Analisis hubungan antara literasi keuangan, perilaku keuangan, dan kepuasan keuangan

The purpose of this research is to investigate causal relationship between financial literacy and financial behavior, financial behavior and financial satisfaction, and between financial literacy and financial satisfaction. The analysis technique used was Granger Causality analysis. The research data was obtained using questionnaire distributed to 100 respondents. The repondents are Indonesian citizens in productive age (15-64 years). The results show a one-way causality between financial literacy and financial behavior, between financial behavior and financial satisfaction, but there is no causality relationship (independence) between financial literacy and financial satisfaction. Abstrak Penelitian ini bertujuan untuk mengetahui hubungan sebab akibat dari literasi finansial dan perilaku finansial, perilaku finansial dan kepuasan finansial, serta antara literasi finansial dan kepuasan finansial. Teknik analisis yang digunakan adalah analisa Granger Causality. Data dikumpulkan dengan menggunakn kuesioner yang dibagikan kepada 100 responden. Para responden adalah warga Indonesia berusia produktif (15-64) tahun. Hasil yang didapatkan menunjukkan adanya hubungan sebab akibat antara literasi finansial dengan perilaku financial, serta antara perilaku finansial dankepuasan finansial. Sementara hubungan antar literasi finansial dan kepuasan finansial tidak ditemukan.

Brain Network to Placebo and Nocebo Responses in Acute Experimental Lower Back Pain: A Multivariate Granger Causality Analysis of fMRI Data

Background and Objective: Placebo and nocebo responses are widely observed. Herein, we investigated the nocebo hyperalgesia and placebo analgesia responses in brain network in acute lower back pain (ALBP) model using multivariate Granger causality analysis (GCA). This approach analyses functional magnetic resonance imaging (fMRI) data for lagged-temporal correlation between different brain areas.Method: After completing the ALBP model, 20 healthy subjects were given two interventions, once during a placebo intervention and once during a nocebo intervention, pseudo-randomly ordered. fMRI scans were performed synchronously during each intervention, and visual analog scale (VAS) scores were collected at the end of each intervention. The fMRI data were then analyzed using multivariate GCA.Results: Our results found statistically significant differences in VAS scores from baseline (pain status) for both placebo and nocebo interventions, as well as between placebo and nocebo interventions. In placebo network, we found a negative lagged-temporal correlation between multiple brain areas, including the dorsolateral prefrontal cortex (DLPFC), secondary somatosensory cortex area, anterior cingulate cortex (ACC), and insular cortex (IC); and a positive lagged-temporal correlation between multiple brain areas, including IC, thalamus, ACC, as well as the supplementary motor area (SMA). In the nocebo network, we also found a positive lagged-temporal correlation between multiple brain areas, including the primary somatosensory cortex area, caudate, DLPFC and SMA.Conclusion: The results of this study suggest that both pain-related network and reward system are involved in placebo and nocebo responses. The placebo response mainly works by activating the reward system and inhibiting pain-related network, while the nocebo response is the opposite. Placebo network also involves the activation of opioid-mediated analgesia system (OMAS) and emotion pathway, while nocebo network involves the deactivation of emotional control. At the same time, through the construction of the GC network, we verified our hypothesis that nocebo and placebo networks share part of the same brain regions, but the two networks also have their own unique structural features.

Computational Mindfulness

We take a computational approach to investigating highly abstract concepts including mindfulness, brain waves, and quantum mechanics. Using Langerian non-meditative mindfulness, defined as the active process of noticing new things, we find that when tested on the authors as subjects in two different ways, induced mindfulness is consistently distinguishable from induced mindlessness, and results in a calmer time series of brain waves as measured on an electroencephalogram. Additional results include a statistical Granger causality analysis of scholarly mindfulness research showing that Langerian mindfulness research causes future mindfulness research but not vice versa, and preliminary results from another study showing substantial differences in responses among subjects induced to view their own faces either mindfully or mindlessly.

Robust unified Granger causality analysis: a normalized maximum likelihood form

Unified Granger causality analysis (uGCA) alters conventional two-stage Granger causality analysis into a unified code-length guided framework. We have presented several forms of uGCA methods to investigate causal connectivities, and different forms of uGCA have their own characteristics, which capable of approaching the ground truth networks well in their suitable contexts. In this paper, we considered comparing these several forms of uGCA in detail, then recommend a relatively more robust uGCA method among them, uGCA-NML, to reply to more general scenarios. Then, we clarified the distinguished advantages of uGCA-NML in a synthetic 6-node network. Moreover, uGCA-NML presented its good robustness in mental arithmetic experiments, which identified a stable similarity among causal networks under visual/auditory stimulus. Whereas, due to its commendable stability and accuracy, uGCA-NML will be a prior choice in this unified causal investigation paradigm.

Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L1/2 norm framework for feature extraction, and uses L2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46–21.81%.

Robust Unified Granger Causality Analysis: A Normalized Maximum Likelihood Form

Unified Granger causality analysis (uGCA) alters conventional two-stage Granger causality analysis into a unified code-length guided framework. We have presented several forms of uGCA methods to investigate causal connectivities, and different forms of uGCA have their own characteristics, which capable of approaching the ground truth networks well in their suitable contexts. In this paper, we considered comparing these several forms of uGCA in detail, then recommend a relatively more robust uGCA method among them, uGCA-NML, to reply to more general scenarios. Then, we clarified the distinguished advantages of uGCA-NML in a synthetic 6-node network. Moreover, uGCA-NML presented its good robustness in mental arithmetic experiments, which identified a stable similarity among causal networks under visual/auditory stimulus. Whereas, due to its commendable stability and accuracy, uGCA-NML will be a prior choice in this unified causal investigation paradigm.