The Relationship Between Automatic Thoughts and Irrational Beliefs Predicting Anxiety and Depression

Cognitive behavioral approaches differ in their views on core cognitions and their hypothesized role in the etiology of depression and anxiety. The present study provides empirical evidence regarding the relationship between irrational beliefs and components of automatic thoughts and their role in the etiology of depression and anxiety. The present study utilized newer and improved questionnaires to assess components of irrational belief. Based on prior research by Safren et al. (Cogn Ther Res 24(3):327–344, 2000), a three-factor structure of the combined automatic thought questionnaires were utilized to measure components of automatic thoughts as they relate to depression and anxiety. Factor analytical methods were utilized to confirm the factor structure of the irrational beliefs and automatic thoughts components. Advanced path modeling was utilized to model the relationship between irrational beliefs and automatic thoughts in predicting anxiety and depression. The study used a sample of N = 542 undergraduate psychology students during stressful exam times. Results indicated that the irrational belief Demandingness represents a primary factor, followed by the secondary irrational beliefs as proposed by Rational Emotive Behavioral Theory. Selfdowning beliefs were fully mediated by depressive automatic thoughts in the case of depressive affect. Low frustration tolerance contributed unique variance to anxious and depressive affect that was not fully mediated by automatic thoughts. Results from the present study add empirical evidence that irrational beliefs indeed represent core and intermediary beliefs that lead to specific automatic thoughts, which is congruent with cognitive behavioral theory as proposed by Rational Emotive Behavioral Therapy.     

The Madonna-Whore Dichotomy: Men Who Perceive Women's Nurturance and Sexuality as Mutually Exclusive Endorse Patriarchy and Show Lower Relationship Satisfaction

The Madonna-Whore Dichotomy (MWD) denotes polarized perceptions of women in general as either “good,” chaste, and pure Madonnas or as “bad,” promiscuous, and seductive whores. Whereas prior theories focused on unresolved sexual complexes or evolved psychological tendencies, feminist theory suggests the MWD stems from a desire to reinforce patriarchy. Surveying 108 heterosexual Israeli men revealed a positive association between MWD endorsement and patriarchy-enhancing ideology as assessed by Social Dominance Orientation (preference for hierarchical social structures), Gender-Specific System Justification (desire to maintain the existing gender system), and sexist attitudes (Benevolent and Hostile Sexism, Sexual Objectification of Women, and Sexual Double Standards). In addition, MWD endorsement negatively predicted men’s romantic relationship satisfaction. These findings support the feminist notion that patriarchal arrangements have negative implications for the well-being of men as well as women. Specifically, the MWD not only links to attitudes that restrict women’s autonomy, but also impairs men’s most intimate relationships with women. Increased awareness of motives underlying the MWD and its psychological costs can help practice professionals (e.g., couple therapists), as well as the general public, to foster more satisfying heterosexual relationships.     

Gender Bias Produces Gender Gaps in STEM Engagement

We explored whether the existence of gender bias causes gender gaps in STEM engagement. In Experiment 1 (n?=?322), U.S. women projected less sense of belonging, positivity toward, and aspirations to participate in STEM than did men when exposed to the reality of STEM gender bias. These gender differences disappeared when participants were told that STEM exhibits gender equality, suggesting that gender bias produces STEM gender gaps. Experiment 2 (n?=?429) explored whether results generalized to a specific STEM department, and whether organizational efforts to mitigate gender bias might shrink gender gaps. U.S. women exposed to a biased chemistry department anticipated more discrimination and projected less sense of belonging, positive attitudes and trust and comfort than did men. These gender differences vanished when participants read about an unbiased department, again suggesting that gender bias promotes STEM gender gaps. Further, moderated mediation analyses suggested that in the presence of gender bias (but not gender equality), women projected less positive attitudes and trust and comfort than did men because they experienced less sense of belonging and anticipated more discrimination. Results were largely unaffected by whether departments completed a diversity training, suggesting that knowledge of diversity initiatives alone cannot close STEM gender gaps.     

Response to Heather B. MacIntosh’s Systematic Review of Journal Articles in Psychoanalytic Couple Therapy

ABSTRACT

I review Heather B. MacIntosh’s research on couple therapy in the analytic literature and provide a history and geography of the development of object relations couple therapy.     

Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation

Measurement invariance is a fundamental assumption in item response theory models, where the relationship between a latent construct (ability) and observed item responses is of interest. Violation of this assumption would render the scale misinterpreted or cause systematic bias against certain groups of persons. While a number of methods have been proposed to detect measurement invariance violations, they typically require advance definition of problematic item parameters and respondent grouping information. However, these pieces of information are typically unknown in practice. As an alternative, this paper focuses on a family of recently proposed tests based on stochastic processes of casewise derivatives of the likelihood function (i.e., scores). These score-based tests only require estimation of the null model (when measurement invariance is assumed to hold), and they have been previously applied in factor-analytic, continuous data contexts as well as in models of the Rasch family. In this paper, we aim to extend these tests to two-parameter item response models, with strong emphasis on pairwise maximum likelihood. The tests’ theoretical background and implementation are detailed, and the tests’ abilities to identify problematic item parameters are studied via simulation. An empirical example illustrating the tests’ use in practice is also provided.     

Automated Item Generation with Recurrent Neural Networks

Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Human experts are a limited resource and testing agencies incur high costs in the process of continuous renewal of item banks to sustain testing programs. Using algorithms instead holds the promise of providing unlimited resources for this crucial part of assessment development. The approach presented here deviates in several ways from previous attempts to solve this problem. In the past, automatic item generation relied either on generating clones of narrowly defined item types such as those found in language free intelligence tests (e.g., Raven’s progressive matrices) or on an extensive analysis of task components and derivation of schemata to produce items with pre-specified variability that are hoped to have predictable levels of difficulty. It is somewhat unlikely that researchers utilizing these previous approaches would look at the proposed approach with favor; however, recent applications of machine learning show success in solving tasks that seemed impossible for machines not too long ago. The proposed approach uses deep learning to implement probabilistic language models, not unlike what Google brain and Amazon Alexa use for language processing and generation.     

Nonlinear Predictive Models for Multiple Mediation Analysis: With an Application to Explore Ethnic Disparities in Anxiety and Depression Among Cancer Survivors

Mediation analysis allows the examination of effects of a third variable (mediator/confounder) in the causal pathway between an exposure and an outcome. The general multiple mediation analysis method (MMA), proposed by Yu et al., improves traditional methods (e.g., estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. Previous studies find that compared with non-Hispanic cancer survivors, Hispanic survivors are more likely to endure anxiety and depression after cancer diagnoses. In this paper, we applied MMA on MY-Health study to identify mediators/confounders and quantify the indirect effect of each identified mediator/confounder in explaining ethnic disparities in anxiety and depression among cancer survivors who enrolled in the study. We considered a number of socio-demographic variables, tumor characteristics, and treatment factors as potential mediators/confounders and found that most of the ethnic differences in anxiety or depression between Hispanic and non-Hispanic white cancer survivors were explained by younger diagnosis age, lower education level, lower proportions of employment, less likely of being born in the USA, less insurance, and less social support among Hispanic patients.     

Combining Indian and Western Spiritual Psychology: Applications to Health and Social Renewal

We introduce the topic of Indian/US collaboration in studying religion and spirituality (R/S), providing both historical and philosophical context. A fully in-depth and comprehensive study of R/S will require taking into account three potential types and sources of knowledge: (1) theoretically and intellectually oriented knowledge as exemplified by William James; (2) empirically oriented knowledge as exemplified by Gordon Allport; and (3) experience- and realization-derived knowledge based on reports from eminent spiritual figures. We sketch the distinct but mutually informative challenges of clinical and epistemic integration. We suggest several topics for future collaboration, arguing that the most comprehensive study of R/S will require combining the western attention to empirical knowledge with the Indian psychology movement’s attention to realization-derived knowledge. Suggested topics include processes of learning from spiritual exemplars and teachers; health effects from engagement in Hindu or other Indian religious practice; psychological and health effects of specific spiritual practices; the importance and influence of the focus used in meditation; the effects of japam, Ramnam, or similar repeated short prayers; how diverse spiritual practices affect mindfulness; and the nature and means of fostering skills for living in a religiously plural society.