|Date and Location||Speaker||Topic||Reading|
|1/15 @ 10:00, ENVD 201||Paul Maglio, UC Merced||Service science and human-centered service systems|
|1/15 @ 12:00, Muenzinger D428||Marie Banich, Psychology and Neuroscience, CU||Drug abuse and the brain
In this talk, I will discuss prior work that I have performed with colleagues at CU Denver on alterations in brain systems found in youth and adults who abuse substances. Following that I will discuss a new national project, the Adolescent Brain Cognitive Development (ABCD) project, which is designed to examine the development of brain systems related to substance abuse, including those responsible for cognitive control and reward processing. This project will also involve collecting a host of other data on cognitive, emotional, and social development. The project will be longitudinal in nature, spanning a 10-year time period, with data collected on over 10,000 individuals at close to 20 sites across the country. CU Boulder has been selected as one of the institutions to be involved in this landmark study, and will be doing so through a joint collaboration between the Institute of Cognitive Science and Institute for Behavioral Genetics. In particular, we, along with three other institutions, will be collecting data on twin pairs to disentangle genetic versus environmental contributions to drug abuse. The on-going design and implications of this study will be discussed.
|1/21 @ 10:00, ENVD 201||Scott Robertson, University of Hawaii||Political decision making in a mixed media world|
|1/22 @ 12:00, Muenzinger D428||Christine Brennan, SLHS, CU||Phonological grain size
sensitivity in auditory cortex is
related to reading skill
Previous evidence reveals regions within the auditory cortex selectively activate based on phonological grain size (number of speech sounds presented) (DeWitt and Rauschecker, 2012). Here, we employed direct testing of phonological grain size in order to confirm and further delineate this organization. While reading impairment is associated with deficits in phonological skill, including difficulty isolating phonemes (Shaywitz et al., 1998; Temple et al., 2001), it is unknown if selective activation for grain size is related to reading skill. Since isolation of small grain units is problematic in dyslexia, reading skill may be related to selectivity of the auditory cortex, especially for small grain stimuli. In this study, we examined the relationship between grain size selectivity and reading skill. Using functional magnetic resonance imaging (fMRI), we studied 20 typical adults under different grain size conditions. Stimuli included speech with one, two, or four phonemes. Stimuli were presented in blocks of seven trials from the same condition. During scanning, an active listening task was completed to ensure participants were awake and attending to stimuli. A timed measure of reading skill (TOWRE) was completed outside the scanner (Torgesen et al., 1999). Using SPM8, we contrasted the phonological grain size conditions. We also completed ROI analyses to examined brain-behavior correlations between activation strength and reading skill. Specifically, we compared the difference in activation for the small versus the large conditions with reading skill. We found significantly greater activation in bilateral middle superior temporal gyrus (m-STG) for 1-2 speech sounds (small grain size) and greater activation in the left middle temporal gyrus (MTG), right anterior STG, and right posterior STG for 4 speech sounds (large grain size). Brain-behavior analyses revealed significant correlation of reading skill with activation for the small compared to large grain size condition (p < .01). Adults who had higher reading skill activated the mid-STG more than lower skill readers. In addition, adults with higher skill engaged the left MTG less than lower skill readers for the large compared to small grain size condition, although this trend was not statistically significant. These results further delineate the organization of the temporal cortex, revealing not only selective activation related to phonological grain size, but also that selectivity is related to reading skill. Higher reading skill was associated with stronger engagement of the m-STG, suggesting that sensitivity to single phonemes may be linked to better reading. Importantly, these results provide a link between phonological grain size sensitivity in the brain and reading skill, consistent with behavioral studies that have shown strong correlations between phoneme awareness and reading ability. The findings have implications for developmental dyslexia, a condition often associated with deficits in phonology. Future studies should investigate phonological organization in children with and without dyslexia to determine if differences in grain size representation underlie the disability.
|1/29 @ 12:00, Muenzinger D428||Thomas Hills, University of Warwick||Control and
Representation in Cognitive Search
The trade-off between exploration and exploitation is a ubiquitous feature of animal life. Neuromolecular and behavioral evidence from across species suggest the ability to mediate this trade-off originated approximately 700 million years ago in a spatial foraging behavior called area-restricted search, allowing animals to modulate foraging behavior in response to resource density. Further evidence suggests this architecture was later exapted in vertebrates to modulate attention and search in internal representations: to maintain goals in the absence of external stimuli and to look, so to speak, before we leapt. In this talk, I will present research from my lab investigating internal search using comparisons of computational models that combine quantitative representations of internal environments with control processes that can navigate these environments. The representations are derived from multiple sources, including unsupervised learning from natural language corpora, social networks, and problem representations based on solution similarity. The control processes consist of random walks and multi-stage models that include dynamic transitions between representations. Over a series of studies, this work suggests that internal search is a form of area-restricted search consistent with the principles of optimal foraging in space and is governed by individual differences in executive control. This approach offers insights into cognitive control not offered by standard proof-of-principle approaches and helps to develop both an evolutionary and process-based account of cognitive control. I will conclude by presenting applications and questions posed by this work for age-related cognitive decline and changes in lexical representation across the lifespan.
|2/5 @ 12:00, Muenzinger D428||Sidney D'Mello, Notre Dame||Between Boredom and
Bewilderment: Coordinating Feeling and
Thinking to Optimize Learning
We study the complex interplay between cognitive and affective states (e.g., confusion, frustration, mind wandering) during learning and leverage insights to develop technologies that coordinate what learners think and feel in addition to what they know and do. Our basic research investigates how complex mental states arise and influence learning via an analysis of interactions among the learners themselves, the learning content, and the learning activity. We then use signal processing and machine learning techniques to build computational models of mental states from facial features, body movements, peripheral physiology, eye gaze, and contextual cues in a variety of digital learning environments, both in the lab and in the wild. Finally, we close the loop by embedding our models in affect- and attention- aware technologies that increase engagement and learning by dynamically adapting to cognition and emotion. This talk will discuss our theoretical foundations, summarize key findings, and discuss our vision for the future of cyberlearning.
|2/8 [Monday] @ 15:30, ATLAS 229 (Schnabel board room)||Kyle Rector, University of Washington||Enhancing Quality of
Life for People who are Blind or Low
Vision Using Computing Technology
Activities such as exercise and participating in the community enhance quality of life, but they are often not accessible to people who are blind or low vision. For example, people who are visually impaired are more likely to be obese, less likely to attend art museums, and less likely to be employed than people who are sighted. The goal of my research is to design, develop, and evaluate systems that enhance the quality of life for people who are blind or low vision.
In this talk, I will first describe a project with in-depth interviews and surveys that inform the design of future eyes-free exercise technologies. I will then describe a solution that I designed and developed called Eyes-Free Yoga, which is an accessible yoga exergame that provides auditory instructions and feedback. Next I will describe the design and evaluation of Eyes-Free Art, an audio proxemic interface to help explore 2D paintings. Finally, I will discuss the opportunities for developing technologies to enhance quality of life for people who are blind or low vision including exercise in the wild, working with children, and improving experiences with art and community.
|2/9 [Tuesday] @ 12:00, Muenzinger D428 (?)||Steven Bethard||Parsing the Language of
Getting a computer to understand the timeline underlying a written narrative is a critical component of tools for review of patient medical histories, analysis of intelligence reports, and tests of reading comprehension. But human language is rarely explicit in the way that would be most convenient for a computer, and events, times, and temporal relations are often implicit, left to be inferred by the reader. In this talk, I will first present a typical computational methodology for constructing timelines from the explicit and implicit cues of language: a series of supervised machine learning components trained on example texts whose timelines have been annotated manually by humans. Then I will show how we can improve this approach by analyzing big data that has not been annotated by humans but nonetheless reveals patterns in how humans talk about time. Finally, I will present an alternative approach to inferring timelines from text that achieves better generalization through modeling the incremental and compositional nature of the language of time.
|2/10 [Wednesday] @ 14:00, ENVD 201 (the Garage, located on 2nd floor of ENVD through door at top of stairs)||Manyan Goel, University of Washington Seattle||Teaching Old Sensors New
A fundamental issue with any new sensing technology is the deployment burden. The new technology often comes with a rigid and perhaps expensive list of pre-requisites; and affects the overall cost-effectiveness, deployability, and accessibility of the solution. For example, a user might find it hard to buy and carry a medical device with them everyday. The primary theme of my Ph.D. research has been to reduce the adoption and deployment barriers of new sensing technologies by using the sensors that are already around us. In this talk, I will present my research on extending the capabilities of the on-board sensors on consumer devices for various applications. First, I will discuss how we can leverage the on-device sensors to enable novel human-computer interactions and make the mobile devices more usable. Next, I will discuss my work on using the on-device sensors for building health technologies that perform similarly to their clinically-approved counterparts; and how they continue to evolve through deployments in various parts of the world. Finally, I will discuss how my recent work in extending the on-device sensors is shaping my future interests and how I plan to continue to lower the adoption barriers for new sensing technologies."
|2/16, 16:00-17:00, Muenzinger E214||
John Gabrieli, Professor, Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, MIT
|Prediction as a
Humanitarian and Pragmatic Contribution from
Human Cognitive Neuroscience
Neuroimaging has greatly enhanced the cognitive neuroscience understanding of the human brain and its variation across individuals (neurodiversity) in both health and disease. Such progress has not yet, however, propelled changes in educational or medical practices that improve people’s lives. We review neuroimaging findings in which initial brain measures (neuromarkers) are correlated with or predict future (1) education, learning, and performance in children and adults; (2) criminality; (3) health-related behaviors; and (4) responses to pharmacological or behavioral treatments. Neuromarkers often provide better predictions (neuroprognosis), alone or in combination with other measures, than traditional behavioral measures. With further advances in study designs and analyses, neuromarkers may offer opportunities to personalize educational and clinical practices that lead to better outcomes for people.
|2/19 @ 12:00, Muenzinger D428||Jeremy Reynolds, Senior Data Scientist, Microsoft||Stories from Industry: How can Cognitive Science Programs enable students to succeed?|
|2/23 @ 12:00, ATLAS 206||Dennis Ramirez||Making Meaningful Systems and Experiences in Video Games|
|2/26 @ 12:00, Muenzinger D428||Peter Foltz, ICS Research Professor||Automated analyses of
language production in clinical tasks
Language provides a window into underlying cognitive structures and mechanisms. In disorders such as schizophrenia, psychosis and bipolar disorder, abnormalities in language production and comprehension are often used as indicators to aid in the diagnosis and in understanding the underlying etiology. This talk will describe research in which we apply automated language analysis techniques to detect differences between groups of control and clinical populations. Over the past 15 years, we have collected samples of language from patients, controls, and unaffected siblings performing a range of tasks in clinical trials and neuropsychological research studies. The tasks include category fluency, logical memory, story recall, story telling, and answering open-ended questions. The analyses use corpus-based statistical models of language to examine semantic and statistical properties of responses. The results indicate that the methods can reliably detect differences in regularities in language between groups. Implications will be discussed for using the approach as a framework for measuring subtle changes in language and understanding the underlying processes that may cause these changes. Finally, I’ll talk about applying the approach for remote monitoring and treatment of cognitive functioning on mobile devices.
|3/1 @ 3:30, Discovery Learning Center (DLC) 170||Chenhao Tao, Cornell||ONLINE SOCIAL INTERACTIONS: A LENS ON HUMANS AND A WORLD FOR
Online social interactions have become an integral part of people's lives, e.g., presidential candidates use Facebook and Twitter to engage with the public, programmers rely on Stackoverflow to write code, and various communities have been forming online. This unprecedented amount of social interaction offers tremendous opportunities to understand human behavior. Such an understanding can induce significant social impact, ranging from influencing election outcomes to better communication for everyone.
Tan's research leverages newly available massive datasets of social interactions to understand human behavior and predict human decisions. These results can be used to build or improve socio-technical systems. In this talk, he will explain my research at both micro and macro levels. At the micro level, he investigates the effect of wording in message sharing via natural experiments. He develops a classifier that outperforms humans in predicting which tweet will be retweeted more. At the macro level, Tan will examine how users engage with multiple communities and find that, surprisingly, users continually explore new communities on Reddit. Moreover, their exploration patterns in their early ``life'' can be used to predict whether they will eventually abandon Reddit. He will finish with some discussion of future research directions in understanding human behavior.
|3/4 @ 12:00, Muenzinger D428||Frank Jaekel, Universität Osnabrück||Categorization: From Psychology to
Machine Learning and Back
Abstract: The ability to categorize is fundamental for cognition—in humans and in machines. Many, if not all, so-called higher cognitive functions, like language or problem-solving, crucially depend on categorization. For this reason, research on categorization plays a central role in cognitive science and artificial intelligence alike. Hence, it is no surprise that many successful machine learning algorithms for categorization were inspired by results and insights from psychology and neuroscience. However, today machine learning is a mature field and more recent methods are usually seen to be grounded in statistics and computer science rather than in cognitive science. Kernel methods, in particular, have gained popularity in machine learning and have proved to be successful in many applied categorization problems. I will describe how similar ideas have developed in psychology and how insights from machine learning can feed back into cognitive science.
|3/11 @ 12:00, Muenzinger D428||Joshua Correll, Psychology and Neuroscience, CU||Of Kith and Kin: Perceptual Enrichment, Expectancy, and
Reciprocal Processing in Face Perception
|3/11 @ 19:30, Math 100 (building just west of the Engineering center)||Stuart Russell, UC Berkeley||The long-term future of (Artificial) Intelligence
The news media in recent months have been full of dire warnings about the risk that AI poses to the human race, coming from well-known figures such as Stephen Hawking, Elon Musk and Bill Gates. Should we be concerned? If so, what can we do about it? While some in the mainstream AI community dismiss these concerns, UC Berkeley’s Stuart Russell will argue instead that a fundamental reorientation of the field is required.
|3/18 @ 12:00, Muenzinger D428||John Trueswell, University of Pennsylvania||The
role of cognitive flexibility in language processing and language
Because children and adults interpret speech in real-time, rapidly making commitments to interpretation essentially on a word-by-word basis, they must learn to deal flexibly with temporary ambiguities that arise in the input. In this talk, I'll present a series of experiments that examine the relationship between language processing, language learning, and cognitive flexibility. It is found that individual differences in how well children flexibly respond to representational conflict during executive function tasks predict how well they deal with temporary syntactic ambiguity during real-time comprehension. I explore how the processing challenges associated with real-time comprehension constrain grammar learning, and might even constrain the types of grammars that arise in languages of the world. The results reveal some of the ways in which the cognitive abilities of the individual shape the linguistic-communicative system of the group.
|4/1 @ 12:00, Muenzinger D428||Derek Lomas, UCSD||Large-Scale Online Experiments Can Accelerate the Production
of Interaction Design Theory
Each day, companies run thousands of design experiments to optimize software designs (e.g., A/B tests). What if just a fraction of these experiments were used to test generalizable theories about how and why interaction designs affect user behavior? How would this impact the field of interaction design?
To illustrate the opportunities for online theory testing, I will share several experiments investigating a classic theory in game design, that games should be neither too hard or too easy. This has been formalized as the hypothesis that “moderate difficulty will produce optimal motivation”. To test this hypothesis, I deployed thousands of variations of an online educational game to >50,000 users. Surprisingly, the results showed that low levels of difficulty consistently produced maximum motivation. Further experiments indicated that the factor of "novelty" plays a critical and rarely recognized role in maintaining player motivation. These findings show how online experiments can be used to build generalizable theories about how designs impact users.
More broadly, these results illustrate how the proliferation of large-scale online theoretical experiments could rapidly produce a large body of empirically-validated interaction design theory. How can we prepare for a "big science" of interaction design? To explore this, I share some additional experimental work with "multi-armed bandits", which suggest how artificial intelligence might participate in the future of large-scale scientific inquiry. I conclude by discussing several of the opportunities, limitations and risks of scientific theory-making in the field of design.
|4/8 @ 12:00, Muenzinger D428||Anu Sharma, SLHS, CU||Cross-modal brain changes in hearing loss across the age
A basic tenet of neuroplasticity is that the brain will re-organize following sensory deprivation. Auditory deprivation appears to tax the brain by changing its normal resource allocation. Compensation for the deleterious effects of hearing loss may include recruitment of alternative or additional brain networks to perform auditory tasks. Our high-density EEG experiments suggest that age-related hearing loss results in significant changes in neural resource allocation, reflecting patterns of increased listening effort, decreased cognitive reserve, which may be associated with dementia-related cognitive decline. Cross-modal plasticity is another form of cortical re-organization associated with deafness. Cross-modal plasticity occurs when an intact sensory modality recruits cortical resources from a deprived sensory modality to increase its processing capabilities as compensation for the effects of sensory deprivation. Our results suggest evidence of recruitment of higher-order auditory cortical areas by visual and somatosensory modalities in hearing loss and deafness. Cross-modal cortical re-organization is evident both in congenital deafness and in age-related mild-moderate hearing loss and shows a strong negative correlation with speech perception performance. Overall, our results suggest that compensatory cortical plasticity secondary to sensory deprivation has important neurological consequences and influences outcomes in children and adults with hearing loss.
|4/22 @ 12:00, Muenzinger D428||Bob Sturm, Queen Mary University of London||Clever Hans, Clever Algorithms: Are your machine learnings
learning what you think?
In machine learning, generalisation is the aim, and overfitting is the bane; but just because one avoids the latter does not guarantee the former. Of particular importance in some applications of machine learning is the “sanity" of the models learnt. In this talk I discuss one discipline in which model sanity is essential -- machine music listening — and how several hundreds of research publications may have unknowingly built, tuned, tested, compared and advertised “horses” instead of solutions. The true cautionary tale of the horse-genius Clever Hans provides the most appropriate illustration, but also ways forward.
Reference: B. L. Sturm, “A simple method to determine if a music information retrieval system is a “horse”,” IEEE Trans. Multimedia, vol. 16, no. 6, pp. 1636-1644, 2014.