Jay I. Myung

According to our database, Jay I. Myung authored at least 18 papers between 2004 and 2020.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2020

The Scaled Target Learning Model: A Novel Computational Model of the Balloon Analogue Risk Task.

Ran Zhou, Jay I. Myung, Mark A. Pitt

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

Machine Learning Optimizes Assessment: New Insights for the Development of Numerosity Estimation.

Sang Ho Lee, Dan Kim, John Opfer, Mark A. Pitt, Jay I. Myung

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019

Optimizing the Design of an Experiment using the ADOpy Package: An Introduction and Tutorial.

Jay I. Myung, Mark A. Pitt, Jaeyeong Yang, Woo-Young Ahn

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Active Learning for a Number-Line Task with Two Design Variables.

Sang Ho Lee, Dan Kim, John Opfer, Mark A. Pitt, Jay I. Myung

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Modeling Delay Discounting using Gaussian Process with Active Learning.

Jorge Chang, Jiseob Kim, Byoung-Tak Zhang, Mark A. Pitt, Jay I. Myung

Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

2018

Assessing the Validity of Three Tasks of Risk-Taking Propensity: Behavioral Measure and Computational Modeling.

Ran Zhou, Jay I. Myung, Carol Mathews, Mark A. Pitt

Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

2017

Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach.

Woojae Kim, Mark A. Pitt, Zhong-Lin Lu, Jay I. Myung

Cogn. Sci., 2017

2015

Workshop on Optimizing Experimental Designs: Theory, Practice, and Applications.

Jay I. Myung, Mark A. Pitt, Maarten Speekenbrink

Proceedings of the 37th Annual Meeting of the Cognitive Science Society, 2015

2014

A Hierarchical Adaptive Approach to Optimal Experimental Design.

Woojae Kim, Mark A. Pitt, Zhong-Lin Lu, Mark Steyvers, Jay I. Myung

Neural Comput., 2014

A Hierarchical Adaptive Approach to the Optimal Design of Experiments.

Woojae Kim, Mark A. Pitt, Zhong-Lin Lu, Mark Steyvers, Hairong Gu, Jay I. Myung

Proceedings of the 36th Annual Meeting of the Cognitive Science Society, 2014

2013

Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach.

Daniel R. Cavagnaro, Richard Gonzalez, Jay I. Myung, Mark A. Pitt

Manag. Sci., 2013

Adaptive Estimation of Psychometric Slope and Threshold with Differential Evolution.

Hairong Gu, Jay I. Myung, Mark A. Pitt, Zhong-Lin Lu

Proceedings of the 35th Annual Meeting of the Cognitive Science Society, 2013

2011

Tutorial on Model Comparison Methods.

Jay I. Myung, Mark A. Pitt

Proceedings of the 33th Annual Meeting of the Cognitive Science Society, 2011

2010

Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science.

Daniel R. Cavagnaro, Jay I. Myung, Mark A. Pitt, Janne V. Kujala

Neural Comput., 2010

2009

Adaptive Design Optimization in Experiments with People.

Daniel R. Cavagnaro, Mark A. Pitt, Jay I. Myung

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008

Measuring Model Flexibility With Parameter Space Partitioning: An Introduction and Application Example.

Mark A. Pitt, Jay I. Myung, Maximiliano Montenegro, James Pooley

Cogn. Sci., 2008

2005

Model selection in cognitive science as an inverse problem.

Jay I. Myung, Mark A. Pitt, Daniel J. Navarro

Proceedings of the Computational Imaging III, San Jose, 2005

2004

Model Comparison Methods.

Jay I. Myung, Mark A. Pitt

Proceedings of the Numerical Computer Methods, Part D, 2004