The Effects of Emotional Schema Therapy and Differentiation Training on Emotional Divorce in Women

Background: Emotional Divorce (ED) is associated with decreased levels of Emotion Regulation (ER), adaptation, and mental health; subsequently, all such pressures raise stress in various dimensions among the affected individuals. Emotional Schema Therapy (EST), as a socio-cognitive model of ER, may improve marital intimacy and reduce couples’ psychological distress. The present study aimed to investigate the effects of EST and differentiation training on the odds of ED among women. Methods: The mean values of ED significantly decreased in both experimental groups, compared to the control group (P<0.05). A significant difference was also observed between the effects of the two interventions on decreasing the ED rate; thus, the effectiveness of EST was greater than that of differentiation training in this respect (P<0.05). Results: The mean values of ED significantly decreased in both experimental groups, compared to the control group (P<0.05). A significant difference was also observed between the effects of the two interventions on decreasing the ED rate; thus, the effectiveness of EST was greater than that of differentiation training in this respect (P<0.05). Conclusion: EST and differentiation training reduced ED among the study participants. These approaches can be adopted as an effective intervention to solve the couples’ problems and improve their marital relationship to reduce the odds of emotional divorce.

“This Virus is a Common Threat to All Humans”: Discourse Representation of COVID-19 in Selected Newspaper Editorials

Existing studies on viruses with bias for COVID-19 have mainly been carried out from non-linguistic fields. Linguistics-related studies have not examined the media representation of COVID-19 since it is a recent development. This study, therefore, identifies the representational strategies, discourse structures and discourse strategies deployed by selected newspapers in representing COVID-19 and associated participants. Data were retrieved from selected COVID-19-related editorials from four purposively selected countries and continents across the world: New York Times (USA, North America), The Guardian (UK, Europe), China Daily (China, Asia) and The Punch (Nigeria, Africa), published in the early periods of the pandemic, and precisely from January 1 – March 31, 2020. Guided by aspects of van Dijk’s socio-cognitive model of critical discourse analysis on ideological discourse structures, data were quantitatively and qualitatively analysed. The newspaper editorials unusually converged to negatively represent an issue – COVID-19 – because it is largely negatively viewed by all. Ten representational strategies (like economic cankerworm, threat to humans, common enemy), six discourse strategies (like demonising, criminalising, condemnation) and twelve ideological discourse structures (like Actor Description, Authority, Burden) and different participant representations and roles (like solver, potential super spreader) were identified in the study. The newspapers largely set the agenda on the negative representation of the virus and its potential havoc on all facets of human endeavours, thereby giving emotional and informational appeal to all to join hands in earnestly silencing the epidemic. Keywords: COVID-19, media representation, newspaper editorials, discourse strategies, discourse structures

Rethinking the Methodological Foundation of Historical Political Science

The basis of a methodology determines whether a research method can fit the core characteristics of a particular academic tradition, and thus, it is crucial to explore this foundation. Keeping in mind the controversy and progress of the philosophy of social sciences, this paper aims to elaborate on four aspects including the cognitive model, the view of causality, research methods, and analysis techniques, and to establish a more solid methodological basis for historical political science. With respect to the “upstream knowledge” of methodology, both positivism and critical realism underestimate the tremendous difference between the natural world and the social world. This leads to inherent flaws in controlled comparison and causal mechanism analysis. Given the constructiveness of social categories and the complexity of historical circumstances, the cognitive model of constructivism makes it more suitable for researchers to engage in macro-political and social analysis. From the perspective of constructivism, the causality in “storytelling,” i.e., the traditional narrative analysis, is placed as the basis of the regularity theory of causality in this paper, thus forming the historical–causal narrative. The historical–causal narrative focuses on how a research object is shaped and self-shaped in the ontological historical process, and thus ideally suits the disciplinary characteristics of historical political science. Researchers can complete theoretical dialogues, test hypotheses, and further explore the law of causality in logic and evidence, thereby achieving the purpose of “learning from history” in historical political science.

Cognitive cascades: How to model (and potentially counter) the spread of fake news

Understanding the spread of false or dangerous beliefs—often called misinformation or disinformation—through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual’s set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade model with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and “alternative facts.”

The job and life satisfaction of teachers: a social cognitive model integrating teachers’ burnout, self-efficacy, dispositional optimism, and social support

The purpose of the study was to test a model of factors predicting teachers’ job and life satisfaction, burnout, dispositional optimism, social support, perceived workload, and self-efficacy. The model extends Lent and Brown’s (J Voc Behav 69(2):236–247, 10.1016/j.jvb.2006.02.006, 2006; J Career Assess 16(1):6–21, 10.1177/1069072707305769, 2008) social cognitive model of the interaction of sources of job and life satisfaction. Specifically, burnout, a condition with a high incidence rate among teachers, was included. The participants were 676 Swiss teachers. Structural equation modeling was used to analyze the data. The results revealed the differential effect of the variables considered on teachers' burnout and job satisfaction, as well as their life satisfaction. Dispositional optimism, social support, and perceived workload might reduce the risk of teacher burnout; dispositional optimism, social support, and teacher self-efficacy seem to positively affect job satisfaction; and dispositional optimism alone, together with burnout and job satisfaction, directly relates to teachers’ life satisfaction. Practical implications of these results are discussed.

A cognitive model for understanding fraudulent behavior in construction industry

PurposePrevious fraud studies focused on the influence of external environmental factors rather than the actor's own cognition or psychological factors. This paper aims to explore the influence of cognitive factors on people's intention to commit fraud in the construction industry.Design/methodology/approachA scenario-based questionnaire survey was conducted with 248 Chinese construction practitioners. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data.FindingsThe findings showed that perceived threat possibility and perceived threat severity positively affected people's attitudes towards fraud. The reward for compliance and response cost had adverse effects on people's attitudes. Attitude towards fraud and response efficacy directly influenced people's intentions to commit fraud.Research limitations/implicationsThe limitations of this study are that only behavioral intention data were collected, and a single scenario was designed. Despite these limitations, this study proposed a cognitive model to understand fraud in the construction industry and provided an empirical analysis using data from Chinese construction practitioners.Originality/valueThis study reveals the impact of cognitive factors on fraud in the construction industry. The results expand the understanding of fraud and propose a cognitive intervention framework to reduce fraud.

A rational account of the repulsion effect

The attraction effect occurs when the presence of an inferior option (the decoy) increases the attractiveness of the option that dominates it (the target). Despite its prominence in behavioral science, recent evidence points to the puzzling existence of the opposite phenomenon—a repulsion effect. In this paper, we formally develop and experimentally test a normative account of the repulsion effect. Our theory is based on the idea that the true values of options are uncertain and must be inferred from available information, which includes the properties of other options. A low-value decoy can signal that the target also has low value when both are believed to be generated by a similar process. We formalize this logic using a hierarchical Bayesian cognitive model that makes predictions about how the strength of the repulsion effect should vary with statistical properties of the decision problem. This theory may help account for several documented phenomena linked to the repulsion effect across both economic and perceptual decision making, as well as new experimental data. Our results shed light on the key drivers of context-dependent judgment across multiple domains and sharpen our understanding of when decoys can be detrimental.

Discursive representation of the Article 370 abrogation: A comparative CDA of the headlines of two major Indian online news publications

The Indian government on 5 August, 2019, unilaterally removed Article 370 of its constitution that provided autonomous status to the disputed region of Jammu and Kashmir. In order to pre-empt any backlash, the authorities put the entire region under strict lockdown and imposed a complete communication blackout including suspension of internet, mobile, and landline phone services. The Indian media vociferously covered the issue of higher “national interest” with no counter-narrative from local news media in the region. Using Van Djik’s socio-cognitive model, the study conducted comparative critical discourse analysis of the headlines from two major Indian online news publications; the English daily The Times of India and the Hindi daily Dainik Jagran to identify the discursive strategies adopted by these newspapers after the revocation of the Article 370. The study aimed to understand how Indian newspapers were shaping the discourse when the Indian government imposed communication restrictions and lockdown in the region. Through CDA, the study located the discursive strategies in the headlines and the ideological standpoints they reflected while covering the Article 370 controversy. The CDA found that the headline discourse in both the news publications was characterized by aggressive nationalistic assertion reinforcing domestic legitimacy for the government’s decision. The analysis further showed substantial evidence for the cultural distances between the English and Hindi language news discourse. Unlike English headlines, the Hindi headlines contained explicit linguistic subjectivities and were overtly hyperbolic in recognizing and blending itself with the nationalist assertion and socio-political expression around the abrogation of Article 370.

Development of object state evaluation method in intelligent decision support systems

Accurate and objective object analysis requires multi-parameter estimation with significant computational costs. A methodological approach to improve the accuracy of assessing the state of the monitored object is proposed. This methodological approach is based on a combination of fuzzy cognitive models, advanced genetic algorithm and evolving artificial neural networks. The methodological approach has the following sequence of actions: building a fuzzy cognitive model; correcting the fuzzy cognitive model and training knowledge bases. The distinctive features of the methodological approach are that the type of data uncertainty and noise is taken into account while constructing the state of the monitored object using fuzzy cognitive models. The novelties while correcting fuzzy cognitive models using a genetic algorithm are taking into account the type of data uncertainty, taking into account the adaptability of individuals to iteration, duration of the existence of individuals and topology of the fuzzy cognitive model. The advanced genetic algorithm increases the efficiency of correcting factors and the relationships between them in the fuzzy cognitive model. This is achieved by finding solutions in different directions by several individuals in the population. The training procedure consists in learning the synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The use of the method allows increasing the efficiency of data processing at the level of 16–24 % using additional advanced procedures. The proposed methodological approach should be used to solve the problems of assessing complex and dynamic processes characterized by a high degree of complexity.