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A hierarchical Bayesian model of human decisionmaking on an optimal stopping problem
 Cognitive Science
, 2006
"... Wiener diffusion accounts of human decisionmaking are among the most successful and best developed formal models in the psychological sciences. We reconsider these models from a Bayesian perspective, using graphical modeling, and Markov Chain MonteCarlo methods for posterior sampling. By analyzing ..."
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Cited by 14 (3 self)
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Wiener diffusion accounts of human decisionmaking are among the most successful and best developed formal models in the psychological sciences. We reconsider these models from a Bayesian perspective, using graphical modeling, and Markov Chain MonteCarlo methods for posterior sampling. By analyzing seminal data from a brightness discrimination task, we show how the Bayesian approach offers several avenues for extending and improving diffusion models. These possibilities include the hierarchical modeling of stimulus properties, and modeling the role of contaminant processes in generating experimental data. We also argue that the Bayesian approach challenges some basic assumptions of previous diffusion models, involving how variability in decisionmaking should be interpreted. We conclude that adopting a Bayesian approach to relating diffusion models and human decisionmaking data will sharpen the theoretical and empirical questions, and improve our understanding of a basic human cognitive ability. BAYESIAN DIFFUSION DECISIONMAKING 2
A tutorial introduction to Bayesian models of cognitive development
"... We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, an ..."
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Cited by 2 (0 self)
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We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.
A Theory of Reaction Time Distributions
"... We present a theory of reaction time (RT) distributions deriving from the distribution of the quotient of two normal random variables: task difficulty (topdown information), and rate of external evidence income (bottomup information). We show that a number of known properties of RT distributions a ..."
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We present a theory of reaction time (RT) distributions deriving from the distribution of the quotient of two normal random variables: task difficulty (topdown information), and rate of external evidence income (bottomup information). We show that a number of known properties of RT distributions are homogeneously accounted for by variations in the value of two easily interpretable parameters, the coefficients of variation of the two normal variables. The theory provides a quantitative and qualitative better account of several large datasets than other distributions families that bave been proposed to account for RTs.
unknown title
"... Bayesian estimation has played a pivotal role in the understanding of individual differences. However, for many models in psychology, Bayesian estimation of model parameters can be difficult. One reason for this difficulty is that conventional sampling algorithms, such as Markov chain Monte Carlo (M ..."
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Bayesian estimation has played a pivotal role in the understanding of individual differences. However, for many models in psychology, Bayesian estimation of model parameters can be difficult. One reason for this difficulty is that conventional sampling algorithms, such as Markov chain Monte Carlo (MCMC), can be inefficient and impractical when little is known about the target distribution – particularly the target distribution’s covariance structure. In this article, we highlight some reasons for this inefficiency and advocate the use of a population MCMC algorithm, called Differential Evolution Markov chain Monte Carlo (DEMCMC) as a means of efficient proposal generation. We demonstrate in a simulation study that the performance of the DEMCMC algorithm is unaffected by the correlation of the target distribution, whereas conventional MCMC performs substantially worse as the correlation increases. We then show that the DEMCMC algorithm can be used to efficiently fit a hierarchical version of the Linear Ballistic Accumulator model to response time data, which has proven to be a difficult task when using conventional MCMC.
A Dynamic and Stochastic Theory of Choice, Response Time, and Confidence
"... The three most basic performance measures used in cognitive research are choice, response time, and confidence. We present a diffusion model that accounts for all three using a common underlying process. The model uses a standard drift diffusion process to account for choice and decision time. To ma ..."
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The three most basic performance measures used in cognitive research are choice, response time, and confidence. We present a diffusion model that accounts for all three using a common underlying process. The model uses a standard drift diffusion process to account for choice and decision time. To make a confidence judgment, we assume that evidence continues to accumulate after the choice. Judges then interrupt the process to categorize the accumulated evidence into a confidence rating. The fully specified model is shown to account qualitatively for the most important interrelationships between all three response variables found in past research.
A Theory of Reaction Time Distributions
, 2009
"... We develop a general theory of reaction time (RT) distributions in psychological experiments, deriving from the distribution of the quotient of two normal random variables, that of the task difficulty (topdown information), and that of the external evidence that becomes available to solve it (botto ..."
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We develop a general theory of reaction time (RT) distributions in psychological experiments, deriving from the distribution of the quotient of two normal random variables, that of the task difficulty (topdown information), and that of the external evidence that becomes available to solve it (bottomup information). The theory provides a unified account of known changes in the shape of the distributions depending on properties of the task and of the participants, and it predicts additional changes that should be observed. A number of known properties of RT distributions are homogeneously accounted for by variations in the value of two easily interpretable parameters: the coefficients of variation of the two normal variables. The predictions of the theory are compared with those of multiple families of distributions that have been proposed to account for RTs, indicating our theory provides a significantly better account of experimental data. For this purpose, we provide comparisons with four large datasets across tasks and modalitities. Finally, we show how the theory links to neurobiological models of response latencies.