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. Author manuscript; available in PMC: 2020 Jan 24.
Published in final edited form as: J Neurosci Methods. 2019 Oct 3;328:108432. doi: 10.1016/j.jneumeth.2019.108432

Fig. 12.

Fig. 12.

Optimizing computational speed is not detrimental to model selection analysis in ChaRTr. Akaike weights averaged over five hypothetical subjects in a model selection analysis with different settings of the random number generator, number of particles, and number of simulated trials per particle for case study 1 (A) and case study 2 (B). For both case studies, ChaRTr reliably identifies the correct data-generating model and in many cases agrees on the second best model for the data. We also note that the exact ranking of the models slightly differ across hyperparameter settings, but the set of identified models is consistent.