As computer programming becomes an increasingly valued skill in the workforce, there is a greater need to understand how people learn to code most effectively.

Statistics show that up to 50% of students who enroll in introductory programming courses in the United States eventually drop out, suggesting a mismatch between how coding is learned and the way it鈥檚 taught. A new study from the 91探花, , examines that issue.

The researchers recorded electrophysiological brain responses of varyingly skilled programmers as they read lines of code written in , a programming language. The brain鈥檚 response to viewing errors in both the syntax (form) and semantics (meaning) of code appeared identical to those that occur when fluent readers process sentences on a word-by-word basis, supporting a resemblance between how people learn computer and natural languages.

91探花News spoke with co-authors , a 91探花professor of psychology, and , a recent 91探花doctoral graduate of psychology, about their research, the future of teaching computer programming and more.

Why is it important to understand how learning computer programming works in the brain?

Iris Kuo: The idea of programming as literacy is something we wanted to focus on. We wanted to approach learning to program from a language learning perspective, specifically from a second language learning perspective. We鈥檝e learned a lot about what makes a second language easy or difficult to learn and why some people are good at it and some people struggle. Now we鈥檙e applying that lens to programming. If we can approach this topic from a different perspective, maybe we can address some myths or bring up new questions.

Chantel Prat: The idea of programming as the literacy of the future is important. There鈥檚 an increasing need and desire for programming in the workforce 鈥� as of 2016 over 20% of listed jobs required coding skills. It used to be this kind of niche skill that software engineers held, but now it鈥檚 central to all STEM fields. Coding is a potential bottleneck to employment, but Intro to Programming continues to be one of these notoriously hard classes with high dropout rates. This is also a field where gender gaps are closing more slowly than other fields.

Everyone wants to tell you what it takes to be a good programmer, but many of their ideas aren鈥檛 substantiated with science. Many of them are tied to culturally-linked ideas about who is already a good programmer. We know a lot about why and for whom learning a natural language is hard or why learning to read is hard. The question now was, can we leverage that expertise to start understanding how people with different levels of expertise understand code?

How did you conduct this research, and what were the main takeaways?

IK: There鈥檚 a lot of literature in the second-language learning community that uses the event-related potential, or ERP, where we place sensors on people鈥檚 heads and record their electrical activity to different stimulus. In this case, they were reading code. There are two distinct markers that indicate someone is processing meaning and when someone is processing form, like grammar. We wanted to use these two indicators to see if someone might react the same way while reading code.

If you鈥檙e a native speaker of a language, or if you鈥檙e really proficient, you tend to react to errors in meaning with a brain response marker called N400. You also tend to react to errors in grammar with a marker called P600. The more proficient you are in a language, the more distinct these markers are. When you鈥檙e first learning a language, you may be able to recognize something wrong with a sentence, but you may not be able to automatically process something as an error in meaning or grammar. Your brain takes time to learn these rules of grammar. Newer second-language learners tend to respond to most errors with the N400 marker, even when the error is grammatical. Over time, they learn to distinguish between something wrong with meaning and something wrong with grammar.

We wanted to see if something like that would happen with coding in people with a wide range of expertise. While all participants responded to errors in meaning and form in code, the higher their level of expertise, the stronger and more distinct their responses to the errors. This matches with what we have traditionally seen in second-language learners, where the more expertise you have in a natural language, the more sensitive you are to errors. This was the first study that realized we could have these neurological markers in coding and that people do process code incrementally.

CP: It was originally thought that N400 and P600 markers were language specific. For a very long time, they were the gold standard for understanding brain processes associated with language comprehension. When research showed you can find them in certain cases for music and math, that was a huge deal. So, these markers aren鈥檛 language-specific; they鈥檙e about making meaning and how we understand what we take in incrementally.

Our study showed that when somebody reads a line of code with a bracket instead of a parenthesis, for example, their brain reacts in the same way as when they read a sentence with the wrong verb ending. And the fact that progression of sensitivity to form and meaning follows the same pattern as second language learning with increasing expertise is what we hoped to find, but it鈥檚 still pretty exciting!

What does the future of this area of research look like, and what is the potential impact on coding education?

IK: We started with the coding language Python because it鈥檚 one of the fastest-growing programming languages and one of the simpler languages for people to learn. It was designed to be really reader friendly. But the reality is, there are hundreds of other programming languages that serve different purposes. Some programming languages are more difficult or easier to learn, just like natural languages. We鈥檙e working toward looking more extensively at the brain and seeing if our results can be replicated with other languages. I think this could impact the way we teach it.

Let鈥檚 say a language is more reliant on structure, can you teach it the same way you teach something like Python? If we want to approach it from a language learning lens, how would we adapt that to accommodate something like Java, which is maybe more difficult for some people to learn?

CP: People have been talking about the gap between the way coding is taught and the way it鈥檚 best learned since at least the 1980s. Coding education originated in an engineering culture 鈥� specifically a software engineering culture. Moving forward, there鈥檚 good reason to support the idea of coding as learning a language, like learning to speak with computers. It should be taught like a language where you have elements of learning syntax, but you also have a lot of practice and 鈥渃onversation鈥� classes where you produce code in small groups. This also creates the option of using coding courses to fulfill second language requirements. There may not be a one-size-fits-all best practice for computer programming education, but I think it鈥檚 useful to understand the way different people learn through a second-language-learning model.

This research was funded by the Office of Naval Research, Cognitive Science of Learning Program.

For more information, contact Kuo at kuoc@uw.edu and Prat at csprat@uw.edu.

Four recent books from 91探花 professors cover a variety of topics including neuroscience, Chinese filial piety and the history of Irvine, California. 91探花News talked with the authors to learn more about their recent publications.

Chantel Prat introduces you to your brain听

In her new book, 91探花psychology professor wants to make one thing clear right away: There鈥檚 no such thing as a 鈥渘ormal鈥� brain.

鈥�,鈥� published this month by Dutton, is the distillation of years of research by Prat into how our brains work 鈥� and a guide for the everyday person into why we think and act the way we do. Every human brain is designed differently, and it鈥檚 those differences, Prat explains, that render a traditional definition of 鈥渘ormal鈥� not only irrelevant, but also inaccurate.

鈥淚 wanted to talk about differences in a different way, debunking the idea of normal as an ideal set point instead of normal as a multidimensional and variable space,鈥� Prat said. 鈥淵ou can鈥檛 even define 鈥榥ormal鈥� without understanding the ways people vary. And within that space, 鈥榙ifferent鈥� doesn鈥檛 have to be better or worse. There鈥檚 a reason we鈥檝e got multiple design choices when brains are engineered. Different types of brains excel in some subset of the variety of environments we put them in.鈥�

The book is organized into two parts: brain designs and brain functions. And within those parts are chapters devoted to the brain鈥檚 two hemispheres and chemistry, for example, as well as sections that dive into how our brains adapt to environments; focus on the different types of information they鈥檙e bombarded with; and affect how we communicate with other people. That interpersonal connection piece is important, Prat notes in the book, because two people might get along swimmingly 鈥� or not so much 鈥� due in part to how their brains are designed.

鈥淭he challenge is that our most instinctive way of understanding others is through 鈥榤irroring鈥欌�攁 process by which our brains activate the programs we would be using if we were engaging in that same behavior. But if the brain of the person you鈥檙e trying to understand doesn鈥檛 work like yours, the inferences you make about why they鈥檙e behaving a particular way are more likely to be wrong,鈥� Prat said.

Prat draws heavily on decades of research 鈥� her own, and that of others, including 91探花colleagues such as Andrea Stocco (her spouse), Patricia Kuhl and Jonathan Kanter in psychology; and Rajesh Rao in computer science and engineering. She also peppers personal anecdotes throughout the text and footnotes and provides a series of quizzes and puzzles to help readers gain insight into their own brains. Prat invites readers to dive deeper by visiting her website and participating in her ongoing research through the very sorts of puzzles she offers in the book.

Prat said she wanted to write a book that was more accurate than the typical 鈥渙ne size fits all鈥� book about neuroscience and accessible to a general audience.

For more information, contact Prat at csprat@uw.edu.

Around the world for clean air

was diagnosed with asthma when she was 2 years old, but she鈥檚 likely had the condition since birth. She spent her childhood in and out of the hospital. Barely able to get out of bed, she fantasized about traveling the world one day.

Holmes-Eber is now an affiliate professor in the Jackson School of International Studies. More than a decade ago, she bicycled a complete circle around the world with her family to raise awareness and funds for clean air and asthma. Holmes-Eber and her husband, Lorenz Eber, documented the 480-day journey in 鈥�.鈥�

鈥淏reathtaking鈥� follows the Ebers and their two daughters, who were homeschooled by their parents, as they travel 9,332 miles on bikes. The family raised $65,000 for World Bike for Breath, a non-profit organization focused on inspiring people to travel by bicycle and promoting clean air. They are the only family on record to complete a full circumnavigation of the world by bicycle.

The journey took the Ebers across four continents 鈥� Europe, Asia, Australia and North America 鈥� and through 24 countries. Holmes-Eber said Mongolia was a highlight of the trip. A traditionally nomadic people, Mongolians have often been forced by outside countries to remain sedentary. But that鈥檚 no longer the case.

鈥淭he downtown core of the capital city, Ulaanbaatar, is maybe 10 blocks of buildings,鈥� Holmes-Eber said. 鈥淎s soon as you get out of that central core, the entire city is ringed with tents. People live throughout the country, still wandering with their animals. We got to stay in the tents with some Mongolian families. I was just so impressed that they kept their culture and their way of life intact and protected it.鈥�

Still, Holmes-Eber said more work needs to be done. People worldwide are living with the levels of pollution and air quality, despite an increase in disease.

鈥淭he numbers of people with asthma have increased,鈥� she said. 鈥淚n fact, all sorts of lung diseases are going up. I thought this book could push that issue, raise awareness and connect people to the reality that we鈥檙e accepting.鈥�

The book was published in June by Falcon.

For more information, contact Holmes-Eber at pholmese@uw.edu.

From country to campus to community: The birth of the city of Irvine

The Southern California suburb of Irvine is home to more than 300,000 people, dozens of corporate headquarters and a University of California campus that has a 21-acre park in the middle.

A little more than 60 years ago, the siting of that campus was the spark for a development that would transform what was then farmland into the sprawling city it is today.

, affiliate instructor of real estate at UW, and co-author Michael Stockstill explain that history in their new book, 鈥�.鈥� What began as the 110,000-acre property assembled by James Irvine from Spanish and Mexican land grants in the 19th century grew to be the site of a UC campus and a master-planned community in the 1960s.

鈥淟arge-scale development on the Irvine Ranch commenced at a time when there was strong interest in creating alternatives to the suburban sprawl that exploded across the United States during the 1950s,鈥� Oliver said.听 鈥淢y coauthor and I thought it important to tell the story of how this largest and most successful example of planned development came to be.鈥�

With a professional background in urban planning, Oliver has worked on planned communities since the 1970s, including at the Irvine Company, which led the planning and development of the Irvine Ranch. In telling the relatively short history of the city of Irvine, Oliver and Stockstill focus just as much on the change to the land as on the people who brought about the change. This includes the competing interests of the Irvine family and the principals of the Irvine Company, as well as conflicts with some environmental groups and community activists.

The UC system, being the catalyst to the master-planned community, features prominently, particularly in a chapter titled 鈥淚nclusions, Exclusions and the Campus that Never Was.鈥�

鈥淣ot all of what was envisioned for the neighborhoods surrounding UC Irvine has come to pass,鈥� the authors write. 鈥淪till, the university has been a significant factor in attracting residents and innovative commercial enterprise to the Irvine Ranch.鈥�

The book was published in June by Routledge.

For more information, contact Oliver at hpo@uw.edu.

Examining the origin and evolution of Chinese filial piety

In 鈥�,鈥� recently published by Routledge, brings an interdisciplinary approach to the analysis of ancient Chinese history.

Porter, professor in the Jackson School of International Studies, uses social neuroscience, cultural evolution, cognitive archaeology and historical analysis to trace the evolution of filial piety.

Commonly attributed to Chinese philosopher Confucius, filial piety is a deep reverance for the lives of preceding generations. In her book, Porter argues that the conceptions of filiality evolved from a structure of feelings that were inherited from the ancestral past, starting with China鈥檚 earliest farmers and their relationship to the stars above.

Porter begins by asking why Confucius views a model filial son as one experiencing psychological grief so devastating that he can no longer participate in life. Borrowing the neuropsychological concept of 鈥渃omplicated grief,鈥� Porter looks to evolutionary conditions that would account for the prominence of this relational emotion.

鈥淚 think neuroscience really opened my mind to understanding how psychological processes are inextricably connected to our nervous system,鈥� Porter said.

This perspective allowed Porter to discern a crucial relationship between inhabitants of a Neolithic settlement and a constellation in the sky, Turtle. This relationship could account for Confucius’ proscribed filiality. For more than 1,000 years, Turtle’s appearance in the sky coincided with the start of agricultural activities in the spring.

This constellation was revered, Porter said, and became like a member of the community due to its reliability. However, due to the gradual shift in the orientation of the earth鈥檚 axis over generations, the constellation eventually moved positions. The people couldn鈥檛 explain why Turtle no longer marked the start of the season. They were shamed, believing the constellation was reluctant to participate in their activities.

鈥淭hat鈥檚 the core of this notion,鈥� Porter said. 鈥淐onfucian filial piety can be understood if we go back to a problematic relationship in mourning Turtle. I establish a line that traces the evolution of the Chinese nervous system and how that system influenced Chinese cultural production to explain Confucius’ vision of a filial son.鈥�

For more information, contact Porter at debzport@uw.edu.

Say, something like this:

What goes in the last box? (The answer and more puzzles are below.)

You look for a pattern, or a rule, and you just can鈥檛 spot it. So you back up and start over.

That鈥檚 your brain recognizing that your current strategy isn鈥檛 working, and that you need a new way to solve the problem, according to new research from the 91探花. With the help of about 200 puzzle-takers, a computer model and functional MRI (fMRI) images, researchers have learned more about the processes of reasoning and decision-making, pinpointing the brain pathway that springs into action when problem-solving goes south.

鈥淭here are two fundamental ways your brain can steer you through life 鈥� toward things that are good, or away from things that aren鈥檛 working out,鈥� said , associate professor of psychology and co-author of the new , published Feb. 23 in the journal Cognitive Science. 鈥淏ecause these processes are happening beneath the hood, you鈥檙e not necessarily aware of how much driving one or the other is doing.鈥�

Using a decision-making task developed by Michael Frank at Brown University, the researchers measured exactly how much 鈥渟teering鈥� in each person鈥檚 brain involved learning to move toward rewarding things as opposed to away from less-rewarding things. Prat and her co-authors were focused on understanding what makes someone good at problem-solving.

The research team first developed a computer model that specified the series of steps they believed were required for solving the Raven鈥檚 Advanced Performance Matrices (Raven鈥檚) 鈥� a standard lab test made of puzzles like the one above. To succeed, the puzzle-taker must identify patterns and predict the next image in the sequence. The model essentially describes the four steps people take to solve a puzzle:

• Identify a key feature in a pattern;
• Figure out where that feature appears in the sequence;
• Come up with a rule for manipulating the feature;
• Check whether the rule holds true for the entire pattern.

At each step, the model evaluated whether it was making progress. When the model was given real problems to solve, it performed best when it was able to steer away from the features and strategies that weren鈥檛 helping it make progress. According to the authors, this ability to know when your 鈥渢rain of thought is on the wrong track鈥� was central to finding the correct answer.

The next step was to see whether this was true in people. To do so, the team had three groups of participants solve puzzles in three different experiments. In the first, they solved the original set of Raven鈥檚 problems using a paper-and-pencil test, along with Frank鈥檚 test which separately measured their ability to 鈥渃hoose鈥� the best options and to 鈥渁void鈥� the worse options. Their results suggested that only the ability to 鈥渁void鈥� the worst options related to problem-solving success. There was no relation between one鈥檚 ability to recognize the best choice in the decision-making test, and to solve the puzzles effectively.

The second experiment replaced the paper-and-pencil version of the puzzles with a shorter, computerized version of the task that could also be implemented in an MRI brain-scanning environment. These results confirmed that those who were best at avoiding the worse options in the decision-making task were also the best problem solvers.

The final group of participants completed the computerized puzzles while having their brain activity recorded using fMRI. Based on the model, the researchers gauged which parts of the brain would drive problem-solving success. They zeroed in on the basal ganglia 鈥� what Prat calls the 鈥渆xecutive assistant鈥� to the prefrontal cortex, or 鈥淐EO鈥� of the brain. The basal ganglia assist the prefrontal cortex in deciding which action to take using parallel paths: one that turns the volume 鈥渦p鈥� on information it believes is relevant, and another that turns the volume 鈥渄own鈥� on signals it believes to be irrelevant. The 鈥渃hoose鈥� and 鈥渁void鈥� behaviors associated with Frank鈥檚 decision-making test relate to the functioning of these two pathways. Results from this experiment suggest that the process of 鈥渢urning down the volume鈥� in the basal ganglia predicted how successful participants were at solving the puzzles.

鈥淥ur brains have parallel learning systems for avoiding the least good thing and getting the best thing. A lot of research has focused on how we learn to find good things, but this pandemic is an excellent example of why we have both systems. Sometimes, when there are no good options, you have to pick the least bad one! What we found here was that this is even more critical to complex problem-solving than recognizing what鈥檚 working.鈥�

Co-authors of the study were , associate professor, and Lauren Graham, assistant teaching professor, in the 91探花Department of Psychology. The research was supported by the 91探花Royalty Research Fund, a 91探花startup fund award and the Bezos Family Foundation.

For more information, contact Prat at csprat@uw.edu.

Want to learn to code? Put down the math book. Practice those communication skills instead.

New research from the 91探花 finds that a natural aptitude for learning languages is a stronger predictor of learning to program than basic math knowledge, or numeracy. That鈥檚 because writing code also involves learning a second language, an ability to learn that language鈥檚 vocabulary and grammar, and how they work together to communicate ideas and intentions. Other cognitive functions tied to both areas, such as problem solving and the use of working memory, also play key roles.

鈥淢any barriers to programming, from prerequisite courses to stereotypes of what a good programmer looks like, are centered around the idea that programming relies heavily on math abilities, and that idea is not born out in our data,鈥� said lead author , an associate professor of psychology at the 91探花and at the Institute for Learning & Brain Sciences. 鈥淟earning to program is hard, but is increasingly important for obtaining skilled positions in the workforce. Information about what it takes to be good at programming is critically missing in a field that has been notoriously slow in closing the gender gap.鈥�

Published online March 2 in Scientific Reports, an open-access journal from the Nature Publishing Group, examined the neurocognitive abilities of more than three dozen adults as they learned , a common programming language. Following a battery of tests to assess their executive function, language and math skills, participants completed a series of online lessons and quizzes in Python. Those who learned Python faster, and with greater accuracy, tended to have a mix of strong problem-solving and language abilities.

In today鈥檚 STEM-focused world, learning to code opens up a variety of possibilities for jobs and extended education. Coding is associated with math and engineering; college-level programming courses tend to require advanced math to enroll and they tend to be taught in computer science and engineering departments. Other research, namely from 91探花psychology professor , has shown that such requirements and perceptions of coding reinforce stereotypes about programming as a masculine field, potentially discouraging women from pursuing it.

But coding also has a foundation in human language: Programming involves creating meaning by stringing symbols together in rule-based ways.

Though a few studies have touched on the cognitive links between language learning and computer programming, some of the data , that are now out of date, and none of them used natural language aptitude measures to predict individual differences in learning to program.

So Prat, who specializes in the neural and cognitive predictors of learning human languages, set out to explore the individual differences in how people learn Python. Python was a natural choice, Prat explained, because it resembles English structures such as paragraph indentation and uses many real words rather than symbols for functions.

To evaluate the neural and cognitive characteristics of 鈥減rogramming aptitude,鈥� Prat studied a group of native English speakers between the ages of 18 and 35 who had never learned to code.

Before learning to code, participants took two completely different types of assessments. First, participants underwent a five-minute electroencephalography scan, which recorded the electrical activity of their brains as they relaxed with their eyes closed. In previous research, Prat showed that patterns of neural activity while the brain is at rest can predict up to 60% of the variability in the speed with which someone can learn a second language (in that case, French).

鈥淯ltimately, these resting-state brain metrics might be used as culture-free measures of how someone learns,鈥� Prat said.

Then the participants took eight different tests: one that specifically covered numeracy; one that measured language aptitude; and others that assessed attention, problem-solving and memory.

To learn Python, the participants were assigned 10 45-minute online instruction sessions using the Codeacademy educational tool. Each session focused on a coding concept, such as lists or if/then conditions, and concluded with a quiz that a user needed to pass in order to progress to the next session. For help, users could turn to a 鈥渉int鈥� button, an informational blog from past users and a 鈥渟olution鈥� button, in that order.

From a shared mirror screen, a researcher followed along with each participant and was able to calculate their 鈥渓earning rate,鈥� or speed with which they mastered each lesson, as well as their quiz accuracy and the number of times they asked for help.

After completing the sessions, participants took a multiple-choice test on the purpose of functions (the vocabulary of Python) and the structure of coding (the grammar of Python). For their final task, they programmed a game 鈥� Rock, Paper, Scissors 鈥� considered an introductory project for a new Python coder. This helped assess their ability to write code using the information they had learned.

Ultimately, researchers found that scores from the language aptitude test were the strongest predictors of participants鈥� learning rate in Python. Scores from tests in numeracy and fluid reasoning were also associated with Python learning rate, but each of these factors explained less variance than language aptitude did.

Presented another way, across learning outcomes, participants鈥� language aptitude, fluid reasoning and working memory, and resting-state brain activity were all greater predictors of Python learning than was numeracy, which explained an average of 2% of the differences between people. Importantly, Prat also found that the same characteristics of resting-state brain data that previously explained how quickly someone would learn to speak French, also explained how quickly they would learn to code in Python.

鈥漈his is the first study to link both the neural and cognitive predictors of natural language aptitude to individual differences in learning programming languages. We were able to explain over 70% of the variability in how quickly different people learn to program in Python, and only a small fraction of that amount was related to numeracy,鈥� Prat said. Further research could examine the connections between language aptitude and programming instruction in a classroom setting, or with more complex languages such as Java, or with more complicated tasks to demonstrate coding proficiency, Prat said.

The study was funded by the Office of Naval Research. Additional co-authors were , a computer scientist and former research assistant professor in the 91探花Department of Radiology; and Chu-Hsuan Kuo and Malayka Mottarella, graduate students in the 91探花Department of Psychology and at I-LABS.

For more information, contact Prat at csprat@uw.edu.

Some adults learn a second language better than others, and their secret may involve the rhythms of activity in their brains.

New findings by scientists at the 91探花 demonstrate that a five-minute measurement of resting-state brain activity predicted how quickly adults learned a second language.

The , published in the June-July issue of the journal Brain and Language, is the first to use patterns of resting-state brain rhythms to predict subsequent language learning rate.

“We’ve found that a characteristic of a person’s brain at rest predicted 60 percent of the variability in their ability to learn a second language in adulthood,” said lead author , a faculty researcher at the Institute for Learning & Brain Sciences and a 91探花associate professor of psychology.

At the beginning of the experiment, volunteers 鈥� 19 adults aged 18 to 31 years with no previous experience learning French 鈥� sat with their eyes closed for five minutes while wearing a commercially available EEG (electroencephalogram) headset. The headset measured naturally occurring patterns of brain activity.

The participants came to the lab twice a week for eight weeks for 30-minute French lessons delivered through an immersive, virtual reality computer program. The U.S. Office of Naval Research 鈥� who funded the current study 鈥� also funded the development of the language training program.

The program, called Operational Language and Cultural Training System (OLCTS), aims to get military personnel functionally proficient in a foreign language with 20 hours of training. The self-paced program guides users through a series of scenes and stories. A voice-recognition component enables users to check their pronunciation.

Watch a video demonstration of the language software:

To ensure participants were paying attention, the researchers used periodic quizzes that required a minimum score before proceeding to the next lesson. The quizzes also served as a measure for how quickly each participant moved through the curriculum.

At the end of the eight-week language program, participants completed a proficiency test covering however many lessons they had finished. The fastest person learned twice as quickly but just as well as the slower learners.

The recordings from the EEG headsets revealed that patterns of brain activity related to language processes were linked the most strongly to the participants’ rate of learning.

So, should people who don’t have this biological predisposition not even try to learn a new language? Prat says no, for two reasons.

“First, our results show that 60 percent of the variability in second language learning was related to this brain pattern 鈥� that leaves plenty of opportunity for important variables like motivation to influence learning,” Prat said.

Second, Prat said it’s possible to change resting-state brain activity using neurofeedback training 鈥� something that she’s studying now in her lab. Neurofeedback is a sort of brain training regimen, through which individuals can strengthen the brain activity patterns linked to better cognitive abilities.

“We’re looking at properties of brain function that are related to being ready to learn well. Our goal is to use this research in combination with technologies such as neurofeedback training to help everyone perform at their best,” she said.

Ultimately, neurofeedback training could help people who want to learn a second language but lack the desirable brain patterns. They’d do brain training exercises first, and then do the language program.

“By studying individual differences in the brain, we’re figuring out key constraints on learning and information processing, in hopes of developing ways to improve language learning, and eventually, learning more generally,” Prat said.

Co-authors of the study are Brianna Yamasaki, Reina Kluender and Andrea Stocco 鈥� all at UW.

Imagine a question-and-answer game played by two people who are not in the same place and not talking to each other. Round after round, one player asks a series of questions and accurately guesses the object the other is thinking about.

Sci-fi? Mind-reading superpowers? Not quite.

91探花 researchers recently used a direct brain-to-brain connection to enable pairs of participants to play a question-and-answer game by transmitting signals from one brain to the other over the Internet. The experiment, detailed today in , is thought to be the first to show that two brains can be directly linked to allow one person to guess what鈥檚 on another person鈥檚 mind.

鈥淭his is the most complex brain-to-brain experiment, I think, that鈥檚 been done to date in humans,鈥� said lead author , an assistant professor of psychology and a researcher at UW鈥檚 .

鈥淚t uses conscious experiences through signals that are experienced visually, and it requires two people to collaborate,鈥� Stocco said.

Here鈥檚 how it works: The first participant, or 鈥渞espondent,鈥� wears a cap connected to an (EEG) machine that records electrical brain activity. The respondent is shown an object (for example, a dog) on a computer screen, and the second participant, or 鈥渋nquirer,鈥� sees a list of possible objects and associated questions. With the click of a mouse, the inquirer sends a question and the respondent answers 鈥測es鈥� or 鈥渘o鈥� by focusing on one of two flashing LED lights attached to the monitor, which flash at different frequencies.

A 鈥渘o鈥� or 鈥測es鈥� answer both send a signal to the inquirer via the Internet and activate a magnetic coil positioned behind the inquirer鈥檚 head. But only a 鈥測es鈥� answer generates a response intense enough to stimulate the visual cortex and cause the inquirer to see a flash of light known as a 鈥�.鈥� The phosphene 鈥� which might look like a blob, waves or a thin line 鈥� is created through a brief disruption in the visual field and tells the inquirer the answer is yes. Through answers to these simple yes or no questions, the inquirer identifies the correct item.

The experiment was carried out in dark rooms in two 91探花labs located almost a mile apart and involved five pairs of participants, who played 20 rounds of the question-and-answer game. Each game had eight objects and three questions that would solve the game if answered correctly. The sessions were a random mixture of 10 real games and 10 control games that were structured the same way.

The researchers took steps to ensure participants couldn’t use clues other than direct brain communication to complete the game. Inquirers wore earplugs so they couldn’t hear the different sounds produced by the varying stimulation intensities of the “yes” and “no” responses. Since noise travels through the skull bone, the researchers also changed the stimulation intensities slightly from game to game and randomly used three different intensities each for “yes” and “no” answers to further reduce the chance that sound could provide clues.

The researchers also repositioned the coil on the inquirer’s head at the start of each game, but for the control games, added a plastic spacer undetectable to the participant that weakened the magnetic field enough to prevent the generation of phosphenes. Inquirers were not told whether they had correctly identified the items, and only the researcher on the respondent end knew whether each game was real or a control round.

“We took many steps to make sure that people were not cheating,” Stocco said.

Participants were able to guess the correct object in 72 percent of the real games, compared with just 18 percent of the control rounds. Incorrect guesses in the real games could be caused by several factors, the most likely being uncertainty about whether a phosphene had appeared.

鈥淭hey have to interpret something they鈥檙e seeing with their brains,鈥� said co-author , a faculty member at the and a 91探花associate professor of psychology. 鈥淚t鈥檚 not something they鈥檝e ever seen before.鈥�

Errors can also result from respondents not knowing the answers to questions or focusing on both answers, or by the brain signal transmission being interrupted by hardware problems.

“While the flashing lights are signals that we’re putting into the brain, those parts of the brain are doing a million other things at any given time too,” Prat said.

The study builds on the 91探花team鈥檚 in 2013, when it was the first to demonstrate a direct brain-to-brain connection between humans. Other scientists have connected the brains of rats and monkeys, and transmitted brain signals from a human to a rat, using electrodes inserted into animals鈥� brains. In the 2013 experiment, the 91探花team used noninvasive technology to send a person鈥檚 brain signals over the Internet to control the hand motions of another person.

The experiment evolved out of research by co-author , a 91探花professor of computer science and engineering, on that enable people to activate devices with their minds. In 2011, Rao began collaborating with Stocco and Prat to determine how to link two human brains together.

In 2014, the researchers received a $1 million grant from the that allowed them to broaden their experiments to decode more complex interactions and brain processes. They are now exploring the possibility of 鈥渂rain tutoring,鈥� transferring signals directly from healthy brains to ones that are developmentally impaired or impacted by external factors such as a stroke or accident, or simply to transfer knowledge from teacher to pupil.

The team is also working on transmitting brain states 鈥� for example, sending signals from an alert person to a sleepy one, or from a focused student to one who has attention deficit hyperactivity disorder, or ADHD.

鈥淚magine having someone with ADHD and a neurotypical student,鈥� Prat said. 鈥淲hen the non-ADHD student is paying attention, the ADHD student鈥檚 brain gets put into a state of greater attention automatically.鈥�

Many technological advancements over the past century, from the telegraph to the Internet, were created to facilitate communication between people. The 91探花team鈥檚 work takes a different approach, using technology to strip away the need for such intermediaries.

鈥淓volution has spent a colossal amount of time to find ways for us and other animals to take information out of our brains and communicate it to other animals in the forms of behavior, speech and so on,鈥� Stocco said. 鈥淏ut it requires a translation. We can only communicate part of whatever our brain processes.

鈥淲hat we are doing is kind of reversing the process a step at a time by opening up this box and taking signals from the brain and with minimal translation, putting them back in another person鈥檚 brain,鈥� he said.

Other co-authors are听 91探花computer science and neurobiology undergraduate student , 91探花bioengineering doctoral student Jeneva Cronin, 91探花bioengineering doctoral student Joseph Wu, and , a research assistant at the 91探花Institute for Learning & Brain Sciences.

91探花 researchers have successfully replicated a direct brain-to-brain connection between pairs of people as part of a scientific study following the team’s a year ago. In the , which involved six people, researchers were able to transmit the signals from one person’s brain over the Internet and use these signals to control the hand motions of another person within a split second of sending that signal.

At the time of the first experiment in August 2013, the 91探花team was the first to demonstrate two human brains communicating in this way. The researchers then tested their brain-to-brain interface in a more comprehensive study, published Nov. 5 in the journal PLOS ONE.

“The new study brings our brain-to-brain interfacing paradigm from an initial demonstration to something that is closer to a deliverable technology,” said co-author , a research assistant professor of psychology and a researcher at UW’s . “Now we have replicated our methods and know that they can work reliably with walk-in participants.”

Collaborator , a 91探花professor of computer science and engineering, is the lead author on this work.

The research team combined two kinds of noninvasive instruments and fine-tuned software to connect two human brains in real time. The process is fairly straightforward. One participant is hooked to an machine that reads brain activity and sends electrical pulses via the Web to the second participant, who is wearing a swim cap with a coil placed near the part of the brain that controls hand movements.

Using this setup, one person can send a command to move the hand of the other by simply thinking about that hand movement.

The 91探花study involved three pairs of participants. Each pair included a sender and a receiver with different roles and constraints. They sat in separate buildings on campus about a half mile apart and were unable to interact with each other in any way 鈥� except for the link between their brains.

Each sender was in front of a computer game in which he or she had to defend a city by firing a cannon and intercepting rockets launched by a pirate ship. But because the senders could not physically interact with the game, the only way they could defend the city was by thinking about moving their hand to fire the cannon.

Across campus, each receiver sat wearing headphones in a dark room 鈥� with no ability to see the computer game 鈥� with the right hand positioned over the only touchpad that could actually fire the cannon. If the brain-to-brain interface was successful, the receiver’s hand would twitch, pressing the touchpad and firing the cannon that was displayed on the sender’s computer screen across campus.

Researchers found that accuracy varied among the pairs, ranging from 25 to 83 percent. Misses mostly were due to a sender failing to accurately execute the thought to send the “fire” command. The researchers also were able to quantify the exact amount of information that was transferred between the two brains.

Another research team from the company Starlab in Barcelona, Spain, recently published in the same journal showing direct communication between two human brains, but that study only tested one sender brain instead of different pairs of study participants and was conducted offline instead of in real time over the Web.

Now, with a new $1 million grant from the , the 91探花research team is taking the work a step further in an attempt to decode and transmit more complex brain processes.

With the new funding, the research team will expand the types of information that can be transferred from brain to brain, including more complex visual and psychological phenomena such as concepts, thoughts and rules.

They’re also exploring how to influence brain waves that correspond with alertness or sleepiness. Eventually, for example, the brain of a sleepy airplane pilot dozing off at the controls could stimulate the copilot’s brain to become more alert.

The project could also eventually lead to “brain tutoring,” in which knowledge is transferred directly from the brain of a teacher to a student.

“Imagine someone who’s a brilliant scientist but not a brilliant teacher. Complex knowledge is hard to explain 鈥� we’re limited by language,” said co-author , a faculty member at the Institute for Learning & Brain Sciences and a 91探花assistant professor of psychology.

Other 91探花co-authors are Joseph Wu of computer science and engineering; Devapratim Sarma and Tiffany Youngquist of bioengineering; and Matthew Bryan, formerly of the UW.

The research published in PLOS ONE was initially funded by the U.S. Army Research Office and the UW, with additional support from the Keck Foundation.

###

For more information, contact Stocco at stocco@uw.edu or 206-685-8610, Rao at rao@cs.washington.edu or 206-685-9141 and Prat at csprat@uw.edu or 206-685-8610.

The ninth annual Allen L. Edwards Psychology Lecture Series will delve into “,” to explain how the brain and an individual’s expectations influence decisions made in uncertain conditions.

The three-part series pairs 91探花 psychologists with experts from outside the university. The free lectures are on Wednesday evenings from Feb. 19 through March 5, beginning at 7 p.m. in Kane Hall 130.

Sheri J. Y. Mizumori, chair of the 91探花psychology department, organized this year’s series with Scott Murray, associate chair for research and associate professor of psychology. Mizumori said they chose the topic because understanding the decision-making process is fundamental to our ability to understand more complex psychological functions such as language and communication, or planning and remembering.

“The choices you make ultimately determine what you learn and remember. Your memories then determine the future choices that you make. It’s all very interconnected,” Mizumori said.

She said the lecture series will explore many factors that go into making decisions, and how your brain determines the best options for future choices.

Descriptions of the 2013 lectures are below, or read more about them :

Feb. 19
Individuals vary widely in their ability to use available information to make good decisions. , 91探花assistant psychology professor, and , psychology and neuroscience professor at the University of Colorado, Boulder, will discuss how individual differences and various brain regions contribute to good decision making.

Feb. 26
In uncertain situations, our brains need to make split-second estimates about the future consequences of taking particular actions. , 91探花professor of psychology, and , psychology professor at the California Institute of Technology, will discuss how rodent and human brains are capable of working out the “risk” and “value” of possible outcomes through trial-and-error experience, and how that information subsequently gets used at the point of decision-making.

March 5
Experts often can provide us with relevant information to help make decisions in uncertain circumstances. , 91探花associate psychology professor, and , psychometrics and quantitative psychology professor at Fordham University, will explore what people understand about uncertainty in decision making, how effectively they incorporate it into the decision-making process, and implications for how best to communicate uncertain information to nonexpert decision-makers.

The lecture series is funded by a bequest from , a 91探花psychology professor from 1944 to his death in 1994, with the support of a bequest from , a 91探花psychology professor from 1936 until his retirement in 1968. Edwards is credited with introducing modern statistical techniques into psychological science.

# # #

Using electrical brain recordings and a form of magnetic stimulation, sent a brain signal to on the other side of the 91探花campus, causing Stocco’s finger to move on a keyboard.

While researchers at Duke University have demonstrated brain-to-brain communication between two rats, and Harvard researchers have demonstrated it between a human and a rat, Rao and Stocco believe this is the first demonstration of human-to-human brain interfacing.

“The Internet was a way to connect computers, and now it can be a way to connect brains,” Stocco said. “We want to take the knowledge of a brain and transmit it directly from brain to brain.”

The researchers captured the full demonstration on video recorded in both labs. The following version has been edited for length. This video and high-resolution photos also are available on the听.

Rao, a 91探花professor of computer science and engineering, has been working on brain-computer interfacing in his lab for more than 10 years and just published a on the subject. In 2011, spurred by the rapid advances in technology, he believed he could demonstrate the concept of human brain-to-brain interfacing. So he partnered with Stocco, a 91探花research assistant professor in psychology at the UW’s听.

On Aug. 12, Rao sat in his lab wearing a cap with electrodes hooked up to an machine, which reads electrical activity in the brain. Stocco was in his lab across campus wearing a purple swim cap marked with the stimulation site for the coil that was placed directly over his left motor cortex, which controls hand movement.

The team had a Skype connection set up so the two labs could coordinate, though neither Rao nor Stocco could see the Skype screens.

Rao looked at a computer screen and played a simple video game with his mind. When he was supposed to fire a cannon at a target, he imagined moving his right hand (being careful not to actually move his hand), causing a cursor to hit the “fire” button. Almost instantaneously, Stocco, who wore noise-canceling earbuds and wasn’t looking at a computer screen, involuntarily moved his right index finger to push the space bar on the keyboard in front of him, as if firing the cannon. Stocco compared the feeling of his hand moving involuntarily to that of a nervous tic.

“It was both exciting and eerie to watch an imagined action from my brain get translated into actual action by another brain,” Rao said. “This was basically a one-way flow of information from my brain to his. The next step is having a more equitable two-way conversation directly between the two brains.”

The technologies used by the researchers for recording and stimulating the brain are both well-known. Electroencephalography, or EEG, is routinely used by clinicians and researchers to record brain activity noninvasively from the scalp. Transcranial magnetic stimulation is a noninvasive way of delivering stimulation to the brain to elicit a response. Its effect depends on where the coil is placed; in this case, it was placed directly over the brain region that controls a person’s right hand. By activating these neurons, the stimulation convinced the brain that it needed to move the right hand.

Computer science and engineering undergraduates Matthew Bryan, Bryan Djunaedi, Joseph Wu and Alex Dadgar, along with bioengineering graduate student Dev Sarma, wrote the computer code for the project, translating Rao’s brain signals into a command for Stocco’s brain.

“Brain-computer interface is something people have been talking about for a long, long time,” said , assistant professor in psychology at the UW’s Institute for Learning & Brain Sciences, and Stocco’s wife and research partner who helped conduct the experiment. “We plugged a brain into the most complex computer anyone has ever studied, and that is another brain.”

At first blush, this breakthrough brings to mind all kinds of science fiction scenarios. Stocco jokingly referred to it as a “Vulcan mind meld.” But Rao cautioned this technology only reads certain kinds of simple brain signals, not a person’s thoughts. And it doesn’t give anyone the ability to control your actions against your will.

Both researchers were in the lab wearing highly specialized equipment and under ideal conditions. They also had to obtain and follow a stringent set of international human-subject testing rules to conduct the demonstration.

“I think some people will be unnerved by this because they will overestimate the technology,” Prat said. “There’s no possible way the technology that we have could be used on a person unknowingly or without their willing participation.”

Stocco said years from now the technology could be used, for example, by someone on the ground to help a flight attendant or passenger land an airplane if the pilot becomes incapacitated. Or a person with disabilities could communicate his or her wish, say, for food or water. The brain signals from one person to another would work even if they didn’t speak the same language.

Rao and Stocco next plan to conduct an experiment that would transmit more complex information from one brain to the other. If that works, they then will conduct the experiment on a larger pool of subjects.

Their research was funded in part by the 听at the UW, the 听and the .

###听

For more information, contact Rao at rao@cs.washington.edu or 206-685-9141, and Stocco at stocco@uw.edu or 206-685-8610. Video and high-resolution photos are available on the .