A cognitively realistic left-corner parser with visual and motor interfaces: An extensible framework for mechanistic processing models

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We introduce a Python3 implementation of ACT-R (Anderson and Lebiere 1998) in which we build an end-to-end simulation of syntactic parsing in a typical self-paced reading experiment. The model uses a left-corner parsing strategy implemented as a skill in procedural memory (following Lewis and Vasishth 2005), makes use of independently motivated components of the ACT-R framework (content-addressable declarative memory, Wagers and Phillips 2009, goal and imaginal buffers etc.), and explicitly models the motor and visual processes involved in self-paced reading. The ACT-R model can be embedded in a (Bayesian) statistical model to estimate its sub-symbolic parameters and do model comparison.

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