It was with great sadness that we in the Cognitive Science community learned of Florence Sullivan’s passing this summer. She was a source of great energy and great expertise in bridges between education and cognitive science. Sullivan was a member of the CogSci Initiative steering committee from its inception, and presented at our third annual workshop in 2017. Follow this link to read the tribute to her in the University News.

If you are writing an external grant this semester, or would like to support or learn from those who are, please consider joining the CogSci grant writing group. E-mail pater@umass.edu by the end of the day Sunday March 15th to join, or for more information. We will meet between biweekly and monthly. The primary focus will be to provide informal feedback to one and other on ideas and writing, but we may engage in more structured activities (e.g. workshops led by the staff at the research office) if there is interest.

Click for more information!

CogSci 2020

Elizabeth Coppock, Boston University, will present “Universals in Superlative Semantics” in the Linguistics colloquium series at 3:30 Fri. 9/27. An abstract follows. All are welcome!

Abstract: This talk reports on the results of a broad cross-linguistic study on the semantics of quantity words such as ‘many’ in the superlative (e.g. ‘most’). While some languages use such a form to express both a relative reading (as in ‘Gloria has visited the most continents’) and a proportional reading (as in ‘Gloria has visited most continents’), the vast majority do not allow the latter, though all allow the former. Absolute readings for the superlatives of ordinary gradable adjectives, in contrast, are universal. I propose an explanation for this cross-linguistic generalization, centered around two core assumptions: quantity words denote gradable predicates of degrees, while proportional readings involve a comparison class of individuals. Proportional readings, I suggest, arise in rare cases when the former assumption is violated.

Brett Jefferson will present on “Using systems factorial technology to understand automation” in Cognitive brown bag Weds. 9/25 at noon in Tobin 521B. Abstract below. All are welcome!

Abstract. Viewing the perception as a systems processing model provides insight and rigor to common questions we have about the way humans understand the world. In this talk, I will discuss one such modeling framework, Systems Factorial Technology, and how it is applied to understanding transparency in automation. Specifically, we apply the capacity coefficient to measure cognitive resource allocation when information about what an automated system is relying on is present to users.

who:  Byron Wallace (Northeastern)
when: Sept 26 (Thursday) 11:45a – 1:15p
where: Computer Science Building Rm 150
food: Athena’s Pizza

What does the evidence say?
Models to help make sense of the biomedical literature

Abstract:

How do we know if a particular medical intervention actually works better than the alternatives for a given condition and outcome? Ideally one would consult all available evidence from relevant trials that have been conducted to answer this question. Unfortunately, such results are primarily disseminated in natural language articles that describe the conduct and results of clinical trials. This imposes substantial burden on physicians and other domain experts trying to make sense of the evidence. In this talk I will discuss work on designing tasks, corpora, and models that aim to realize natural language technologies that can extract key attributes of clinical trials from articles describing them, and infer the reported findings regarding these. The hope is to use such methods to help domain experts (such as physicians) access and make sense of unstructured biomedical evidence.

More specifically, I will discuss models to automatically extract trial population characteristics (e.g., conditions), interventions/comparators (treatments), and outcomes studied in a given clinical trial; together these “PICO” elements compose well-formed clinical questions. I will then present ongoing work on corpora and models for inferring the comparative effectiveness of a given treatment, as compared to a specified comparator, and with respect to a particular outcome of interest. If successfully realized (a big if), such models would effectively facilitate real-time clinical question answering over reports of clinical trials, in turn enabling evidence-based care.
Bio:
Byron Wallace is an assistant professor in the Khoury College of Computer Sciences at Northeastern University. He holds a PhD in Computer Science from Tufts University, where he was advised by Carla Brodley. He has previously held faculty positions at the University of Texas at Austin and at Brown University. His research is in machine learning and natural language processing, with an emphasis on their application in health informatics.

Wallace’s work has been supported by grants from the National Science Foundation (including a CAREER award), the National Institutes for Health, and the Army Research Office. He won the Tufts University 2012 Outstanding Graduate Researcher award, and his thesis work was recognized as The Runner Up for the 2013 ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG KDD) Dissertation Award. He co-authored the winning submission for the Health Care Data Analytics Challenge at the 2015 IEEE International Conference on Healthcare Informatics, and co-authored the 2017 Distinguished Clinical Research Informatics Paper Award winner at the American Medical Informatics Association Joint Summits on Translational Sciences. He also received the 2018 Early Career Award from the Society for Research Synthesis.

Michael C. Frank of Stanford University (http://langcog.stanford.edu/) will be presenting “Children’s language learning; developing understanding of social world” in a talk organized by the Developmental Science Initiative and co-sponsored by the Cognitive Science Initiative. It will take place Thursday November 7th from 1-2 pm at a location to be announced later.

who:  Yoon Kim (Harvard University)

when: 09/19 (Thursday) 11:45a – 1:15p

where: Computer Science Building Rm 150

food: Athena’s Pizza

“Neural Grammar Induction“

Abstract:

Bio:

Yoon Kim is a fifth-year Ph.D. candidate in computer science at Harvard University. He is advised by Alexander Rush. He is supported by a Google Fellowship.

From Jeff Starns

I’m writing to invite you to a discussion entitled “What should we do with p-values?” that will be held next Wed. (9/18) at noon in Tobin 423. Misuse of p-values (p hacking, interpreting p as the chance of error, etc.) is a common theme in discussions about the replication crisis, but you don’t see a lot of discussion on the *proper* use of p-values. We will explore the latter in an open discussion format. We’ll have some Psych 240 instructors to moderate, but we are trying for a conversation, not a lecture. All are welcome to come share their approach to using (or eschewing) p-values. Maybe we’ll be able to add some “Thou shalt”s to all the “Thou shalt not”s in the p-value rule book. This is technically the cognitive brownbag spot, but we would like to get people from other
areas joining the discussion. Hope to see you there!