The multidimensional measurement of the positive symptoms of psychosis

The measures most frequently used to assess psychotic symptoms fail to reflect important dimensions. The Psychotic Symptom Rating Scale (PSYRATS) aims to capture the multidimensional nature of auditory hallucinations and delusions. Individuals (N = 276) who had recently relapsed with positive symptoms completed the auditory hallucinations and delusions PSYRATS scales. These scores were compared with the relevant items from the SAPS and PANSS, and with measures of current mood. Total scores and distribution of items of the PSYRATS scales are presented and correlated with other measures. Positive symptom items from the SAPS and PANSS reflected the more objective aspects of PSYRATS ratings of auditory hallucinations and delusions (frequency and conviction) but were relatively poor at measuring distress. A major strength of the PSYRATS scales is the specific measurement of the distress dimension of symptoms, which is a key target of psychological intervention. It is advised that the PSYRATS should not be used as a total score alone, whilst further research is needed to clarify the best use of potential subscales. Copyright (c) 2007 John Wiley & Sons, Ltd.

The psychological, neurochemical and functional neuroanatomical mediators of the effects of positive and negative mood on executive functions

In this review we evaluate the cognitive and neural effects of positive and negative mood on executive function. Mild manipulations of negative mood appear to have little effect on cognitive control processes, whereas positive mood impairs aspects of updating, planning and switching. These cognitive effects may be linked to neurochemistry: with positive mood effects mediated by dopamine while negative mood effects may be mediated by serotonin levels. Current evidence on the effects of mood on regional brain activity during executive functions, indicates that the prefrontal cortex is a recurrent site of integration between mood and cognition. We conclude that there is a disparity between the importance of this topic and awareness of how mood affects, executive functions in the brain. Most behavioural and neuroimaging studies of executive function in normal samples do not explore the potential role of variations in mood, yet the evidence we outline indicates that even mild fluctuations in mood can have a significant influence on neural activation and cognition. (c) 2006 Elsevier Ltd. All rights reserved.

Positive schizotypy and trauma-related intrusions

The current study extends previous investigation of schizotypy as a vulnerability factor for trauma-related intrusions through the use of a clinical sample. Fifty people seeking psychological interventions after experiencing a distressing or traumatic event completed measures of positive schizotypy, posttraumatic stress disorder symptomatology, peritraumatic dissociation, and mood. Individuals scoring high in positive schizotypy were vulnerable to experiencing more frequent trauma-related intrusions along with wider posttraumatic stress disorder symptomatology, including hypervigilance, avoidance, and low mood. Results are discussed within a theoretical context, suggesting that certain information processing styles associated with high schizotype individuals may account for a vulnerability to trauma-related intrusions.

Picture book exposure elicits positive visual preferences in toddlers

Although the relationship between "mere exposure" and attitude enhancement is well established in the adult domain, there has been little similar work with children. This article examines whether toddlers' visual attention toward pictures of foods can be enhanced by repeated visual exposure to pictures of foods in a parent-administered picture book. We describe three studies that explored the number and nature of exposures required to elicit positive visual preferences for stimuli and the extent to which induced preferences generalize to other similar items. Results show that positive preferences for stimuli are easily and reliably induced in children and, importantly, that this effect of exposure is not restricted to the exposed stimulus per se but also applies to new representations of the exposed item. (C) 2009 Elsevier Inc. All rights reserved.

Stabilizing swarm intelligence search via positive feedback resource allocation

A novel Swarm Intelligence method for best-fit search, Stochastic Diffusion Search, is presented capable of rapid location of the optimal solution in the search space. Population based search mechanisms employed by Swarm Intelligence methods can suffer lack of convergence resulting in ill defined stopping criteria and loss of the best solution. Conversely, as a result of its resource allocation mechanism, the solutions SDS discovers enjoy excellent stability.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.