Brandon M. Turner is an Associate Professor in the Psychology Department at The Ohio State University. He received a B.S. from Missouri State University in mathematics and psychology in 2008, a MAS in statistics from The Ohio State University in 2010, and a Ph.D. from The Ohio State University in 2011. He then spent one year as a postdoctoral researcher at University of California, Irvine, and two years as a postdoctoral fellow at Stanford University. His research interests include dynamic models of cognition and perceptual decision making, efficient methods for performing likelihood-free and likelihood-informed Bayesian inference, and unifying behavioral and neural explanations of cognition. His current focus is on understanding how external factors such as the environment, and internal factors such as working memory interact to shape an observer’s perception of the world, and ultimately how this perception drives their decisions. Vita [@ 1/20/2020]
Emily Weichart is a postdoctoral researcher. She received a B.S. in Psychology in 2013 and an M.A. in Cognitive Psychology in 2017 from The Ohio State University. She completed her Ph.D. at the University of Virginia in 2020, working in the Computational Memory Lab. She has a background in model-based approaches to cognitive assessment and investigating the cognitive effects of clinical treatments. Her recent work has explored time-varying mechanisms of decision making during perceptual tasks, with the broader goal of understanding the complex dynamics that connect visual attention, cognitive control, and decision making processes.
Giwon Bahg is a fourth-year doctoral student. He received a B.A. in Psychology and Philosophy in 2013, and an M.A. in Psychology in 2015 from Seoul National University. He is interested in computational modeling, Bayesian methods, and temporal dynamics of human cognition, particularly in the context of thinking processes (e.g., categorization, reasoning, decision-making). His current work aims to implement adaptive design optimization for fMRI experiments using a joint modeling approach. He is also investigating dynamics of internal representations in decision-making, as well as its neural and computational bases.
Qingfang Liu is a fourth-year graduate student. She graduated from Beijing Normal University in China in 2016, with a B.S. in Psychology. Now she is studying for a doctoral degree in Cognitive Psychology and a MAS degree in Statistics. She is interested in perceptual and economical decision-making, computational cognitive models (e.g. sequential sampling models) and Bayesian methods. Her current focus is identifying neural correlates of intertemporal choice and constructing joint models by linking behavioral and neural data. She is also working as a course associate for Data Analysis in Psychology (Psych 2220).
Nate Haines is a fourth-year doctoral student studying clinical psychology at The Ohio State University (OSU). He received his B.A. from OSU in 2015, and his M.A. from OSU in 2017. Nate is interested in the role that emotion plays in how humans process, learn from, and make decisions based on rewards—particularly in the case of drug addiction and other externalizing disorders (e.g., ADHD). Currently, he is focusing on: (1) the role of emotion in learning and valuation, and (2) the relationship between impulsive and anxious personality traits and risky behavior. Nate uses cognitive modeling and machine learning techniques to explore these questions.
Inhan Kang is a third-year doctoral student in the Quantitative Psychology program. He graduated from Seoul National University with a B.A. in Psychology and a B.S. in Statistics in 2014 and an M.A. in Quantitative Psychology in 2016. His research interests include the generalized latent variable modeling and its extension to cognitive modeling and neuroscience, mathematical modeling, Bayesian analysis, and stochastic processes. His current focus is on joint modeling of different data modalities and statistical methods to achieve a parsimonious explanation of complex brain-behavior connections.
Matthew Galdo is a second-year graduate student. He graduated from Ohio State University in 2018 with a B.S. in Neuroscience. Now, he is working toward a Ph.D. in Cognitive Psychology and a MAS degree in Statistics. His research interests include: the dynamics of decision making, in particular how abnormal patterns of decision behavior (e.g. addiction) and their underlying mechanisms evolve over time; merging cognitive and psychiatric theory; Bayesian statistical methodology; and the link between neural data and cognition. Currently, his main focus is exploring how connectome data can interface with joint models of neural and behavioral data.
Woojong Yi is a second-year doctoral student in the Psychology Department at The Ohio State University. He received a B.A. from The Catholic University of Korea in Psychology in 2010, and an M.A. from Seoul National University in Interdisciplinary program in Cognitive Science (Concentration in Quantitative Psychology) in 2016. He is interested in finding and understanding of processing stages of cognition (e.g., self-control and decision-making), Bayesian statistical methods, computational modeling, joint modeling of behavioral and neural measures, and ultimately, theoretical understanding of cognition. He is currently working on discovering discrete processing stages of self-control using Hidden Markov Models.
Daniel G. Evans is a research assistant. He received a B.A. in Psychology in 2016 from Butler University and has spent the past three years as a research assistant at George Washington University and The Ohio State University. His research background combines a breadth of topics (including attention, memory, linguistics, cognitive control, and multiple sclerosis) with behavioral, eye-tracking, and fMRI methods. The overarching theme of his research is to investigate the neural basis of how attention interacts with other cognitive processes (e.g., decision-making, learning, object tracking). Currently, he is using simultaneous eye-tracking and fMRI to develop computational models of hierarchical category learning. In his spare time, Dan enjoys playing hockey, going hiking, and brewing beer.