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10/12/21

By Jessica Weiss ’05

Students of science in the United States are likely to recognize the names and discoveries of Sir Isaac Newton, Galileo and Charles Darwin. Fewer may know of the many influential curanderos, cosmologists and agriculturists from across the Americas whose work has impacted science across the globe for centuries. 

Thanks to a $400,000 grant from the National Science Foundation, a new project led by Professor of History Karin Rosemblatt aims to establish how Latin America’s popular, Indigenous and Afro-descendant communities were “never on the periphery of scientific developments.” 

“We aim to shift emphasis away from the discoveries of a few scientific geniuses and to foreground instead the many contributors to scientific work—porters, local guides, wives and family members, technicians, herbal specialists,” said Rosemblatt, who is also the director of UMD’s Nathan and Jeanette Miller Center for Historical Studies. 

The project, “Placing Latin America and the Caribbean in the History of Science, Technology, Environment, and Medicine,” will bring together senior and established researchers and graduate students in the field of HSTEM (History of Science, Technology, Environment and Medicine) in Latin America and the Caribbean. The network will secure ties among researchers in North and South America, produce publications that make their research widely available and provide training and mentoring to graduate students.  

Rosemblatt, whose research focuses on the transnational study of gender, race, ethnicity and class, has already coordinated a 13-person steering committee made up of scholars at different stages of their careers working in Latin America and the United States. The committee members specialize in different time periods, geographic regions and topics. They include: Miruna Achim (Universidad Autónoma Metropolitana-Cuajimalpa, Mexico City); Eve Buckley (University of Delaware); Marcos Cueto (Oswaldo Cruz Foundation, Rio de Janeiro); Sebastián Gil-Riaño (University of Pennsylvania); Pablo F. Gómez (University of Wisconsin-Madison); Carlos López Beltrán (National Autonomous University of Mexico); Camilo Quintero (UNIANDES, Colombia); Megan Raby (University of Texas at Austin); Julia Rodriguez (University of New Hampshire); Carlos Sanhueza Cerda (Universidad de Chile); Elisa Sevilla Perez (Universidad de San Francisco, Quito); and Adam Warren (University of Washington, Seattle). Ana Luísa Reis Castro (MIT) will serve as graduate student representative. 

Next steps involve growing the network and building out a website. 

Through the materials produced by the network, teachers of students of all ages will also gain access to bibliographies, lesson plans, essays and collections of syllabi that allow them to cover a broader range of scientific endeavors and a more diverse community of scientists, Rosemblatt said. 

“We hope to convince other historians, students and the broader public that the Western scientific tradition developed in conversation with other, often colonized, peoples,” she said.   

Image: “Two views of Cabo Tres Montes” (Chile), 1891, via memoriachilena.cl

10/28/21

There are myriad benefits to learning a new language—from conversing with people from other backgrounds, to easing international travel, to advancing your career. But acquiring a new language as an adult is not always easy, particularly if a person is trying to distinguish phonetic sounds not often heard in their native language.

With funding from the National Science Foundation (NSF), researchers in the Computational Linguistics and Information Processing (CLIP) Laboratory at the University of Maryland are exploring this phenomenon, using computational modeling to investigate learning mechanisms that can help listeners adapt their speech perception of a new language.

Naomi Feldman (left), an associate professor of linguistics with an appointment in the University of Maryland Institute for Advanced Computer Studies, is principal investigator of the $496K grant(link is external).

Feldman is overseeing five students in the CLIP Lab who are heavily involved in the project, including two who are pictured below. Craig Thorburn(link is external) (right), is a fourth-year doctoral student in linguistics, and Saahiti Potluri (left), is an undergraduate double majoring in applied mathematics and finances.

For their initial work, the researchers are taking a closer look at the specific difficulties native Japanese speakers face when learning English.

As an adult, it is often difficult to alter the speech categories that people have experienced since childhood, particularly as it relates to non-native or unfamiliar speech sounds. For example, native English speakers can easily distinguish between the “r” and “l” sound, which native Japanese speakers are not accustomed to.

Feldman’s research team is developing two types of computational models based on adult perceptual learning data: probabilistic cue weighting models, which are designed to capture fast, trial-by-trial changes in listeners’ reliance on different parts of the speech signal; and reinforcement learning models, which are designed to capture longer term, implicit perceptual learning of speech sounds. Thorburn and Potluri are working on the latter models.

With guidance from Feldman, the two researchers are exploring a reward-based mechanism that research suggests is particularly effective in helping adults acquire difficult sound contrasts when learning a second language.

“We're trying to uncover the precise mechanism that makes learning so effective in this paradigm,” Thorburn says. “This appears to be a situation in which people are able to change what they learned as an infant, something we refer to as having plasticity—the ability of the brain to adapt—in one’s representations. If we can pin down what is happening in this experiment, then we might be able understand what causes plasticity more generally.”

Potluri says that the powerful computational resources provided by UMIACS are critical to the project, noting that the model they are working with goes through hundreds of audio clips and “learns” over thousands of trials.

“The lab's servers can run these experiments in a matter of hours. Whereas with less computational power, it would literally take days to run a single experiment,” she says. “After running the model, we also need to analyze the massive datasets generated by the trials, and they are easier to store and manipulate—without concerning memory issues—on the lab's servers.”

Potluri says it was her interest in learning languages and a desire to get involved in linguistics research that drew her to apply to work in CLIP as an undergraduate. Despite having very little previous coursework in the subject, she and Feldman found that the NSF-funded project was a great area for her to exercise her knowledge in math while gaining new skills.

Feldman says the complementary skill sets of Thorburn and Potluri make them a good team to assist on the project.

“Craig and Saahiti have interests that are very interdisciplinary—spanning everything from language science to computer science to applied math—which makes them a perfect fit for research that uses computational models to study how people learn language,” she says. “Their collaborative work has already proven to be very impressive, and I am glad to have them on our team.”

—Story by Melissa Brachfeld

10/8/21

By Jessica Weiss ’05

Starting next summer, University of Maryland language scholars will have a new place to conduct their research and a new source of participants for their studies: the Planet Word museum in downtown Washington, D.C. and its visitors.

A new $440,000 grant from the National Science Foundation funds a partnership between UMD, Howard University and Gallaudet University and Planet Word to advance research and public understanding about the science of language.

For example, experiments may look at what non-signing people believe about what makes various American Sign Language signs hard or easy to learn, why it’s easier to understand the speech of people we know rather than strangers, or whether we think differently when reading a text message versus formal writing.

The experiments will be interactive and fun, said Assistant Research Professor in UMD’s Maryland Language Science Center Charlotte Vaughn, who is leading the project.

“Language is already the topic of conversation at the museum, so there’s an unparalleled opportunity for our studies and activities about language science to be a seamless and memorable part of visitors’ experience,” she said.

Planet Word, opened in late 2020 and housed in the historic Franklin School building, aims to show the depth, breadth and fun of words, language and reading. Faculty from UMD’s Maryland Language Science Center, the Department of Linguistics, the Department of Hearing and Speech Sciences and the Department of English were involved in shaping the museum’s vision and programming. It has been a hope of the museum’s founder, Ann Friedman, to also have it be a space for research and discovery.

In addition to Vaughn, the lead project team includes Associate Professor in the Department of Hearing and Speech Sciences Yi Ting Huang and postdoc affiliate in the Department of Hearing and Speech Sciences Julie Cohen at UMD, as well as Assistant Professor of Psychology at Howard University Patrick Plummer and Assistant Professor of Linguistics at Gallaudet University Deanna Gagne. Other personnel include Jan Edwards and Rochelle Newman, both professors in the Department of Hearing and Speech Sciences at UMD; Colin Phillips, professor in the Department of Linguistics at UMD; and Laura Wagner, professor in the Department of Psychology at the Ohio State University.

Vaughn said the opportunity to partner with a historically Black university and the world's only liberal arts university for Deaf and hard-of-hearing people will allow for significant progress on issues central to the field.

“Engaging the diverse Planet Word audience in our activities will make our research stronger, more representative, and more widely accessible,” Vaughn said. “At the same time, our collaborative partnership, plus offering unique research experiences to students underrepresented in the field, works toward diversifying the future of the language sciences.”

The grant also funds the development of a training course in public-facing research, which will be offered for the first time at Planet Word next summer. Though offered through UMD, the course will be open to students from across the region. Those who take part will help lead the research studies, set to begin around the same time.

“Participating in public-facing research is an excellent opportunity for students,” said Huang. “Communicating science to broad audiences involves developing ways to hook people into engaging with questions when they have limited familiarity with the topic and unraveling scientific puzzles through the format of conversations.”

9/1/21

Support for research on constraints on movement, and on exceptive constructions.

Congratulations to Adam Liter and to Maria Polinsky, whose work has earned new support from the National Science Foundation. Adam has received a Doctoral Dissertation Research Improvement Grant for work with his supervisor, Jeff Lidz, on “Subjacency, the Empty Category Principle, and the nature of constraints on phrase movement.” Masha is the recipient of a Collaborative Research Award on “Variation in exceptive structures,” on how languages express thoughts like ‘everybody laughed except you,' a project on which Hisao Kurokami has already begun to work. See the abstracts below.

Adam Liter and Jeffrey Lidz, BCS #2116270, Subjacency, the Empty Category Principle, and the nature of constraints on phrase movement

In general, it is possible to form a question by 'moving' a wh-phrase like "who” or "which boy" out of a seemingly arbitrary number of clauses, as in "Who did Allie say that Amy saw?", "Who did Alicia hear that Allie said that Amy saw?", and so on. In these questions, "who" is the logical object of "saw" yet appears at the beginning of the sentence. However, there are certain syntactic environments, commonly called 'islands,' in which question formation is not possible. A question like "Who did the book by delight everyone?"--whose intended meaning is 'who is the person such that the book by that person delighted everyone'--sounds unnatural to speakers of English, suggesting that it is not a possible question despite having a reasonable meaning. Some linguists have claimed that these constraints disappear when the offending structure is elided, such as in a sentence like "Amy said that the book by someone delighted everyone, but I don't remember who". Such sentences sound a bit more natural to speakers of English, but their status isn't entirely clear. This dissertation project will advance linguistic theory by using recent experimental techniques to ascertain whether such sentences are grammatical. In advancing the field, this project will also support education and diversity by training an undergraduate research assistant in these experimental techniques, scientific thinking, and statistical analysis.

Using behavioral methods, this doctoral dissertation project probes the link between speakers' reported judgments and their sensitivity to structure in questions with and without ellipsis. The goal is to determine whether the same principles apply to dependencies involving ellipsis as those that do not, with the longer term goal of identifying the computational principles governing syntactic locality. More generally, the project addresses the consequences of mismatches between reported acceptability and subliminal sensitivity to structure in acceptability judgments.

Maria Polinsky, BCS #2116344, Variation in exceptive structures

All languages are able to express universal statements, even though we realize that they are seldom literally true. Consequently, languages also have means of expressing exceptions to such generalizations, via exceptive constructions. English examples include "Everybody but Sandy laughed" and "Everybody laughed except Sandy". Linguistic means of expressing exclusion have received modest attention from philosophers of language and semanticists, whose focus has been primarily on English. Beyond that small body of work, little is known about exceptive constructions across the world's languages: how they are built, what their distribution is within individual languages and across languages, and how they compare to other constructions expressing comparison or contrast. This research project fills this gap as the first cross-linguistic investigation of lexical, morphological, and syntactic properties of the construction. Understanding exceptive constructions allows linguists to create better theories of language structure and to predict the range of variation in natural languages; it helps computer scientists build better parsing models; it gives language educators new dimensions that should be emphasized in language teaching, and it provides cultural anthropologists with additional tools to study societal (dis)similarities in the concept of exclusion. 

This research project employs methodologies from linguistic typology, theoretical syntax, and formal semantics to carry out in-depth investigations of exceptive constructions in a wide range of the world's languages. The project aims for maximum linguistic coverage by using sampling techniques of modern linguistic typology. Theoretically, the project addresses a range of questions that arise from the empirical findings. In particular, it analyzes the contrast between free and connected exceptives, phrasal and clausal exceptives, and coordinated and subordinated exceptives. The project develops diagnostics that reliably identify the different types of exceptives and identifies independent linguistic properties that correlate with these different types of exceptives in a language. Therefore, it allows researchers to predict the type of exceptive constructions in an individual language. Beyond developing a picture of exceptive structure cross-linguistically, the project has notable implications for current theories of ellipsis. The project provides data on low-resource and endangered languages and highlights the importance of linguistic diversity for a complete understanding of the human language system.

 

8/18/21

By Chris Carroll

As the clouds of mental illness gather, it can be difficult for patients to recognize their own symptoms and find necessary help to navigate storms like episodes of depression or schizophrenia.

With $1.2 million in new funding from the National Science Foundation, University of Maryland researchers are creating a computerized framework that could one day lead to a system capable of a mental weather forecast of sorts. It would meld language and speech analysis with machine learning and clinical expertise to help patients and mental health clinicians connect and head off crises while dealing with a sparsely resourced U.S. mental health care system.

“We’re addressing what has been called the ‘clinical white space’ in mental health care, when people are between appointments and their doctors have little ability to help monitor what’s happening with them,” said Philip Resnik, a professor of linguistics with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS) who is helping to lead the research.

The project was born with the help of a seed grant through the AI + Medicine for High Impact (AIM-HI) Challenge Awards, which bring together scholars at the University of Maryland, College Park (UMCP) with medical researchers at the University of Maryland, Baltimore (UMB) on major research initiatives that link artificial intelligence and medicine. Deanna Kelly, a professor of psychiatry at the University of Maryland School of Medicine, is another of the project’s leaders, as are electrical and computer engineering Professor Carol-Espy Wilson and computer science Assistant Professor John Dickerson, both at UMCP.

The new funding will help the research team pour their diverse expertise into a single framework, which would then be developed into a deployable system for testing in a clinical setting.

How would such a system work? Users might answer a series of questions about physical and emotional well-being, with the system employing artificial intelligence to analyze word choice and language use—Resnik’s area of focus in the project. It could also monitor the patient’s speech patterns, analyzing changes in the timing and degree of movement made by the lips and different parts of the tongue, and comparing it to a baseline sample taken from healthy control subjects or earlier when the participant was in remission, said Espy-Wilson, who has an appointment in the Institute for Systems Research.

People generally overlap neighboring sounds when speaking, beginning the next sound before finishing the previous one, a process called co-production. But someone suffering from depression, for instance, has simpler coordination, and their sounds don’t overlap to the same extent.

“You can't think as fast, you can't talk as fast when you’re depressed,” said Espy-Wilson. “And when you talk, you have more and longer pauses … You have to think more about what you want to say. The more depressed you are, the more of the psychomotor slowing you're going to have.”

While the final form of the system has yet to take shape, it could potentially live in an app on patients’ phones, and with their permission, automatically monitor their mental state and determine their level of need for clinical intervention, as well as what resources are available to help.

If the system simply directed streams of patients at already overloaded doctors or facilities with no open beds, it could potentially make things worse for everyone, said Dickerson, who has a joint appointment in UMIACS.

He’s adding his expertise to work that Resnik and Espy-Wilson have been pursuing for years, and taking on the central challenge—using an approach known in the machine learning field as the “multi-armed bandit” problem—of creating a system that can deploy limited clinical resources while simultaneously determining how to best meet a range of evolving patient needs. During development and testing, the AI system’s determinations will always be monitored by a human overseer, said Dickerson.

The World Health Organization estimated a decade ago that the cost of treating mental health issues between 2011 and 2030 would top $16 trillion worldwide, exceeding cardiovascular diseases. The stresses of the COVID-19 pandemic have exacerbated an already high level of need, and in some cases resulted in breakdown conditions for the system, said Kelly, director of the Maryland Psychiatric Research Center’s Treatment Research Program.

As the project develops, the technology could not only connect patients with a higher level of care to prevent worsening problems (avoiding costlier care), but also might help clinicians understand which patients don’t need hospitalization. Living in the community with necessary supports is often healthier than staying in a psychiatric facility—plus it’s cheaper and frees up a hospital bed for someone who needs it, she said.

“Serious mental illness makes up a large portion of health care costs here in the U.S. and around the world,” Kelly said. “Finding a way to assist clinicians in preventing relapses and keeping people well could dramatically improve people’s lives, as well as save money.”

Aadit Tambe M.Jour. ’22 contributed to this article.

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