Adapting Cognitive Processing Therapy to Treat Co-Occurring Posttraumatic Stress Disorder and Mild Traumatic Brain Injury: A Case Study

This case study aims to provide evidence for the effectiveness of adapting a particular manualized cognitive behavioral therapy intervention to treat co-occurring posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI). This study presents the treatment of a woman who experienced co-occurring mTBI and PTSD following a motor vehicle accident, a dual diagnosis that was established through a flexible assessment approach involving interviews as well as standardized psychological, neuropsychological, and neurobehavioral testing. Treatment planning led to a-priori adaptation of Cognitive Processing Therapy (CPT) to treat both her PTSD symptoms and the sequelae associated with her mTBI. The therapist maintained fidelity to the manualized structure and content of CPT protocol, adapting portions of the treatment to add specific emphasis on issues of identity confusion and role loss in service of addressing these common functional impairments that can accompany mTBI. Discussion focuses on application of CPT for future treatment of comorbid PTSD and TBI amidst complicating factors, including role losses and medical and safety issues. This case study is especially relevant due to the prevalence of co-occurring PTSD and TBI across a variety of populations.     

Modelling the human lane-change execution behaviour through Multilayer Perceptrons and Convolutional Neural Networks

Driving is a highly complex task that involves the execution of multiple cognitive tasks belonging to different levels of abstraction. Traffic emerges from the interaction of a big number of agents implementing those behaviours, but until recent years, modelling it by the interaction of these agents in the so called micro-simulators was a nearly impossible task as their number grows. However, with the growing computing power it is possible to model increasingly large quantities of individual vehicles according to their individual behaviours. These models are usually composed of two sub-models for two well-defined tasks: car-following and lane-change. In the case of lane-change the literature proposes many different models, but few of them use Computational Intelligence (CI) techniques, and much less use personalization for reaching individual granularity. This study explores one of the two aspects of the lane-change called lane-change acceptance, where the driver performs or not a lane-change given his intention and the vehicle environment. We demonstrate how the lane-change acceptance of a specific driver can be learned from his lane change intention and surrounding environment in an urban scenario using CI techniques such as feed-forward Artificial Neural Network (ANN). We work with Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN) architectures. How they perform one against the other and how the different topologies affect both to the generalization of the problem and the learning process are studied.     

Enterprise credit risk evaluation based on neural network algorithm

To explore the enterprise credit risk evaluation, the application effect of several common neural network models in Chinese small and medium-sized enterprise data sets was compared and the optimal parameters for each model were determined. In addition, the classification accuracy and the applicability of the model were compared, and finally the common problem of optimization neural network algorithm based on population was solved: need to determine the dimensions in advance. The experimental results showed that the probabilistic neural network (PNN) had the minimum error rate and second types of errors, while the PNN model had the highest AUC value and was robust. To sum up, the algorithm makes some contributions to solve the financing problem of small and medium-sized enterprises in China.     

A review of social disinhibition after traumatic brain injury

Acquired social disinhibition refers to a debilitating behavioural syndrome commonly reported after a severe traumatic brain injury (TBI) and is characterized by inappropriate social behaviour, often described as immaturity and insensitivity towards others. These behaviours can have enduring effects on the social capability of the individual and their relationships with others. However, research into socially disinhibited behaviour after TBI has been thwarted by a lack of consensus in the literature on definition and measurement. This review provides an overview of our current understanding of the definition, measurement, prevalence, associated outcomes, neuropathology, and underlying mechanisms of social disinhibition after TBI. In addition, suggestions are made for future research to further our understanding of this syndrome with the eventual aim of rehabilitating problematic behaviours. It is concluded that an improved understanding of what causes disinhibited behaviour after TBI will be necessary for the development of effective treatment strategies aimed at the rehabilitation of underlying impairments.     

Attentional orienting and awareness: evidence from a discrimination task

We used several cue–target SOAs (100, 500, 1000 ms) and three different degrees of cue predictability (Non-predictive-50%, Predictive-75%, Counter-predictive-25%), to investigate the role of awareness of cue–target predictability on cueing effects. A group of participants received instructions about the informative value of the cue, while another group did not receive such instructions. Participants were able to extract the predictive value of a spatially peripheral cue and use it to orient attention, whether or not specific instructions about the predictive value of the cue were given, and no matter their ability to correctly report it in a post-test questionnaire. In the non-predictive block, bad estimators who received no instructions showed regular cueing effects, while good estimators exhibited smaller and non-significant facilitatory effects at the short SOA and an absence of significant IOR at longer SOAs. However, for the instructions group, the pattern of results reversed.     

Un regard structural sur l’inégalité des genres au travail : La causalité des différences « genrées » dans les caractéristiques de réseau de l’employé

This study aims at identifying gender differences in network structure and, hence, at understanding the underlying nature of gender inequalities in organisations. The hypotheses address four ego-network characteristics: network size and density, and ego’s centrality and brokerage. Contrary to the existing body of literature, none significant isolated gender difference in network structure is identified. Instead, those gender differences are triggered exclusively by organisational status, that is to say, they are due to the increased level of power. Thus, organisational status is considered as moderating the relationship between gender and network structure’s characteristics. Some further perspectives are suggested to clarify and generalise the phenomena observed.     

人类社会认知的神经机制:来自社会脑研究的证据

社会认知是指从环境互动的过程中推测他人意图、情感和思想等内部状态的心理加工过程。社会脑假说认为,包括人类在内的灵长类大脑内肯定存在着一个旨在认识和理解他人表情的神经机制,在社会交往中通过该中枢迅速处理与他人相互作用的各种信息。本文通过回顾社会脑的构成与基本功能,镜像神经元的发现及功能,以及社会脑、镜像神经元对社会认知影响的相关研究,提出了人类社会认知不同于其他物种的三个主要区别。