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- Daniel R. Cavagnaro, Jay I. Myung, Mark A. Pitt, Janne V. Kujala
- Neural Computation
- 2010

Discriminating among competing statistical models is a pressing issue for many experimentalists in the field of cognitive science. Resolving this issue begins with designing maximally informative experiments. To this end, the problem to be solved in adaptive design optimization is identifying experimental designs under which one can infer the underlying… (More)

- Daniel R Cavagnaro, Mark A Pitt, Richard Gonzalez, Jay I Myung
- Journal of risk and uncertainty
- 2013

Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within cumulative prospect theory. While several different parametric forms have been proposed, their qualitative similarities make it challenging to discriminate among them empirically. In this paper, we use… (More)

- Mark A Pitt, Woojae Kim, Daniel J Navarro, Jay I Myung
- Psychological review
- 2006

To model behavior, scientists need to know how models behave. This means learning what other behaviors a model can produce besides the one generated by participants in an experiment. This is a difficult problem because of the complexity of psychological models (e.g., their many parameters) and because the behavioral precision of models (e.g., interval-scale… (More)

The Minimum Description Length (MDL) principle is an information theoretic approach to inductive inference that originated in algorithmic coding theory. In this approach, data are viewed as codes to be compressed by the model. From this perspective, models are compared on their ability to compress a data set by extracting useful information in the data… (More)

- Jay I Myung, Daniel R Cavagnaro, Mark A Pitt
- Journal of mathematical psychology
- 2013

Experimentation is ubiquitous in the field of psychology and fundamental to the advancement of its science, and one of the biggest challenges for researchers is designing experiments that can conclusively discriminate the theoretical hypotheses or models under investigation. The recognition of this challenge has led to the development of sophisticated… (More)

- Jay I Myung, Yun Tang, Mark A Pitt
- Methods in enzymology
- 2009

Computational models are powerful tools that can enhance the understanding of scientific phenomena. The enterprise of modeling is most productive when the reasons underlying the adequacy of a model, and possibly its superiority to other models, are understood. This chapter begins with an overview of the main criteria that must be considered in model… (More)

Theories of decision making are often formulated in terms of deterministic axioms, which do not account for stochastic variation that attends empirical data. This study presents a Bayesian inference framework for dealing with fallible data. The Bayesian framework provides readily applicable statistical procedures addressing typical inference questions that… (More)

- Woojae Kim, Mark A. Pitt, Zhong-Lin Lu, Mark Steyvers, Jay I. Myung
- Neural Computation
- 2014

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 scans, responses from infant participants). A major interest of researchers is designing experiments that lead to maximal accumulation of information about the phenomenon under study with the fewest… (More)

- Daniel R Cavagnaro, Mark A Pitt, Jay I Myung
- Psychonomic bulletin & review
- 2011

An ideal experiment is one in which data collection is efficient and the results are maximally informative. This standard can be difficult to achieve because of uncertainties about the consequences of design decisions. We demonstrate the success of a Bayesian adaptive method (adaptive design optimization, ADO) in optimizing design decisions when comparing… (More)

- Mark A. Pitt, Jay I. Myung, Maximiliano Montenegro, James Pooley
- Cognitive Science
- 2008

A primary criterion on which models of cognition are evaluated is their ability to fit empirical data. To understand the reason why a model yields a good or poor fit, it is necessary to determine the data-fitting potential (i.e., flexibility) of the model. In the first part of this article, methods for comparing models and studying their flexibility are… (More)