How cognitive modeling can benefit from hierarchical Bayesian models.

@article{Lee2011HowCM,
  title={How cognitive modeling can benefit from hierarchical Bayesian models.},
  author={Michael D. Lee},
  journal={Journal of Mathematical Psychology},
  year={2011},
  volume={55},
  pages={1-7}
}
  • Michael D. Lee
  • Published 1 February 2011
  • Psychology
  • Journal of Mathematical Psychology
Abstract Hierarchical Bayesian modeling provides a flexible and interpretable way of extending simple models of cognitive processes. To introduce this special issue, we discuss four of the most important potential hierarchical Bayesian contributions. The first involves the development of more complete theories, including accounting for variation coming from sources like individual differences in cognition. The second involves the capability to account for observed behavior in terms of the… Expand

Figures from this paper

BAYESIAN METHODS IN COGNITIVE MODELING 2 Introduction
Bayesian statistical methods provide a flexible and principled framework for relating cognitive models to behavioral data. They allow for cognitive models to be formalized, evaluated, and applied,Expand
Hierarchical Bayesian models of cognitive development
TLDR
The Bayesian modeling approach is compared with the connectionist and nativist modeling paradigms and considered in view of Marr’s three description levels of information-processing mechanisms. Expand
Determining informative priors for cognitive models
TLDR
This work surveys several sources of information that can help to specify priors for cognitive models, discusses some of the methods by which this information can be formalized in a prior distribution, and identifies a number of benefits of including informative priors in cognitive modeling. Expand
Bayesian statistical approaches to evaluating cognitive models.
TLDR
This work explores three key elements used to formally evaluate cognitive models: parameter estimation, model prediction, and model selection, and compares and contrast traditional approaches with Bayesian statistical approaches to performing each of these three elements. Expand
Bayesian Q 2 statistical approaches to evaluate cognitive models
Cognitive models aim to explain complex human behavior in terms of hypothesized mechanisms of the mind. These mechanisms can be formalized in terms of mathematical structures containing parametersExpand
Bayesian estimation in hierarchical models
TLDR
This work provides an introduction to the ideas of hierarchical models and to the Bayesian estimation of their parameters, illustrated with two extended examples, and discusses Bayesian model comparison as a case of hierarchical modeling. Expand
Using Bayesian regression to test hypotheses about relationships between parameters and covariates in cognitive models
TLDR
This work develops a comprehensive solution to the covariate problem in the form of a Bayesian regression framework that can be easily added to existing cognitive models and allows researchers to quantify the evidential support for relationships between covariates and model parameters using Bayes factors. Expand
A Bayesian hierarchical mixture approach to individual differences: Case studies in selective attention and representation in category learning ☆
Abstract We demonstrate the potential of using a Bayesian hierarchical mixture approach to model individual differences in cognition. Mixture components can be used to identify latent groups ofExpand
Using hierarchical Bayesian methods to examine the tools of decision-making
Hierarchical Bayesian methods offer a principled and comprehensive way to relate psychological models to data. Here we use them to model the patterns of information search, stopping and deciding in aExpand
Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition
TLDR
It is argued that the expressive power of current Bayesian models must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition, and this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 88 REFERENCES
Three case studies in the Bayesian analysis of cognitive models
TLDR
This article presents Bayesian analyses of three influential psychological models: multidimensional scaling models of stimulus representation, the generalized context model of category learning, and a signal detection theory model of decision making. Expand
Ability, Breadth, and Parsimony in Computational Models of Higher-Order Cognition
TLDR
This article argues that three important aspects of a model of higher-order cognition to evaluate are its ability to reason, solve problems, converse, and learn as well as people do; the breadth of situations in which it can do so; and the parsimony of the mechanisms it posits. Expand
A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods
TLDR
It is argued that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a more thorough evaluation of models in the cognitive sciences. Expand
Exemplars, Prototypes, Similarities, and Rules in Category Representation: An Example of Hierarchical Bayesian Analysis
TLDR
This work shows how inferences about these parameters, and about the category representations they generate, can be used to evaluate data in terms of the ongoing exemplar versus prototype and similarity versus rules debates in the literature. Expand
Hierarchical models of simple mechanisms underlying confidence in decision making
TLDR
A hierarchical model of raw confidence judgments using the beta distribution is developed, and two simple confidence mechanisms are implemented within it, using Bayesian methods to fit to data from a two-alternative confidence experiment. Expand
Bayesian data analysis.
  • J. Kruschke
  • Computer Science, Medicine
  • Wiley interdisciplinary reviews. Cognitive science
  • 2010
TLDR
A fatal flaw of NHST is reviewed and some benefits of Bayesian data analysis are introduced and illustrative examples of multiple comparisons in Bayesian analysis of variance and Bayesian approaches to statistical power are presented. Expand
A hierarchical model for estimating response time distributions
TLDR
A hierarchical Bayesian model that provides a means of estimating the shape, scale, and location of RT distributions and provides a principled and efficient means of pooling information across disparate data from different individuals is presented. Expand
A general latent assignment approach for modeling psychological contaminants
Abstract Data from psychological experiments are rife with ‘contaminants’, which can generally be defined as data generated by psychological processes different from those intended as the object ofExpand
Constructing informative model priors using hierarchical methods
Abstract Despite their negative reputation, informative priors are very useful in inference. Priors that express psychologically meaningful intuitions damp out random fluctuations in the data due toExpand
Understanding memory impairment with memory models and hierarchical Bayesian analysis
a b s t r a c t The study of human episodic memory is a topic that interests cognitive and mathematical psychologists as well as clinicians interested in the diagnosis and assessment of Alzheimer'sExpand
...
1
2
3
4
5
...