A software toolkit for modeling human sentence parsing: An approach using continuous-time, discrete-state stochastic dynamical systems

Abstract

We present a new software toolkit for implementing a broad class oftheories of sentence processing. In this framework, processing a word ina sentence is viewed as a continuous-time random walk through a set ofdiscrete states that encode information about the emerging structure of thesentence so far. The state space includes one or more special absorbingstates, which, when reached, indicate the decision to move on to the nextword of the sentence. This setup allows us to ask how how long it takesto reach an absorbing state and what the probability of reaching this stateis. We summarize a number of important statistics that can be directlyrelated to human reading times and comprehension question performance.To illustrate the use of the toolkit, we model two types of garden paths,local coherence effects, and the ambiguity advantage using three qualitativelydifferent theories of sentence processing. While the modeler must still makedefensible theoretical and implementation choices, this framework representsan improvement over the descriptive, paper-pencil modeling that is thenorm in psycholinguistics by facilitating quantitative evaluations of modelperformance and laying the groundwork for Bayesian fitting of free parametersin a model. An open-source Python package is provided.