Human, Animal, and Machine Learning: Experiment and Theory
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.
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.
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.
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.
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.
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:
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.
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.
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.
Rob Nowak: All of Machine Learning
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.
Martha Alibali (firstname.lastname@example.org), Tim Rogers (email@example.com), Jerry Zhu (firstname.lastname@example.org)