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Approximating Bayesian Inference through Model Simulation
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DOI：10.1016/j.tics.2018.06.003.png)
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摘要
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The ultimate test of the validity of a cognitive theory is its ability to predict patterns of empirical data. Cognitive models formalize this test by making specific processing assumptions that yield mathematical predictions, and the mathematics allow the models to be fitted to data. As the field of cognitive science has grown to address increasingly complex problems, so too has the complexity of models increased. Some models have become so complex that the mathematics detailing their predictions are intractable, meaning that the model can only be simulated. Recently, new Bayesian techniques have made it possible to fit these simulation-based models to data. These techniques have even allowed simulation-based models to transition into neuroscience, where tests of cognitive theories can be biologically substantiated.
Keyword:
DECISION FIELD-THEORY
PERCEPTUAL DECISION
COMPUTATIONAL MODELS
TIME
TUTORIAL
CORTEX
MEMORY
CHOICE
CATEGORIZATION
EXPRESSIONS
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