The Experience in Personal Social Systems Questionnaire (EXIS.pers): Development and Psychometric Properties

This study presents the theoretical background, development, and psychometric properties of the German and English versions of the Experience in Personal Social Systems Questionnaire (EXIS.pers). It assesses how the members of a personal social system experience their situation within that system. It is designed as a research tool for interventions in which only one member of the system participates (e.g., Family Constellation Seminars). The EXIS.pers was created to measure change on the individual level relating to one's own important personal social system. In Study 1, we used exploratory factor analysis (EFA) for latent variable identification of the original German EXIS.pers (n = 179). In Studies 2 and 3, we used confirmatory factor analysis (CFA) to examine the dimensionality of the German (n = 634) and English (n = 310) EXIS.pers. Internal consistencies and cross‐cultural structural equivalence were assessed. EFA indicated that a four‐factor model provided best fit for the German EXIS.pers. For both the German and English EXIS.pers, CFA provided the best fit for a five‐factor bi‐level model that included a general factor (Experience In Personal Social Systems) and four dimensions (Belonging, Autonomy, Accord, Confidence). Good internal consistencies, external associations, and cross‐cultural structural equivalence were demonstrated. This study provides first evidence for the German and English EXIS.pers as an economical and reliable measure of an individual's experience within his or her personal social systems.     

The emergence of the ecological mind in Hua‐Yen/Kegon Buddhism and Jungian psychology

The complexity associated with deep interconnectedness in nature is beginning to be articulated and elaborated in the field of ecological studies. While some parallels to the psyche have been made and the field of Eco‐psychology has been developing, Jung's explicit contribution by way of the image of rhizomes has not been considered in detail. Philosopher Gilles Deleuze acknowledges borrowing the term from Jung, though he disagreed with Jung's Empedoclean use of the term. The paper presents some fundamental properties of rhizomes along with contemporary scientific research on mycorrhizal (fungal) networks. Comparisons are made, first with classical symbolic forms, demonstrating some overlap but also some differences. Then comparison of rhizomal networks is made to those found both in mammalian brains and in recent images of the ‘cosmic web’. While no hard conclusions can be drawn from these images, their remarkable similarities are suggestive of a need to reconsider what is meant by ‘intelligence’. The cosmic web is one of the largest structures in the known universe (clusters of galaxies which form into filaments and walls) with empty spaces in between. Exploration of the structure of this web leads to a discussion of dark matter and dark energy, current hot topics in science, probing into the mysteries of our ‘Big‐Bang’ cosmology. An additional comparison of the emerging image of the universe as a whole with the ancient Chinese Buddhist cosmological vision from the Hua‐Yen School (Kegon in Japan) again reveals profound parallels. The potential convergence of aspects of subjective, or meditative, explorations with objective scientific constructions is striking and offers links between East and West, as well as potential confirmation of the objective aspects of empathy.     

Diversity and Community: The Role of Agent‐Based Modeling

Community psychology involves several dialectics between potentially opposing ideals, such as theory and practice, rights and needs, and respect for human diversity and sense of community. Some recent papers in the American Journal of Community Psychology have examined the diversity‐community dialectic, some with the aid of agent‐based modeling and concepts from network science. This paper further elucidates these concepts and suggests that research in community psychology can benefit from a useful dialectic between agent‐based modeling and the real‐world concerns of community psychology.     

Effects of Group Functional Behavior-Based Cognitive-Behavioral Therapy for Obsessive-Compulsive Behavior in a Youth With Autism Spectrum Disorder

Individuals with Autism Spectrum Disorder (ASD) often present with comorbid Obsessive Compulsive Behaviors (OCBs), but little research exists on effective intervention for OCBs. Using a single-case experimental design, this study highlights the efficacy of a Group Functional Behavior-based Cognitive-Behavior Therapy (Fb-CBT) to reduce OCBs in an 11-year-old youth with ASD. Tailored to the individual needs of this youth, Fb-CBT included traditional CBT components (e.g., psychoeducation, cognitive-behavioral skills training, and exposure and response prevention) coupled with functional behavior assessment and intervention. Time-series parent report data and standardized measures showed clinically significant decreases in OCBs, an increase in psychosocial functioning, and high consumer satisfaction.     

Does Parent-Child Interaction Therapy Reduce Maternal Stress,Anxiety, and Depression Among Mothers of Children with Autism Spectrum Disorder?

Maternal stress, anxiety, and depression are associated with ineffective parenting strategies for families of children with Autism Spectrum Disorders (ASD). We present the use of Parent-Child Interaction Therapy (PCIT) with three mother-child dyads to reduce maternal stress, anxiety, and depression and child disruptive behavior in children with ASD. Results included increases in positive parenting skills, and decreases in maternal anxiety, depression, and stress, as well as child behavior problems. PCIT is a promising alternative to more intensive and costly interventions, both at reducing disruptive behaviors and improving maternal health. Implications of PCIT for dyads affected by ASD are discussed.     

Generating and sharing of market intelligence in sales teams: an economic social network perspective

In this research, we take a multimethod approach to shed light on the potential costs to sales teams that generate and share market intelligence (MI). First, we introduce an analytical model to propose the respective levels of effort that sales managers, experts, and team members spend generating and sharing MI. To test our propositions, we utilize social network data from 40 independent, business-to-business (B2B) sales teams, representing 287 salespeople. Interestingly, our results support the premise that team members become dependent (reduce MI efforts) when their sales manager or team expert shares MI among the team. We term this a “sharing tax” that sales managers and team experts pay when they share MI. Consequently, sales managers demonstrate greater MI-generation efforts the more they share MI. We also find that experts who share more (less) also show greater (lesser) MI-generation efforts, but only for teams where sales managers share low (high) levels of MI. In summary, our research innovatively conducts an empirical test of the Nash Equilibrium pattern of sales team effort to show that two critical team members, the sales manager and expert, are at a disadvantage when they share valuable MI.     

Stability and Change in Cognitive Behavior Therapy: Considering the Implications of ACT and RFT

Acceptance and Commitment Therapy (ACT) and Relational Frame Theory (RFT) are part of the new wave of treatments and analyses that seem to be emerging in cognitive behavior therapy. In this article, data in support of these new approaches are provided, and evidence that ACT works through different processes than traditional CBT are presented. The integrative proposals of Ciarrochi and Robb, and Ciarrochi, Robb, and Godsell are then considered. In the long run, whether such integrations are useful is an empirical matter, but concerns are raised about the effects of focusing on the content of beliefs, and the role of logical–empirical challenges to belief. Address correspondence to Steven C. Hayes, Department of Psychology/298, University of Nevada, Reno, NV 89557-0062, USA.     

Semantic computations of truth based on associations already learned

This article sets forth a detailed theoretical proposal of how the truth of ordinary empirical statements, often atomic in form, is computed. The method of computation draws on psychological concepts such as those of associative networks and spreading activation, rather that the concepts of philosophical or logical theories of truth. Axioms for a restricted class of cases are given, as well as some detailed examples.     

An evolutionary system for neural logic networks using genetic programming and indirect encoding

Nowadays, intelligent connectionist systems such as artificial neural networks have been proved very powerful in a wide area of applications. Consequently, the ability to interpret their structure was always a desirable feature for experts. In this field, the neural logic networks (NLN) by their definition are able to represent complex human logic and provide knowledge discovery. However, under contemporary methodologies, the training of these networks may often result in non-comprehensible or poorly designed structures. In this work, we propose an evolutionary system that uses current advances in genetic programming that overcome these drawbacks and produces neural logic networks that can be arbitrarily connected and are easily interpretable into expert rules. To accomplish this task, we guide the genetic programming process using a context-free grammar and we encode indirectly the neural logic networks into the genetic programming individuals. We test the proposed system in two problems of medical diagnosis. Our results are examined both in terms of the solution interpretability that can lead in knowledge discovery, and in terms of the achieved accuracy. We draw conclusions about the effectiveness of the system and we propose further research directions.     

Logic programs and connectionist networks

One facet of the question of integration of Logic and Connectionist Systems, and how these can complement each other, concerns the points of contact, in terms of semantics, between neural networks and logic programs. In this paper, we show that certain semantic operators for propositional logic programs can be computed by feedforward connectionist networks, and that the same semantic operators for first-order normal logic programs can be approximated by feedforward connectionist networks. Turning the networks into recurrent ones allows one also to approximate the models associated with the semantic operators. Our methods depend on a well-known theorem of Funahashi, and necessitate the study of when Funahashi's theorem can be applied, and also the study of what means of approximation are appropriate and significant.