This talk presents a theory and process model of human sentence comprehension based on the following claim: Sentence comprehension can be understood as a series of cue-based retrievals from short-term (and long-term) memory. Conceiving of sentence processing in this way lets us incorporate important ideas from memory research in cognitive psychology, including similarity-based retrieval interference, activation decay, and confusable position codes for serial order information. These principles, together with an ontology of features from linguistic theory, provide explanatory accounts of many parsing phenomena (such as difficulty on embeddings and recency effects), and generate novel predictions which can be empirically tested. I'll summarize some relevant new data from several languages. This includes reading experiments in English showing an interesting cross-over effect of interference and decay on attachment (the process of incorporating a new word into an existing partial interpretation) and reanalysis (recovering from a misinterpretation of a local ambiguity): attachment processes show strong effects of interference, but small effects of decay, while reanalysis shows just the opposite pattern. We will also consider data from head-final languages that begin to answer one of the basic questions motivated by the theoretical framework: exactly what kinds of similarity (syntactic, semantic, phonological, positional) matters in sentence processing? The process model is based on a computational cognitive architecture, ACT-R, which embodies a set of independently motivated hypotheses about memory retrieval and cognitive skill. The model yields the kind of detailed behavioral trace required to adequately bring theory into contact with temporally-rich paradigms such as eye-tracking. This is joint work with Julie Van Dyke, JJ Nakayama, and Shravan Vasishth.

Student Host: John Hale