Spring 2012
Topics In Cognitive Science
CSCI 7772 | EDUC 7775 | LING 7775 | PHIL 7775 | PSYC 7775 | SLHS 7775

Fri 12:00-14:00
Muenzinger D430


Professor Michael Mozer
Department of Computer Science
Engineering Center Office Tower 741
Office Hours:  Wed 2:30-4:30

Course Objectives

The intent of this course is to expose students to the breadth and depth of current research issues in the field of Cognitive Science. Students will attend presentations of innovative theories and methodologies of Cognitive Science that they will be expected to critically evaluate. Students will participate in the ICS Colloquium Series and also the ICS Distinguished Speakers series that hosts internationally recognized Cognitive Scientists who share and discuss their current research. Following colloquia, students will have the opportunity to engage in analysis and discussion of the work that was presented to further their understanding of the material.


This course is a requirement for students interested in obtaining either the joint Cognitive Science Ph.D.  or a graduate certificate in Cognitive Science.  Others may enroll in the course as space is available.

There are no pre-required courses. This course is primarily offered for one unit of credit, and students in one of the Cognitive Science academic programs must enroll in this course for two semesters.  Students who have strong need to obtain two units of credit for this course in one semester may potentially do so with the instructor's permission. For each unit of credit, students must attend at least 7 talks and write commentaries on these talks.

Course requirements

Talk attendance

The primary requirement for the course is to attend at least 7 (or 14) colloquia in Cognitive Science, including the ICS Distinguished Speaker series. The primary opportunity to attend Cognitive Science colloquia is via the ICS speaker series, the schedule for which can be found at  When I am alerted to talks on campus with significant cognitive science content, but which are not part of the ICS speaker series, I will announce them to the class. If you hear of one that I haven't announced, please forward the time, location, abstract, and title to me, and if I deem it to have significant cognitive science content, I will announce the talk to the class as an option.  If for any reason we have a shortage of talks this semester, I can supplement the ICS schedule by bringing in advanced graduate students and faculty during the ICS colloquium slot.

Background readings

For the ICS colloquia, students are responsible for reading appropriate background materials (e.g., journal articles, book chapters) provided by the speaker to serve as grounding for the colloquium.  These materials will be distributed to students 1-2 weeks prior to the colloquium.


For each talk attended, students are to write a commentary of no more than one page.  For the commentary, I want to get your reactions to the talk.  The commentary can include:
  • a summary of what you think the main or most interesting ideas are behind the work, However, there is no need to provide a complete abstract of the talk. I want to know the student's perspective on what the work had to offer.
  • questions about the material for further discussion, either clarification questions or points of disagreement with the authors (``I don't see how such and such will work as the speaker claims...'').
  • comments on how the talk relates to other talks the student has attended or other papers the student has read.
  • a critique of the work. What are the flaws in the ideas presented? What are the limitations? Did the speaker place their work in the appropriate theoretical context? Did the speaker overstate their results? 
  • If the talk gave you inspiration for new research ideas, describe these ideas.  The ideas might be extensions of the work, new directions to move in, or applications of the work to other subfields of cognitive science.
Think of the commentary as your opportunity to share your thoughts and insights with both me and the entire class. I have created a google group,, and your commentaries should be sent to the group for reaction by the professor and other students. To simplify our lives, I'd like the commentaries to be mailed to the group, with a email subject line "[STUDENT LAST NAME] COMMENTARY [SPEAKER]". You are invited to comment on commentaries and to add your reactions to the commentaries, so that we get a group electronic discussion going.

You may find that the number of emails associated with the group is too large. If you wish, you may change your group settings so that you receive weekly summaries of the group discussions or that you log into the group to read the discussions.

You will recieve an invitation from me to join the google group.  You may join either using your University of Colorado login as your google login (including the, or you may join with a gmail address. You may have to request an invitation to join with your gmail address.

Commentaries are due within two weeks of the talk date. I did not have this rule in Fall 2011, and many students delayed writing the commentaries until the end of the semester, which made discussions of the talks temporally disjointed. For the benefit of our discussions, as well as for your own memory,  the deadline requirement has been added. I would tighten the deadline even further, but some participants mentioned that they needed a bit of time to digest a talk and read some of the opinions of their classmates before having the confidence to broadcast their views.

All commentaries are due by May 5.


To receive an "A" grade in the course, the student must submit 7 (or 14) commentaries that reflect an informed opinion on the research discussed, and student must participate in some electronic discussion of others' commentaries and of the talks in general.  Students will fail the course if they do not submit at least 5 commentaries.

Interesting colloquia

Date and Location Speaker Topic Reading
1/20/2012, 12:00-13:00, ICS colloquium
(Muenzinger D430)
Tal Yarkoni,
University of Colorado
Bridging the mind/brain gap with text: a novel framework for large-scale automated synthesis of functional MRI data

The explosive growth of the human neuroimaging literature has led to major advances in understanding of human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. In this talk, I discuss some of the major challenges neuroimaging researchers face, and describe a novel brain mapping framework (Neurosynth) that uses text mining, meta-analysis and machine learning techniques to help address some of these challenges. The Neurosynth framework can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature (e.g., how to infer cognitive states from distributed activity patterns), and support 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. I illustrate these applications with concrete examples from several domains, and introduce a web interface that provides access to the data and tools (, before concluding with a discussion of future directions and potential avenues for integration with other tools.
Yarkoni et al.
1/26/2012, 15:30-16:30
CS Colloqiuum
(ECCR 265)
Ma Schwager, Boston University
Active Estimation for Multi-Robot Teams in Hazardous Environments

This talk will discuss the problem of deploying a group of robots into an environment for state estimation tasks (e.g. target localization, field estimation, or mapping), while avoiding hazards at unknown positions that cause the robots to fail. Some regions of the environment may be hazardous, for example, due to fire, severe weather, caustic chemicals, or the presence of adversarial agents. A probabilistic model is formulated, under which recursive Bayesian filters are used to estimate the state and hazards online. The robots move both to avoid hazards and to provide useful sensor information by following the gradient of mutual information. Efforts toward overcoming the challenges of decentralization and scalability will also be discussed.
Warning: this will be a technical talk. I've included it because those of you interested in modeling will find it interesting for the particular matehmatical techniques.
2/3/2012, 12:00-13:30, Muenzinger E214 Danny Oppenheimer, Princeton Using metacognitive disfluency to improve decision quality and educational outcomes
2/7/2012, 3:30-4:30, Discovery Learning Center 170 David McDonald, U. Washington
Social Computational Systems: A Research Agenda for HCC

The rise of large-scale systems that allow a diverse community of individuals to each contribute their unique talents, perspectives and skills has necessitated a shift in how we think about the relationship between people and computing. Systems like Wikipedia, PatientsLikeMe, InnoCentive, or Mechanical Turk illustrate the potential for these new systems to interleave the talents of people and machines to begin solving problems that neither people nor machines can solve alone. I define these types of systems as Social Computational Systems (SoCS). One aspect of SoCS is that any single disciplinary stance (computational, behavioral, or social) is insufficient to elaborate characteristics for which we must account when designing and building future SoCS.

In the talk I claim that the paradigm of Human Centered Computing (HCC) is shifting to encompass larger numbers of connected participating users and, as a result, SoCS is important to the future of HCC. I outline a small selection of prior research that illustrates a progression of my own research thinking about how to study, characterize, design and build systems where computation and people are essential to the way the systems perform. I illustrate working in a disciplinary intersection through a study that applies machine learning techniques to understand how members of one large online community identify behavioral patterns of other members of the community. The talk concludes by outlining key challenges for a Social Computational Systems research agenda.

2/10/2012, 12:00-13:00, ICS Colloquium Alice Healy, University of Colorado, Psychology & Neuroscienc

Specificity and Transfer of Learning

Knowledge is often highly specific to the conditions of acquisition, so there is limited transfer of learning from training to testing.  A series of studies is reported examining specificity and transfer of learning in three very different tasks, including digit data entry, speeded aiming, and time production.  These studies address a variety of theoretical issues, including those involving mental practice, variability of practice, and task integration.  Despite these differences across studies, they converge on the conclusion that specificity and transfer of learning are not mutually exclusive.  That is, significant specificity can occur even when participants appear to transfer their learning from training to testing.  Furthermore, the studies show that the extent of transfer and its direction (i.e., positive or negative) is largely dependent on the definition of transfer employed, the baseline level during training (i.e., start or end of training), and the dependent measure used to assess performance (e.g., initiation time or execution time).

Healy & Wohldmann
2/17/2012, 12:00-13:00, ICS Colloquium Kurt van Lehn, Arizona State University
Now that Intelligent Tutoring Systems are as effective as human tutors, how can they become even better?

Abstract:  It is often said that human tutors are 2 standard deviations more effective than classroom instruction and that Intelligent Tutoring Systems (ITS) are 1 standard deviation more effective.  This hypothesis, which inspired many important studies of human tutoring and many efforts to replicate human tutoring with natural language tutoring systems, now seems false.  Although research continues, the current best fitting hypothesis is that both human tutors and ITS have the same effect size, namely 0.75 standard deviations above no-tutoring instruction.   The first part of the talk will support this claim with a meta-analysis of relevant experiments, illustrated with specific experiments from several labs.  However, this finding does not imply that ITS researchers should declare victory and retire.  The studies found that both human tutors and ITS were far from perfect.  ITS researchers should continue to try to achieve a 2 standard deviation effect size.  The second part of the talk discusses three methods with promising preliminary results:  (1) using machine learning to improve tutorial decision making; (2) teaching and fading an explicit meta-cognitive strategy; and (3) prompting reflection during problem solving.  

2/21/2012,11-12,  DLC 170 Chris Le Dantec, Georgia Tech Human-computer communication 
2/22/2012, 11-12, DLC 170 Tom Yeh, U. Maryland Human-computer communication
2/27/2012, 4-5, Humanities 125 Zygmunt Frajzyngier, CU Boulder
Theoretical bases for differential marking of noun phrases: the proper domain for  argument-adjunct distinction

The immediate aim of the study is to provide an explanation for why certain noun phrases  are formally less marked (‘arguments’) and others aremore marked (‘adjuncts’) within a  clause. Rejecting the widespread assumption that verbs have an ‘argument structure’ and  that verbs assign grammatical relations, as well as the absolutist notions of core and  peripheral grammatical relations being determined by verbs, the study provides a nonaprioristic explanation for the phenomena that led to the emergence of such notions.  The  proposed theory explains a hitherto unexplained phenomenon, viz., why verbs and nouns  having the same referential meanings across languages  have different syntactic properties.
3/2/2012, 12:00-13:00, ICS Colloquium Dan Roth, University of Illinois, Computer Science Learning From Natural Instructions

Machine learning is traditionally formalized as the study of learning concepts and decision functions from labeled examples, thus requiring representations that encode information about the target function’s domain.  We are interested in providing a way for a human teacher to interact with an automated learner using natural instructions which communicate relevant domain expertise to the learner without necessarily knowing a thing about the internal representation or the learning program.  This talk focuses on the machine learning aspects of this problem.  The key challenge is to learn natural language interpretations without being given direct supervision at that level; for example, a plausible feedback could be the success or failure of the performed instruction.  We will present research on Constrained Conditional Models (CCMs), a framework that augments probabilistic models with declarative constraints in order to support learning such interpretations.  In CCMs we formulate natural language interpretation problems as Integer Linear Programs and learning of the objective functions is done via constraints-­-driven learning and through global inference that account for the interdependencies among interpretation’s components.  In particular, we will focus on new algorithms for training these global models using easy-­-to-­-get indirect supervision signals and show the contribution of indirect supervision to other NLP tasks such as Information Extraction, Transliteration and Textual Entailment. 
recent paper
3/16/2012, 12:00-13:00, ICS Colloquium Juia Evans, San Diego State University The impact implicit learning and development of conceptual knowledge in children with Specific Language Impairment

It has recently been suggested that SLI is a domain general deficit in implicit learning. Ullman and colleagues have argued that the implicit learning impairments in SLI are restricted to procedural learning impairments and that these procedural learning deficits impact the acquisition and use of bound morphology and syntax; leaving the acquisition and use of the mental lexicon largely intact in children with SLI. In this talk I will present behavioral, EEG, and aMEG data from our lab that suggests that implicit learning deficits in SLI may extend beyond procedural learning to include other aspects of implicit learning as well; and show how these implicit learning deficits result in a qualitatively different developmental trajectory of the acquisition and use of lexical conceptual knowledge for children with SLI as compared to typically developing children.
reading 1
reading 2
3/21/2012, 17:00-18:30, Koelbel  330 Paul Rozin, University of Pennsylvania The Psychology Of Food
3/23/2012, 12:00-13:00,  ICS Colloquium  Jordan Boyd-Graber, University of Maryland, iSchool and Institute for Advanced Computer Studies Making Topic Models more Human(e)

Imagine you need to get the gist of what's going on in a large text dataset such as all tweets that mention Obama, all e-mails sent within a company, or all newspaper articles published by the New York Times in the 1990s.  Topic models, which automatically discover the themes which permeate a corpus, are a popular tool for discovering what's being discussed.  However, topic models aren't perfect; errors hamper adoption of the model, performance in downstream computational tasks, and human understanding of the data.  However, humans can easily diagnose and fix these errors.  We present a statistically sound model to incorporate hints and suggestions from humans to iteratively refine topic models to better model large datasets.  We also examine how topic models can be used to understand topic control in debates and discussions.  We demonstrate a technique that can identify when speakers are "controlling" the topic of a conversation, which can identify events such as when participants in a debate don't answer a question, when pundits steer a conversation toward talking points, or when a moderator exerts her influence on a discourse.
4/6/2012, 12:00-13:00,  ICS Colloquium Nikolaus Correll, Comp Sci, University of Colorado swarm robotics

(Nikolaus is giving a CS colloquium earlier in the semester. This will be a different talk, and aimed more for a cog sci audience.)
4/13/2012, 12:00-13:00,  ICS Colloqiuium
Greg Burns, Emory University
Neuroimaging of Brain-Culture Interactions

I will present the results of two studies that examine the effects of society and culture on individual brain regions associated with decision making. 1)  Sacred values, such as those associated with religious or ethnic identity, underlie many important individual and group decisions in life, and individuals typically resist attempts to trade-off their sacred values in exchange for material benefits.  We utilized an experimental paradigm that used integrity as a proxy for sacredness and which paid real money to induce individuals to sell their personal values.  Using functional magnetic resonance imaging (fMRI), we found that values that people refused to sell (sacred values) were associated with increased activity in the left temporoparietal junction and ventrolateral prefrontal cortex, regions previously associated with semantic rule retrieval.  This suggests that sacred values affect behavior through the retrieval and processing of deontic rules and not through a utilitarian evaluation of costs and benefits.  2) Finally, we use neuroimaging to predict cultural popularity - something that is popular in the broadest sense and appeals to a large number of individuals. We used fMRI to measure the brain responses of a relatively small group of adolescents while listening to songs of largely unknown artists. As a measure of popularity, the sales of these songs were totaled for the three years following scanning, and brain responses were then correlated with these "future" earnings. Although subjective likability of the songs was not predictive of sales, activity within the ventral striatum was significantly correlated with the number of units sold. These results suggest that the neural responses to goods are not only predictive of purchase decisions for those individuals actually scanned, but such responses generalize to the population at large and may be used to predict cultural popularity.
reading 1
reading 2
4/18/2012, 10:30-11:30 Discovery Learning Center (Engineering Center) DLC 170 Sean Munson, University of Michigan
Preferences & Nudges in Sociotechnical Systems
Every day, millions of people make decisions in digital environments or supported by software tools. Designers of sociotechnical systems influence the choices people make, both intentionally and inadvertently, with their design decisions.
In this talk, I will discuss my research on individual preferences and systems designed to nudge people to be their better selves. I will present, in detail, a study of people's preferences for political opinion information and efforts to nudge those preferences; this study has shown the importance of individual differences in selective exposure theory. I will then give an overview of my current research platforms, designed to help people live healthier and happier lives, and my research questions and expected contributions.
4/26/2012, 15:30-16:30
CS Colloquium
Andrew McCallum, UMass Amherst machine learning and language understanding

This talk will be more technical, and aimed at a computer science audience.
4/27/2012, 12:00-13:00,  ICS Colloquium  Andrew McCallum, UMass Amherst machine learning and language understanding

This talk will be different than the 4/26  talk. Students may count each talk separately.

Additional information for students (click to read)