Impact of Social Desirability and Environmental Awareness on Ecological Behavior among Students

In present scenario, Indian government has regulated many policies and pro environment action with an aim of being aware about environment under which we continue to live. Although, it is not only government responsibility towards developing pro environment attitude rather it should be emerges from us for our nature. However, people become more sensitive for their livelihood needs than environmental concerns which are remarkable notion. So, the present study attempted to study the pro environment attitude and ecological behavior dynamics with an influence of social desirability.

Co-Evolution of Communication and System Performance in Engineering Systems Design: A Stochastic Network-Behavior Dynamics Model

The socio-technical perspective on engineering system design emphasizes the mutual dynamics between interdisciplinary interactions and system design outcomes. How different disciplines interact with each other depends on technical factors such as design interdependence and system performance. On the other hand, the design outcomes are influenced by social factors such as the frequency of interactions and their distribution. Understanding this co-evolution can lead to not only better behavioral insights, but also efficient communication pathways. In this context, we investigate how to quantify the temporal influences of social and technical factors on interdisciplinary interactions and their influence on system performance. We present a stochastic network-behavior dynamics model that quantifies the design interdependence, discipline-specific interaction decisions, the evolution of system performance, as well as their mutual dynamics. We employ two datasets, one of student subjects designing an automotive engine and the other of NASA engineers designing a spacecraft. Then, we apply statistical Bayesian inference to estimate model parameters and compare insights across the two datasets. The results indicate that design interdependence and social network statistics both have strong positive effects on interdisciplinary interactions for the expert and student subjects alike. For the student subjects, an additional modulating effect of system performance on interactions is observed. Inversely, the total number of interactions, irrespective of their discipline-wise distribution, has a weak but statistically significant positive effect on system performance in both cases. However, excessive interactions mirrored with design interdependence and inflexible design space exploration reduce system performance. These insights support the case for open organizational boundaries as a way for increasing interactions and improving system performance.

Social Network Approaches to Bullying and Victimization

Social network research is the way to examine bullying as a group process. Cross-sectional network studies allow us to examine who bullies whom or who defends whom, as well as the agreement on these dyadic relationships. Longitudinal network studies allow us to particularly examine selection and influence processes. The longitudinal studies with the most power have shown that selection and influence processes play a role for bullies. For victims, selection and influence processes have been found in adolescence (secondary education), but not in childhood (elementary education). Social network dynamics in bullying and victimization can also be linked to research on the impact of social norms or the evaluation of an intervention. Recent studies have also started to examine interdependencies between multiple positive and negative relationships. Most social network research on bullying and victimization has been done in late childhood or early adolescence. A few studies, however, have shown that it is also feasible to examine network-behavior dynamics at younger ages. Further research is necessary on whether and how individuals in a network, relationship patterns, or the entire network structure can be targeted by interventions.

Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder

Adaptive behavior requires balancing approach and avoidance based on the rewarding and aversive consequences of actions. Imbalances in this evaluation are thought to characterize mood disorders such as major depressive disorder (MDD). We present a novel application of the drift diffusion model (DDM) suited to quantify how offers of reward and aversiveness, and neural correlates thereof, are dynamically integrated to form decisions, and how such processes are altered in MDD. Hierarchical parameter estimation from the DDM demonstrated that the MDD group differed in three distinct reward-related parameters driving approach-based decision making. First, MDD was associated with reduced reward sensitivity, measured as the impact of offered reward on evidence accumulation. Notably, this effect was replicated in a follow-up study. Second, the MDD group showed lower starting point bias towards approaching offers. Third, this starting point was influenced in opposite directions by Pavlovian effects and by nucleus accumbens activity across the groups: greater accumbens activity was related to approach bias in controls but avoid bias in MDD. Cross-validation revealed that the combination of these computational biomarkers were diagnostic of patient status, with accumbens influences being particularly diagnostic. Finally, within the MDD group, reward sensitivity and nucleus accumbens parameters were differentially related to symptoms of perceived stress and depression. Collectively, these findings establish the promise of computational psychiatry approaches to dissecting approach-avoidance decision dynamics relevant for affective disorders.