Search Results - "Myung, Jay I."

Efficient Closed-loop Maximization of Carbon Nanotube Growth Rate using Bayesian Optimization by Chang, Jorge, Nikolaev, Pavel, Carpena-Núñez, Jennifer, Rao, Rahul, Decker, Kevin, Islam, Ahmad E., Kim, Jiseob, Pitt, Mark A., Myung, Jay I., Maruyama, Benji

“…A major technological challenge in materials research is the large and complex parameter space, which hinders experimental throughput and ultimately slows down…”
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A hierarchical adaptive approach to optimal experimental design by Kim, Woojae, Pitt, Mark A, Lu, Zhong-Lin, Steyvers, Mark, Myung, Jay I

“…Experimentation is at the core of research in the behavioral and neural sciences, yet observations can be expensive and time-consuming to acquire (e.g., MRI…”
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Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm by Ahn, Woo-Young, Gu, Hairong, Shen, Yitong, Haines, Nathaniel, Hahn, Hunter A., Teater, Julie E., Myung, Jay I., Pitt, Mark A.

“…Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. In…”
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Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science by Cavagnaro, Daniel R, Myung, Jay I, Pitt, Mark A, Kujala, Janne V

“…Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins…”
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A model-based analysis of decision making under risk in obsessive-compulsive and hoarding disorders by Aranovich, Gabriel J., M.D, Cavagnaro, Daniel R., Ph.D, Pitt, Mark A., Ph.D, Myung, Jay I., Ph.D, Mathews, Carol A., M.D

“…Abstract Attitudes towards risk are highly consequential in clinical disorders thought to be prone to “risky behavior”, such as substance dependence, as well…”
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Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach by Cavagnaro, Daniel R., Gonzalez, Richard, Myung, Jay I., Pitt, Mark A.

“…Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict…”
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Probabilistic Decision-Making in Children With Dyslexia by Pereira, Christa L Watson, Zhou, Ran, Pitt, Mark A, Myung, Jay I, Rossi, P Justin, Caverzasi, Eduardo, Rah, Esther, Allen, Isabel E, Mandelli, Maria Luisa, Meyer, Marita, Miller, Zachary A, Gorno Tempini, Maria Luisa

“…Neurocognitive mechanisms underlying developmental dyslexia (dD) remain poorly characterized apart from phonological and/or visual processing deficits…”
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The scaled target learning model: Revisiting learning in the balloon analogue risk task by Zhou, Ran, Myung, Jay I., Pitt, Mark A.

“…The Balloon Analogue Risk Task (BART) is a sequential decision making paradigm that assesses risk-taking behavior. Several computational models have been…”
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Evaluating the performance of the quick CSF method in detecting contrast sensitivity function changes by Hou, Fang, Lesmes, Luis Andres, Kim, Woojae, Gu, Hairong, Pitt, Mark A, Myung, Jay I, Lu, Zhong-Lin

“…The contrast sensitivity function (CSF) has shown promise as a functional vision endpoint for monitoring the changes in functional vision that accompany eye…”
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Optimal Experimental Design for Model Discrimination by Myung, Jay I, Pitt, Mark A

“…Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design…”
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A number-line task with a Bayesian active learning algorithm provides insights into the development of non-symbolic number estimation by Lee, Sang Ho, Kim, Dan, Opfer, John E., Pitt, Mark A., Myung, Jay I.

“…To characterize numerical representations, the number-line task asks participants to estimate the location of a given number on a line flanked with zero and an…”
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ADOpy: a python package for adaptive design optimization by Yang, Jaeyeong, Pitt, Mark A., Ahn, Woo-Young, Myung, Jay I.

“…Experimental design is fundamental to research, but formal methods to identify good designs are lacking. Advances in Bayesian statistics and machine learning…”
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Data-driven experimental design and model development using Gaussian process with active learning by Chang, Jorge, Kim, Jiseob, Zhang, Byoung-Tak, Pitt, Mark A., Myung, Jay I.

“…•Propose a novel data-driven, model-free framework for optimal experimentation and model development.•The framework is built upon two ideas: nonparametric…”
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Assessing the validity of three tasks of risk‐taking propensity by Zhou, Ran, Myung, Jay I., Mathews, Carol A., Pitt, Mark A.

“…Risk‐taking propensity is a general personality disposition that has been studied using survey, behavioral, and cognitive modeling approaches, but the…”
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Mechanisms Underlying the Spacing Effect in Learning: A Comparison of Three Computational Models by Walsh, Matthew M, Gluck, Kevin A, Gunzelmann, Glenn, Jastrzembski, Tiffany, Krusmark, Michael, Myung, Jay I, Pitt, Mark A, Zhou, Ran

“…The spacing effect is one of the most widely replicated results in experimental psychology: Separating practice repetitions by a delay slows learning but…”
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Questioning Psychological Constructs: Current Issues and Proposed Changes by De Boeck, Paul, Pek, Jolynn, Walton, Katherine, Wegener, Duane T., Turner, Brandon M., Andersen, Barbara L., Beauchaine, Theodore P., Lecavalier, Luc, Myung, Jay I., Petty, Richard E.

“…Constructs are central to psychology. We describe two current trends as responses to dissatisfactions with the abstract nature of constructs and with uncertain…”
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Discriminating among probability weighting functions using adaptive design optimization by Cavagnaro, Daniel R., Pitt, Mark A., Gonzalez, Richard, Myung, Jay I.

“…Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within…”
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On the functional form of temporal discounting: An optimized adaptive test by Cavagnaro, Daniel R., Aranovich, Gabriel J., McClure, Samuel M., Pitt, Mark A., Myung, Jay I.

“…The tendency to discount the value of future rewards has become one of the best-studied constructs in the behavioral sciences. Although hyperbolic discounting…”
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A Call for Keeping Doors Open and for Parallel Efforts by De Boeck, Paul, Pek, Jolynn, Walton, Katherine, Wegener, Duane T., Turner, Brandon M., Andersen, Barbara L., Beauchaine, Theodore P., Lecavalier, Luc, Myung, Jay I., Petty, Richard E.

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A hierarchical Bayesian approach to adaptive vision testing: A case study with the contrast sensitivity function by Gu, Hairong, Kim, Woojae, Hou, Fang, Lesmes, Luis Andres, Pitt, Mark A, Lu, Zhong-Lin, Myung, Jay I

“…Measurement efficiency is of concern when a large number of observations are required to obtain reliable estimates for parametric models of vision. The…”
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