PDF(Pubmed)
Abstract：
UNASSIGNED: While previous studies have suggested close association of psychological variables of students withtheir higher-order cognitive abilities, such studies have largely been lacking for third world countries like India, with their unique socio-economic-cultural set of challenges. We aimed to investigate the relationship between psychological variables (depression, anxiety and stress) and cognitive functions among Indian students, and to predict cognitive performance as a function of these variables.
UNASSIGNED: Four hundred and thirteen university students were systematically selected using purposive sampling. Widely used and validated offline questionnaires were used to assess their psychological and cognitive statuses. Correlational analyses were conducted to examine the associations between these variables. An Artificial Neural Network (ANN) model was applied to predict cognitive levels based on the scores of psychological variables.
UNASSIGNED: Correlational analyses revealed negative correlations between emotional distress and cognitive functioning. Principal Component Analysis (PCA) reduced the dimensionality of the input data, effectively capturing the variance with fewer features. The feature weight analysis indicated a balanced contribution of each mental health symptom, with particular emphasis on one of the symptoms. The ANN model demonstrated moderate predictive performance, explaining a portion of the variance in cognitive levels based on the psychological variables.
UNASSIGNED: The study confirms significant associations between emotional statuses of university students with their cognitive abilities. Specifically, we provide evidence for the first time that in Indian students, self-reported higher levels of stress, anxiety, and depression are linked to lower performance in cognitive tests. The application of PCA and feature weight analysis provided deeper insights into the structure of the predictive model. Notably, use of the ANN model provided insights into predicting these cognitive domains as a function of the emotional attributes. Our results emphasize the importance of addressing mental health concerns and implementing interventions for the enhancement of cognitive functions in university students.
UNASSIGNED: Four hundred and thirteen university students were systematically selected using purposive sampling. Widely used and validated offline questionnaires were used to assess their psychological and cognitive statuses. Correlational analyses were conducted to examine the associations between these variables. An Artificial Neural Network (ANN) model was applied to predict cognitive levels based on the scores of psychological variables.
UNASSIGNED: Correlational analyses revealed negative correlations between emotional distress and cognitive functioning. Principal Component Analysis (PCA) reduced the dimensionality of the input data, effectively capturing the variance with fewer features. The feature weight analysis indicated a balanced contribution of each mental health symptom, with particular emphasis on one of the symptoms. The ANN model demonstrated moderate predictive performance, explaining a portion of the variance in cognitive levels based on the psychological variables.
UNASSIGNED: The study confirms significant associations between emotional statuses of university students with their cognitive abilities. Specifically, we provide evidence for the first time that in Indian students, self-reported higher levels of stress, anxiety, and depression are linked to lower performance in cognitive tests. The application of PCA and feature weight analysis provided deeper insights into the structure of the predictive model. Notably, use of the ANN model provided insights into predicting these cognitive domains as a function of the emotional attributes. Our results emphasize the importance of addressing mental health concerns and implementing interventions for the enhancement of cognitive functions in university students.
摘要：
■虽然以前的研究表明，学生的心理变量与他们的高阶认知能力密切相关，像印度这样的第三世界国家基本上缺乏这样的研究，他们独特的社会经济文化挑战。我们的目的是调查心理变量(抑郁，焦虑和压力)和印度学生的认知功能，并根据这些变量预测认知表现。
■使用目的抽样系统地选择了四十三名大学生。广泛使用和验证的离线问卷用于评估他们的心理和认知状态。进行相关分析以检查这些变量之间的关联。应用人工神经网络（ANN）模型根据心理变量的得分来预测认知水平。
■相关分析显示情绪困扰和认知功能之间呈负相关。主成分分析(PCA)降低了输入数据的维数，用更少的特征有效地捕获方差。特征权重分析表明每个心理健康症状的均衡贡献，特别强调其中一个症状。人工神经网络模型表现出中等的预测性能，根据心理变量解释认知水平的一部分差异。
■该研究证实了大学生的情绪状态与认知能力之间的显着关联。具体来说,我们首次提供证据表明，在印度学生中，自我报告的压力水平较高，焦虑,抑郁症与认知测试中的较低表现有关。PCA和特征权重分析的应用为预测模型的结构提供了更深入的见解。值得注意的是,ANN模型的使用提供了作为情感属性的函数来预测这些认知领域的见解。我们的结果强调了解决心理健康问题和实施干预措施以增强大学生认知功能的重要性。
■使用目的抽样系统地选择了四十三名大学生。广泛使用和验证的离线问卷用于评估他们的心理和认知状态。进行相关分析以检查这些变量之间的关联。应用人工神经网络（ANN）模型根据心理变量的得分来预测认知水平。
■相关分析显示情绪困扰和认知功能之间呈负相关。主成分分析(PCA)降低了输入数据的维数，用更少的特征有效地捕获方差。特征权重分析表明每个心理健康症状的均衡贡献，特别强调其中一个症状。人工神经网络模型表现出中等的预测性能，根据心理变量解释认知水平的一部分差异。
■该研究证实了大学生的情绪状态与认知能力之间的显着关联。具体来说,我们首次提供证据表明，在印度学生中，自我报告的压力水平较高，焦虑,抑郁症与认知测试中的较低表现有关。PCA和特征权重分析的应用为预测模型的结构提供了更深入的见解。值得注意的是,ANN模型的使用提供了作为情感属性的函数来预测这些认知领域的见解。我们的结果强调了解决心理健康问题和实施干预措施以增强大学生认知功能的重要性。
