Generation Mechanism of Belief in Conspiracy Theories: Three Explanations from Social Cognitive Perspective

Social psychology treats conspiracy theories as an ideological belief which is defined as people’s tendency to interpret major social and political events as powerful groups or individuals who secretly deliberately plan to achieve their intended purpose. Belief in conspiracy theories is not just about believing in a specific conspiracy theory, but a general belief in all kinds of conspiracy theories. The following two findings in previous studies are sufficient to prove this point: one is the strong correlation between beliefs about the conspiracy theories behind different events; the other is that people may simultaneously embrace contradictory conspiracy theories about the same event. These two findings also indicate that belief in conspiracy theories is an integral and coherent psychological structure, which can be included into the category of psychological research as an independent variable. The negative influence of conspiracy theories is greater than the positive influence has become the consensus of researchers. To make a reasonable intervention in belief in conspiracy theories, we must first clarify the mechanism it generates. Previous studies have attempted to explain why people choose to believe in conspiracy theories from different perspectives. The results of studies on the relationship between belief in conspiracy theories and the Big Five personality are often difficult to replicate, so it seems that belief in conspiracy theories cannot be simply described by the Big Five personality dimensions. From the perspective of motivation, cognitive motives of reducing cognitive uncertainty and understanding the external world, existential motives of avoiding external threats and enhancing the sense of control and security, social motives of maintaining the positive image of competence and morality of individuals and inner groups can induce individuals to generate belief in conspiracy theories. Compared with other research perspectives, the perspective of social cognition seems to better reflect the internal psychological process of forming an ideological belief. This paper mainly introduces three explanations for the emergence of individual’s belief in conspiracy theories from the perspective of social cognition. Illusory pattern perception leads to people's cognitive tendency to establish connections between unrelated events and even impose causal relationships to generate belief in conspiracy theories. Hypersensitive agency detection affects people to look for the agency in the environment, and even over-perceive and assume the agency, purpose and intention behind the event, thus generating belief in conspiracy theories. Projection s people to infer the thoughts and behaviors of others in the event based on their own understanding and knowledge, and project their self-perception of "I am willing to participate in this event" onto others, thus promoting the belief in conspiracy theories that "others really plotted this event”. Although these three factors have corresponding theoretical and empirical support and have certain explanatory power for the generation of belief in conspiracy theories, it is difficult to explain that any of them are independent of other psychological mechanisms and independently generate belief in conspiracy theories. In the future study, it is suggested to combine social cognition, motivation, personality and other research perspectives, attach importance to experimental design and vertical research, expand the group of subjects, broaden measurement methods, and carry out cross-perspective, interdisciplinary and cross-cultural in-depth and systematic research on the generation mechanism of belief in conspiracy theories

How does economic inequality shape conspiracy theories? Empirical evidence from China

Conspiracy theories tend to be prevalent, particularly in societies with high economic inequality. However, few studies have examined the relationship between economic inequality and belief in conspiracy theories. We propose that economic inequality leads people to believe conspiracy theories about economically advantaged groups (i.e., upwards conspiracy theories) and that moral evaluations of those groups mediate this relationship. Study 1 (N=300) found support for these ideas in a survey among Chinese residents. Study 2 (N=160) manipulated participants' perceptions of economic inequality in a virtual society. The manipulation shaped moral evaluations of economically advantaged groups, and conspiracy beliefs, in the predicted manner. In Study 3 (N = 191) and Study 4 (N = 210), we experimentally manipulated participants' perceptions of economic inequality in real Chinese society and replicated the results of Study 2. In addition, in Study 4, we find that economic inequality predicts belief in conspiracy theories about economically disadvantaged groups (i.e., downward conspiracy theories), which was mediated by anomie. We conclude that perceived economic inequality predicts conspiracy theories about economically advantaged groups and that moral evaluations account for this effect. Also, upward and downward conspiracy theory beliefs are associated with different psychological processes

Porous polydimethylsiloxane films with specific surface wettability but distinct regular physical structures fabricated by 3D printing

Porous polydimethylsiloxane (PDMS) films with special surface wettability have potential applications in the biomedical, environmental, and structural mechanical fields. However, preparing porous PDMS films with a regular surface pattern using conventional methods, such as chemical foaming or physical pore formation, is challenging. In this study, porous PDMS films with a regular surface pattern are designed and prepared using 3D printing to ensure the formation of controllable and regular physical structures. First, the effect of the surface wettability of glass substrates with different surface energies (commercial hydrophilic glass and hydrophobic glass (F-glass) obtained by treating regular glass with 1H,1H,2H,2H-perfluorooctyl-trichlorosilane) on the structural characteristics of the 3D printed PDMS filaments is investigated systematically. Additionally, the effect of the printing speed and the surface wettability of the glass substrate on the PDMS filament morphology is investigated synchronously. Next, using the F-glass substrate and an optimized printing speed, the effects of the number of printed layers on both the morphologies of the individual PDMS filaments and porous PDMS films, and the surface wettability of the films are studied. This study reveals that regularly patterned porous PDMS films with distinct structural designs but the same controllable surface wettability, such as anisotropic surface wettability and superhydrophobicity, can be easily fabricated through 3D printing. This study provides a new method for fabricating porous PDMS films with a specific surface wettability, which can potentially expand the application of porous PDMS films

The relationship between triglyceride, cholesterol and lipoprotein levels, and immune responses to hepatitis B vaccine

Cholesterol homeostasis disorder and hypertriglyceridemia, as common metabolic conditions, have rarely been reported to affect the immune responses to the hepatitis B vaccine. Our study found that higher high-density lipoprotein (HDL) level showed a significant relationship with positive anti-HBs results (cOR = 1.479, 95% CI: 1.150, 1.901, p = 0.002; aOR = 1.304, 95% CI: 1.006, 1.691, p = 0.045), especially in individuals aged 18- to 40-year-old, female, smoking more than 100 cigarettes in life, and drinking more than 12 times every year. Lower low-density lipoprotein (LDL) level was associated with a negative anti-HBs result among participants aged 18- to 40-year-old, and participants who were obese. Higher level of HDL and lower level of LDL may be protective factors of better immune effect of hepatitis B vaccine. More research should be conducted to investigate the influence of the cholesterol level on the immune responses to the hepatitis B vaccine, and more in-depth research should be performed to uncover the mechanism

System threat during a pandemic: How conspiracy theories help to justify the system

During the COVID-19 pandemic, many people have endorsed conspiracy theories about foreign governments yet shown increased trust and support for their own government. Whether there is a potential correlation between these social phenomena and the psychological mechanisms behind them is still unclear. Integrating insights from the existential threat model of conspiracy theories and system justification theory, two experimental studies were conducted to investigate whether belief in out-group conspiracy theories can play a mediating role in the effects of system threat on people's system justification beliefs against the background of the pandemic. The results show that system threat positively predicts individuals’ system-justifying belief, and belief in out-group conspiracy theories mediated this relationship

Determining the Clinical Value and Critical Pathway of GTPBP4 in Lung Adenocarcinoma Using a Bioinformatics Strategy: A Study Based on Datasets from The Cancer Genome Atlas

Lung cancer is the leading cause of cancer-related death worldwide, and the most common histologic subtype is lung adenocarcinoma (LUAD). Due to the significant mortality and morbidity rates among patients with LUAD, the identification of novel biomarkers to guide diagnosis, prognosis, and therapy is urgent. Guanosine triphosphate-binding protein 4 (GTPBP4) has been found to be associated with tumorigenesis in recent years, but the underlying molecular mechanism remains to be elucidated. In the present study, we demonstrate that GTPBP4 is significantly overexpressed in LUAD primary tumors. A total of 55 genes were identified as potential targets of GTPBP4. GO enrichment analysis identified the top 25 pathways among these target genes, among which, ribosome biogenesis was shown to be the most central. Each target gene demonstrated strong and complex interactions with other genes. Of the potential target genes, 12 abnormally expressed candidates were associated with survival probability and correlated with GTPBP4 expression. These findings suggest that GTPBP4 is associated with LUAD progression. Finally, we highlight the importance of the role of GTPBP4 in LUAD in vitro. GTPBP4 knockdown in LUAD cells inhibited proliferation and metastasis, promoted apoptosis, and enhanced sensitivity to TP. Overall, we conclude that GTPBP4 may be considered as a potential biomarker of LUAD

Fast Self-Attention Deep Detection Network Based on Weakly Differentiated Plant Nematodess

High-precision, high-speed detection and classification of weakly differentiated targets has always been a difficult problem in the field of image vision. In this paper, the detection of phytopathogenic Bursaphelenchus xylophilus with small size and very weak inter-species differences is taken as an example. Our work is aimed at the current problem of weakly differentiated target detection: We propose a lightweight self attention network. Experiments show that the key feature recognition areas of plant nematodes found by our Self Attention network are in good agreement with the experience and knowledge of customs experts, and the feature areas found by this method can obtain higher detection accuracy than expert knowledge; In order to optimize the computing power brought by the whole image input, we use low resolution images to quickly obtain the location coordinates of key features, and then obtain the information of high resolution feature regions based on the coordinates; The adaptive weighted multi feature joint detection method based on heat map brightness is adopted to further improve the detection accuracy; We have constructed a more complete high-resolution training data set, involving 24 species of Equisetum and other common hybrids, with a total data volume of more than 10,000. The algorithm proposed in this paper replaces the tedious extensive manual labelling in the training process, improves the average training time of the model by more than 50%, reduces the testing time of a single sample by about 27%, optimizes the model storage size by 65%, improves the detection accuracy of the ImageNet pre-trained model by 12.6%, and improves the detection accuracy of the no-ImageNet pre-trained model by more than 48%

Fast Self-Attention Deep Detection Network Based on Weakly Differentiated Plant Nematodess

High-precision, high-speed detection and classification of weakly differentiated targets has always been a difficult problem in the field of image vision. In this paper, the detection of phytopathogenic Bursaphelenchus xylophilus with small size and very weak inter-species differences is taken as an example. Our work is aimed at the current problem of weakly differentiated target detection: We propose a lightweight self attention network. Experiments show that the key feature recognition areas of plant nematodes found by our Self Attention network are in good agreement with the experience and knowledge of customs experts, and the feature areas found by this method can obtain higher detection accuracy than expert knowledge; In order to optimize the computing power brought by the whole image input, we use low resolution images to quickly obtain the location coordinates of key features, and then obtain the information of high resolution feature regions based on the coordinates; The adaptive weighted multi feature joint detection method based on heat map brightness is adopted to further improve the detection accuracy; We have constructed a more complete high-resolution training data set, involving 24 species of Equisetum and other common hybrids, with a total data volume of more than 10,000. The algorithm proposed in this paper replaces the tedious extensive manual labelling in the training process, improves the average training time of the model by more than 50%, reduces the testing time of a single sample by about 27%, optimizes the model storage size by 65%, improves the detection accuracy of the ImageNet pre-trained model by 12.6%, and improves the detection accuracy of the no-ImageNet pre-trained model by more than 48%