  As models of cognition grow in complexity and number of parameters, Bayesian
inference with standard methods can become intractable, especially when the
data-generating model is of unknown analytic form. Recent advances in
simulation-based inference using specialized neural network architectures
circumvent many previous problems of approximate Bayesian computation.
Moreover, due to the properties of these special neural network estimators, the
effort of training the networks via simulations amortizes over subsequent
evaluations which can re-use the same network for multiple datasets and across
multiple researchers. However, these methods have been largely underutilized in
cognitive science and psychology so far, even though they are well suited for
tackling a wide variety of modeling problems. With this work, we provide a
general introduction to amortized Bayesian parameter estimation and model
comparison and demonstrate the applicability of the proposed methods on a
well-known class of intractable response-time models.
