* In-person seminars are cancelled effective March 13, 2020.  Zoom links will be provided for the talks listed below and to those on the mailing list.

The Fall 2020/Spring 2021 COGNITIVE SCIENCE COLLOQUIUM SERIES schedule is shown below.  Details will be posted as soon as they are available.  As usual, the colloquium will be held on Fridays (unless otherwise noted), from 12:00 - 1:30 p.m., in the Speech, Language, and Hearing Sciences Building, Room 205, 1131 E Second Street. Recordings of these talks are available on Panopto, which can be accessed with a UA NetID.

Since 2012, an annual feature of the colloquium series is a special talk given by the Roger N. Shepard Distinguished Visiting Speaker. Please follow the link for a list of past speakers.

If you would like to receive email announcements about these and other events, please contact Program Coordinator Kirsten Cloutier Grabo at to be added to the colloquium listserv.

Information about previous talks during this academic year can be found at the bottom of this list. Other past talks can be found at COGNITIVE SCIENCE COLLOQUIUM ARCHIVE.


FALL 2020


October 30, 2020

Jennie Gubner,  Assistant Professor, Music, University of Arizona


Re-imagining Dementia Education through Ethnomusicology and Arts-based Research

Abstract: How can the field of ethnomusicology contribute to dementia research and education? How can arts-based research methods be used to produce new kinds of knowledge that enrich the ways students from within and beyond the health sciences understand aging and cognitive decline? How might ethnographic and arts-based dementia education models help us reimagine how we can better train students to be active agents in the production of age-friendly, inclusive societies. In this presentation I will speak about my role as an ethnomusicologist working in the field of dementia research and education over the last 5 years. I will draw on my experiences designing college courses about music and dementia and participating in inter-professional dementia research and training opportunities in collaboration with geriatricians and neurologists. As a faculty member on the UA Health Sciences Strategic Initiative "Next-Generation Models of Healthy Aging," I will advocate the potential of arts-based dementia education models in teaching interdisciplinary listening, recruiting workforce pipeline to support older adults, and reshaping perceptions of dementia through relationship-centered models of creative engagement and public storytelling. 



November 6, 2020

Molly Gebrian, Assistant Professor, Fred Fox School of Music, University of Arizona


Music and Early Language Acquisition

Abstract: The phrase “music is the universal language” is ubiquitous in our culture, but this talk will explore the idea that, rather than music being a language, language can best be understood as a type of music. Infants first experience language not as a content-rich utterance, but rather as pure sound, devoid of any concrete meaning but full of interesting and varied acoustic information. Music perception is often treated as an ancillary ability in our culture and is thought to mature more slowly than language perception and acquisition. This talk will demonstrate that, to the contrary, not only do music and language abilities develop in parallel, but musical hearing and ability are essential for successful language acquisition. A review of the relevant literature in infant and child development, cognitive neuroscience, and musicology will show that the ability to hear musically is fundamental to our identity and linguistic abilities as human beings.



November 13, 2020

Gina Kuperberg, Professor, Cognitive Science, Tufts University


Does hierarchical predictive coding mediate language comprehension? Evidence from multimodal neuroimaging



November 20, 2020

Sol Lim, Assistant Professor, Systems and Industrial Engineering, University of Arizona


Human Motion Analysis with Inertial Sensing and Predictive Modeling for Improved Health and Well-being

Abstract: Wearable sensing technologies that gained popularity with health and fitness tracking present many new opportunities for human factors & ergonomics research. Obtaining interpretable and actionable information from the vast amounts of data generated by these sensors will require merging traditional ergonomics theory and first principles with statistical techniques adept at handling large data. My research presents a framework for combining wearable inertial sensing, biomechanical modeling, and predictive modeling techniques for ergonomics assessment. Examples include estimating exposures to manual material handling tasks of different intensity and duration, with insights on the body’s biomechanical response to external loads in dynamic tasks. This study was motivated by the high prevalence of overexertion injuries from high force exertions and awkward postures during manual material handling which account for one-third of all work-related injuries costing the US economy $13.7 billion annually. The developed approach is aimed at overcoming some of the conventional challenges of manually measuring workers’ exposures to force exertions and work postures in non-routinized work such as in variable material handling (e.g., UPS/Amazon fulfillment centers), patient-care (e.g., nurses, patient transporters), and construction work. I will conclude with an overview of other on-going studies to illustrate the broad potential of low-cost wearable sensing and predictive modeling for improve human health and well-being.


December 4, 2020

Stacey Tecot, Associate Professor, School of Anthropology, University of Arizona


The socioendocrinology of raising babies: insights from defiant lemurs

Determining how species mitigate environmental stress to survive and reproduce is central to an understanding of human and non-human primate evolution and health, and is also critical for forecasting the fates of species in the face of climate change and habitat degradation. An estimated 98% of lemurs are threatened with extinction, and the strategies that they have evolved to cope with challenges in their environment may be insufficient to respond to more recent, relatively abrupt challenges caused by human activities. Here, I focus on the evolution and proximate mechanisms of shared infant care, reproductive timing, and the stress response. I use a combination of detailed behavioral and physiological data on the red-bellied lemur as well as comparative data across species to determine how lemurs negotiate reproduction and survival in a naturally dynamic environment, and how anthropogenic factors might impact those strategies. 





January 22, 2021

Roeland Hancock, Assistant Research Professor, Assistant Director, Brain Imaging Research Center, University of Connecticut



January 29, 2021

Liz Chrastil, Assistant Professor, Neurobiology and Behavior, University of California, Irvine



February 5, 2021

Alison Hawthrone Deming, Professor, English, University of Arizona

Evan MacLean, Assistant Professor, Anthropology, University of Arizona



February 12, 2021

Miriam Spering, Assistant Professor, Neuroscience, University of British Columbia



February 19, 2021

Sarah Aronowitz, Assistant Professor, Philosophy and Cognitive Science, University of Arizona



February 26, 2021

Andrew Besler, Professor, School of Theatre, Film & Television, University of Arizona



March 5, 2021

Debbie Kelly, Professor, Psychology Department, University of Manitoba



March 19, 2021

Catherine Brooks, Associate Professor, Center for Digital Socity and Data Studies, School of Information, University of Arizona



March 26, 2021

Anne Charity-Hudley, Professor, Department of Linguistics, University of California, Santa Barbara




April 9, 2021


Melville Wohlgemuth, Assistant Professor, Neuroscience, University of Arizona



April 16, 2021

Peter Turkeltaub, Associate Professor, Neurology, Georgetown University Medical Center



April 23, 2021

Dina Spano, Research Fellow, Human Neuroimaging, University College London



April 30, 2021





COLLOQUIUM SPEAKERS who have already visited 2020/21


September 4, 2020

Adarsh Pyarelal, Research Scientist, School of Information, University of Arizona

Building machines that understand humans

Abstract: As anyone who's been frustrated by Siri or Alexa can readily verify, computers just don't get us. While artificially intelligent (AI) agents are getting quite good at understanding explicit instructions, they still struggle to understand implicit information conveyed through prosody, facial expressions, informal/imprecise language, etc. This difficulty presents a major obstacle to the development of AI 'theory of mind', i.e., the ability to infer the beliefs, desires, and intentions of humans.  In this talk, I will give an overview of ToMCAT (Theory of Mind-based Cognitive Architecture for Teams) - a 4-year project aimed at developing AI agents with social intelligence and testing them in a Minecraft-based virtual testbed.



September 11, 2020

Chris Baldassano, Assistant Professor of Psychology, Columbia University

Cognitive maps: events, spaces, semantics, and development

Abstract: Understanding and remembering realistic experiences in our everyday lives requires activating many kinds of structured knowledge about the world, including spatial maps, temporal event scripts, and semantic relationships. My recent projects have explored the ways in which we build up this schematic knowledge (during a single experiment and across developmental timescales) and can strategically deploy these cognitive maps to construct event representations that we can store in memory or use to make predictions. I will describe my lab's ongoing work developing new experimental and analysis techniques for conducting functional MRI experiments using narratives, movies, poetry, virtual reality, and "memory experts" to study complex naturalistic schemas.




September 25, 2020

Samuel Gershman, Roger N. Shepard Visiting Scholar, Associate Professor, Department of Psychology, Harvard University

Predictive maps in the brain

Abstract: In this talk, I will present a theory of reinforcement learning based on a "predictive map" that can be used to efficiently evaluate different states of the environment. I show how such a map explains many aspects of hippocampal representation. The map can be decomposed to reveal latent structure resembling entorhinal grid cells. I will then present evidence that humans employ such a predictive map to solve reinforcement learning tasks. Finally, I will discuss the role of dopamine error signals in learning the predictive map.



October 2, 2020

Dwight Kravitz, Associate Professor, Cognitive Neuroscience, Department of Psychological & Brain Sciences, George Washington University

Predicting functional organization and its effects on behavior

Abstract: In many ways, cognitive neuroscience is the attempt to use physiological observation to clarify the mechanisms that shape behavior. Over the past 25 years, fMRI has provided a system-wide and yet somewhat spatially precise view of the response in human cortex evoked by a wide variety of stimuli and task contexts. The current talk focuses on the other direction of inference; the implications of this observed functional organization for behavior. To begin, we must interrogate the methodological and empirical frameworks underlying our derivation of this organization, partially by exploring its relationship to and predictability from gross neuroanatomy. Next, across a series of studies, the implications of two properties of functional organization for behavior will be explored: 1) the co-localization of visual working memory and perceptual processing and 2) implicit learning in the context of distributed responses. In sum, these results highlight the limitations of our current approach and hint at a new general mechanism for explaining observed behavior in context with the neural substrate. 



October 9, 2020

Gondy Leroy, Professor, Department of Management Information Systems, Eller Fellow, Eller College of Management, University of Arizona

Design and Development of Decision Support Tools for Surveillance of Autism Spectrum Disorders (ASD) using EHR

In this presentation, I will provide a high level overview of a natural language processing (NLP) information system that can help improve, speed up, and facilitate reporting of cases of ASD using free text in EHR. I will show the approach and results of rule-based and machine learning algorithms to automatically recognize phenotype expression of ASD in text as well as label a child as ASD or not through review of the EHR free text.  I will discuss current results and problems encountered because this is a low resource area. I will also show examples of new analyses made possible with this type of data creation and biases to be taken into account. I hope to conclude with a discussion with the audience of new promising areas and potential extensions, applications, and collaborations.



October 16, 2020

Winslow Burleson, Professor, School of Information, University of Arizona

Motivational Environments: Cyberlearning, Digital Health, and Society’s Grand Challenges

Abstract: Novel forms of human computer interaction and learning sciences, applied to health, technology, education, and innovation are radically transforming the socio-technical environments in which we interact. Affective learning companions personally tailor interactions to mitigate Stuck and promote Flow. Supportive dressing systems adapt to the needs of persons living with cognitive impairment. Smart home and assistive technologies can foster independence for people living with autism. The University of Arizona Holodeck, a powerful Experiential Supercomputer, is empowering transdisciplinary collaborations advancing convergent research, education, and innovation.



October 23, 2020

Maureen Ritchey, Assistant Professor, Psychology Department, Boston College

Making memories: Brain networks supporting episodic binding and reconstruction

Abstract: When we remember an event, we weave together its specific features into a coherent episode. In effect, we rebuild the world in our minds. How does the human brain accomplish this feat? In this talk, I will discuss the hippocampal and cortical network interactions that transform experience into memory. This transformation process begins at encoding, as feature representations are bound through the hippocampus and embedded within the spatiotemporal structure of events. As memories are retrieved, cortico-hippocampal networks interact to reconstruct these features into a richly detailed experience. I will highlight recent work suggesting that, within the posterior medial cortico-hippocampal network, there are distinct subnetwork alliances that support different aspects of episodic representations. Finally, I will discuss ongoing efforts to modulate the reconstruction of emotional memories, leveraging what we know about making memories to make them feel better.