Adult attachment, anxiety, and pain self-efficacy as predictors of pain intensity and disability

Pain self-efficacy and anxiety have each been shown to contribute substantially to pain intensity and pain-related disability. Although adult attachment theory has been related separately to chronic pain, anxiety, and self-efficacy, it has not before been investigated with either pain self-efficacy or anxiety in the context of chronic pain. This study investigated the interrelations between these aspects of the chronic pain experience and their relative contributions towards pain intensity and disability. A clinical sample of 152 chronic pain patients participated in this study, completing self-report measures of attachment, self-efficacy, pain intensity, and disability, prior to attending a multidisciplinary pain clinic. Results revealed that fearful and preoccupied (anxious) attachment categories were associated with low pain self-efficacy, while high scores on the attachment dimension of comfort with closeness were linked with high pain self-efficacy, particularly for males. Insecure attachment (whether defined in terms of categories or dimensions) was related to higher levels of anxiety. Pain self-efficacy proved a stronger predictor of pain intensity than did anxiety and was a stronger predictor of disability than pain intensity or anxiety. In addition, comfort with closeness moderated the associations between pain self-efficacy and disability, pain self-efficacy and pain intensity, and anxiety and disability. Together, these findings support the value of adopting an attachment theoretical approach in the context of chronic pain. Treatment considerations and future research directions are considered. (c) 2006 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

Factor analysis of the self-report Dysexecutive (DEX-S) Questionnaire

The self-rating Dysexecutive Questionnaire (DEX-S) is a recently developed standardized self-report measure of behavioral difficulties associated with executive functioning such as impulsivity, inhibition, control, monitoring, and planning. Few studies have examined its construct validity, particularly for its potential wider use across a variety of clinical and nonclinical populations. This study examines the factor structure of the DEX-S questionnaire using a sample of nonclinical (N = 293) and clinical (N = 49) participants. A series of factor analyses were evaluated to determine the best factor solution for this scale. This was found to be a 4-factor solution with factors best described as inhibition, intention, social regulation, and abstract problem solving. The first 2 factors replicate factors from the 5-factor solutions found in previous studies that examined specific subpopulations. Although further research is needed to evaluate the factor structure within a range of subpopulations, this study supports the view that the DEX has the factor structure sufficient for its use in a wider context than only with neurological or head-injured patients. Overall, a 4-factor solution is recommended as the most stable and parsimonious solution in the wider context.

The role of self-efficacy in predicting rule-following behaviors in shelters for homeless youth: A test of the theory of planned behavior

Through a prospective study of 70 youths staying at homeless-youth shelters, the authors tested the utility of I. Ajzen's (1991) theory of planned behavior (TPB), by comparing the constructs of self-efficacy with perceived behavioral control (PBC), in predicting people's rule-following behavior during shelter stays. They performed the 1st wave of data collection through a questionnaire assessing the standard TPB components of attitudes, subjective norms, PBC, and behavioral intentions in relation to following the set rules at youth shelters. Further, they distinguished between items assessing PBC (or perceived control) and those reflecting self-efficacy (or perceived difficulty). At the completion of each youth's stay at the shelter, shelter staff rated the rule adherence for that participant. Regression analyses revealed some support for the TPB in that subjective norm was a significant predictor of intentions. However, self-efficacy emerged as the strongest predictor of intentions and was the only significant predictor of rule-following behavior. Thus, the results of the present study indicate the possibility that self-efficacy is integral to predicting rule adherence within this context and reaffirm the importance of incorporating notions of people's perceived ease or difficulty in performing actions in models of attitude-behavior prediction.

Bias, precision and heritability of self-reported and clinically measured height in Australian twins

Many studies of quantitative and disease traits in human genetics rely upon self-reported measures. Such measures are based on questionnaires or interviews and are often cheaper and more readily available than alternatives. However, the precision and potential bias cannot usually be assessed. Here we report a detailed quantitative genetic analysis of stature. We characterise the degree of measurement error by utilising a large sample of Australian twin pairs (857 MZ, 815 DZ) with both clinical and self-reported measures of height. Self-report height measurements are shown to be more variable than clinical measures. This has led to lowered estimates of heritability in many previous studies of stature. In our twin sample the heritability estimate for clinical height exceeded 90%. Repeated measures analysis shows that 2-3 times as many self-report measures are required to recover heritability estimates similar to those obtained from clinical measures. Bivariate genetic repeated measures analysis of self-report and clinical height measures showed an additive genetic correlation > 0.98. We show that the accuracy of self-report height is upwardly biased in older individuals and in individuals of short stature. By comparing clinical and self-report measures we also showed that there was a genetic component to females systematically reporting their height incorrectly; this phenomenon appeared to not be present in males. The results from the measurement error analysis were subsequently used to assess the effects of error on the power to detect linkage in a genome scan. Moderate reduction in error (through the use of accurate clinical or multiple self-report measures) increased the effective sample size by 22%; elimination of measurement error led to increases in effective sample size of 41%.

GTM: the generative topographic mapping

Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline.

GTM: the generative topographic mapping

Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline.

Developments of the generative topographic mapping

The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput. 10(1), 215-234) as a probabilistic re- formulation of the self-organizing map (SOM). It offers a number of advantages compared with the standard SOM, and has already been used in a variety of applications. In this paper we report on several extensions of the GTM, including an incremental version of the EM algorithm for estimating the model parameters, the use of local subspace models, extensions to mixed discrete and continuous data, semi-linear models which permit the use of high-dimensional manifolds whilst avoiding computational intractability, Bayesian inference applied to hyper-parameters, and an alternative framework for the GTM based on Gaussian processes. All of these developments directly exploit the probabilistic structure of the GTM, thereby allowing the underlying modelling assumptions to be made explicit. They also highlight the advantages of adopting a consistent probabilistic framework for the formulation of pattern recognition algorithms.

Antecedents and consequences of service quality in consumer evaluation of self-service internet technologies

Advances in technology coupled with increasing labour costs have caused service firms to explore self-service delivery options. Although some studies have focused on self-service and use of technology in service delivery, few have explored the role of service quality in consumer evaluation of technology-based self-service options. By integrating and extending the self-service quality framework the service evaluation model and the Technology Acceptance Model the authors address this emerging issue by empirically testing a comprehensive model that captures the antecedents and consequences of perceived service quality to predict continued customer interaction in the technology-based self-service context of Internet banking. Important service evaluation constructs like perceived risk, perceived value and perceived satisfaction are modelled in this framework. The results show that perceived control has the strongest influence on service quality evaluations. Perceived speed of delivery, reliability and enjoyment also have a significant impact on service quality perceptions. The study also found that even though perceived service quality, perceived risk and satisfaction are important predictors of continued interaction, perceived customer value plays a pivotal role in influencing continued interaction.

The effects of negative affectivity on self-reported activity limitations in stroke patients:testing the symptom perception, disability and psychosomatic hypotheses

This study investigated whether Negative Affectivity (NA) causes bias in self-report measures of activity limitations or whether NA has a real, non-artifactual association with activity limitations. The Symptom Perception Hypothesis (NA negatively biases self-reporting), Disability Hypothesis (activity limitations cause NA) and Psychosomatic Hypothesis (NA causes activity limitations) were examined longitudinally using both self-report and objective activity limitations measures. Participants were 101 stroke patients and their caregivers interviewed within two weeks of discharge, six weeks later and six months post-discharge. NA and self-report, proxy-report and observed performance activity (walking) limitations were assessed at each interview. NA was associated with activity limitations across measures. Both the Disability and Psychosomatic Hypotheses were supported: initial NA predicted objective activity limitations at six weeks but, additionally, activity limitations at six weeks predicted NA at six months. These results suggest that NA both affects and is affected by activity limitations and does not simply influence reporting.

The influence of memory in a threshold model for distributed task assignment

A nature inspired decentralised multi-agent algorithm is proposed to solve a problem of distributed task selection in which cities produce and store batches of different mail types. Agents must collect and process the mail batches, without a priori knowledge of the available mail at the cities or inter-agent communication. In order to process a different mail type than the previous one, agents must undergo a change-over during which it remains inactive. We propose a threshold based algorithm in order to maximise the overall efficiency (the average amount of mail collected). We show that memory, i.e. the possibility for agents to develop preferences for certain cities, not only leads to emergent cooperation between agents, but also to a significant increase in efficiency (above the theoretical upper limit for any memoryless algorithm), and we systematically investigate the influence of the various model parameters. Finally, we demonstrate the flexibility of the algorithm to changes in circumstances, and its excellent scalability.

Quantifying the complexity of chaos in multibasin multidimensional dynamics of molecular systems

The simulated classical dynamics of a small molecule exhibiting self-organizing behavior via a fast transition between two states is analyzed by calculation of the statistical complexity of the system. It is shown that the complexity of molecular descriptors such as atom coordinates and dihedral angles have different values before and after the transition. This provides a new tool to identify metastable states during molecular self-organization. The highly concerted collective motion of the molecule is revealed. Low-dimensional subspaces dynamics is found sensitive to the processes in the whole, high-dimensional phase space of the system. © 2004 Wiley Periodicals, Inc.

General Aspects of Constructing an Autonomous Adaptive Agent

There are a great deal of approaches in artificial intelligence, some of them also coming from biology and neirophysiology. In this paper we are making a review, discussing many of them, and arranging our discussion around the autonomous agent research. We highlight three aspect in our classification: type of abstraction applied for representing agent knowledge, the implementation of hypothesis processing mechanism, allowed degree of freedom in behaviour and self-organizing. Using this classification many approaches in artificial intelligence are evaluated. Then we summarize all discussed ideas and propose a series of general principles for building an autonomous adaptive agent.

Intelligent Computations for Flood Monitoring

Floods represent the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a method to flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations. In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and classification. We tested our approach to process data from three different satellite sensors: ERS-2/SAR (during flooding on Tisza river, Ukraine and Hungary, 2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADARSAT-1 (during flooding on Huaihe river, China, 2007). Obtained results showed the efficiency of our approach.

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences.

Self-aware and self-expressive camera networks

Smart cameras perform on-board image analysis, adapt their algorithms to changes in their environment, and collaborate with other networked cameras to analyze the dynamic behavior of objects. A proposed computational framework adopts the concepts of self-awareness and self-expression to more efficiently manage the complex tradeoffs among performance, flexibility, resources, and reliability. The Web extra at http://youtu.be/NKe31-OKLz4 is a video demonstrating CamSim, a smart camera simulation tool, enables users to test self-adaptive and self-organizing smart-camera techniques without deploying a smart-camera network.