This work presents an evaluation of two well-known models of retrieval processes in sentence comprehension, the activation-based model and the direct-access model. We implemented these models in a Bayesian hierarchical framework and showed that some aspects of the data can be explained better by the direct access model. Specifically, the activation-based cannot predict that, on average, incorrect retrievals would be faster than correct ones. More generally, our work leverages the capabilities of Stan to provide a powerful framework for flexibly developing computational models of competing theories of retrieval, and demonstrates how these models’ predictions can be compared in a Bayesian setting.

Code and data available at github.com/stan-dev/stancon_talks