Singular Value Decomposition (SVD)

By Lowell Thompson and Ashley Hou

This is a collaborative tutorial aimed at simplifying a common machine learning method known as singular value decomposition. Learn how these techniques impact computational neuroscience research as well!

Singular value decomposition is a method to factorize an arbitrary m \times n matrix, A, into two orthonormal matrices U and V, and a diagonal matrix \Sigma. A can be written as U\Sigma V^T. The diagonal entries of \Sigma, called singular values, are arranged to be in decreasing magnitude. The columns of U and V are composed of the left and right singular vectors. Therefore, we can express U \Sigma V^T as a weighted sum of outer products of the corresponding left and right singular vectors, \sigma_i u_i v_i^T.

In neuroscience applications, we have a matrix R of the firing rate of a given neuron, where the first dimension represents different frontoparallel motion directions and the second represents disparity (a measure that helps determine the depth of an object). Relationships between preferences for direction and depth could serve as a potential mechanism underlying 3D motion perception. SVD can be used to determine whether a neuron’s joint tuning function for these properties is separable or inseparable. Separability entails a constant relationship between the two properties: a particular direction preference will be maintained across all disparity levels, and vice versa. If this were the case, then all of the vectors in the firing matrix could be described in terms of a single linearly independent vector, or function. This is also known as a rank 1 matrix.

Using SVD, we can approximate R by \sigma_1 u_1 v_1^T, which is obtained by truncating the sum after the 1st singular value. This will be a low-rank approximation of R. If R is fully separable in direction and disparity, only the first singular value will be non-zero, indicating the matrix is of rank 1. \sigma_1 u_1 v_1^T will then be a close approximation of R. In general, the closer R is to being separable, the more dominant the first singular value \sigma_1 will be over the other singular values, and the closer the approximation \sigma_1 u_1 v_1^T will be to the original matrix R.

Below is an interactive example that can help you visualize this concept. On the left is the representation of a neuron’s joint tuning function, given by a matrix who’s rows and columns are defined by the “Direction” and “Disparity” properties. The values within each cell of the matrix are an example neuron’s firing rate for the given combination of these properties. On the right, we have altered the representation of the matrix by plotting the firing rate of the neuron across different disparity levels for each direction of lateral motion. The peak firing rate is deemed the neuron’s disparity preference at that particular frontoparallel motion direction. Notice that regardless of motion direction, this neuron maintains a similar, slightly negative disparity preference (preferring objects near the observer). These cell types are predominantly found in the middle temporal (MT) cortex of rhesus macaques, an area of the brain that seems to be specialized for both 2D and 3D motion processing (Smolyanskaya, Ruff, & Born, 2013; Sanada & DeAngelis, 2014).

Using the slider on the bottom of the graph, you are manipulating the example neuron’s separability. As you move the slider to the far right side, representing the largest degree of inseparability for this example, the disparity tuning curves develop a peculiar pattern. That is, for directions of motion that are nearly opposite to one another (~180 degrees apart), the disparity preference of the neuron is flipped. These types of neurons are predominantly found an area that lies just above MT in the cortical hierarchy, the medial superior temporal area (MST). Cells of this type have been deemed “direction-dependent disparity selectivity” (DDD) neurons, and are potentially useful in differentiating self-motion from object-motion, although this is hypothesis has not been critically evaluated (Roy et al., 1992b; Roy & Wurtz, 1990; Yang et al., 2011).

 

Another plot is displayed below that illustrates how the singular values of a matrix will change depending on the cell’s separability. Notice as the cell becomes less separable, the magnitude of the first singular value decreases, and the contribution of other singular values begins to increase. The inset plot illustrates this using a common metric for evaluating separability, known as the degree of inseparability. This is simply the ratio of the first singular value compared to the sum of all the singular values.

Lastly, we’ve provided another interactive graph where the left portion is the same example neuron from the previous graph. On the right, is the prediction generated from \sigma_1 u_1 v_1^T. As you move the slider to the right, increasing the degree of inseparability, you’ll notice the prediction becomes increasingly dissimilar to the actual firing matrix.

Posted in LUCID, Machine Learning, Resources

What is a Computational Cognitive Model?

By Rui Meng and Ayon Sen

A computational cognitive model explores the essence of cognition and various cognitive functionalities through developing detailed, process-based understanding by specifying corresponding computation models.

Sylvain Baillet discusses various aspects of cognitive computation models

Computational model is a mathematical model using computation to study complex systems. Typically one sets up a simulation with the desired parameters and lets the computer run. One then looks at the output to interpret the behavior of the model.

Computational cognitive models are computational models used in the field of cognitive science. Models in cognitive science can be generally categorized into computational, mathematical or verbal-conceptual models. At present, computational modeling appears to be the most promising approach in many respects and offers more flexibility and expressive power than other approaches.

Computational models are mostly process based theories i.e., they try to answer how human performance comes about and by what psychological mechanism.

A general structure of a Neural Network

Among the different computational cognitive models, neural networks are by far the most commonly used connectionist model today. The prevailing connectionist approach today was originally known as parallel distributed processing (PDP). It is an artificial neural network approach that stressed the parallel nature of neural processing, and the distributed nature of neural representation. It is now common to fully equate PDP and connectionism.

Recurrent Layer Neural Network

A general structure for the Recurrent Neural Network

Neural networks were inspired by the structure of human brains. One particular variant of the neural network is called the recurrent neural network. It embodies the philosophy that learning requires remembering. Recurrent neural networks are also becoming a popular computational model for cognitive sciences.

 

Try it yourself: Play with a neural network to see how it works. A Neural Network Playground

Real World Example: Optimizing Teaching

Cognitive Computational models are used to do tasks that are hard or impossible to do with a lab experiments e.g., too many people are involved for the experiment to be feasible. For example, let us assume a teacher wants to teach 100 problems to children in class. But due to time constraint she can only teach 30 problems to the students. The teacher would preferably like to select 30 problems such that the children learn the most i.e., perform well on all 100 problems. Note that, there are 100C30 = 2.93e25 possible question sets. To evaluate the question sets, the teacher would need to teach each dataset to one group of children and evaluate their performance. Let there be 30 children in each class then the total number of children required would be 30 X 2.93e25 = 8.81e26 which is large. This makes identifying an optimal question set infeasible.

This task can be simplified if a cognitive computational model of children for that particular task can be devised. Then the teacher only needs to test the question sets on the cognitive model to figure out which one is the best. This saves a lot of time and is feasible.

Additional Resources:

Posted in LUCID Library, Resources

Martina Rau on Learning with Visuals

Lucid Faculty, Educational Psychology Professor, Director of Learning, Representations, & Technology Lab as well as Computer Sciences Affiliate, Martina Rau is interested in educational technologies to support more effective learning with visuals.

While we generally think of visuals as helpful tools, Rau highlights the fact that visuals can be confusing if  students do not know how to interpret visuals, construction visuals, or make connections among multiple visuals.

She created a video blog, Learning with Visuals, that aims to translate the research conducted on campus and share findings in a more accessible and useful approach to teaching and learning with visuals. She aims to help students looking for effective study strategies, parents wanting to help with children learn, teachers who notice that students often have difficulties understanding visuals, and of course prospective researchers in educational psychology.

 Involving Students from Multiple Disciplines in Research

 

Here she discusses the importance of students from multiple disciplines learning from each other and gaining an understanding of what it takes to conduct research in a different field.

 

Collaborating with Visuals

 

Here she discusses how educational technologies can support students to more effectively collaborate with visuals.

 

 

 

Translating Research into Everyday Language

 

Here she discusses the importance of finding new ways to learn and teach with visuals as well as how she plans to make her research accessible to non-scientists, such as teachers, parents, and students.

Posted in LUCID Library, Resources

Poolmate: Pool-Based Machine Teaching

Poolmate provides a command-line interface to algorithms for searching teaching sets among a candidate pool. Poolmate is designed to work with any learner which can be communicated with through a file-based API.

Developed by Ara Vartanian (aravart@cs.wisc.edu), Scott Alfeld (salfeld@amherst.edu), Ayon Sen (ayonsn@cs.wisc.edu) and Jerry Zhu (jerryzhu@cs.wisc.edu), poolmate details can be found on aravart or github

If machine learning is to discover knowledge, then machine teaching is to pass it on.

-Jerry Zhu

Machine teaching is an inverse problem to machine learning. Given a learning algorithm and a target model, machine teaching finds an optimal (e.g. the smallest) training set. For example, consider a “student” who runs the Support Vector Machine learning algorithm. Imagine a teacher who wants to teach the student a specific target hyperplane in some feature space (never mind how the teacher got this hyperplane in the first place). The teacher constructs a training set D=(x1,y1) … (xn, yn), where xi is a feature vector and yi a class label, to train the student. What is the smallest training set that will make the student learn the target hyperplane? It is not hard to see that n=2 is sufficient with the two training items straddling the target hyperplane. Machine teaching mathematically formalizes this idea and generalizes it to many kinds of learning algorithms and teaching targets. Solving the machine teaching problem in general can be intricate and is an open mathematical question, though for a large family of learners the resulting bilevel optimization problem can be approximated.

Machine teaching can have impacts in education, where the “student” is really a human student, and the teacher certainly has a target model (i.e. the educational goal). If we are willing to assume a cognitive learning model of the student, we can use machine teaching to reverse-engineer the optimal training data — which will be the optimal, personalized lesson for that student. We have shown feasibility in a preliminary cognitive study to teach categorization. Another application is in computer security where the “teacher” is an attacker and the learner is any intelligent system that adapts to inputs. More details are from this research overview: Machine Teaching

Machine teaching, the problem of finding an optimal training set given a machine learning algorithm and a target model. In addition to generating fascinating mathematical questions for computer scientists to ponder, machine teaching holds the promise of enhancing education and personnel training.

-Jerry Zhu in Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education

Jerry Zhu is a LUCID faculty member and CS professor, Ayon Sen is a LUCID trainee and CS graduate student.

Poolmate is based upon work supported by the National Science Foundation under Grant No. IIS-0953219. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Posted in Resources

LUCIDtalks: Machine Learning with Ayon Sen

As part of our LUCID program, the graduate students meet weekly and discuss topics that are interesting to both the computation and behavioral graduate students.

In this video Ayon provides an overview of Machine Learning in 20 minutes. Ayon explains linear and non-linear learners, the overfitting vs. bias-variance trade off and provides resources for those interested in learning more about machine learning.

Posted in LUCID, LUCID Library, Machine Learning

Apply to LUCID!

We are currently accepting applications for Fall 2018!

Please read at our LUCID overview and diversity statement

Details for how to apply to the LUCID program at UW–Madison:

1. Apply to UW-Madison at Graduate School
2. Apply to a Ph.D. program in one of the four core departments in LUCID:

Psychology

Educational Psychology

Electrical and Computer Engineering (ECE)

Computer Sciences (CS)

3. Apply to LUCID:

Email your statement of interest to ceiverson(at)wisc(dot)edu

In your statement of interest please answer the following:

  • Please tell us about your background and how your experience contributes to the LUCID diversity mission.
  • Describe your interest and background in research that connects computation, cognition, and learning.
  • Finally, what do you hope to gain from being a LUCID trainee?
Posted in LUCID

Watch Our LUCID Video

Our new LUCID video demonstrates our collaborative learning approach to tackling the real-world projects.

This video highlights the LUCID program and focusses on four pedagogical approaches: interdisciplinary groups, practical problems, prof-and-peer mentoring, and science communication.

Posted in LUCID

HAMLET

HAMLET  (Human, Animal, and Machine Learning: Experiment and Theory)

HAMLET is an interdisciplinary proseminar series that started in 2008. The goal is to provide behavioral and computational graduate students with a common grounding in the learning sciences. Guest speakers give a talk each week, followed by discussions. It has been an outlet for fresh research results from various projects. Participants are typically from Computer Sciences, ECE, Psychology, Educational Psychology as well as other parts of the campus. Multiple federal research grants and publications at machine learning and cognitive psychology venues have resulted from the interactions of HAMLET participants.

Meetings: Fridays 3:45 p.m. – 5 p.m. Berkowitz room (338 Psychology Building)

Subscribe/Unsubscribe: HAMLET mailing list

Fall 2018 Schedule:

Sep 14, Tim Rogers

Investigating the covariance structure of widely-held false beliefs

ABSTRACT: It has become increasingly clear that our society is suffering a crisis of false belief. Across a variety of domains–including science, health, economics, politics, history, and current events–there often exists vociferous disagreement about what ought to be matters of fact. I’d like to know whether cognitive science and machine learning together can help us to understand why this is happening. In prior work we have studied the formation of false beliefs in small well-controlled lab studies with no real-world stakes. In this talk I will describe some preliminary work looking at real false beliefs occurring in the wild. In collaboration with a summer PREP student and colleagues in Psychology and in the School of Journalism, we generated a long list of incorrect but putatively widely-held beliefs spanning many different knowledge domains. Using Amazon Mechanical Turk, we asked people about the degree
to which they endorsed or rejected the belief. We also collected a variety of data about each respondent, including sociodemographic data, political leanings, media trust, IQ, and so on. The aim was to measure how susceptibility to false beliefs pattern across individuals, and to understand what properties of individuals predict susceptibility to which kinds of false beliefs. Our early results provide some provocative evidence contradicting some straight-forward hypotheses about false belief formation, but there is a lot more work to be done. We would like to connect with others interested in these problems from a machine-learning-and-social-media perspective, and in particular would like to consider ways of using social network analytics to assess the degree to which media consumption may be causing the patterns we observe.

Sep 21, LUCID faculty meeting – no HAMLET

Sep 28,

Oct 5,

Oct 12,

Oct 19, LUCID faculty meeting – no HAMLET

Oct 26,

Nov 2,

Nov 9,

Nov 16,

Nov 23,

Nov 30,

Dec 7,

Dec 14,

 

 

 

 


Spring 2018 Schedule:

Feb 2, Blake Mason

Title: Low-Dimensional Metric Learning with Application to Perceptual Feature Selection

Abstract: I will discuss recent work investigating the theoretical foundations of metric learning, focused on four key topics: 1) how to learn a general low-dimensional (low-rank) metrics as well as sparse metrics; 2) upper and lower (minimax) bounds on the generalization error; 3) how to quantify the sample complexity of metric learning in terms of the dimension of the feature space and the dimension/rank of the underlying metric; 4) the accuracy of the learned metric relative to the underlying true generative metric. As an application of these ideas, I will discuss work with collaborators in Educational Psychology that applies metric learning for perceptual feature detection in non-verbally mediated cognitive processes.

Feb 9, Adrienne Wood

Form follows function: Emotion expressions as adaptive social tools

Emotion expressions convey people’s feelings and behavioral intentions, and influence, in turn, the feelings and behaviors of perceivers. I take a social functional approach to the study of emotion expression, examining how the physical forms of emotion expression are flexible and can be adapted to accomplish specific social tasks. In this talk, I discuss two lines of research, the first of which applies a social functional lens to smiles and laughter. I present work suggesting that smiles and laughter vary in their physical form in order to achieve three distinct tasks of social living: rewarding others, signaling openness to affiliation, and negotiating social hierarchies. My approach, which generalizes to other categories of expressive behavior, accounts for the form and context of the occurrence of the expressions, as well as the nature of their influence on social partners. My second line of research examines how cultural and historical pressures influence emotional expressiveness. Cultures arising from the intersection of many other cultures, such as in the U.S., initially lacked a clear social structure, shared norms, and a common language. Recent work from my collaborators and myself suggests such cultures increase their reliance on emotion expressions, establishing a cultural norm of expressive clarity. I conclude by presenting plans to quantify individual differences in the tendency to synchronize with and accommodate to the emotion expressive style of a social partner, and relate those differences to people’s social network positions. Given the important social functions served by emotion expression, I suggest that the ability to use it flexibly is associated with long-term social integration.

Feb 16 Nils Ringe, Professor, Political Science

Speaking in Tongues: The Politics of Language and the Language of Politics in the European Union

Politics in the European Union primarily takes place between political actors who do not share a common language, yet this key feature of EU politics has not received much attention from political scientists. This project investigates if and how multilingualism affects political processes and outcomes. Its empirical backbone is a series of in-depth interviews with almost 100 EU policy-makers, administrators, translators, and interpreters, but it also involves at least two potential components where political science, linguistic, and computational approaches overlap. The first involves the analysis of oral legislative negotiations in the European Parliament, where non-native English speakers interact using English as their shared language, and in native-English speaking parliamentary settings (in Ireland, Scotland, and/or the UK), to determine if “EU English” differs syntactically and semantically from “regular” English. The expectation is that speech in the EP is simpler, more neutral, and more utilitarian. The second component involves the identification of languages spoken in EP committee meetings using computational methods, to determine the language choices members of the EP make.

Feb 23 Andreas Obersteiner

Does 1/4 look larger than 1/3? The natural number bias in comparing symbolic and nonsymbolic fractions

When people compare the numerical values of fractions, they are often more accurate and faster when the larger fraction has the larger natural number components (e.g., 2/5 > 1/5) than when it has the smaller components (e.g., 1/3 > 1/4). However, recent studies produced conflicting evidence of this “natural number bias” when the comparison problems were more complex (e.g., 25/36 vs. 19/24). Moreover, it is unclear whether the bias also occurs when fractions are presented visually as shaded parts of rectangles rather than as numerical symbols. I will first present data from a reaction time study in which university students compared symbolic fractions. The results suggest that the occurrence and strength of the bias depends on the specific type of comparison problems and on people’s ability to activate overall fraction magnitudes. I will then present preliminary data from an eye tracking study in which university students compared rectangular fraction visualizations. Participants’ eye movements suggest that the pure presence of countable parts encouraged them to use unnecessary counting strategies, although the number of countable parts did not bias their decisions. The results have implications for mathematics education, which I will discuss in the talk.

Mar 2 Nicole Beckage, University of Kansas

Title: Multiplex network optimization to capture attention to features

Abstract: How does attention to features and current context affect people’s search in mental and physical spaces? I will examine how procedures for optimally searching through “multiplex” networks — networks with multiple layers or types of relationships — capture human search and retrieval patterns. Prior work on semantic memory, people’s memory for facts and concepts, has primarily focused on modeling similarity judgments of pairs of words as distances between points in a high-dimensional space (e.g., LSA by Laudauer et al, 1998; Word2Vec by Mikolov et al. 2013). While these decisions seem to accurately account for human similarity in some contexts, it’s very difficult to interpret high dimensional spaces, making it hard to use such representations for scientific research. Further, it is difficult to adapt these spaces to a specific context or task. Instead, I define a series of individual feature networks to construct a multiplex network, where each network in the multiplex captures a “sense” or type of similarity between items. I then optimize the “influence” of each of these feature networks within the multiplex framework, using real world search behavior on a variety of tasks. These tasks include semantic memory search in a cognitive task and information search in Wikipeida. The resulting weighting of the multiplex can capture aspects of human attention and contextual information in these diverse tasks. I explore how this method can provide interpretability to multi-relational data in psychology and other domains by developing an optimization framework that considers not only the presence or absence of relationships but also the nature of the relationships. While I focus on applications of semantic memory, I discuss mathematical proofs and simulation experiments that apply more generally to optimization problems in the multiplex network literature.

Mar 9 Psychology visit day Data Blitz! (WID Orchard Room)

The UW-Madison Psychology department will be hosting its second annual data blitz for prospective graduate students in the Orchard Room of the WID. The data blitz will feature speakers from across the department presenting their research in an easily digestible format. Each talk will be 5 minutes long with an additional 2 minutes for questions at the end of each talk. All are welcome to attend. Below you will find a list of the speakers and the titles of their talks:

    • Rista Plate “Unsupervised learning shifts emotion category boundaries”
    • Ron Pomper “Familiar object salience affects novel word learning”
    • Elise Hopman “Measuring meaning alignment between different languages”
    • Anna Bartel “How diagrams influence students’ mental models of mathematical story problems”
    • Aaron Cochrane “Chronic and phasic interactions between video game playing and addiction”
    • Pierce Edmiston “Correlations between programming languages and beliefs about programming”
    • Chris Racey “Neural processing underlying color preference judgments”
    • Sofiya Hupalo “Corticotropin-releasing factor (CRF) modulation of frontostriatal circuit function”

Mar 16 Varun Jog, Assistant Professor, ECE

Title: Mathematical models for social learning

Abstract: Individuals in a society learn about the world and form opinions not only through their own experiences, but also through interactions with other members in the society. This is an incredibly complex process, and although it is difficult to describe it completely using simple mathematical models, valuable insights may be obtained through such a study. Such social learning models have turned out to be a rich source of problems for probabilists, statisticians, information theorists, and economists. In this talk, we survey different social learning models, describe the necessary mathematical tools to analyze such models, and give examples of results that one may prove through such an approach.

Mar 23 Josh Cisler, Assistant Professor, Department of Psychiatry

Title: Real-time fMRI neurofeedback using whole-brain classifiers with an adaptive implicit emotion regulation task: analytic considerations

Most fMRI neuroimaging studies manipulate a psychological or cognitive variable (e.g., happy versus neutral faces) and observe the manipulations impact on brain function (e.g., amygdala activity is greater for happy faces). As such, the causal inferences that can be drawn from these studies is the effect of cognition on brain function, and not the effect of brain function on cognition. Real-time fMRI refers to processing of fMRI data simultaneous with data acquisition, enabling feedback of current brain states to be presented back to the participant in (near) real-time, thus enabling the participant to use the feedback signals to modify brain states. We are conducting an experiment using real-time fMRI neurofeedback where the feedback signal consists of classifier output (hyperplane distances) from a SVM trained on all grey matter voxels in the brain. The feedback signal is embedded within a commonly used implicit emotion regulation task, such that the task becomes easier or harder depending on the participant’s brain state. This type of ‘closed loop’ design allows for testing whether manipulations of brain state (via feedback) have a measurable impact on cognitive function (task performance). The purpose of this presentation will be to present the experimental design and resulting data properties for the purpose of obtaining feedback and recommendations for understanding and analyzing the complex dynamical systems relations between the feedback signal, brain state, and task performance.

Mar 30 (no meeting, spring break)

Apr 6 Student Research Presentations

Mini talk 1: Ayon Sen, Computer Sciences

For Teaching Perceptual Fluency, Machines Beat Human Experts

In STEM domains, students are expected to acquire domain knowledge from visual representations. Such learning requires perceptual fluency: the ability to intuitively and rapidly see what concepts visuals show and to translate among multiple visuals. Instructional problems that enhance perceptual fluency are highly influenced by sequence effects. Thus far, we lack a principled approach for identifying a sequence of perceptual-fluency problems that promote robust learning. Here, we describe a novel educational data mining approach that uses machine learning to generate an optimal sequence of visuals for perceptual-fluency problems. In a human experiment realted to chemistry, we show that a machine-generated sequence outperforms both a random sequence and a sequence generated by a human domain expert. To our knowledge, our study is the first to show that an educational data mining approach can yield desirable difficulties for perceptual learning.

Mini talk 2: Evan Hernandez, Ara Vartanian, Computer Sciences

Block-based programming environments are popular in computer science education, but the click-and-drag style of these environments render them inaccessible by students with motor impairments. Vocal user interfaces (VUIs) offer a popular alternative to traditional keyboard and mouse interfaces. We design a VUI for Google Blockly in the traditional Turtle/LOGOS setting and discuss the relevant design choices. We then investigate augmentations to educational programming environments. In particular, we describe a method of program synthesis for completing the partial or incorrect programs of students, and ask how educational software may leverage program synthesis to enhance student learning.

Apr 13 (no meeting)

Apr 20

Rob Nowak: All of Machine Learning

Apr 27 (STARTING AT 4PM instead of 3:45pm). Tyler Krucas, The Wisconsin Gaming Alliance.

An Industry Perspective on Data in Game Design and Development

The Wisconsin game development industry offers a surprisingly comprehensive cross section of the types of individuals and teams that develop video games. This includes everything from studios that collaborate on AAA titles such as Call of Duty and Bioshock Infinite, to studios that work largely on mobile or free-to-play games, to studios that primarily work on educational games or games for impact. In all cases, data collection and analysis is an important tool in every step of the game development process. However, the scale of the data collected and its use can vary dramatically from developer to developer. In my talk, I will provide an overview of the the game development ecosystem in Wisconsin, as well as examples of the different types data collection and use practices found in the regional industry. Critically, I’ll frame this discussion in the context of possible links with the HAMLET group – in terms of possible sources of data to address fundamental questions surrounding human learning or behavior as well as possible collaborations.

Fall 2017 Schedule:

Sept 15, Virtual and Physical: Two Frames of Mind, Bilge Mutlu (CS)

In creating interactive technologies, virtual and physical embodiments are often seen as two sides of the same coin. They utilize similar core technologies for perception, planning, and interaction and engage people in similar ways. Thus, designers consider these embodiments to be broadly interchangeable and choice of embodiment to primarily depend on the practical demands of an application. In this talk, I will make the case that virtual and physical embodiments elicit fundamentally different frames of mind in the users of the technology and follow different metaphors for interaction. These differences elicit different expectations, different forms of engagement, and eventually different interaction outcomes. I will discuss the design implications of these differences, arguing for different domains of interaction serving as appropriate context for virtual and physical embodiments.

October 13, Learning semantic representations for text: analysis of recent word embedding methods, Yingyu Liang (CS)

Recent advances in natural language processing build upon the approach of embedding words as low dimensional vectors. The fundamental observation that empirically justifies this approach is that these vectors can capture semantic relations. A probabilistic model for generating text is proposed to mathematically explain this observation and existing popular embedding algorithms. It also reveals surprising connections to classical notions such as Pointwise Mutual Information in computational linguistics, and allows to design novel, simple, and practical algorithms for applications such as embedding sentences as vectors.

October 20, Vlogging about research, Martina Rau (Ed Psych)

October 27, LUCID faculty meeting, No large group meeting

November 3, A Discussion of Open Science Practices, Martha W. Alibali (Psych)

This HAMLET session will be a discussion of open-science practices, led by Martha Alibali. We will start with brief discussion of the “replication crisis” and “questionable research practices”. We will then discuss solutions, including better research practices, data sharing and preregistration. Please read at least some of the provided papers, and come prepared to ask questions and share your experiences.

Replication crisis paper

Questionable Research Practices (QRPs) and solutions paper paper

Data sharing paper http://deevybee.blogspot.co.uk/2014/05/data-sharing-exciting-but-scary.html

Preregistration paper pic https://www.psychologicalscience.org/observer/seven-selfish-reasons-for-preregistration

November 10, Systematic misperceptions of 3D motion explained by Bayesian inference, Bas Rokers (Psych)

Abstract: Over the years, a number of surprising, but seemingly unrelated errors in 3D motion perception have been reported. Given the relevance of accurate motion perception to our everyday life, it is important to understand the cause of these perceptual errors. We considered that these perceptual errors might arise as a natural consequence of estimating motion direction given sensory noise and the geometry of 3D viewing. We characterized the retinal motion signals produced by objects moving along arbitrary trajectories through three dimensions and developed a Bayesian model of perceptual inference. The model predicted a number of known errors, including a lateral bias in the perception of motion trajectories, and a dependency of this bias on stimulus contrast and viewing distance. The model also predicted a number of previously unknown errors, including a dependency of perceptual bias on eccentricity, and a surprising tendency to misreport approaching motion as receding and vice versa. We then used standard 3D displays as well as a virtual reality (VR) headsets to test these predictions in naturalistic settings, and established that people make the predicted errors. In sum, we developed a quantitative model of 3D motion perception and provided a parsimonious account for a range of systematic perceptual errors in naturalistic environments.

November 17, Total variation regression under highly correlated designs, Becca Willett (ECE)

Abstract: I will describe a general method for solving high-dimensional linear inverse problems with highly correlated variables. This problem arises regularly in applications like neural decoding from fMRI data, where we often have two orders of magnitude more brain voxels than independent scans. Our approach leverages a graph structure that represents connections among voxels in the brain. This graph can be estimated from side sources, such as diffusion-weighted MRI, or from fMRI data itself. We will explore the underlying models, computational methods, and initial empirical results. This is joint work with Yuan Li and Garvesh Raskutti.

November 24 Thanksgiving Holiday

December 1, Micro-(Shape-And-Motion)-Scopes, Mohit Gupta (CS)

Imagine a drone looking for a safe landing site in a dense forest, or a social robot trying to determine the emotional state of a person by measuring her micro-saccade movements and skin-tremors due to pulse beats, or a surgical robot performing micro-surgery inside the body. In these applications, it is critical to resolve fine geometric details, such as tree twigs; to recover micro-motion due to biometric signals; and the precise motion of a robotic arm. Such precision is more than an order-of-magnitude beyond the capabilities of traditional vision techniques. I will talk about our recent work on designing extreme (micro) resolution 3D shape and motion sensors using unconventional, but low-cost optics, and computational techniques. These methods can measure highly subtle motions (< 10 microns), and highly detailed 3D geometry (<100 microns). These sensors can potentially detect a persons pulse or micro-saccade movements, and resolve fine geometric details such as a facial features, from a long distance.

December 8, Influence maximization in stochastic and adversarial settings, Po-Ling Loh (ECE)

We consider the problem of influence maximization in fixed networks, for both stochastic and adversarial contagion models. Such models may be used to model infection spreads in epidemiology, as well as the diffusion of information in viral marketing. In the stochastic setting, nodes are infected in waves according to linear threshold or independent cascade models. We establish upper and lower bounds for the influence of a subset of nodes in the network, where the influence is defined as the expected number of infected nodes at the conclusion of the epidemic. We quantify the gap between our upper and lower bounds in the case of the linear threshold model and illustrate the gains of our upper bounds for independent cascade models in relation to existing results. In the adversarial setting, an adversary is allowed to specify the edges through which contagion may spread, and the player chooses sets of nodes to infect in successive rounds. Our main result is to establish upper and lower bounds on the regret for possibly stochastic strategies of the adversary and player. This is joint work with Justin Khim (UPenn) and Varun Jog (UW-Madison).

For other events check out our calendar: Seminar and Events  This content and updates can be found: HAMLET


HAMLET Archives

Fall 2015 archive
Fall 2012 archive
Fall 2011 archive
Spring 2011 archive
Fall 2010 archive
Fall 2009 archive
Spring 2009 archive
Fall 2008 archive

 

Posted in Events

eLUCID8

eLUCID8-02

THANK YOU to all who contributed to the success of eLUCID8!

Data Science and Human Behavior  |  In the Lab and In the Wild

Wisconsin Institute for Discovery, Madison, WI
August 14th – August 15th, 2017

eLUCID8 featured interactive presentations, talks, and roundtables intended to discuss LUCID related projects and potential collaborations with government agencies, non-profits and industry groups.

Scroll down or click for the Agenda.

For communication during the conference (your questions, announcements and evaluation links) please sign up for eLUCID on Slack: https://join.slack.com/t/elucid8/signup

We truly appreciate and value your feedback. Please let us know what your experience was like at eLUCID8 in the following short surveys: Monday eLUCID8, Tuesday eLUCID8. Our students also respect and honor your feedback for their presentations. Please let us know what you think: LUCID Presentations

 

Keynote Speakers

bobmankoff

Monday, August 14th
6pm

Bob Mankoff
Former Cartoon Editor of The New Yorker,
Present Cartoon and Humor Editor of Esquire

“Crowdsourcing Humor”

Humor is traditionally at the hands of its author. What happens when the audience picks the punchline?

Each week, on the last page of the magazine, The New Yorker provides a cartoon in need of a caption. Readers submit captions, the magazine chooses three finalists, readers vote for their favorites. It’s humor—crowdsourced—and with more than 3 million submissions provided by 600,000 participants, it provides tremendous insight as to what makes us laugh.

In a fast-paced and funny talk, Bob Mankoff, The New Yorker‘s cartoon editor, will analyze the lessons we learn from crowdsourced humor. Along the way, he’ll explore how cartoons work (and sometimes don’t); how he makes decisions about what cartoons to include; and what crowds can tell us about a good joke.

 


mozer-largeTuesday, August 15th
4pm
Michael C. Mozer
Department of Computer Science 
and Institute of Cognitive Science
University of Colorado

“Amplifying Human Capabilities on Visual Categorization Tasks”

 

 

We are developing methods to improve human learning and performance on challenging visual categorization tasks, e.g., bird species identification, diagnostic dermatology. Our approach involves inferring _psychological embeddings_ — internal representations that individuals use to reason about a domain. Using predictive cognitive models that operate on an embedding, we perform surrogate-based optimization to determine efficient and effective means of training domain novices as well as amplifying an individual’s capabilities at any stage of training. Our cognitive models leverage psychological theories of: similarity judgement and generalization, contextual and sequential effects in choice, attention shifts among embedding dimensions. Rather than searching over all possible training policies, we focus our search on policy spaces motivated by the training literature, including manipulation of exemplar difficulty and the sequencing of category labels. We show that our models predict human behavior not only in the aggregate but at the level of individual learners and individual exemplars, and preliminary experiments show the benefits of surrogate-based optimization on learning and performance.

This work was performed in collaboration with Brett Roads at the University of Colorado.

Michael Mozer received a Ph.D. in Cognitive Science at the University of California at San Diego in 1987. Following a postdoctoral fellowship with Geoffrey Hinton at the University of Toronto, he joined the faculty at the University of Colorado at Boulder and is presently an Professor in the Department of Computer Science and the Institute of Cognitive Science. He is secretary of the Neural Information Processing Systems Foundation and has served as chair of the Cognitive Science Society. He is interested both in developing machine learning algorithms that leverage insights from human cognition, and in developing software tools to optimize human performance using machine learning methods.

 


Agenda

Monday August 14th

9:00-9:30      Welcome to eLUCID8 from our LUCID Director, Tim Rogers
9:30-10:30     Learning in Childhood with Jenny Saffran, Ed Hubbard and Chuck Kalish
10:30-10:45   Break
10:45-Noon    Machine Learning & Human Behavior with Varun Jog, Dimitris Papailiopoulos, Joe Austerweil and Jerry Zhu
Noon-1:30      LUNCH
1:30-2:15        Making Sense of the Ineffable with Karen Schloss and Paula Niedenthal
2:15-2:45       Data Science in the Wild with our Keynote Speakers Bob Mankoff and Michael Mozer
2:45-3:00       Break
3:00-4:15        Data Blitz
4:15-5:00       Poster and Movie Session
5:00-6:00      Posters, Mingling and Cash Bar
6:00-7:00      KEYNOTE: Bob Mankoff

Tuesday August 15th

9:30-10:30     Science Communication Panel with Veronica Reuckert, host of “Central Time” on WPR, Jordan Ellenberg, author of How Not to be Wrong: The Power of Mathematical Thinking, and Mark Seidenberg, author of Language at the Speed of Sight: How We Read, Why So Many Can’t, and What Can Be Done About It

10:30-12:00   LUCID Science Talks
12:00-1:00     LUNCH
1:00-1:45        University-Industry Partnerships  from Susan LaBelle, UW–Madison Office of Corporate Relations
1:45-2:00       Break
2:00-3:20      Data Science in the Wild with City of Madison, 4W, POLCO, and Lands’ End
3:20-4:00      Breakouts and Group Discussion
4:00-5:00      KEYNOTE: Michael C. Mozer

 

Posted in Events

CogSci 2018 Poster Design Contest for $100

Would you like to see your artwork displayed across the nation and the globe? Support a prestigious international scientific conference? Have a chance to win a cash prize of $100? If so, please submit your poster design for CogSci 2018 by June 12th!

The Cognitive Science Society is the world’s largest academic society focusing on how the mind works. In 2018 the CogSci annual meeting will be held in Madison, and we are seeking original artwork to advertise the conference. The conference title is ‘Changing Minds’ a focus that brings together disciplines such as cognitive psychology, machine learning, education, development, and neuroscience. Themes for the conference include:

changing minds: connecting human and machine learning
changing brains: neural mechanisms of cognitive change
changing knowledge: cognition, education, and technology
changing society: cognition, persuasion, and politics

We would like a poster that captures these themes with a compelling graphic, together with text providing further details about the event.

Here are examples from previous conferences:

cogsciposter_2011_smallercogsci_2012CogSci2015cogsci_2015_poster-small2cogsci_2016CogSci2017-Poster

 

 

 

 

 

 

 

 

 

 

For the poster text use llorum ipsum or dummy text to demonstrate font and color you recommend to work with your design elements. Please send PDF files to ceiverson@wisc.edu.

The competition due date is June 12. A shortlist will be determined via crowd-sourced adaptive sampling using the NEXT system, and the conference committee will select a winner by June 15.

Posted in Uncategorized