Cognitive Processes Underlying Verbal Fluency in Multiple Sclerosis

Background: Verbal fluency (VF) has been associated with several cognitive functions, but the cognitive processes underlying verbal fluency deficits in Multiple Sclerosis (MS) are controversial. Further knowledge about VF could be useful in clinical practice, because these tasks are brief, applicable, and reliable in MS patients. In this study, we aimed to evaluate the cognitive processes related to VF and to develop machine-learning algorithms to predict those patients with cognitive deficits using only VF-derived scores.Methods: Two hundred participants with MS were enrolled and examined using a comprehensive neuropsychological battery, including semantic and phonemic fluencies. Automatic linear modeling was used to identify the neuropsychological test predictors of VF scores. Furthermore, machine-learning algorithms (support vector machines, random forest) were developed to predict those patients with cognitive deficits using only VF-derived scores.Results: Neuropsychological tests associated with attention-executive functioning, memory, and language were the main predictors of the different fluency scores. However, the importance of memory was greater in semantic fluency and clustering scores, and executive functioning in phonemic fluency and switching. Machine learning algorithms predicted general cognitive impairment and executive dysfunction, with F1-scores over 67–71%.Conclusions: VF was influenced by many other cognitive processes, mainly including attention-executive functioning, episodic memory, and language. Semantic fluency and clustering were more explained by memory function, while phonemic fluency and switching were more related to executive functioning. Our study supports that the multiple cognitive components underlying VF tasks in MS could serve for screening purposes and the detection of executive dysfunction.

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Is there disproportionate impairment in semantic or phonemic fluency in schizophrenia?

Journal of the International Neuropsychological Society ◽

10.1017/s1355617703910095 ◽

2003 ◽

Vol 9 (1) ◽

pp. 79-88 ◽

Cited By ~ 38

Author(s):

WILLIAM S. KREMEN ◽

LARRY J. SEIDMAN ◽

STEPHEN V. FARAONE ◽

MING T. TSUANG

Keyword(s):

Bipolar Disorder ◽

Verbal Fluency ◽

Executive Dysfunction ◽

Small Degree ◽

Semantic Fluency ◽

Diagnostic Specificity ◽

Semantic Organization ◽

Phonemic Fluency ◽

Frontal Lobe Function ◽

Normal Controls

Phonemic and semantic fluency involve the capacity to generate words beginning with particular letters or belonging to particular categories, respectively. The former has been associated with frontal lobe function and the latter with temporoparietal function, but neuroimaging studies indicate overlap of underlying neural networks. Schizophrenia patients may experience disproportionate semantic fluency impairment owing to abnormal semantic organization; however, executive dysfunction in schizophrenia suggests possible disproportionate phonemic fluency impairment. Moreover, little is known about the diagnostic specificity of either verbal fluency deficit to schizophrenia or their stability over time. We examined 83 schizophrenia patients, 15 bipolar disorder patients, and 83 normal controls. Both fluency types were impaired in schizophrenia patients. Schizophrenia patients as a whole manifested disproportionate semantic fluency impairment relative to bipolar disorder patients, but only a subset of schizophrenia patients manifested disproportionate semantic fluency impairment relative to controls. Few characteristics, except to some extent paranoid-nonparanoid subtype, meaningfully differentiated schizophrenia patients with and without this disproportionate impairment. Verbal fluency measures were moderately stable over a 4-year period in schizophrenia patients and controls (.48 < rs < .79). These results mirror a literature that overall suggests a small degree of disproportionate semantic fluency impairment in schizophrenia, but also some heterogeneity in fluency deficits. (JINS, 2003, 9, 79–88.)

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Supplemental Material for One Model to Rule Them All? Using Machine Learning Algorithms to Determine the Number of Factors in Exploratory Factor Analysis

Psychological Methods ◽

10.1037/met0000262.supp ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Factor Analysis ◽

Exploratory Factor Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Number Of Factors

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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The Unlearnable Checkerboard Pattern

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.1.2.holloway.1 ◽

2019 ◽

Vol 1 (2) ◽

pp. 78-80

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Checkerboard Pattern ◽

Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms. Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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