Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-10
Abstract： Integrative complexity is a concept used in psychology to measure the structure of an individual’s thinking in two aspects: differentiation and integration. The measurement of integrative complexity relies primarily on manual analysis of textual content, which can be written materials, speeches, interview transcript large language models, or any other form of oral or written expression. To solve the problems of high cost of manual assessment methods, low accuracy of automated assessment methods, and the lack of Chinese text assessment scheme, this study designed an automated assessment scheme for integrative complexity on Chinese and English texts. We utilized text data enhancement technique of the large language model and the model migration technique for the assessment of integrative complexity, and explored the automated assessment methods for the two sub-structures of integrative complexity, namely, the fine integration complexity and the dialectical integration complexity. In this paper, two studies are designed and implemented. Firstly, a prediction model for the integration complexity of English text is implemented based on the text data enhancement technology of large language model; secondly, a prediction model for the integration complexity of Chinese text is implemented based on the model transfer technology. The results showed that: 1) We used GPT-3.5-Tubo for English text data enhancement, a pre-trained multilingual Roberta model for word vector extraction, and a text convolutional neural network model as a downstream model. The Spearman correlation coefficient between this model’s prediction of integration complexity and the manual scoring results was 0.62, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity Spearman correlation coefficient of 0.60. It is superior to machine learning methods and neural network models without data enhancement. 2) In Study 2, a model with the same structure as the neural network in Study 1 was established, and the final model parameters in Study 1 were also transferred to the model in this study to train the integration complexity prediction model based on Chinese text. In the case of zero samples, the Spearman correlation coefficients of the transfer learning model for integrative complexity are 0.31, the Spearman correlation coefficient of dialectical integration complexity is 0.31, and the correlation coefficient of fine integration complexity is 0.33, all of which are better than the model in the case of random parameters (integrative complexity: 0.17, dialectical integrative complexity: 0.10, fine integrative complexity: 0.10). In the case of small samples, the Spearman correlation coefficient of the transfer learning model was 0.73, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity correlation coefficient of 0.73.
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Subjects: Psychology >> Applied Psychology submitted time 2024-04-10
Abstract： Integrative complexity is a concept used in psychology to measure the structure of an individual’s thinking. It mainly involves two aspects: differentiation and integration. Differentiation refers to the ability of an individual to identify and understand different viewpoints or elements in the information. Integration refers to the ability of individuals to combine these different ideas or elements into a logical and coherent whole. The measurement of integrative complexity relies primarily on manual analysis of textual content, which can be written materials, speeches, interview transcripts, or any other form of oral or written expression. Integrative complexity has demonstrated its interdisciplinary value and extensive research potential in the fields of management psychology, political psychology and cultural psychology. In the field of management psychology, the level of integrated complexity of leaders affects how they approach complex management challenges, develop strategies, and promote team diversity. In political psychology, researchers use integrative complexity to analyze the thinking styles of political leaders, the foreign policy decision-making process, and the political attitudes and behaviors of the masses. Cultural psychology uses integrative complexity to explore the thinking patterns and information processing strategies of individuals in different cultural contexts. But in the field of health psychology, the integrative complexity has not been fully studied. Integrated complexity, as a measure of the structure of thought, can explain how individuals process information and deal with stress and negative emotions, which is very important for individual mental health. According to the suicide escape theory, individuals may escape unbearable self-consciousness and emotional pain through suicidal behavior. Under this theoretical framework, low integration complexity may be a risk factor for suicidal behavior, because low integration complexity may make it difficult for individuals to see multiple aspects of problems and possible solutions while facing stress and psychological pain, and thus leading to helpless and hopeless. This study explores the effect of integration complexity on suicidal ideation and suicidal behavior through social network media data. The results show that the complexity of dialectical integration negatively affects individual suicidal ideation, the complexity of fine integration positively affects individual suicidal ideation, and the complexity of dialectical integration negatively regulates the impact of negative emotions on suicidal ideation. Individuals with low dialectical integration complexity are more likely to be disturbed by negative emotions, and thus more likely to show suicidal ideation; Individuals with high dialectical integration complexity are less likely to be disturbed by negative emotions and thus less likely to exhibit suicidal ideation, but this pattern is not stable and may be disturbed by cultural background and other factors. On the eve of suicidal behavior, the integration complexity of the individual will continue to decrease.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2022-03-18
Abstract：
This paper introduces the big data research method in psychology in details, taking Ninety-Nine Articles website as an example. Using the collected textual data, we calculated word frequencies as features, then trained machine learning models, and used models to predict life satisfaction for texts crawled from Ninety-Nine Articles website, providing inspiration and help for beginners in big data research. This paper introduces text-based word frequency calculation using Python and sentiment dictionary through specific examples, and completes the training, testing and application of the machine learning model using Python's scikit-learn library. Furthermore, we uploaded the accompanying source program for direct operation. This paper introduces the big data research method of machine learning modeling via text-based word frequency. Our article emphasizes how to apply the technology, and thus we introduce the technology in a more basic way with less involvement of the technical principles.
Peer Review Status:Awaiting Review
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