Introduction to Statistics Using R

Akinkunmi, Mustapha

doi:10.2200/s00899ed1v01y201902mas024

“…Collinearity between model variables, or multicollinearity when dealing with several variables, reduces the reliability of regressor coefficient estimation in linear regression 48 . Accordingly, it was necessary to take a series of preprocessing steps to reduce multicollinearity in order to have confidence that model performance was accurate.…”

Section: Methodsmentioning

confidence: 99%

Neural cognitive signals during spontaneous movements in the macaque

Tremblay

¹

,

Testard

²

,

DiTullio

³

et al. 2022

Preprint

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The single neuron basis of cognitive processing in primates has mostly been studied in laboratory settings where movements are severely restricted. It is unclear, therefore, how natural movements might affect neural signatures of cognition in the brain. Moreover, studies in mice indicate that body movements, when measured, account for most of the neural dynamics in the cortex. To examine this issue, we recorded from single neuron ensembles in the prefrontal cortex in moving monkeys performing a cognitive task and characterized eyes, head, and body movements using video tracking. Despite significant trial-to-trial movement variability, single neuron tuning could be precisely measured and decision signals accurately decoded on a single-trial basis. Creating or abolishing spontaneous movements through head restraint and task manipulations had no measurable impact on neural responses. However, encoding models showed that uninstructed movements explained as much neural variance as task variables, with most of them aligned to task events. These results demonstrate that cognitive signals in the cortex are robust to natural movements, but also that unmeasured movements are potential confounds in cognitive neurophysiology experiments.

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“…The linear encoding model we used does not have a method for dealing with missing data points, accordingly these time points with missing data were removed before fitting the model (mean = 46%, SD = 6%). Linear Encoding Model: Mitigation of multicollinearity Collinearity between model variables, or multicollinearity when dealing with several variables, reduces the reliability of regressor coefficient estimation in linear regression 48 . Accordingly, it was necessary to take a series of preprocessing steps to reduce multicollinearity in order to have confidence that model performance was accurate.…”

Section: Task-aligned Vs Task-independent Contributionmentioning

confidence: 99%

Neural cognitive signals during spontaneous movements in the macaque

Tremblay

¹

,

Testard

²

,

DiTullio

³

et al. 2022

View full text Add to dashboard Cite

The single neuron basis of cognitive processing in primates has mostly been studied in laboratory settings where movements are severely restricted. It is unclear, therefore, how natural movements might affect neural signatures of cognition in the brain. Moreover, studies in mice indicate that body movements, when measured, account for most of the neural dynamics in the cortex. To examine this issue, we recorded from single neuron ensembles in the prefrontal cortex in moving monkeys performing a cognitive task and characterized eyes, head, and body movements using video tracking. Despite significant trial-to-trial movement variability, single neuron tuning could be precisely measured and decision signals accurately decoded on a single-trial basis. Creating or abolishing spontaneous movements through head restraint and task manipulations had no measurable impact on neural responses. However, encoding models showed that uninstructed movements explained as much neural variance as task variables, with most of them aligned to task events. These results demonstrate that cognitive signals in the cortex are robust to natural movements, but also that unmeasured movements are potential confounds in cognitive neurophysiology

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“…The clustering technique consist of k-means. The decision tree technique consist of random forest technique (Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion as reported by (Bose et al, 2018). This research has produced a taxonomy which serves as a guide for the choice of techniques in sentiment analysis.…”

Section: Resultsmentioning

confidence: 99%

Sentiment Analysis Techniques and Application-Survey and Taxonomy

Umar

¹

,

Bena

²

,

Wadata

³

2021

ETJ

2

0

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Nowadays, social media platforms, blogs, and e-commerce are commonly use to express opinion on politics, movies, products, education respectively; for election forecasting, business boosting and improvement of teaching and learning. As a result, data generation becomes easier; producing big data which requires appropriate techniques and tools to analyse easily, accurately and timely. Thus, making sentiment analysis very demanding research area. This study will investigate on what basis (sentiment classification level) or area of application (data source) do supervised machine learning approaches particularly Support Vector Machine (SVM), Naïve Bayes, and Maximum Entropy algorithms, and other technique-lexicon-based approach give the best result in sentiment analysis. Based on the review of the literature there is a contradiction on the point that SVM generated the best result in analyzing student sentiment on document level. This study also discovers that sentiment analysis differs from system to system based on polarity (types of the classes to predict: positive or negative, subjective or objective), different levels of classification (sentence, phrase, or document level) and language that is processed. This research produces a taxonomy which serves as a guide for the choice of techniques in sentiment analysis. The taxonomy explores the sentiment classification levels and data preprocessing stages. It also explores that sentiment analysis techniques were organised in to three (3) groups; Machine learning, Lexicon and hybrid or combination. The machine learning techniques were sub-grouped in to two (2) namely; supervised and unsupervised. The supervised were organized in to two (2): Classification and Regression. un-supervised machine learning techniques includes clustering and association. The clustering technique consist of k-means. Decision tree which is a classification based under supervised type of machine learning technique consist of random forest,(Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion. The commonly used tools are Weka, Python compiler, and R programming tool.

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Introduction to Statistics Using R

Cited by 8 publications

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Neural cognitive signals during spontaneous movements in the macaque

Neural cognitive signals during spontaneous movements in the macaque

Neural cognitive signals during spontaneous movements in the macaque

Sentiment Analysis Techniques and Application-Survey and Taxonomy

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