 30%) of the differences in our heights. The remaining 7%? That’s due to our unique environmental experiences. Please note that this says nothing about our own individual height. As a 5’ 4” female from the United States, this does NOT mean that 63% of my height is due to genetics. These numbers are only meaningful in explaining the differences in our heights across a population. To emphasize how population-driven heritability estimates are, on MaTCH’s left navigation menu, click on “Country.” Here you will see the data for height (if you were looking at the height variable) broken down by country. The ‘r’s are the correlations. Scroll to the right to see the heritability and common environment numbers. Canada, for example, shows 34% heritability for height and 60% for common environment, leaving 6% for unique environment. These numbers are very different from, say, the data for the United States. The U.S. shows 85% for heritability and 8% for common environment, leaving 7% for unique environment. Why might this be? Maybe Canadians are more genetically alike than are people in the U.S., thus differences amongst Canadians in their height must be more due to environment. Or maybe there just isn’t enough Canadian data. In the second column of that table, we see that three studies were used to calculate the Canadian estimates whereas 29 studies were used to calculate the U.S. data. There is much data here to explore. Before you dive too deeply into this website, watch this 15-minute tutorial video. Video Link : 1731 If you want to tackle this with your Intro Psych students, perhaps wherever you cover genetics, send your students to the MaTCH website to choose a psychologically relevant trait. Give your students a template like this to complete. The correlation for identical twins (mzall) on ______________ (enter trait name) is ________ (first line in the blue chart). The correlation for fraternal twins (dzall) on ______________ (enter trait name) is ________ (fourth line in the blue chart). The differences in  ______________ (enter trait name) within a population are _____% (h2_all) due to genetics, _____% (c2_all) due to a shared environment, and _____% (100 minus h2_all minus c2_all) due to a unique environment.   If students can’t find the trait they are interested in from the drop-down menu, they can click on “Find my Trait” in the top navigation bar. Searching on “intelligence” for example, tells us that that trait is lumped under “Higher-Level Cognitive Functions”. References Adam, G. (2012, September 6). What is heritability? Retrieved from Science 2.0: Join the Revolution: http://www.science20.com/gerhard_adam/what_heritability-93424   Onion, A. (2016, July 3). Why have Americans stopped growing taller? Retrieved from ABC News: http://abcnews.go.com/Technology/story?id=98438&page=1  ... View more

Originally posted on May 5, 2014. From an early age, I wanted to be an astronaut. I memorized Mercury astronaut missions. I dreamt of using a manned maneurvering unit to glide through space. I cried when the Challenger exploded. I still dream of going to space, but I know it’s a long shot. Still, space exploration captivates me. What will be the biggest obstacle to a successful Mars mission? It won’t be inadequate fuel, faulty aerodynamics, or clunky helmets. Social isolation is the greatest barrier to interplanetary travel. Don’t believe me? Think about the past 520 days of your life (about a year and five months). That’s how long it takes to travel to Mars and return. How many people did you see during that time? How many conversations did you have? Did you attend a sporting event? A play? A worship service? Maybe a loved one was born or passed away. Now wipe those experiences away. Instead, imagine that during this period of your life you lived in cramped quarters with only five other people, no fresh air, and no sunlight. This is not a mere thought experiment. The experiment happened, with funds from the Russian Academy of Sciences. What happened? Quite a bit. In research recently reported in the Proceedings of the National Academy of Sciences, the six volunteer marsonauts completed lots of tasks to keep their minds fresh. They also slept like babies without the daily rigmarole of daily work commutes, grocery shopping, or other daily drivel. Then the guys started sleeping like polar bears in hibernation. Then they started doing less, becoming even more sedentary amidst almost endless boredom. Space is only cool for so long. The good news? They all made it. There weren’t any major scuffles, and the guys probably formed lifelong friendships. They even showed signs of cognitive improvement. But the marsonaut volunteers each handled the prolonged social isolation differently. One of them shifted to a 25-hr sleep-wake schedule, which meant that he was alone (awake or asleep) 20% of the 520 day mock mission. As the researchers sift through their massive data set (to put it in perspective, they measured 4.396 million minutes of sleep!), I’m sure we’ll learn more about the psychological consequences of prolonged social isolation. For now, we can still look into the night sky, find the Red Planet, and dream of people visiting sometime in our generation. We know they’ll sleep well—and a lot. ... View more

Originally posted on May 16, 2014. Being around men increases stress. Do countries with more man than women have higher stress levels? In my last post, I promised to answer this question. But it’s a harder question than it seems. How do you measure a country’s level of stress? Some organizations, such as Gallup, do an excellent job surveying people around the world. I don’t work at Gallup, nor do I have access to their data. So I had to do the best I could. First, I gathered country gender composition data from our friends at the World Bank. I separated countries according to whether they had a majority of male or female citizens. The average was 50.77% women (standard deviation 1.19; Minimum: 48.19%, Maximum: 54.30%). Of the 74 countries for which data were available, 19 were male-majority and 55 were female-majority. Next, I searched for a good, comprehensive measure of country-level stress. Bloombergmade things easy. They computed a country’s stress score by combining seven factors: Annual Homicide Rate per 100,000 Gross Domestic Product (GDP) per capita Income inequality (Gini coefficient) Corruption (as measured by Transparency International) Unemployment rate Urban air pollution (micrograms per cubic meter) Life expectancy (years at birth) Finally, I compared country-level stress between male-majority and female-majority countries. This would give me an initial answer to my question. What were the results? Countries with more men than women, compared to their female-majority counterparts, had higher levels of stress. Three factors drove the effect: corruption, pollution, and life expectancy. In each case, more men than women equaled a more corrupt, polluted, and shorter lived society. A close fourth, which wasn’t quite statistically significant (p= .063), was Gross Domestic Product per capita. If a country had a male majority (vs. female majority), GDP was lower. These findings offer a novel extension to the finding that being around men, versus women, increases rodent stress. But unlike those careful laboratory experiments, people weren’t randomly assigned to live in male- or female-majority countries. We can’t infer cause and effect. All we can conclude is that when men are present, stress seems to rise instead of fall. ... View more

Originally posted on February 5, 2015. Even though the smartphone has only been around for the past seven or eight years, it’s sometimes difficult to remember what life was like before we had so much information at our fingertips. You could argue with a friend about what year “Back to the Future, Part 2” came out, or in what year the “future” was set. (It was released in 1989. The future, filled with flying cars and floating skateboards, was set in 2015.) Back then, you couldn’t resolve discussions by swiping a screen and touching a button. Siri wasn’t even a twinkle in Steve Jobs’s eye. If you got lost, you had to consult a map or stop and ask for directions, and if you got bored while waiting in line, you couldn’t pass the time by playing Candy Crush or perusing Instagram. Luddites argue that life was better before the smart phone, whereas others tout the benefits of instant communication and information. But one thing is certain: The smartphone has changed our lives. And our thumbs. Yes, when we spend time on smartphones using a touchscreen, it changes the way our thumbs and brains work together, according to a new study by researchers from the University of Zurich and ETH Zurich in Switzerland. Our obsession with smartphones presented the perfect opportunity to explore the everyday plasticity of our brains. With smartphones, we are using our fingertips—especially our thumbs—in a new way, and we do it a lot. And because our phones keep track of how we use them, they carry a wealth of information that can be studied. In the study, the research team used electroencephalography (EEG) to record brain response to the touch of the thumb, index finger, and middle fingerprints of touchscreen phone users compared to people who still use flip phones or other old-school devices. They found that the electrical activity in the brains of smartphone users was enhanced when all three fingertips were touched. The amount of activity in the brain’s cortex associated with the thumb and index fingertips was directly proportional to the amount of phone use. Repetitive movements over the touchscreen surface might reshape sensory processing from the hand. Cortical sensory processing in our brains is constantly shaped by personal digital technology. So, the next time you use your thumbs to tweet, answer email, or jot yourself a note, remember that you’re training your brain. Keep in mind, too, that excessive phone usage is linked with motor dysfunction and pain. Remember the so-called “BlackBerry thumb”? ... View more

Originally posted on April 3, 2014. My friend Ed Diener, the Jedi Master of happiness research, presented a wonderful keynote talk on “The Remarkable Progress of National Accounts of Subjective Well-Being” at the recent one-day “Happiness and Well-Being” conference.  He documented the social and health benefits of positive well-being, and celebrated the use of at least simple well-being measures by 41 nations as of 2013. In displaying the health accompaniments of positive emotions, Ed introduced me to a 2011 PNAS (Proceedings of the National Academy of Sciences) study by Andrew Steptoe and Jane Wardle that I’d somehow missed.  Steptoe and Wardle followed 3,853 fifty-two to seventy-nine year olds in England for 60 months.  This figure displays the number surviving, among those with high, medium, and low positive affect—which was assessed by averaging four mood reports across a single day at the study’s beginning.  Those with a “blue” mood that day were twice as likely as the good mood folks to die in the ensuing five years!{cke_protected_1}{cke_protected_2} ... View more