Category: Behavior/Psychology Page 15 of 28

Pinpointing Where Durham’s Nicotine Addicts Get Their Fix

On August 10, 2017

In Behavior/Psychology, Data, Medicine, Statistics, Students, Visualization

DURHAM, N.C. — It’s been five years since Durham expanded its smoking ban beyond bars and restaurants to include public parks, bus stops, even sidewalks.

While smoking in the state overall may be down, 19 percent of North Carolinians still light up, particularly the poor and those without a high school or college diploma.

Among North Carolina teens, consumption of electronic cigarettes in particular more than doubled between 2013 and 2015.

Now, new maps created by students in the Data+ summer research program show where nicotine addicts can get their fix.

Studies suggest that tobacco retailers are disproportionately located in low-income neighborhoods.

Living in a neighborhood with easy access to stores that sell tobacco makes it easier to start young and harder to quit.

The end result is that smoking, secondhand smoke exposure, and smoking-related diseases such as lung cancer, are concentrated among the most socially disadvantaged communities.

If you’re poor and lack a high school or college diploma, you’re more likely to live near a store that sells tobacco. Photo from Pixabay.

Where stores that sell tobacco are located matters for health, but for many states such data are hard to come by, said Duke statistics major James Wang.

Tobacco products bring in more than a third of in-store sales revenue at U.S. convenience stores — more than food, beverages, candy, snacks or beer. Despite big profits, more than a dozen states don’t require businesses to get a special license or permit to sell tobacco. North Carolina is one of them.

For these states, there is no convenient spreadsheet from the local licensing agency identifying all the businesses that sell tobacco, said Duke undergraduate Nikhil Pulimood. Previous attempts to collect such data in Virginia involved searching for tobacco retail stores by car.

“They had people physically drive across every single road in the state to collect the data. It took three years,” said team member and Duke undergraduate Felicia Chen.

Led by UNC PhD student in epidemiology Mike Dolan Fliss, the Duke team tried to come up with an easier way.

Instead of collecting data on the ground, they wrote an automated web-crawler program to extract the data from the Yellow Pages websites, using a technique called Web scraping.

By telling the software the type of business and location, they were able to create a database that included the names, addresses, phone numbers and other information for 266 potential tobacco retailers in Durham County and more than 15,500 statewide, including chains such as Family Fare, Circle K and others.

Map showing the locations of tobacco retail stores in Durham County, North Carolina.

When they compared their web-scraped data with a pre-existing dataset for Durham County, compiled by a nonprofit called Counter Tools, hundreds of previously hidden retailers emerged on the map.

To determine which stores actually sold tobacco, they fed a computer algorithm data from more than 19,000 businesses outside North Carolina so it could learn how to distinguish say, convenience stores from grocery stores. When the algorithm received store names from North Carolina, it predicted tobacco retailers correctly 85 percent of the time.

“For example we could predict that if a store has the word “7-Eleven” in it, it probably sells tobacco,” Chen said.

As a final step, they also crosschecked their results by paying people a small fee to search for the stores online to verify that they exist, and call them to ask if they actually sell tobacco, using a crowdsourcing service called Amazon Mechanical Turk.

Ultimately, the team hopes their methods will help map the more than 336,000 tobacco retailers nationwide.

“With a complete dataset for tobacco retailers around the nation, public health experts will be able to see where tobacco retailers are located relative to parks and schools, and how store density changes from one neighborhood to another,” Wang said.

The team presented their work at the Data+ Final Symposium on July 28 in Gross Hall.

Data+ is sponsored by Bass Connections, the Information Initiative at Duke, the Social Science Research Institute, the departments of mathematics and statistical science and MEDx. This project team was also supported by Counter Tools, a non-profit based in Carrboro, NC.

Writing by Robin Smith; video by Lauren Mueller and Summer Dunsmore

Mental Shortcuts, Not Emotion, May Guide Irrational Decisions

By Kara Manke

On March 30, 2017

In Behavior/Psychology, Guest Post, Neuroscience, Students

If you participate in a study in my lab, the Huettel Lab at Duke, you may be asked to play an economic game. For example, we may give you $20 in house money and offer you the following choice:

Keep half of the $20 for sure
Flip a coin: heads you keep all $20; tails you lose all $20

In such a scenario, most participants choose 1, preferring a sure win over the gamble.

Now imagine this choice, again starting with $20 in house money:

Lose half of the $20 for sure
Flip a coin: heads you keep all $20; tails you lose all $20

In this scenario, most participants prefer the gamble over a sure loss.

If you were paying close attention, you’ll note that both examples are actually numerically identical – keeping half of $20 is the same as losing half of $20 – but changing whether the sure option is framed as a gain or a loss results in different decisions to play it safe or take a risk. This phenomenon is known as the Framing Effect. The behavior that it elicits is weird, or as psychologists and economists would say, “irrational”, so we think it’s worth investigating!

Brain activity when people make choices consistent with (hot colors) or against (cool colors) the Framing Effect.

In a study published March 29 in the Journal of Neuroscience, my lab used brain imaging data to test two competing theories for what causes the Framing Effect.

One theory is that framing is caused by emotion, perhaps because the prospect of accepting a guaranteed win feels good while accepting a guaranteed loss feels scary or bad. Another theory is that the Framing Effect results from a decision-making shortcut. It may be that a strategy of accepting sure gains and avoiding sure losses tends to work well, and adopting this blanket strategy saves us from having to spend time and mental effort fully reasoning through every single decision and all of its possibilities.

Using functional magnetic resonance imaging (fMRI), we measured brain activity in 143 participants as they each made over a hundred choices between various gambles and sure gains or sure losses. Then we compared our participants’ choice-related brain activity to brain activity maps drawn from Neurosynth, an analysis tool that combines data from over 8,000 published fMRI studies to generate neural maps representing brain activity associated with different terms, just as “emotions,” “resting,” or “working.”

As a group, when our participants made choices consistent with the Framing Effect, their average brain activity was most similar to the brain maps representing mental disengagement (i.e. “resting” or “default”). When they made choices inconsistent with the Framing Effect, their average brain activity was most similar to the brain maps representing mental engagement (i.e. “working” or task”). These results supported the theory that the Framing Effect results from a lack of mental effort, or using a decision-making shortcut, and that spending more mental effort can counteract the Framing Effect.

Then we tested whether we could use individual participants’ brain activity to predict participants’ choices on each trial. We found that the degree to which each trial’s brain activity resembled the brain maps associated with mental disengagement predicted whether that trial’s choice would be consistent with the Framing Effect. The degree to which each trial’s brain activity resembled brain maps associated with emotion, however, was not predictive of choices.

Our findings support the theory that the biased decision-making seen in the Framing Effect is due to a lack of mental effort rather than due to emotions.

This suggests potential strategies for prompting people to make better decisions. Instead of trying to appeal to people’s emotions – likely a difficult task requiring tailoring to different individuals – we would be better off taking the easier and more generalizable approach of making good decisions quick and easy for everyone to make.

Guest post by Rosa Li

The Fashion Trend Sweeping East Campus

By Meg Shieh

On March 23, 2017

In Behavior/Psychology, Computers/Technology, Field Research, Students

During the months of January and February, there was one essential accessory seen on many first-year Duke students’ wrists: the Jawbone. The students were participating in a study listed on DukeList by Ms. Madeleine George solely for first-year students regarding their lives at Duke. The procedures for the study were simple:

Do a preliminary test involving a game of cyberball, a game psychologists have adapted for data collection.
Wear the Jawbone for the duration of the study (10 days)
Answer the questions sent to your phone every four hours. You will need to answer five a day. The questions are brief.
Answer all the questions every day (you can miss one of the question times) and get $32.

About a hundred first-year Duke students participated.

Some of the questions on the surveys asked how long you slept, how stressed you felt, what time did you woke up, did you talk to your parents today, how many texts did you send, and so on. It truly did feel as though it were a study on the daily life of Duke students. However, there was a narrower focus on this study.

Ms. Madeleine George

Ms. George is a Ph.D. candidate in developmental psychology in her 5^th year at Duke. She is interested in relationships and how daily technology usage and social support such as virtual communication can influence adolescent and young adult well-being.

Her dissertation is about how parents may be able to provide daily support to their children in their first year of college as face to face interactions are replaced by virtual communication through technology in modern society. This was done in three pieces.

The jawbone study is the third part. George is exploring why these effects occur, if they are uniquely a response to parents, or if people can simply feel better from other personal interactions. Taking the data from the surveys, George has been using models that allow for comparison between each person to themselves and basic ANOVA tests that allow her to examine the differences between groups. She’s still working on that analysis.

For her first test, she found that students who talked to their parents were feeling worse. But, on days students had a stressor, they were in a better mood after talking to their parents. In addition, based on the cyberball experiment where students texted a parent, stranger, or no one, George infers that texting anyone is better than no one because it can make people feel supported.

So far, George seems to have found that technology doesn’t necessarily take away relationship value and quality. Online relationships tend to reflect offline relationships. While talking with parents might not always make a student feel better, there can be circumstances where it can be beneficial.

Post by Meg Shieh.

What is Money Really Worth?

By Shanen Ganapathee

On March 7, 2017

In Behavior/Psychology, Neuroscience

“Yesterday, I was at an event and I sat next to an economist,” Brian Hare told my class. “I asked him: how old is money? He was completely lost.”

I was in Hare’s class on a Monday at noon, laughing at his description of the interaction. We had so far been exploring the origins of humans’ particular ways of making sense of the world through his course in Human Cognitive Evolution and we were faced with a slide that established the industrial period as less than 200 years old. As compared to a hunting and gathering lifestyle, this stretch of time is minuscule on an evolutionary scale.

Slide from Dr. Hare’s class. Reproduced with permission.

Why then do so many studies employ money as a proxy for the measurement of human behaviors that have been shaped by hundreds of thousands of years? This kind of research is trying to get at “prosociality,” (the ability to be altruistic and cooperative towards others) or empathy and guilt aversion, just to name a few.

I had started to wonder about this months before as a summer intern at the University of Tokyo. As I listened to a graduate student describe an experiment employing money to understand how humans behaved cooperatively, I grew puzzled. I eventually asked: Why was money used in this experiment? The argument was made that money was enough of a motivator for this sample population of college students to generalize that if they chose to share it, it must mean something.

During a panel discussion about prosociality at the American Association for the Advancement of Science meeting in Boston last month, my chance came to ask the question again. Alan Sanfey, professor at the Donders Institute for Brain, Cognition and Behavior, used experimental paradigms that rewarded participants with money to tease out the particular effects of guilt on generous behavior.

“Is money a good proxy for understanding evolutionarily ancient behavior?” I asked. Robin Dunbar, professor of evolutionary psychology at Oxford University took a dig at my question and mentioned that the barter system would have likely been the best ancient representative of money. However, the barter system likely came to life during the agricultural period, which itself is less than 10,000 years old.

Dollar bills. Public domain.

Stephen Pluháček, an attendee at the event and a senior scholar at the University of New Hampshire, said in a followup email to me that he “was interested in [my] question to the panel and disappointed by their response — which I found indicative of the ways we can become so habituated to a way of looking at things that we find it difficult to even hear questions that challenge our foundational assumptions.”

“As I said in our brief conversation, I am not convinced that money can stand as a proxy for prosocial behavior (trust, generosity) in humans prior to the advent of agriculture,” Pluháček wrote. “And even barter or gift exchange may be limited in their applicability to early humans (as well as to modern humans prior to the cognitive revolution).”

So, I’m not alone in my skepticism. However, in my discussion with Leonard White, my advisor and associate director for education in the Duke Institute for Brain Sciences, he pointed out:

“The brain is remarkably facile. We have this amazing capacity for proxy substitution.”

In essence, this would mean that our brain would be able to consider money as a reward just like any reward that might have mediated the evolution of our behavior over time. We would thus be able to test subjects with “modern” stimuli, it appears.

It is clear that an evolutionary narrative is important to creating a more complete picture of contemporary human behavior. But sometimes the proxies we choose to make these measures don’t fit very well with our long history.

By Shanen Ganapathee

Creating Technology That Understands Human Emotions

By Anika Radiya-Dixit

On January 30, 2017

In Behavior/Psychology, Computers/Technology, Data, Engineering, Neuroscience, Science Communication & Education, Students, Visualization

“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

By Karl Bates

On December 30, 2016

In Behavior/Psychology, Biology, Guest Post, Neuroscience

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

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.”

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

Evolutionary Genetics Shaping Health and Behavior

By Karl Bates

On December 28, 2016

In Animals, Behavior/Psychology, Biology, Field Research, Guest Post

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)

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 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.

Guest 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?

By Karl Bates

On November 16, 2016

In Behavior/Psychology, Field Research, Mathematics, Statistics

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.”

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.

“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 )

Post by Karl Leif Bates

Depression Screening Questions Seem to Miss Men

By Karl Bates

On November 10, 2016

In Behavior/Psychology, Guest Post, Medicine, Neuroscience

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.

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

“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.

Guest Post by Eric Ferreri, Duke News and Communications

Mapping the Brain With Stories

By Meg Shieh

On November 8, 2016

In Behavior/Psychology, Computers/Technology, Data, Lecture, Neuroscience, Visualization

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 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.

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_100hed Post by Meg Shieh