Dataset Information

The Predictive Value of Tagalog Voice Morphology in Filler-Gap Dependency Formation.

ABSTRACT: Research has shown that when processing filler-gap dependencies, comprehenders do not wait until they encounter all of the bottom-up information in the input. Instead, they use various types of linguistic information to predictively posit a gap that would allow the dependency to be resolved. They can use syntactic (Traxler and Pickering, 1996), lexical (Trueswell et al., 1994), morphological (Kamide et al., 2003), and prosodic (Nagel et al., 1994) information. Here we examine whether Tagalog comprehenders use the language's voice morphology to guide their incremental interpretations. We hypothesized that voice allows comprehenders to commit to an interpretation upon encountering the verb, since they have information about the event structure at this point in time and by virtue of the voice morphology, the thematic role of the filler. In experiment 1, using an acceptability judgment study, we found that comprehenders differed in how they used the different voices in different filler-gap contexts to detect the licitness of displacements. These differences may have consequences for how voice is used in real-time. In experiments 2 and 3, using the stops-making-sense paradigm (Boland et al., 1990), we found that comprehenders used voice as a cue to actively associate the filler with the gap. However, in experiment 3, the way in which they used voice varied by type and varied across types of filler-gap dependencies. We argue that comprehenders were using construction-specific cue validities when processing filler-gap dependencies. However, they also engaged with other classes of linguistic information, including (but not limited to) information about the structural similarities and the thematic complexity of the dependencies involved, and the relative frequency of the different types of voices in the language. These interactions resulted in processing asymmetries.

SUBMITTER: Pizarro-Guevara JS

PROVIDER: S-EPMC7174735 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The Predictive Value of Tagalog Voice Morphology in Filler-Gap Dependency Formation.

Pizarro-Guevara Jed Sam JS Wagers Matthew M

Frontiers in psychology 20200415

Research has shown that when processing filler-gap dependencies, comprehenders do not wait until they encounter all of the bottom-up information in the input. Instead, they use various types of linguistic information to predictively posit a gap that would allow the dependency to be resolved. They can use syntactic (Traxler and Pickering, 1996), lexical (Trueswell et al., 1994), morphological (Kamide et al., 2003), and prosodic (Nagel et al., 1994) information. Here we examine whether Tagalog com ...[more]

PMID: 32351420

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Project description:BackgroundRisk stratification is crucial to improve tailored therapy in patients with suspected coronary artery disease (CAD). This study investigated the ability of targeted proteomics to predict presence of high-risk plaque or absence of coronary atherosclerosis in patients with suspected CAD, defined by coronary computed tomography angiography (CCTA).MethodsPatients with suspected CAD (n = 203) underwent CCTA. Plasma levels of 358 proteins were used to generate machine learning models for the presence of CCTA-defined high-risk plaques or complete absence of coronary atherosclerosis. Performance was tested against a clinical model containing generally available clinical characteristics and conventional biomarkers.FindingsA total of 196 patients with analyzable protein levels (n = 332) was included for analysis. A subset of 35 proteins was identified predicting the presence of high-risk plaques. The developed machine learning model had fair diagnostic performance with an area under the curve (AUC) of 0·79 ± 0·01, outperforming prediction with generally available clinical characteristics (AUC = 0·65 ± 0·04, p < 0·05). Conversely, a different subset of 34 proteins was predictive for the absence of CAD (AUC = 0·85 ± 0·05), again outperforming prediction with generally available characteristics (AUC = 0·70 ± 0·04, p < 0·05).InterpretationUsing machine learning models, trained on targeted proteomics, we defined two complementary protein signatures: one for identification of patients with high-risk plaques and one for identification of patients with absence of CAD. Both biomarker subsets were superior to generally available clinical characteristics and conventional biomarkers in predicting presence of high-risk plaque or absence of coronary atherosclerosis. These promising findings warrant external validation of the value of targeted proteomics to identify cardiovascular risk in outcome studies. FUND: This study was supported by an unrestricted research grant from HeartFlow Inc. and partly supported by a European Research Area Network on Cardiovascular Diseases (ERA-CVD) grant (ERA CVD JTC2017, OPERATION). Funders had no influence on trial design, data evaluation, and interpretation.

Dataset Information

The Predictive Value of Tagalog Voice Morphology in Filler-Gap Dependency Formation.

Publications

The Predictive Value of Tagalog Voice Morphology in Filler-Gap Dependency Formation.

Similar Datasets

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