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A Tutorial

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Part of the Computational Approaches to Cognition and Perception book series (CACP)

Abstract

In this chapter, we apply some of the likelihood-free algorithms discussed in previous chapters in a tutorial fashion with an application to the Minerva 2 model, a global matching model of recognition memory. First, we will briefly describe the model. Then, we will fit the Minerva 2 model to simulated data using three different likelihood-free algorithms and compare how well each technique provides fits to the data. Finally, we will fit a hierarchical version of the Minerva 2 model to real-world data using a combination of the probability density approximation method and the Gibbs ABC algorithm.

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Fig. 3.1
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Fig. 3.3
Fig. 3.4
Fig. 3.5
Fig. 3.6
Fig. 3.7

Notes

  1. 1.

    The responses are arbitrarily coded as either a one for an “old” response, or a zero for a “new” response.

  2. 2.

    In our simulations, we tested a few different values of δ ABC until we arrived at the smallest value that still produced good mixing behavior across the chains.

  3. 3.

    Dennis et al. [4] also used words of different frequency (high and low) to construct their study and test lists. Because Minerva 2 lacks a mechanism for explaining word frequency effects in recognition memory, for the purposes of this demonstration we collapsed across both word frequency classes to produce a single hit and false alarm rate for each experimental condition.

  4. 4.

    After fitting the model, we noticed that no marginal distribution for η i went below four, so while our choice was made out of convenience, it had little effect on the posterior estimates.

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Palestro, J.J., Sederberg, P.B., Osth, A.F., Zandt, T.V., Turner, B.M. (2018). A Tutorial. 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_3

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