Abstract
In this chapter, we highlight a few real-world applications where truly simulation-based models (with intractable likelihood functions) are fit using likelihood-free techniques. First, we use likelihood-free techniques to fit a hierarchical version of the Retrieving Effectively from Memory model to data from a recognition memory task. Next, we fit a dynamic extension of the signal detection theory model and compare the fit generated using likelihood-free methods to hand-held fits previously attained using the approximate least squares method. Finally, we fit two complex, stochastic accumulator models of decision making to data from a perceptual decision-making task.
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Notes
- 1.
Sometimes evaluating the likelihood function is more arduous than simply simulating the model, as in our BCDMEM example from Chap. 4.
- 2.
Although later instantiations of REM incorporate both item and context noise, for our purposes we only consider the pure item-noise version for demonstration.
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Palestro, J.J., Sederberg, P.B., Osth, A.F., Zandt, T.V., Turner, B.M. (2018). Applications. 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_5
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DOI: https://doi.org/10.1007/978-3-319-72425-6_5
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