Zanuttini gives Freeman lecture Fri. April 1 at 3:30

Raffaella Zanuttini (Yale University) will give the Freeman Lecture this year on Friday, April 1 in room S211 of the Integrative Learning Center at 3:30. Her talk, “Discovering Grammatical Diversity in American
English” is based on the research carried out by the Yale Grammatical Diversity Project which she founded.

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Bloggers welcome!

For the first time, we’ve set up a post in this newsletter to accept comments (“What is the birthdate of cognitive science?”). We’d like to encourage other members of the Cognitive Science community at UMass to use this platform to communicate with one another and the rest of the world. We have set up a separate “Blog” page on the website that will contain only those posts that have comments enabled (as well as this one). If you would like to post something to the CogSci website and newsletter, please include in an e-mail to

1. The title of the post
2. The contents of the post
3. Whether you’d like comments enabled
4. Whether you’d like your name at the top, as well as website and e-mail (please include these)

As well as discussion pieces, we’re also happy to publish announcements (publications, conferences, jobs, etc.) and anything else you’d like to have reach the 140 subscribers to our weekly newsletter.


Reinforcement Learning and AlphaGo

Andy Barto and Sridhar Mahadevan of Computer Science appeared in a piece by the UMass news office last week on the role of Reinforcement Learning in Google DeepMind’s AlphaGo. Andy had this interesting follow-up with his analysis of the factors leading to AlphaGo’s success, noting that the news office piece may have given a bit too much credit to Reinforcement Learning:

I am writing a description of AlphaGo for the 2nd edition of our Reinforcement Learning book. I think what makes it work as well as it does is that it cleverly combines three existing technologies: 1) a type of multi-layer neural network called a deep convolutional network, which is specialized for processing images, or other spatial arrays of data, 2) value function learning from self-play, which descends from Samuel’s famous checkers playing program of the 1960s and Tesauro’s backgammon playing system of the 1990s—this is mostly where reinforcement learning plays a part; and 3) Monte Carlo Tree Search, which is relatively recent and was a big advance for Go playing programs. They did clever engineering in combining these methods. A lot of smart people at DeepMind.


Jung in MLFL Thursday 3/31 at noon

what: Learning Performance Models for Post-Stroke
who: Hee-Tae Jung, UMass CICS
when: 12:00pm Thursday, 3/31
where: cs151
pizza: Antonio’s
generous sponsor: Yahoo!

The use of robots and games is becoming a popular trend in stroke rehabilitation research. Despite the acknowledged importance, however, research on evaluating the patient’s qualitative motor performance, customizing the initial task workspace based on the patient’s performance, and generating visualized performance reports of autonomous sessions for the therapist have received little attention. The essence of these is to model the therapist’s decision criteria to evaluate the qualitative motor performance and to use such evaluation to assess difficulties in the different regions of the task workspace for the individual stroke patient. In this talk, first I will motivate the needs of such models by introducing the results of a single-subject case study, and then propose a computational framework to learn the necessary models from therapist’s demonstration. The experiment results with two patients and two therapists suggest that the proposed framework is promising.

Hee-Tae Jung is a PhD candidate at the College of Information and Computer Sciences, University of Massachusetts Amherst, and a member of the Perceptual Robotics Lab. He received his BS in Computer Science from Yonsei University, Korea in 2007 and MS in Computer Science from Stanford University, USA in 2009. His research focus is on developing and analyzing intelligent assistive technologies for the population with special needs. He is the recipient of the Robin Popplestone Scholarship and the Graduate School Dissertation Research Grant. He was selected as an ACM/IEEE Human-Robot Interaction (HRI) Pioneer in 2015 and was an elected PC Co-Chair for the ACM/IEEE HRI Pioneers Workshop 2016. He served as an editorial assistant for the Journal of Robotics and Autonomous Systems from 2013 through 2014.


Lane in CSSI Friday April 1 at 12:30

In collaboration with the Institute for Social Science Research and the Department of Economics, the UMass Computational Social Science Institute invites you to the weekly CSSI seminar:

Julia Lane
New York University, Center for Urban Science Progress (CUSP)
Friday, April 1, 2016 • 12:30 p.m.-2:00 p.m.
Computer Science Building, Room 150/151
Lunch will be provided, beginning at 12:00
Talk begins at 12:30

Title: What Social Science Research Brings to Big Data Research: A View From Experience
Abstract: The world of big data offers enormous opportunities for social science, but social scientists have a great deal to offer, to a (data) world that is currently looking to computer scientists to provide answers. Three major areas in which social scientists can contribute, based on decades of experience and work with end users, include causal inference, data quality and addressing privacy and confidentiality. This talk discusses each of these in turn using examples drawn from a new largescale social science data infrastructure constructed using big data techniques.
Bio: Julia Lane is a Professor in the Wagner School of Public Policy at New York University. She is also a Provostial Fellow in Innovation Analytics and a Professor in the Center for Urban Science and Policy at NYU. Dr. Lane is an economist and has authored over 65 refereed articles and edited or authored seven books. She has been working with a number of national governments to document the results of their science investments. Her work has been featured in several publications including Science and Nature. Dr. Lane started at the National Science Foundation (as Senior Program Director of the Science of Science and Innovation Policy Program) to quantify the results of federal stimulus spending, which is the basis of the new Institute for Research on Innovation and Science at the University of Michigan. Dr. Lane has had leadership positions in a number of policy and data science initiatives at her other previous appointments, which include Senior Managing Economist at the American Institutes for Research; Senior Vice President and Director, Economics Department at NORC/University of Chicago; various consultancy roles at The World Bank; and Assistant, Associate and Full Professor at American University. Dr. Lane received her PhD in Economics and Master’s in Statistics from the University of Missouri.


Symposium – Tracking the Human Mind in Attitude and Speech Reports. Saturday April 16th

From Angelika Kratzer

You are all cordially invited to a symposium featuring some of the fellows of the 2015/2016 SIAS (Some Institutes for Advanced Study) Summer Institute. The topic of the symposium is:

Tracking the Human Mind in Attitude and Speech Reports

Saturday, April 16, 10:00 AM to 1:00 PM. Integrative Learning Center N400. The symposium will be followed by a reception.

A detailed program will follow.

Being one of the most interdisciplinary working groups of the 2015/2016 SIAS Summer Institute, the group on Attitude Ascriptions & Speech Reports, designed two projects that promise to make a positive contribution to the longstanding communication problem between neuroscience and linguistic research. In her dissertation, the neuroscientist in the group, Jorie Koster-Hale, found that epistemic properties of other people’s beliefs are represented via response patterns of neural populations in canonical belief ascription regions in the brain (so-called ‘Theory of Mind’ regions). These properties relate to the kind of evidence that ground a belief: whether it was good evidence or not, or whether it was visual or auditory evidence. Those kinds of properties do not only play a major role in philosophical discussions of knowledge ascriptions, they are also grammaticalized in verbal inflectional paradigms in many lesser-known languages (so-called “evidentials”). In addition, they trigger a significant dichotomy in the class of attitude verbs across languages: verbs in the believe family (believe that, suspect that, conjecture that) can be used to report false beliefs, while verbs in the know family (know that, discover that, reveal that, hear that, see that) cannot – those ‘factive’ verbs can only describe attitudes that are properly connected to reality. The SIAS Attitude Ascription and Speech Report group recognized those fascinating connections and found a common language to construct joint projects that will bring together their collective expertise in neuroscience, cognitive development, language acquisition, epistemology, theoretical linguistics and semantic typology under headings like ‘factivity/veridicality’ and ‘knowledge first’. Projects of this kind could become models for collaboration between researchers in the sciences and the humanities.

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What is the birthdate of Cognitive Science?

From Joe Pater (

I’m currently working on a piece called “1957: The Birth of Cognitive Science” (as part of a larger project on debates in cognitive science). I have picked that date since it is the publication date of Noam Chomsky’s Syntactic Structures, and Frank Rosenblatt’s “The perceptron: a perceiving and recognizing automaton”, which can be taken as the genesis of generative linguistics and neural network modeling of cognition respectively (there are other possibilities for the latter, such as McCulloch and Pitts’ and Hebb’s work, but I’m particularly interested in explaining how many of the features of later neural network models and their associated research strategies first appeared in Rosenblatt’s work). The clash between these two paradigms was of course a major feature of cognitive science in the late 1980s and 1990s, and there continues to be considerable productive tension between these approaches, as well as integrative work.

When I suggested this date with this rationale to a colleague in Cognitive Psychology, I was glad to get the assessment that it was “As good a point in time as any, better than most.” I’d be curious, though, to hear other suggestions for birthdates.


Strategic planning in Cognitive Science

The steering committee of the CogSci Initiative is currently deeply involved in the development of a strategic plan for the formation of an Institute. If you have ideas about future directions for Cognitive Science education and research on campus, and ways in which we can be working together to meet our goals, as well as the goals of our Department, College / School, and University’s own strategic plans, please contact a member of the steering committee.


Florence Sullivan of Education on the Steering Committee

Welcome to Florence Sullivan of Education as the newest faculty member of the CogSci Initiative steering committee! Florence has research interests that overlap with many of us. For example, she describes her current NSF funded project as follows:

The goal of the Microgenetic learning analytics project is to develop a new computational method for analyzing talk recorded in co-present small group interactions.

The rest of the steering committee looks forward to working with Florence in developing new initiatives in interdisciplinary cognitive science involving Education.


Sánchez in LARC 12:20 Weds. 3/23

Covadonga Sánchez from the Spanish linguistics unit of LLC will be presenting to the Language Acquisition Research Center Wednesday 3/23 at 12:20 in room N451 of the ILC on “The realization of subject focus in L2 Spanish: Results from a Pilot Study” Everyone is welcome!