Search

Both prediction and working memory constraints have been established as key factors in characterizing incremental sentence processing difficulty. Here we investigate the less explored question: Whether and how predictive expectation and working memory interact with each other using data from naturalistic reading time corpora. We provide broad-coverage evaluations of two hypotheses that make divergent predictions regarding the interaction of expectation and memory constraints: the Information Locality and Prediction Maintenance hypotheses. We first confirmed the predictions of both expectation- and working memory-based theories. Regarding their interactions, we find support the Information Locality hypothesis: Strong mutual predictability can enhance locality effects. We argue that future theory building in sentence processing should therefore take into consideration both prediction and memory constraints, as well as their potential interaction.

Cue-based retrieval theories of sentence processing assume that subject-verb dependencies are resolved through a content-addressable search in memory. The model assumes that multiple nouns with similar syntactic or semantic features increase dependency completion difficulty. English eyetracking data (reading) are consistent with model predictions; interestingly, a similar experiment with German --a language marking case overtly-- suggests that only syntactic features affect dependency completion difficulty. Why would German show different behavior than English? Using a computational implementation of the cue-based retrieval model and model comparison using Bayes factors, we show that the reason is systematic variation at the individual-participant level: German participants overwhelmingly give higher weighting to syntactic cues over semantic cues, whereas English participants mostly give equal weighting to syntactic and semantic cues. The richer morphosyntax of German leads to syntactic cues being favoured; if such cues are largely absent (as in English) the parser relies on both cue types equally.

Studies of the speed-accuracy trade-off (SAT) have been influential in arguing for the direct-access model of retrieval in sentence processing. The direct-access model assumes that long-distance dependencies rely on a content-addressable search for the correct representation in memory. Here, we address two important weaknesses in the statistical methods standardly used for analysing SAT data. First, these methods are based on non-hierarchical modelling. We show how a hierarchical model can be fit to SAT data, and we test parameter recovery in this more conservative model. The parameters most relevant to the direct-access account cannot be accurately estimated, and we attribute this to the standard SAT model being overparameterised for the limited data available to fit it. Second, the power properties of SAT studies are unknown. We conduct a power analysis and show that inferences from null results to the null hypothesis, though commonplace in the SAT literature, may be unwarranted.

Studies on similarity-based interference in subject-verb number agreement dependencies have found a consistent facilitatory effect in ungrammatical sentences but no conclusive effect in grammatical sentences. Existing models propose that interference is caused either by a faulty representation of the input (encoding-based models) or by difficulty in retrieving the subject based on cues at the verb (retrieval-based models). Neither class of model captures the observed patterns in human reading time data. We propose a new model that integrates a feature encoding mechanism into an existing cue-based retrieval model. Our model outperforms the cue-based retrieval model in explaining interference effect data from both grammatical and ungrammatical sentences. These modeling results yield a new insight into sentence processing, encoding modulates retrieval. Nouns stored in memory undergo feature distortion, which in turn affects how retrieval unfolds during dependency completion.

Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer-i Cancho, 2008, 2015; Hawkins, 1994). Consistent with this claim, corpus studies have shown that the average distance between a head (e.g., verb) and its dependent (e.g., noun) tends to be short cross-linguistically (Ferrer-i Cancho, 2014; Futrell, Mahowald, & Gibson, 2015; Liu, Xu, & Liang, 2017). This implies that on average languages avoid inefficient or complex structures for simpler structures. But a number of studies in psycholinguistics (Konieczny, 2000; Levy & Keller, 2013; Vasishth, Suckow, Lewis, & Kern, 2010) show that the comprehension system can "adapt" to the typological properties of a language, for example, verb-final order, leading to more complex structures, for example, having longer linear distance between a head and its dependent. In this paper, we conduct a corpus study for a group of 38 languages, which were either Subject-Verb-Object (SVO) or Subject-Object-Verb (SOV), in order to investigate the role of word order typology in determining syntactic complexity. We present results aggregated across all dependency types, as well as for specific verbal (objects, indirect objects, and adjuncts) and nonverbal (nominal, adjectival, and adverbial) dependencies. The results suggest that dependency distance in a language is determined by the default word order of a language, and crucially, the "direction" of a dependency (whether the head precedes the dependent or follows it; e.g., whether the noun precedes the verb or follows it). Particularly we show that in SOV languages (e.g., Hindi, Korean) as well as SVO languages (e.g., English, Spanish), longer linear distance (measured as number of words) between head and dependent arises in structures when they mirror the default word order of the language. In addition to showing results on linear distance, we also investigate the influence of word order typology on hierarchical distance (HD; measured as number of heads between head and dependent). The results for HD are similar to that of linear distance. At the same time, in comparison to linear distance, the influence of adaptability on HD seems less strong. In particular, the results show that most languages tend to avoid greater structural depth. Together, these results show evidence for "limited adaptability" to the default word order preferences in a language. Our results support a large body of work in the processing literature that highlights the importance of linguistic exposure and its interaction with working memory constraints in determining sentence complexity. Our results also point to the possible role of other factors such as the morphological richness of a language and a multifactor account of sentence complexity remains a promising area for future investigation.

Ulcer is one of the most common diseases affecting throughout the world population. The allopathic treatment of ulcer adversely affects the health by causing harmful side effects. Currently, many herbal plants and secondary metabolites have been used for the ulcer treatment. In the present review, many herbal plants and their parts (root, rhizome, bark, leaves and fruits) have been listed in the table are currently being used for ulcer treatment. These metabolites are responsible for ulcer-neutralization or anti-inflammatory properties. In silico study, plant metabolites showed interaction between protodioscin (secondary metabolites of Asparagus racemosus) and interferon-γ (virulent factor of gastric ulcer) during molecular docking. All the residues of interferon-γ exhibited hydrophobic interactions with plant metabolites. These interactions helps in understanding the plant secondary metabolites vis a vis will open a new door in the research field of new drug discovery and designing for the ulcer treatment.