Predicting without modeling: a critique of Trabasso and Bartolone (2003)

T. Trabasso and J. Bartolone (2003) used a computational model of narrative text comprehension to account for empirical findings. The authors show that the same predictions are obtained without running the model. This is caused by the model's computational setup, which leaves most of the model's input unchanged.     

Coherence-driven resolution of referential ambiguity: A computational model

We present a computational model that provides a unified account of inference, coherence, and disambiguation. It simulates how the build-up of coherence in text leads to the knowledge-based resolution of referential ambiguity. Possible interpretations of an ambiguity are represented by centers of gravity in a high-dimensional space. The unresolved ambiguity forms a vector in the same space. This vector is attracted by the centers of gravity, while also being affected by context information and world knowledge. When the vector reaches one of the centers of gravity, the ambiguity is resolved to the corresponding interpretation. The model accounts for reading time and error rate data from experiments on ambiguous pronoun resolution and explains the effects of context informativeness, anaphor type, and processing depth. It shows how implicit causality can have an early effect during reading. A novel prediction is that ambiguities can remain unresolved if there is insufficient disambiguating information.