Observers use gesture to disambiguate contrastive expressions of preference

We present two studies exploring how participants respond when a speaker contrasts two ideas, then expresses an ambiguous preference towards one of them. Study 1 showed that, when reading a speaker’s preference as text, participants tended to choose whatever was said last as matching the speaker’s preference, reflecting the recent-mention bias of anaphora resolution. In Study 2, we asked whether this pattern changed for audio versions of our stimuli. We found that it did not. We then asked whether observers used gesture to disambiguate the speaker’s preference. Participants watched videos in which two statements were spoken. Co-speech gestures were produced during each statement, in two different locations. Next, an ambiguous preference for one option was spoken. In ‘gesture disambiguating’ trials, this statement was accompanied by a gesture in the same spatial location as the gesture accompanying the first statement. In ‘gesture non-disambiguating’ trials, no third gesture occurred. Participants chose the first statement as matching the speaker’s preference more often for gesture disambiguating compared to non-disambiguating trials. Our findings add to the literature on resolution of ambiguous anaphoric reference involving concrete entities and discourse deixis, and we extend this literature to show that gestures indexing abstract ideas are also used during discourse comprehension.

EXPRESS: Misretrieval but not misrepresentation: A feature misbinding account of post-interpretive effects in number attraction.

Attraction effects in comprehension have reliably shown a grammaticality asymmetry in which mismatching plural attractors confer facilitatory interference for ungrammatical verbs but no processing cost for grammatical verbs (Tanner et al., 2014; Wagers et al., 2009). While this has favored cue-based retrieval accounts of attraction phenomena in comprehension, Patson and Husband (2016) offered offline evidence suggesting comprehenders systematically misrepresent number information in attraction phrases, leaving open the possibility for faulty NP representations later in processing. The current study employs two self-paced reading discourse experiments to test for number attraction misrepresentations in real-time. Specifically, the attraction phrases occurred as embedded direct object phrases, allowing for a direct test of the role of attractor noun number in head noun number misrepresentation (i.e. no number cue from verb). Although no on-line evidence for misrepresentation was found, a third single-sentence RSVP experiment showed error rates to offline probes corroborating the post-interpretive findings from Patson and Husband (2016), suggesting that a search in memory for associative features may not employ the same processes as the formation of dependencies in discourse comprehension. The findings are discussed in the framework of feature misbinding in memory in line with recent post-interpretive accounts of offline comprehension errors.

Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.

Narrative event segmentation in the cortical reservoir

Recent research has revealed that during continuous perception of movies or stories, humans display cortical activity patterns that reveal hierarchical segmentation of event structure. Thus, sensory areas like auditory cortex display high frequency segmentation related to the stimulus, while semantic areas like posterior middle cortex display a lower frequency segmentation related to transitions between events. These hierarchical levels of segmentation are associated with different time constants for processing. Likewise, when two groups of participants heard the same sentence in a narrative, preceded by different contexts, neural responses for the groups were initially different and then gradually aligned. The time constant for alignment followed the segmentation hierarchy: sensory cortices aligned most quickly, followed by mid-level regions, while some higher-order cortical regions took more than 10 seconds to align. These hierarchical segmentation phenomena can be considered in the context of processing related to comprehension. In a recently described model of discourse comprehension word meanings are modeled by a language model pre-trained on a billion word corpus. During discourse comprehension, word meanings are continuously integrated in a recurrent cortical network. The model demonstrates novel discourse and inference processing, in part because of two fundamental characteristics: real-world event semantics are represented in the word embeddings, and these are integrated in a reservoir network which has an inherent gradient of functional time constants due to the recurrent connections. Here we demonstrate how this model displays hierarchical narrative event segmentation properties beyond the embeddings alone, or their linear integration. The reservoir produces activation patterns that are segmented by a hidden Markov model (HMM) in a manner that is comparable to that of humans. Context construction displays a continuum of time constants across reservoir neuron subsets, while context forgetting has a fixed time constant across these subsets. Importantly, virtual areas formed by subgroups of reservoir neurons with faster time constants segmented with shorter events, while those with longer time constants preferred longer events. This neurocomputational recurrent neural network simulates narrative event processing as revealed by the fMRI event segmentation algorithm provides a novel explanation of the asymmetry in narrative forgetting and construction. The model extends the characterization of online integration processes in discourse to more extended narrative, and demonstrates how reservoir computing provides a useful model of cortical processing of narrative structure.

Neural Correlates of Causal Inferences in Discourse Understanding and Logical Problem-Solving: A Meta-Analysis Study

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.