Nonverbal Semantics Test (NVST)—A Novel Diagnostic Tool to Assess Semantic Processing Deficits: Application to Persons with Aphasia after Cerebrovascular Accident

Assessment of semantic processing capacities often relies on verbal tasks which are, however, sensitive to impairments at several language processing levels. Especially for persons with aphasia there is a strong need for a tool that measures semantic processing skills independent of verbal abilities. Furthermore, in order to assess a patient’s potential for using alternative means of communication in cases of severe aphasia, semantic processing should be assessed in different nonverbal conditions. The Nonverbal Semantics Test (NVST) is a tool that captures semantic processing capacities through three tasks—Semantic Sorting, Drawing, and Pantomime. The main aim of the current study was to investigate the relationship between the NVST and measures of standard neurolinguistic assessment. Fifty-one persons with aphasia caused by left hemisphere brain damage were administered the NVST as well as the Aachen Aphasia Test (AAT). A principal component analysis (PCA) was conducted across all AAT and NVST subtests. The analysis resulted in a two-factor model that captured 69% of the variance of the original data, with all linguistic tasks loading high on one factor and the NVST subtests loading high on the other. These findings suggest that nonverbal tasks assessing semantic processing capacities should be administered alongside standard neurolinguistic aphasia tests.

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A Theoretical Account of Lexical and Semantic Naming Deficits in Bilingual Aphasia

Journal of Speech Language and Hearing Research ◽

10.1044/1092-4388(2012/12-0091) ◽

2013 ◽

Vol 56 (4) ◽

pp. 1314-1327 ◽

Cited By ~ 20

Author(s):

Teresa Gray ◽

Swathi Kiran

Keyword(s):

Language Proficiency ◽

Language Processing ◽

Semantic Processing ◽

Language Deficits ◽

Theoretical Account ◽

Language Assessments ◽

Processing Deficits ◽

Bilingual Aphasia ◽

Naming Deficits ◽

The Relationship

Purpose The purpose of this study was to examine premorbid language proficiency and lexical and semantic processing deficits in bilingual aphasia and develop a theoretical account of bilingual language processing. Method Nineteen Spanish–English patients with bilingual aphasia completed a language use questionnaire (LUQ) and were administered Spanish and English standardized language assessments. The authors analyzed the data to (a) identify patterns of lexical and semantic processing deficits and conceptualize a theoretical framework that accounts for language deficits, (b) determine LUQ measures that predict poststroke language deficits, and (c) evaluate the relationship between predictive LUQ measures and poststroke language deficits in order to identify impairment patterns. Results On the basis of the results, the authors obtained significant correlations on several measures between language input and output. They identified prestroke language ability rating as the strongest predictor of poststroke outcomes. On the basis of these data, 2 distinct groups were identified: (a) patients who lost the same amount of language in Spanish and English and (b) patients who lost different amounts of Spanish and English. Conclusions These findings suggest that it is possible to identify relationships between language patterns and deficits in patients with bilingual aphasia and that these trends will be instrumental in clinical assessments of this understudied population.

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Psychometric Properties of the Snaith–Hamilton Pleasure Scale and a Facet-Level Analysis of the Relationship Between Anhedonia and Extraversion in a Nonclinical Sample

Psychological Reports ◽

10.1177/0033294118756336 ◽

2018 ◽

Vol 122 (1) ◽

pp. 360-375 ◽

Cited By ~ 5

Author(s):

Eva Langvik ◽

Sigrun Borgen Austad

Keyword(s):

Principal Component Analysis ◽

Factor Analysis ◽

Confirmatory Factor Analysis ◽

Factor Model ◽

Principal Component ◽

Social Anhedonia ◽

Confirmatory Factor ◽

Nonclinical Sample ◽

Physical Anhedonia ◽

The Relationship

The aim of this study is to investigate the psychometric properties of the Snaith–Hamilton Pleasure Scale (SHAPS) and look at facets of extraversion as predictors of anhedonia. SHAPS is hypothesized to be multidimensional, stable over time in a nonclinical sample, and related to extraversion on both dimension and facet level. Data collection was conducted at baseline ( N = 362) and at a 10-week follow-up ( N = 94). The structural properties of SHAPS were analyzed using principal component analysis and confirmatory factor analysis. Multiple regression explored facets of extraversion as predictors of anhedonia. The results show that SHAPS is stable across time ( r = .71, p < .001), with high internal consistency (α = .89). In the principal component analysis, a two-factor model emerged (Social and Physical anhedonia). The confirmatory factor analysis indicated that the two-factor model consisting of Physical anhedonia (α = .81) and Social anhedonia (α = .87) had a better fit than the one-factor model. Higher scores on Gregariousness and Positive emotions at baseline predicted higher scores on the SHAPS total and Social and Physical anhedonia ( p < .05). Lower scores on Assertiveness predicted higher scores on Social anhedonia ( p < .05). These results support the view of anhedonia as a multidimensional concept that should be regarded as a trait, rather than a state or mere bypassing symptom. The relationship between anhedonia and extroversion is best understood by applying a multidimensional approach to anhedonia and by focusing on the facet level of extroversion.

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Relationship between cognition and primary negative symptoms sub-domains in schizophrenia

European Psychiatry ◽

10.1016/j.eurpsy.2017.01.2112 ◽

2017 ◽

Vol 41 (S1) ◽

pp. S188-S188

Author(s):

O.O. Capatina ◽

I.V. miclutia ◽

A. toma

Keyword(s):

Negative Symptoms ◽

Factor Model ◽

Principal Component ◽

Assessment Tools ◽

Cognitive Factor ◽

Competing Interest ◽

Meta Analyses ◽

Relationship Of ◽

The Relationship ◽

Number Of Authors

IntroductionThe relationship between negative symptoms and cognition in schizophrenia is not clear, a number of authors whom studied this relationship came up with inconsistent findings and meta-analyses show that there is a small moderate associations between the two domains.Objectives and aimsThe aim of this study was to investigate the relationship between cognition and the primary negative symptoms sub-domains.MethodsSixty-seven female patients with schizophrenia were evaluated using PANSS ans NSA-16 scales. Correlation and regression analyses were used in the present study to investigate the relationship between the primary negative symptoms sub-domains obtained by using the principal component analysis, and cognition evaluated with the PANSS using the 5 factor model as described by Lindenmayer.ResultsNo relationship was found between the PANSS Cognitive factor and Negative factor, but when investigating the relationship of the Cognitive PANSS factor with the negative sub-domains: diminished expression (DE) and avolition-apathy (AA), it was shown that there is a significant association between cognition and AA domain, but there was shown no association with the DE domain, and there was just a small association with the composit score of the NAS-16.ConclusionsOur study reveals the relative independence of cognitive factor from the negative domain of the psychopathology, even though the association with AA domain was clear. These findings also support the need of using appropriate assessment tools in order to get a refined understanding of the phenomenology of schizophrenia.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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What Does Human Development Index Rating Mean in Terms of Individualism/Collectivism?

European Psychologist ◽

10.1027//1016-9040.8.2.97 ◽

2003 ◽

Vol 8 (2) ◽

pp. 97-100 ◽

Cited By ~ 3

Author(s):

Maria José Sotelo ◽

Luis Gimeno

Keyword(s):

Human Development ◽

Human Development Index ◽

Principal Component ◽

General Idea ◽

Development Index ◽

Low Levels ◽

The Relationship

The authors explore an alternative way of analyzing the relationship between human development and individualism. The method is based on the first principal component of Hofstede's individualism index in the Human Development Index rating domain. Results suggest that the general idea that greater wealth brings more individualism is only true for countries with high levels of development, while for middle or low levels of development the inverse is true.

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HUBUNGAN KEPADATAN BIVALVIA DENGAN PARAMETER LINGKUNGAN DI PESISIR TANJUNG PURA KABUPATEN BANGKA TENGAH

Akuatik Jurnal Sumberdaya Perairan ◽

10.33019/akuatik.v13i2.1434 ◽

2020 ◽

Vol 13 (2) ◽

pp. 112-121

Author(s):

Sudiyar . ◽

Okto Supratman ◽

Indra Ambalika Syari

Keyword(s):

Negative Impact ◽

Principal Component ◽

Environmental Parameters ◽

Data Retrieval ◽

Distribution Patterns ◽

Random System ◽

System Method ◽

Relationship Of ◽

The Relationship ◽

Anadara Granosa

The destructive fishing feared will give a negative impact on the survival of this organism. This study aims to analyze the density of bivalves, distribution patterns, and to analyze the relationship of bivalves with environmental parameters in Tanjung Pura village. This research was conducted in March 2019. The systematic random system method was used for collecting data of bivalves. The collecting Data retrieval divided into five research stasions. The results obtained 6 types of bivalves from 3 families and the total is 115 individuals. The highest bivalve density is 4.56 ind / m², and the lowest bivalves are located at station 2,1.56 ind / m², The pattern of bivalve distribution in the Coastal of Tanjung Pura Village is grouping. The results of principal component analysis (PCA) showed that Anadara granosa species was positively correlated with TSS r = 0.890, Dosinia contusa, Anomalocardia squamosa, Mererix meretrix, Placamen isabellina, and Tellinella spengleri were positively correlated with currents r = 0.933.

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A psychologically-inspired fuzzy-based approach for user personality prediction in rumor propagation across social networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189864 ◽

2021 ◽

pp. 1-15

Author(s):

V. Indu ◽

Sabu M. Thampi

Keyword(s):

Social Networks ◽

Factor Model ◽

Five Factor Model ◽

Principal Component ◽

Common People ◽

Rumor Propagation ◽

Usage Patterns ◽

User Personality ◽

Personality Prediction ◽

High Degree

Social networks have emerged as a fertile ground for the spread of rumors and misinformation in recent times. The increased rate of social networking owes to the popularity of social networks among the common people and user personality has been considered as a principal component in predicting individuals’ social media usage patterns. Several studies have been conducted to study the psychological factors influencing the social network usage of people but only a few works have explored the relationship between the user’s personality and their orientation to spread rumors. This research aims to investigate the effect of personality on rumor spread on social networks. In this work, we propose a psychologically-inspired fuzzy-based approach grounded on the Five-Factor Model of behavioral theory to analyze the behavior of people who are highly involved in rumor diffusion and categorize users into the susceptible and resistant group, based on their inclination towards rumor sharing. We conducted our experiments in almost 825 individuals who shared rumor tweets on Twitter related to five different events. Our study ratifies the truth that the personality traits of individuals play a significant role in rumor dissemination and the experimental results prove that users exhibiting a high degree of agreeableness trait are more engaged in rumor sharing activities and the users high in extraversion and openness trait restrain themselves from rumor propagation.

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EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

Applied Spectroscopy ◽

10.1177/0003702820987847 ◽

2021 ◽

pp. 000370282098784

Author(s):

James Renwick Beattie ◽

Francis Esmonde-White

Keyword(s):

Principal Components ◽

Principal Component ◽

Original Data ◽

Specific Chemical ◽

Successive Refinement ◽

Minimal Loss ◽

Components Analysis ◽

The Mathematical Model ◽

Application Specific ◽

Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing âconcentrationâ or âweightsâ. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

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Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽

10.3390/atmos12010012 ◽

2020 ◽

Vol 12 (1) ◽

pp. 12

Author(s):

Yulia Ivanova ◽

Anton Kovalev ◽

Vlad Soukhovolsky

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Principal Component ◽

Stem Growth ◽

Forest Growth ◽

Parabolic Approximation ◽

New Approach ◽

The Relationship ◽

Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

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Implicit Semantic Processing of Linguistic and Non-linguistic Stimuli in Adults with Autism Spectrum Disorder

Journal of Autism and Developmental Disorders ◽

10.1007/s10803-020-04736-5 ◽

2021 ◽

Author(s):

Emme O’Rourke ◽

Emily L. Coderre

Keyword(s):

Autism Spectrum Disorder ◽

Language Processing ◽

Semantic Processing ◽

Autistic Traits ◽

Autism Spectrum ◽

Event Related Potential ◽

Spectrum Disorder ◽

Adults With Autism ◽

Priming Task ◽

Adults With Asd

AbstractWhile many individuals with autism spectrum disorder (ASD) experience difficulties with language processing, non-linguistic semantic processing may be intact. We examined neural responses to an implicit semantic priming task by comparing N400 responses—an event-related potential related to semantic processing—in response to semantically related or unrelated pairs of words or pictures. Adults with ASD showed larger N400 responses than typically developing adults for pictures, but no group differences occurred for words. However, we also observed complex modulations of N400 amplitude by age and by level of autistic traits. These results offer important implications for how groups are delineated and compared in autism research.

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Quantify Coal Macrolithotypes of a Whole Coal Seam: A Method Combing Multiple Geophysical Logging and Principal Component Analysis

Energies ◽

10.3390/en14010213 ◽

2021 ◽

Vol 14 (1) ◽

pp. 213

Author(s):

Chao Cui ◽

Suoliang Chang ◽

Yanbin Yao ◽

Lutong Cao

Keyword(s):

Principal Component Analysis ◽

Coal Seam ◽

Coalbed Methane ◽

Principal Component ◽

Component Analysis ◽

Gas Production ◽

Index Model ◽

Geophysical Logging ◽

The Relationship ◽

Observation Results

Coal macrolithotypes control the reservoir heterogeneity, which plays a significant role in the exploration and development of coalbed methane. Traditional methods for coal macrolithotype evaluation often rely on core observation, but these techniques are non-economical and insufficient. The geophysical logging data are easily available for coalbed methane exploration; thus, it is necessary to find a relationship between core observation results and wireline logging data, and then to provide a new method to quantify coal macrolithotypes of a whole coal seam. In this study, we propose a L-Index model by combing the multiple geophysical logging data with principal component analysis, and we use the L-Index model to quantitatively evaluate the vertical and regional distributions of the macrolithotypes of No. 3 coal seam in Zhengzhuang field, southern Qinshui basin. Moreover, we also proposed a S-Index model to quantitatively evaluate the general brightness of a whole coal seam: the increase of the S-Index from 1 to 3.7, indicates decreasing brightness, i.e., from bright coal to dull coal. Finally, we discussed the relationship between S-Index and the hydro-fracturing effect. It was found that the coal seam with low S-Index values can easily form long extending fractures during hydraulic fracturing. Therefore, the lower S-Index values indicate much more favorable gas production potential in the Zhengzhuang field. This study provides a new methodology to evaluate coal macrolithotypes by using geophysical logging data.

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