Results of Bayesian hierarchical modeling with the use of cognitive strategies as the dependent variable and participants and strategy items as random effects in Study 1.</p
Recall that we were discussing hierarchical models last lecture. As an example, we were examining a ...
Bayesian models of cognition provide a powerful way to understand the behavior and goals of individu...
This thesis focuses on the application of the hierarchical Bayesian (HB) methodology to real data. T...
Results of Bayesian hierarchical modeling with the use of metacognitive strategies as the dependent ...
The hierarchical Bayesian approach to cognitive modeling often provides a quality of inference that ...
Introduction: The need for hierarchical models Those of us who study human cognition have no easy ta...
Psychological experiments often yield data that are hierarchically structured. A number of popular s...
Bayesian data analysis involves describing data by meaningful mathematical models, and allocating cr...
From a computational perspective, the primary goal of cognitive science is to infer the influence of...
To be useful, cognitive models with fitted parameters should show generalizability across time and a...
Abstract To be useful, cognitive models with fitted parame-ters should show generalizability across ...
Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models ...
An important tool in the advancement of cognitive science are quantitative models that represent dif...
Human response time (RT) data are widely used in experimental psychology to evaluate theories of men...
Many theories of human cognition postulate that people are equipped with a repertoire of strategies ...
Recall that we were discussing hierarchical models last lecture. As an example, we were examining a ...
Bayesian models of cognition provide a powerful way to understand the behavior and goals of individu...
This thesis focuses on the application of the hierarchical Bayesian (HB) methodology to real data. T...
Results of Bayesian hierarchical modeling with the use of metacognitive strategies as the dependent ...
The hierarchical Bayesian approach to cognitive modeling often provides a quality of inference that ...
Introduction: The need for hierarchical models Those of us who study human cognition have no easy ta...
Psychological experiments often yield data that are hierarchically structured. A number of popular s...
Bayesian data analysis involves describing data by meaningful mathematical models, and allocating cr...
From a computational perspective, the primary goal of cognitive science is to infer the influence of...
To be useful, cognitive models with fitted parameters should show generalizability across time and a...
Abstract To be useful, cognitive models with fitted parame-ters should show generalizability across ...
Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models ...
An important tool in the advancement of cognitive science are quantitative models that represent dif...
Human response time (RT) data are widely used in experimental psychology to evaluate theories of men...
Many theories of human cognition postulate that people are equipped with a repertoire of strategies ...
Recall that we were discussing hierarchical models last lecture. As an example, we were examining a ...
Bayesian models of cognition provide a powerful way to understand the behavior and goals of individu...
This thesis focuses on the application of the hierarchical Bayesian (HB) methodology to real data. T...
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