Duke Research Blog

Following the people and events that make up the research community at Duke.

Category: Behavior/Psychology (Page 2 of 23)

Creating Technology That Understands Human Emotions

“If you – as a human – want to know how somebody feels, for what might you look?” Professor Shaundra Daily asked the audience during an ECE seminar last week.

“Facial expressions.”
“Body Language.”
“Tone of voice.”
“They could tell you!”

Over 50 students and faculty gathered over cookies and fruits for Dr. Daily’s talk on designing applications to support personal growth. Dr. Daily is an Associate Professor in the Department of Computer and Information Science and Engineering at the University of Florida interested in affective computing and STEM education.

Dr. Daily explaining the various types of devices used to analyze people’s feelings and emotions. For example, pressure sensors on a computer mouse helped measure the frustration of participants as they filled out an online form.

Affective Computing

The visual and auditory cues proposed above give a human clues about the emotions of another human. Can we use technology to better understand our mental state? Is it possible to develop software applications that can play a role in supporting emotional self-awareness and empathy development?

Until recently, technologists have largely ignored emotion in understanding human learning and communication processes, partly because it has been misunderstood and hard to measure. Asking the questions above, affective computing researchers use pattern analysis, signal processing, and machine learning to extract affective information from signals that human beings express. This is integral to restore a proper balance between emotion and cognition in designing technologies to address human needs.

Dr. Daily and her group of researchers used skin conductance as a measure of engagement and memory stimulation. Changes in skin conductance, or the measure of sweat secretion from sweat gland, are triggered by arousal. For example, a nervous person produces more sweat than a sleeping or calm individual, resulting in an increase in skin conductance.

Galvactivators, devices that sense and communicate skin conductivity, are often placed on the palms, which have a high density of the eccrine sweat glands.

Applying this knowledge to the field of education, can we give a teacher physiologically-based information on student engagement during class lectures? Dr. Daily initiated Project EngageMe by placing galvactivators like the one in the picture above on the palms of students in a college classroom. Professors were able to use the results chart to reflect on different parts and types of lectures based on the responses from the class as a whole, as well as analyze specific students to better understand the effects of their teaching methods.

Project EngageMe: Screenshot of digital prototype of the reading from the galvactivator of an individual student.

The project ended up causing quite a bit of controversy, however, due to privacy issues as well our understanding of skin conductance. Skin conductance can increase due to a variety of reasons – a student watching a funny video on Facebook might display similar levels of conductance as an attentive student. Thus, the results on the graph are not necessarily correlated with events in the classroom.

Educational Research

Daily’s research blends computational learning with social and emotional learning. Her projects encourage students to develop computational thinking through reflecting on the community with digital storytelling in MIT’s Scratch, learning to use 3D printers and laser cutters, and expressing ideas using robotics and sensors attached to their body.

VENVI, Dr. Daily’s latest research, uses dance to teach basic computational concepts. By allowing users to program a 3D virtual character that follows dance movements, VENVI reinforces important programming concepts such as step sequences, ‘for’ and ‘while’ loops of repeated moves, and functions with conditions for which the character can do the steps created!

 

 

Dr. Daily and her research group observed increased interest from students in pursuing STEM fields as well as a shift in their opinion of computer science. Drawings from Dr. Daily’s Women in STEM camp completed on the first day consisted of computer scientist representations as primarily frazzled males coding in a small office, while those drawn after learning with VENVI included more females and engagement in collaborative activities.

VENVI is a programming software that allows users to program a virtual character to perform a sequence of steps in a 3D virtual environment!

In human-to-human interactions, we are able draw on our experiences to connect and empathize with each other. As robots and virtual machines grow to take increasing roles in our daily lives, it’s time to start designing emotionally intelligent devices that can learn to empathize with us as well.

Post by Anika Radiya-Dixit

Life Lessons from a Neuroscientist

I recently had the privilege of sitting down with Dr. Anne Buckley, a professor and  neuropathologist working in Dr. Chay Kuo’s cell biology lab at Duke. I got a first-hand account of her research on neuron development and function in mice. But just as fascinating to me were the life lessons she had learned during her time as a researcher.

Anne Buckley, M.D. Ph.D., is an assistant professor of pathology

Anne Buckley, M.D. Ph.D., is an assistant professor of pathology

Buckley’s research looks at brain tumors in mice. She recently found that some of the mice developed the tumors in an area full of neurons, the roof of the fourth ventricle, which is of particular interest because humans have developed tumors in the same location. This discovery could show how neurological pathways affect tumor formation and progression.

Buckley also gave me some critical words of advice, cautioning me that research isn’t for everyone.

“Research is not glamorous, and not always rewarding,” she warned me. When she first started research, Buckley learned a hard lesson: work doesn’t necessarily lead to results. “For every question I went after, I found ten more unresolved,” she said. “To be a researcher, it takes a lot of perseverance and resilience. A lot of long nights.”

But that’s also the beauty of research. Buckley says that she’s learned to find happiness in the small successes, and that she “enjoys the process, enjoys the challenge.”

And when discoveries happen?

“When I look at data, and I see something unexpected, I get really excited,” she says. “I know something that no one else knows. Tomorrow, everyone will know. But tonight, I’m the only person in the world who knows.”

kendra_zhong_headshotGuest Post by Kendra Zhong, North Carolina School of Science and Math, Class of 2017

Evolutionary Genetics Shaping Health and Behavior

Dr. Jenny Tung is interested in the connections between genes and behavior: How does behavior influence genetic variation and regulation and how do genetic differences influence behavior?

A young Amboseli baboon hitches a ride with its mother. (Photo by Noah Snyder-Mackler)

A young Amboseli baboon hitches a ride with its mother. (Photo by Noah Snyder-Mackler)

An assistant professor in the Departments of Evolutionary Anthropology and Biology at Duke, Tung is interested in evolution because it gives us a window into why the living world is the way it is. It explains how organisms relate to one another and their environment. Genetics explains the actual molecular foundation for evolutionary change, and it gives part of the answer for trait variation. Tung was drawn as an undergrad towards the combination of evolution and genetics to explain every living thing we see around us; she loves the explanatory power and elegance to it.

Tung’s longest collaborative project is the Amboseli Baboon Research Project (ABRP), located in the Amboseli ecosystem of East Africa. She co-leads it with Susan Alberts, chair of evolutionary anthropology at Duke, Jeanne Altmann at Princeton, and Beth Archie at Notre Dame.

Tung has spent months at a time on the savannah next to Mount Kilimanjaro for this project. The ABRP monitors hundreds of baboons in several social groups and studies social processes at several levels. Recently the project has begun to include genetics and other aspects of baboon biology, including the social behaviors within the social groups and populations, and how these behaviors have changed along with the changing Amboseli ecosystem. Tung enjoys different aspects of all of her projects, but is incredibly grateful to be a part of the long-term Amboseli study.

Jenny Tung

Jenny Tung is an assistant professor in evolutionary anthropology and biology.

The process of discovery excites Tung. It is hard for her to pin down a single thing that makes research worth it, but “new analyses, discussions with students who teach me something new, seeing a great talk that makes you think in a different way or gives you new research directions to pursue” are all very exciting, she said.

Depending on the project, the fun part varies for her; watching a student develop as a scientist through their own project is rewarding, and she loves collaborating with extraordinary scientists. Specific sets of collaborators make the research worth it. “When collaborations work, you really push each other to be better scientists and researchers,” Tung said.

Raechel ZellerGuest post by Raechel Zeller, North Carolina School of Science and Math, Class of 2017

Would You Expect a ‘Real Man’ to Tweet “Cute” or Not?

There’s nothing cute about stereotypes, but as a species, we seem to struggle to live without them.

In a clever new study led by Jordan Carpenter, who is now a postdoctoral fellow at Duke, a University of Pennsylvania team of social psychologists and computer scientists figured out a way to test just how accurate our stereotypes about language use might be, using a huge collection of real tweets and a form of artificial intelligence called “natural language processing.”

Wordclouds show the words in tweets that raters mistakenly attributed to Female authors (left) or Males (right).

Word clouds show the words in tweets that raters mistakenly attributed to Female authors (left) or Males (right). The larger the word appears, the more often the raters were fooled by it. Word color indicates the frequency of the word; gray is least frequent, then blue, and dark red is the most frequent. <url> means they used a link in their tweet.

Starting with a data set that included the 140-character bon mots of more than 67,000 Twitter users, they figured out the actual characteristics of 3,000 of the authors. Then they sorted the authors into piles using four criteria – male v. female; liberal v. conservative; younger v. older; and education (no college degree, college degree, advanced degree).

A random set of 100 tweets by each author over 12 months was loaded into the crowd-sourcing website Amazon Mechanical Turk. Intertubes users were then invited to come in and judge what they perceived about the author one characteristic at a time, like age, gender, or education, for 2 cents per rating. Some folks just did one set, others tried to make a day’s wage.

The raters were best at guessing politics, age and gender. “Everybody was better than chance,” Carpenter said. When guessing at education, however, they were worse than chance.

Jordan Carpenter is a newly-arrived Duke postdoc working with Walter Sinnott-Armstrong in philosophy and brain science.

Jordan Carpenter is a newly-arrived Duke postdoc working with Walter Sinnott-Armstrong in philosophy and brain science.

“When they saw the word S*** [this is a family blog folks, work with us here] they most often thought the author didn’t have a college degree. But where they went wrong was they overestimated the importance of that word,” Carpenter said. Raters seemed to believe that a highly-educated person would never tweet the S-word or the F-word. Unfortunately, not true! “But it is a road to people thinking you’re not a Ph.D.,” Carpenter wisely counsels.

The raters were 75 percent correct on gender, by assuming women would be tweeting words like Love, Cute, Baby and My, interestingly enough. But they got tricked most often by assuming women would not be talking about News, Research or Ebola or that the guys would not be posting Love, Life or Wonderful.

Female authors were slightly more likely to be liberal in this sample of tweets, but not as much as the raters assumed. Conservatism was viewed by raters as a male trait. Again, generally true, but not as much as the raters believed.

Youthful authors were correctly perceived to be more likely to namedrop a @friend, or say Me and Like and a few variations on the F-bomb, but they could throw the raters for a loop by using Community, Our and Original.

And therein lies the social psychology takeaway from all this: “An accurate stereotype should be one with accurate social judgments of people,” but clearly every stereotype breaks down at some point, leading to “mistaken social judgement,” Carpenter said. Just how much stereotypes should be used or respected is a hot area of discussion within the field right now, he said.

The other value of the paper is that it developed an entirely new way to apply the tools of Big Data analysis to a social psychology question without having to invite a bunch of undergraduates into the lab with the lure of a Starbucks gift card. Using tweets stripped of their avatars or any other identifier ensured that the study was testing what people thought of just the words, nothing else, Carpenter said.

The paper is “Real Men Don’t Say “Cute”: Using Automatic Language Analysis To Isolate Inaccurate Aspects Of Stereotypes.”  You can see the paper in Social Psychology and Personality Science, if you have a university IP address and your library subscribes to Sage journals. Otherwise, here’s a press release from the journal. (DOI: 10.1177/1948550616671998 )

Karl Leif BatesPost by Karl Leif Bates

Depression Screening Questions Seem to Miss Men

Women may be more likely to be diagnosed and treated for anxiety and depression not because they are, but because they’re more willing than men to honestly answer the questions used to diagnose mental health problems, a new Duke study finds.

man drinking - Wellcome Images

Asking men about their drinking might identify more cases of the blues like this guy. (Blauwe Week 1936 advertisement against alcohol. From Wellcome Images via Wikimedia Commons)

Jen’nan Read, a Duke sociologist and lead-author of the study, said men seem to adhere to a societal stigma to remain “macho” and are less likely to open up about their feelings. Her findings appear in Sociological Forum available online now and will appear in print in December.

Read’s study examines connections between mental and physical health in both men and women and suggests that the criteria used to examine mental health should be expanded beyond depression to include questions on substance abuse, which is another form of expressing mental distress, and more common among men.

The study finds that while depression is often how women express problems with mental health, men do so by drinking alcohol. The Duke study found that questioning men about alcohol use is a better way to diagnose both mental and physical health problems.

“Depression gives a lopsided picture,” Read said. “It makes mental health look like a women’s issue.”

A common set of questions include asking how often people have trouble getting to sleep or staying asleep, felt sad, lonely or like ‘you couldn’t shake the blues.’

Jen'nan Read is a Duke sociologist

Jen’nan Read is a Duke sociologist

“It’s more acceptable for women to answer affirmatively to these questions,” Read said. “Men are less likely to say they have feelings of anxiety. Issues of masculinity lead many to mask their problems.”

The result is often missed diagnoses of mental health problems in men.

The study crunches data from the Aging Status and Sense of Control Survey, in which people answer questions about their mental and physical health, diet, family situation, access and use of health care and other life factors. The average of women surveyed is about 54, and the average age of men was about 51.

Read’s study found that both men and women suffering from poor mental health are likely to suffer physical problems as well, like high blood pressure, diabetes and other issues.

The study was conducted by Read, Jeremy R. Porter, a sociologist with the City University of New York – Brooklyn College, and Bridget K. Gorman, a sociologist at Rice University.

Eric FerreriGuest Post by Eric Ferreri, Duke News and Communications

Mapping the Brain With Stories

alex-huth_

Dr. Alex Huth. Image courtesy of The Gallant Lab.

On October 15, I attended a presentation on “Using Stories to Understand How The Brain Represents Words,” sponsored by the Franklin Humanities Institute and Neurohumanities Research Group and presented by Dr. Alex Huth. Dr. Huth is a neuroscience postdoc who works in the Gallant Lab at UC Berkeley and was here on behalf of Dr. Jack Gallant.

Dr. Huth started off the lecture by discussing how semantic tasks activate huge swaths of the cortex. The semantic system places importance on stories. The issue was in understanding “how the brain represents words.”

To investigate this, the Gallant Lab designed a natural language experiment. Subjects lay in an fMRI scanner and listened to 72 hours’ worth of ten naturally spoken narratives, or stories. They heard many different words and concepts. Using an imaging technique called GE-EPI fMRI, the researchers were able to record BOLD responses from the whole brain.

Dr. Huth explaining the process of obtaining the new colored models that revealed semantic "maps are consistent across subjects."

Dr. Huth explaining the process of obtaining the new colored models that revealed semantic “maps are consistent across subjects.”

Dr. Huth showed a scan and said, “So looking…at this volume of 3D space, which is what you get from an fMRI scan…is actually not that useful to understanding how things are related across the surface of the cortex.” This limitation led the researchers to improve upon their methods by reconstructing the cortical surface and manipulating it to create a 2D image that reveals what is going on throughout the brain.  This approach would allow them to see where in the brain the relationship between what the subject was hearing and what was happening was occurring.

A model was then created that would require voxel interpretation, which “is hard and lots of work,” said Dr. Huth, “There’s a lot of subjectivity that goes into this.” In order to simplify voxel interpretation, the researchers simplified the dimensional subspace to find the classes of voxels using principal components analysis. This meant that they took data, found the important factors that were similar across the subjects, and interpreted the meaning of the components. To visualize these components, researchers sorted words into twelve different categories.

img_2431

The Four Categories of Words Sorted in an X,Y-like Axis

These categories were then further simplified into four “areas” on what might resemble an x , y axis. On the top right was where violent words were located. The top left held social perceptual words. The lower left held words relating to “social.” The lower right held emotional words. Instead of x , y axis labels, there were PC labels. The words from the study were then colored based on where they appeared in the PC space.

By using this model, the Gallant could identify which patches of the brain were doing different things. Small patches of color showed which “things” the brain was “doing” or “relating.” The researchers found that the complex cortical maps showing semantic information among the subjects was consistent.

These responses were then used to create models that could predict BOLD responses from the semantic content in stories. The result of the study was that the parietal cortex, temporal cortex, and prefrontal cortex represent the semantics of narratives.

meg_shieh_100hedPost by Meg Shieh

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