Fig.1 1.Ī schematic view of incremental language comprehension. The incremental view of language processing is summarized in Fig. When this function is applied successively to the symbols in an utterance, it results in the listener's final interpretation of the utterance. Under a strong assumption of incrementality, the process of language comprehension is fully characterized by an integration function which takes a representation r and an input symbol w and produces an output representation r'. This is an integration of two parts: The listener must combine a representation r, built based on what she has heard so far, with the current symbol w to form a new representation r'. Over the years, extensive evidence from experiments as well as theoretical considerations has led to the conclusion that this process is incremental: The information contained in each word is immediately integrated into the listener's representation of the speaker's intent. Furthermore, we demonstrate that dependency locality effects, a signature prediction of memory‐based theories, can be derived from lossy‐context surprisal as a special case of a novel, more general principle called information locality.įor a human to understand natural language, they must process a stream of input symbols and use them to build a representation of the speaker's intended message. ![]() We show that this model provides an intuitive explanation for an outstanding puzzle involving interactions of memory and expectations: language‐dependent structural forgetting, where the effects of memory on sentence processing appear to be moderated by language statistics. Our model, lossy‐context surprisal, holds that the processing difficulty at a word in context is proportional to the surprisal of the word given a lossy memory representation of the context-that is, a memory representation that does not contain complete information about previous words. In this work, we present a new model of incremental sentence processing difficulty that unifies and extends key features of both kinds of models. ![]() Models that explain and predict this difficulty can be broadly divided into two kinds, expectation‐based and memory‐based. ![]() A key component of research on human sentence processing is to characterize the processing difficulty associated with the comprehension of words in context.
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