Investigating Managers' Fine-Grained Evaluation Processes in Organizations: Exploring Two Dual-Process Perspectives.

The dual-process theory is a significant theory in both organizational theory and social psychology and two conjectures about this theory are considered in this manuscript; the default-interventionist vs. parallel-competitive account. Our research goal is to empirically investigate these two views. In concrete terms, by using event-related potentials (ERPs), we seek to study the fine-grained brain processes and self-reported feelings involved in managers' evaluations of target employees within an economic context (firing employees) vs. a social network context (excluding employees). Using the stereotype content model categories, each target employee has high (or low) warmth and high (or low) levels of competence. In the fine-grained ERP analysis of the brain process, we focus on three time windows of interest: novelty detection (N2) and goal violation detection (N400) at the unconscious level, and we then evaluate conscious emotional arousal (late positive potential, LPP). Finally, we focus on the self-reported feelings when having to fire or exclude target employees. As goal pursuit theory predicts, the brain dynamics and self-reported measures differ widely across the two organizational contexts; in concrete terms, processes at a later stage overrule early stages depending on the context. This implies that the data bespeaks more for the parallel-competitive account than the default-interventionist account. We discuss the implications of these findings for research in management and management practice.

Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models.

Anxiety disorders are a group of mental illnesses that cause constant and overwhelming feelings of anxiety and fear. Excessive anxiety can make an individual avoid work, school, family get-togethers, and other social situations that in turn might amplify these symptoms. According to the World Health Organization (WHO), one in thirteen persons globally suffers from anxiety. It is high time to understand the roles of various clinical biomarker measures that can diagnose the types of anxiety disorders. In this study, we apply machine learning (ML) techniques to understand the importance of a set of biomarkers with four types of anxiety disorders-Generalized Anxiety Disorder (GAD), Agoraphobia (AP), Social Anxiety Disorder (SAD) and Panic Disorder (PD). We used several machine learning models and extracted the variable importance contributing to a type of anxiety disorder. The study uses a sample of 11,081 Dutch citizens' data collected by the Lifelines, Netherlands. The results show that there are significant and low correlations among GAD, AP, PD and SAD and we extracted the variable importance hierarchy of biomarkers with respect to each type of anxiety disorder which will be helpful in designing the experimental setup for clinical trials related to influence of biomarkers on type of anxiety disorder.

Highly Recommended? How Relation-Specific Attachment Styles Bias Customers Willingness to Recommend.

Recently concepts from attachment theory are being applied to business situations. In this paper we focus on how relationship specific (RS) versus general (G) attachment styles affect the willingness-to-recommend (WtR) by customers. Such WtR refers to the likelihood of customers to recommend the services of their service provider to other customers, based on their experiences with the provider. This WtR is often measured by means of the Net Promoter Score (NPS) which is assumed to be a reliable (credible) market signal as it originates from customers themselves and not from the firm. This study provides insights in this issue using data from 798 members of an online panel from the Netherlands, covering four service industries. Customers are surveyed on their RS and G attachment styles, trust in, satisfaction with, and commitment to their service provider, as well as their WtR this provider. Findings emerge from econometric parallel mediation analyses. This study shows that customers' RS but not the G attachment styles bias their appraisal of trust in, satisfaction with and commitment to the service provider, which in turn affects their WtR. More specifically, across the four service industries, customers scoring higher on RS anxiety and/or avoidance show systematically lower levels of trust in and satisfaction with, and commitment to the firm, ultimately leading to lower WtR. Firms should especially target those customers that score higher on RS avoidance (possibly in combination with higher levels of RS anxiety) as their WtR is strongly biased which might create uncertainty for other customers about the firm's reputation.

Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081).

Machine Learning has been on the rise and healthcare is no exception to that. In healthcare, mental health is gaining more and more space. The diagnosis of mental disorders is based upon standardized patient interviews with defined set of questions and scales which is a time consuming and costly process. Our objective was to apply the machine learning model and to evaluate to see if there is predictive power of biomarkers data to enhance the diagnosis of depression cases. In this research paper, we aimed to explore the detection of depression cases among the sample of 11,081 Dutch citizen dataset. Most of the earlier studies have balanced datasets wherein the proportion of healthy cases and unhealthy cases are equal but in our study, the dataset contains only 570 cases of self-reported depression out of 11,081 cases hence it is a class imbalance classification problem. The machine learning model built on imbalance dataset gives predictions biased toward majority class hence the model will always predict the case as no depression case even if it is a case of depression. We used different resampling strategies to address the class imbalance problem. We created multiple samples by under sampling, over sampling, over-under sampling and ROSE sampling techniques to balance the dataset and then, we applied machine learning algorithm "Extreme Gradient Boosting" (XGBoost) on each sample to classify the mental illness cases from healthy cases. The balanced accuracy, precision, recall and F1 score obtained from over-sampling and over-under sampling were more than 0.90.

Framing a trust game as a power game greatly affects interbrain synchronicity between trustor and trustee.

We used dual electroencephalography (EEG) to measure brain activity simultaneously in pairs of trustors and trustees playing a 15-round trust game framed as a "trust game" versus a "power game". Four major findings resulted: first, earnings in each round were higher in the trust than in the power game. Second, in the trust game, reaction time for strategic deliberations was significantly longer for the trustee than the trustor. In the power game, however, the trustee took longer to think about how much money to repay, whereas the trustor took longer to think about how much money to invest. Third, prediction accuracy for the amount exchanged was higher in the trust game than in the power game. Fourth, interbrain synchronicity gauged with the phase-locking value of alpha bands in the brain - especially the frontal and central regions - was higher in the power game than in the trust game. We infer that this last finding reflects elevated mutual strategic deliberation in the power game. These behavioral and neuroscience-based findings give a better understanding of the framing effects of a trust game on the strategic deliberations of both trustor and trustee seeking to attain wealth.

Facial Attractiveness as a Function of Athletic Prowess.

We investigate the relationship between facial attractiveness and athletic prowess. We study the connection between subjective facial attractiveness (measured on a 5-point scale of judged facial attractiveness) and athletes by gender and age of respondents. Five age classes were investigated in Studies 1-5: preadolescents (average age: 8.85 years: n = 92), adolescents (average age: 15.8 years; n = 82), young adults (average age: 21.6 years; n = 181), middle-aged adults (average age: 47.5 years; n = 189), and older adults (65 years old; n = 183). The findings show that world-class athletes are perceived as more facially attractive than amateur athletes, with women athletes perceived as more facially attractive than men, and these findings generally occur to a greater extent for female than male respondents. These findings hold for preadolescents, adolescents, young adults, and older adults. However, results were mixed for middle-aged adults where generally amateur athletes were evaluated more attractive than world-class and men athletes more attractive than women.

Epigenetic modification of the oxytocin and glucocorticoid receptor genes is linked to attachment avoidance in young adults.

Attachment in the context of intimate pair bonds is most frequently studied in terms of the universal strategy to draw near, or away, from significant others at moments of personal distress. However, important interindividual differences in the quality of attachment exist, usually captured through secure versus insecure - anxious and/or avoidant - attachment orientations. Since Bowlby's pioneering writings on the theory of attachment, it has been assumed that attachment orientations are influenced by both genetic and social factors - what we would today describe and measure as gene by environment interaction mediated by epigenetic DNA modification - but research in humans on this topic remains extremely limited. We for the first time examined relations between intra-individual differences in attachment and epigenetic modification of the oxytocin receptor (OXTR) and glucocorticoid receptor (NR3C1) gene promoter in 109 young adult human participants. Our results revealed that attachment avoidance was significantly and specifically associated with increased OXTR and NR3C1 promoter methylation. These findings offer first tentative clues on the possible etiology of attachment avoidance in humans by showing epigenetic modification in genes related to both social stress regulation and HPA axis functioning.

"I am resting but rest less well with you." The moderating effect of anxious attachment style on alpha power during EEG resting state in a social context.

We took EEG recordings to measure task-free resting-state cortical brain activity in 35 participants under two conditions, alone (A) or together (T). We also investigated whether psychological attachment styles shape human cortical activity differently in these two settings. The results indicate that social context matters and that participants' cortical activity is moderated by the anxious, but not avoidant attachment style. We found enhanced alpha, beta and theta band activity in the T rather than the A resting-state condition, which was more pronounced in posterior brain regions. We further found a positive correlation between anxious attachment style and enhanced alpha power in the T vs. A condition over frontal and parietal scalp regions. There was no significant correlation between the absolute powers registered in the other two frequency bands and the participants' anxious attachment style.

fMRI activities in the emotional cerebellum: a preference for negative stimuli and goal-directed behavior.

Several studies indicate that the cerebellum might play a role in experiencing and/or controlling emphatic emotions, but it remains to be determined whether there is a distinction between positive and negative emotions, and, if so, which specific parts of the cerebellum are involved in these types of emotions. Here, we visualized activations of the cerebellum and extracerebellar regions using high-field fMRI, while we asked participants to observe and imitate images with pictures of human faces expressing different emotional states or with moving geometric shapes as control. The state of the emotions could be positive (happiness and surprise), negative (anger and disgust), or neutral. The positive emotional faces only evoked mild activations of crus 2 in the cerebellum, whereas the negative emotional faces evoked prominent activations in lobules VI and VIIa in its hemispheres and lobules VIII and IX in the vermis. The cerebellar activations associated with negative emotions occurred concomitantly with activations of mirror neuron domains such as the insula and amygdala. These data suggest that the potential role of the cerebellum in control of emotions may be particularly relevant for goal-directed behavior that is required for observing and reacting to another person's (negative) expressions.