Parameter recovery for the Leaky Competing Accumulator model

The Leaky Competitive Accumulator (LCA) model for perceptual discrimination is rapidly growing in popularity due to its neural plausibility. The model assumes that perceptual choices and associated response times are the consequence of the accrual of evidence for the various response alternatives up to a certain predetermined threshold. In addition, accrual of evidence is influenced by temporal leakage of information and mutual inhibition between the accumulators. In this paper we provide an overview of fitting routines that may be used to identify the parameter values used for generating data under the LCA assumptions. We find that because (a) there is no closed-form solution to the likelihood function of the LCA model, and (b) there are strong trade-offs between accumulation rate, leakage, and inhibition, it is extremely difficult to faithfully recover the parameters of the LCA model. To minimize these problems, we recommend to use DE-MCMC sampling, collect very large amounts of data, and constrain the parameter space where possible.