Sentence Processing as Skilled Memory Retrieval: A Computational Model

Shravan Vasishth 　

Abstract:
A detailed process theory is presented of the moment-by-moment working memory retrievals and associated control structure that subserve sentence comprehension. The theory is derived from the application of independently motivated principles of memory and cognitive skill to the specialized task of sentence parsing. The theory construes sentence processing as a series of skilled associative memory retrievals modulated by similarity-based interference and fluctuating activation. The cognitive principles appealed to are formalized in computational form in the ACT-R architecture, and the process model is realized in ACT-R. The results of seven simulations are presented:  
- Five simulations provide quantitative accounts of the effects of length and structural interference on both unambiguous and garden path structures  
- One simulation provides a graded taxonomy of double center-embeddings ranging from relatively easy to extremely difficult. The explanation of center-embedding difficulty is a novel one that derives from the model's complete reliance on discriminating retrieval cues in the absence of an explicit representation of serial order information.  
- A final simulation models a surprising result due to Drenhaus et al. (2005) involving the processing of ungrammatical versus grammatical negative polarity items.  
The first six fits were obtained with only one free scaling parameter fixed across the simulations (the final simulation involved one further parameter, the implications of which will be discussed); all other parameters were ACT-R defaults. The modeling results support the hypothesis that fluctuating activation and similarity-based interference are the key factors shaping working memory in sentence processing. The theory and empirical predictions are compared with several related accounts of sentence processing complexity.