Does unconscious stress play a role in prolonged cardiovascular stress recovery?

Item does not contain fulltextAccording to recent insights, humans might not be aware of a substantial part of their cognitive stress representations while these still have prolonged physiological effects. Unconscious stress' can be measured by implicit affect (IA) tests. It was shown that IA predicts physiological stress responses, in fact better than explicit (conscious') affect. It is not known yet whether IA is associated with concurrent prolonged stress responses. In two studies (n=62 and 123), anger harassment was used to induce stress. Blood pressure (BP) and heart rate (HR) were measured continuously. During BP and HR recovery, IA was measured by an anger' version of the implicit association test (IAT) or the implicit positive and negative affect test (IPANAT). Blood pressure and HR increased during anger harassment and recovery afterwards. When using the IPANAT BP recovery levels were lower when positive IA was high and higher when negative IA was high, independent of explicit affect and rumination. These results were not found using the IAT. These results provide preliminary evidence that physiological stress recovery is associated with IA. This is in line with the theory that unconscious stress is responsible for a possibly considerable part of unhealthy prolonged stress-related physiological activity.9 p

Social Network Analysis of Co-fired Fuzzy Rules

Abstract. The popularity of modern online social networks has grown up so quickly in the last few years that, nowadays, social network analysis has become one of the hottest research lines in the world. It is important to highlight that social network analysis is not limited to the analysis of networks connecting peo-ple. Indeed, it is strongly connected with the classical methods widely recognized in the context of graph theory. Thus, social network analysis is applied to many different areas like for instance economics, bibliometrics, and so on. This contri-bution shows how it can also be successfully applied in the context of designing interpretable fuzzy systems. The key point consists of looking at the rule base of a fuzzy system as a fuzzy inference-gram (fingram), i.e., as a social network made of nodes representing fuzzy rules. In addition, nodes are connected through edges that represent the interaction between rules, at inference level, in terms of co-fired rules, i.e., rules fired at the same time by a given input vector. In short, fingram analysis consists of studying the interaction among nodes in the network for the purpose of understanding the structure and behavior of the fuzzy rule base under consideration. It is based on the basic principles of social network analysis which have been properly adapted to the design of fuzzy systems.