Assessing aspects of reading by a connectionist model

Anthropocentrism of computational systems is totally justified when the task concerns to natural language. Computational linguistics systems usually rely on mathematical and statistical formalisms, which are efficient and useful but far from human procedures and therefore not so skilled. The presented work proposes a computational model of natural language reading, called cognitive reading indexing model (CRIM), inspired by some aspects of human cognition, trying to become as psychologically plausible as possible. The model relies on a semantic neural network and it produces nets of activated concepts as text representations. The experimental evaluation shows that the system is suitable to model human reading, and it provides a framework to validate and assess hypothesis concerning reading from other cognitive science fields.