Encoding interference effects support self-organized sentence processing

According to cue-based retrieval theories of sentence comprehension, establishing the syntactic dependency between a verb and the grammatical subject is susceptible to interference from other noun phrases in the sentence. At the verb, the subject must be retrieved from memory, but non-subject nouns that are similar on dimensions that are relevant to subject-verb agreement, like number marking, can make the retrieval more difficult. However, cue-based retrieval models fail to account for a class of interference effects, conventionally called encoding interference, that cannot be due to retrieval interference. In this paper, we implement a self-organized sentence processing model that provides a more parsimonious explanation of encoding interference effects than otherwise reasonable extensions that could be made to the cue-based retrieval approach. We first also present new behavioral evidence for encoding interference using a semantic similarity manipulation in two self-paced reading studies of subject-verb number agreement. The results of these experiments are more compatible with the self-organizing account. We argue that self-organization, which reduces all parsing to fallible feature match optimization and makes no a priori distinction between encoding and retrieval, can provide a unifying approach to similarity-based interference in sentence comprehension.

Automated summary

According to cue-based retrieval theories of sentence comprehension, the syntactic dependency between a verb and the subject is susceptible to interference from other noun phrases in the sentence; a self-organized sentence processing model provides a more parsimonious explanation on encoding interference effects; self-organization approach reduces parsing to feature match optimization, offering a unified method for similarity-based interference in sentence comprehension.