Abstract
In this chapter, we provide assurance that the likelihood-free algorithms we have described in the previous chapters can correctly recover posterior distributions for a number of cognitive modeling applications. First, we show how the parameters of a popular model of episodic memory can be accurately recovered using the ABCDE algorithm. Second, we show how a hierarchical version of the classic signal detection theory model can be recovered using the Gibbs ABC algorithm and a kernel-based approach. Finally, we show how the parameters of the Linear Ballistic Accumulator model, a simple model of perceptual decision making, can be accurately estimated using the PDA method, but not using the synthetic likelihood approach. Through each of these validations, we demonstrate that the likelihood-free algorithms can accurately recover the true posterior distributions.
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We treated the correct and incorrect RT distributions as the accumulators themselves, rather than the response alternatives.
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Palestro, J.J., Sederberg, P.B., Osth, A.F., Zandt, T.V., Turner, B.M. (2018). Validations. In: Likelihood-Free Methods for Cognitive Science. Computational Approaches to Cognition and Perception. Springer, Cham. https://doi.org/10.1007/978-3-319-72425-6_4
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DOI: https://doi.org/10.1007/978-3-319-72425-6_4
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