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Discourse understanding is hampered when missing or conflicting context information is given. In four experiments, we investigated what happens (a) when the definite determiner "the," which presupposes existence and uniqueness, does not find a unique referent in the context or (b) when the appropriate use of the indefinite determiner is violated by the presence of a unique referent (Experiment 1 and Experiment 2). To focus on the time-course of processing the uniqueness presupposition of the definite determiner, we embedded the determiner in different sentence structures and varied the context (Experiment 3 and Experiment 4). Reading time served as an index of processing difficulty in a word-by-word self-paced reading task and acceptability judgments provided hints for a possible repair of a presupposition violation. Our results showed that conflicting and missing context information lowered acceptability ratings and was associated with prolonged reading times. The pattern of results differed depending on the nature of the presupposition (Experiments 1 and 2) and whether supplementing missing context information was possible (Experiment 3 and Experiment 4). Our findings suggest that different cognitive processes come into play when interpreting presuppositions in order to get a meaningful interpretation of a discourse.

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Humor is a special human expression style, an important "lubricant" for daily communication for people; people can convey emotional messages that are not easily expressed through humor. At present, artificial intelligence is one of the popular research domains; "discourse understanding" is also an important research direction, and how to make computers recognize and understand humorous expressions similar to humans has become one of the popular research domains for natural language processing researchers. In this paper, a humor recognition model (MLSN) based on current humor theory and popular deep learning techniques is proposed for the humor recognition task. The model automatically identifies whether a sentence contains humor expression by capturing the inconsistency, phonetic features, and ambiguity of a joke as semantic features. The model was experimented on three publicly available wisecrack datasets and compared with state-of-the-art language models, and the results demonstrate that the proposed model has better humor recognition accuracy and can contribute to the research on discourse understanding.

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In discourse comprehension, we need to draw inferences to make sense of discourse. Previous neuroimaging studies have investigated the neural correlates of causal inferences in discourse understanding. However, these findings have been divergent, and how these types of inferences are related to causal inferences in logical problem-solving remains unclear. Using the activation likelihood estimation (ALE) approach, the current meta-analysis analyzed 19 experiments on causal inferences in discourse understanding and 20 experiments on those in logical problem-solving to identify the neural correlates of these two cognitive processes and their shared and distinct neural correlates. We found that causal inferences in discourse comprehension recruited a left-lateralized frontotemporal brain system, including the left inferior frontal gyrus, the left middle temporal gyrus (MTG), and the bilateral medial prefrontal cortex (MPFC), while causal inferences in logical problem-solving engaged a nonoverlapping brain system in the frontal and parietal cortex, including the left inferior frontal gyrus, the bilateral middle frontal gyri, the dorsal MPFC, and the left inferior parietal lobule (IPL). Furthermore, the pattern similarity analyses showed that causal inferences in discourse understanding were primarily related to the terms about language processing and theory-of-mind processing. Both types of inferences were found to be related to the terms about memory and executive function. These findings suggest that causal inferences in discourse understanding recruit distinct neural bases from those in logical problem-solving and rely more on semantic knowledge and social interaction experiences.