Decreased symptoms of depression after mindfulness-based stress reduction: potential moderating effects of religiosity, spirituality, trait mindfulness, sex, and age

Objective: mindfulness-based stress reduction (MBSR) is a secular meditation training program that reduces depressive symptoms. Little is known, however, about the degree to which a participant's spiritual and religious background, or other demographic characteristics associated with risk for depression, may affect the effectiveness of MBSR. Therefore, this study tested whether individual differences in religiosity, spirituality, motivation for spiritual growth, trait mindfulness, sex, and age affect MBSR effectiveness.Methods: as part of an open trial, multiple regression was used to analyze variation in depressive symptom outcomes among 322 adults who enrolled in an 8-week, community-based MBSR program.Results: as hypothesized, depressive symptom severity decreased significantly in the full study sample (d=0.57; p<0.01). After adjustment for baseline symptom severity, moderation analyses revealed no significant differences in the change in depressive symptoms following MBSR as a function of spirituality, religiosity, trait mindfulness, or demographic variables. Paired t tests found consistent, statistically significant (p<0.01) reductions in depressive symptoms across all subgroups by religious affiliation, intention for spiritual growth, sex, and baseline symptom severity. After adjustment for baseline symptom scores, age, sex, and religious affiliation, a significant proportion of variance in post-MBSR depressive symptoms was uniquely explained by changes in both spirituality (?=?0.15; p=0.006) and mindfulness (?=?0.17; p<0.001).Conclusions: these findings suggest that MBSR, a secular meditation training program, is associated with improved depressive symptoms regardless of affiliation with a religion, sense of spirituality, trait level of mindfulness before MBSR training, sex, or age. Increases in both mindfulness and daily spiritual experiences uniquely explained improvement in depressive symptom

Modelling knowledge in Electronic Study Books

Knowledge graphs are a new form of knowledge representation. They are closely related to semantic networks and can be looked upon as in line with Schank's conceptual dependency theory and Sowa's conceptual graphs. The special feature of knowledge graphs is the use of a very restricted set of types of relations, that is considered to be the basic set of primitive relations. The theory of knowledge graphs is outlined in the first part of the paper. In the second part the possibilities of knowledge graphs for solving problems posed by Electronic (Study) Books will be discussed

The algebra of lexical semantics

Abstract. The current generative theory of the lexicon relies primar-ily on tools from formal language theory and mathematical logic. Here we describe how a different formal apparatus, taken from algebra and automata theory, resolves many of the known problems with the gener-ative lexicon. We develop a finite state theory of word meaning based on machines in the sense of Eilenberg [11], a formalism capable of de-scribing discrepancies between syntactic type (lexical category) and se-mantic type (number of arguments). This mechanism is compared both to the standard linguistic approaches and to the formalisms developed in AI/KR. 1 Problem Statement In developing a formal theory of lexicography our starting point will be the informal practice of lexicography, rather than the more immediately related for-mal theories of Artificial Intelligence (AI) and Knowledge Representation (KR). Lexicography is a relatively mature field, with centuries of work experience an

Eliminating Ditransitives

Abstract. We discuss how higher arity verbs such as give or promise can be treated in an algebraic framework that admits only unary and binary relations and does not rely on event variables

Distributed situation awareness in dynamic systems: Theoretical development and application of an ergonomics methodology

The purpose of this paper is to propose foundations for a theory of situation awareness based on the analysis of interactions between agents (i.e., both human and non-human) in subsystems. This approach may help promote a better understanding of technology-mediated interaction in systems, as well as helping in the formulation of hypotheses and predictions concerning distributed situation awareness. It is proposed that agents within a system each hold their own situation awareness which may be very different from (although compatible with) other agents. It is argued that we should not always hope for, or indeed want, sharing of this awareness, as different system agents have different purposes. This view marks situation awareness as a 1 dynamic and collaborative process that binds agents together on tasks on a moment-by-moment basis. Implications of this viewpoint for development of a new theory of, and accompanying methodology for, distributed situation awareness are offered

Dynamics of Transformation from Segregation to Mixed Wealth Cities

We model the dynamics of the Schelling model for agents described simply by a continuously distributed variable - wealth. Agents move to neighborhoods where their wealth is not lesser than that of some proportion of their neighbors, the threshold level. As in the case of the classic Schelling model where segregation obtains between two races, we find here that wealth-based segregation occurs and persists. However, introducing uncertainty into the decision to move - that is, with some probability, if agents are allowed to move even though the threshold level condition is contravened - we find that even for small proportions of such disallowed moves, the dynamics no longer yield segregation but instead sharply transition into a persistent mixed wealth distribution. We investigate the nature of this sharp transformation between segregated and mixed states, and find that it is because of a non-linear relationship between allowed moves and disallowed moves. For small increases in disallowed moves, there is a rapid corresponding increase in allowed moves, but this tapers off as the fraction of disallowed moves increase further and finally settles at a stable value, remaining invariant to any further increase in disallowed moves. It is the overall effect of the dynamics in the initial region (with small numbers of disallowed moves) that shifts the system away from a state of segregation rapidly to a mixed wealth state. The contravention of the tolerance condition could be interpreted as public policy interventions like minimal levels of social housing or housing benefit transfers to poorer households. Our finding therefore suggests that it might require only very limited levels of such public intervention - just sufficient to enable a small fraction of disallowed moves, because the dynamics generated by such moves could spur the transformation from a segregated to mixed equilibrium.Comment: 12 pages, 7 figure

The Rationality of Prejudices

We model an -player repeated prisoner's dilemma in which players are given traits (e.g., height, age, wealth) which, we assume, affect their behavior. The relationship between traits and behavior is unknown to other players. We then analyze the performance of “prejudiced” strategies—strategies that draw inferences based on the observation of some or all of these traits, and extrapolate the inferred behavior to other carriers of these traits. Such prejudiced strategies have the advantage of learning rapidly, and hence of being well adapted to rapidly changing conditions that might result, for example, from high migration or birth rates. We find that they perform remarkably well, and even systematically outperform both Tit-For-Tat and ALLD when the population changes rapidly

Modeling Abnormal Priming in Alzheimer's Patients with a Free Association Network

Alzheimer's Disease irremediably alters the proficiency of word search and retrieval processes even at its early stages. Such disruption can sometimes be paradoxical in specific language tasks, for example semantic priming. Here we focus in the striking side-effect of hyperpriming in Alzheimer's Disease patients, which has been well-established in the literature for a long time. Previous studies have evidenced that modern network theory can become a powerful complementary tool to gain insight in cognitive phenomena. Here, we first show that network modeling is an appropriate approach to account for semantic priming in normal subjects. Then we turn to priming in degraded cognition: hyperpriming can be readily understood in the scope of a progressive degradation of the semantic network structure. We compare our simulation results with previous empirical observations in diseased patients finding a qualitative agreement. The network approach presented here can be used to accommodate current theories about impaired cognition, and towards a better understanding of lexical organization in healthy and diseased patients