Dreaming and the default network: A review,synthesis, and counterintuitive research proposal

This article argues that the default network, augmented by secondary visual and sensorimotor cortices, is the likely neural correlate of dreaming. This hypothesis is based on a synthesis of work on dream content, the findings on the contents and neural correlates of mind-wandering, and the results from EEG and neuroimaging studies of REM sleep. Relying on studies in the 1970s that serendipitously discovered episodes of dreaming during waking mind-wandering, this article presents the seemingly counterintuitive hypothesis that the neural correlates for dreaming could be further specified in the process of carrying out EEG/fMRI studies of mind-wandering and default network activity. This hypothesis could be tested by asking participants for experiential reports during moments of differentially high levels of default network activation, as indicated by mixed EEG/fMRI criteria. Evidence from earlier EEG/fMRI studies of mind-wandering and from laboratory studies of dreaming during the sleep-onset process is used to support the argument.     

How the Size of Our Social Network Influences Our Semantic Skills

People differ in the size of their social network, and thus in the properties of the linguistic input they receive. This article examines whether differences in social network size influence individuals’ linguistic skills in their native language, focusing on global comprehension of evaluative language. Study 1 exploits the natural variation in social network size and shows that individuals with larger social networks are better at understanding the valence of restaurant reviews. Study 2 manipulated social network size by randomly assigning participants to learn novel evaluative words as used by two (small network) versus eight (large network) speakers. It replicated the finding from Study 1, showing that those exposed to a larger social network were better at comprehending the valence of product reviews containing the novel words that were written by novel speakers. Together, these studies show that the size of one's social network can influence success at language comprehension. They thus open the door to research on how individuals’ lifestyle and the nature of their social interactions can influence linguistic skills.     

Attentional orienting and executive control are affected by different types of meditation practice

Several studies have demonstrated the beneficial effects of meditation on attention. The present study investigated the relationship between focused attention (FA) and open monitoring (OM) meditation skills and the various functions of attention. In Experiment 1, we executed the attention network test and compared the performance of experts on dandao meditation with that of ordinary people on this test. The results indicated that the experts specializing in OM meditation demonstrated greater attentional orienting ability compared with those specializing in FA meditation and the control group. In addition, both expert groups registered improvements in their executive control abilities compared with the control group. In Experiment 2, we trained beginners in FA meditation for 3 months. The results showed that the experimental group exhibited significantly enhanced executive control ability. We infer that FA meditation skills promote executive control function and OM meditation skills promote both executive control and attentional orienting functions.     

同伴团体对青少年问题行为的影响：一项基于社会网络分析的研究

以675名初中和高中学生为被试,采用社会网络分析方法,获得506名青少年在其班级中的网络中心度,并确定他们所属的同伴团体,在此基础上考察同伴团体的行为规范对其问题行为的影响。结果发现：（1）在控制了班级层次的问题行为水平和其他相关变量后,同伴团体的问题行为水平能够正向预测青少年自身的问题行为;（2）青少年在同伴团体内部的地位能负向预测青少年的问题行为,青少年在班级社交网络中的度数中心度能正向预测其问题行为,而中介中心度能负向预测其问题行为;（3）交互作用分析表明：同伴团体的问题行为水平主要对低中介中心度的青少年产生显著影响;仅在问题行为水平较高的同伴团体中,青少年的度数中心度正向预测其问题行为。     

社交网站成瘾与青少年抑郁的关系:认知负载和核心自我评价的中介作用

为探讨社交网站成瘾对青少年抑郁的影响及其作用机制,在素质-压力模型的视角下,采用社交网站成瘾量表、认知负载量表、核心自我评价量表和流调中心抑郁量表,对武汉市三所全日制中学886名初中生进行调查。结果表明:(1)社交网站成瘾、认知负载、核心自我评价和抑郁两两间存在显著的相关,且社交网站成瘾对抑郁具有显著的正向预测作用;(2)认知负载和核心自我评价能在社交网站成瘾与抑郁的关系中起完全中介作用。具体而言,社交网站成瘾通过三条路径影响抑郁:一是认知负载的单独中介作用;二是核心自我评价的单独中介作用;三是认知负载-核心自我评价的链式中介作用。本研究揭示了社交网站成瘾与抑郁的关系及其作用机制,拓展了社交网站成瘾对个体心理社会适应的研究。     

Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks

Globally, motor vehicle crashes account for over 1.2 million fatalities per year and are the leading cause of death for people aged 15–29 years. The majority of road crashes are caused by human error, with risk heightened among young and novice drivers learning to negotiate the complexities of the road environment. Direct feedback has been shown to have a positive impact on driving behaviour. Methods that could detect behavioural changes and therefore, positively reinforce safer driving during the early stages of driver licensing could have considerable road safety benefit. A new methodology is presented combining in-vehicle telematics technology, providing measurements forming a personalised driver profile, with neural networks to identify changes in driving behaviour. Using Long Short-Term Memory (LSTM) recurrent neural networks, individual drivers are identified based on their pattern of acceleration, deceleration and exceeding the speed limit. After model calibration, new, real-time data of the driver is supplied to the LSTM and, by monitoring prediction performance, one can assess whether a (positive or negative) change in driving behaviour is occurring over time. The paper highlights that the approach is robust to different neural network structures, data selections, calibration settings, and methodologies to select benchmarks for safe and unsafe driving. Presented case studies show additional model applications for investigating changes in driving behaviour among individuals following or during specific events (e.g., receipt of insurance renewal letters) and time periods (e.g., driving during holiday periods). The application of the presented methodology shows potential to form the basis of timely provision of direct feedback to drivers by telematics-based insurers. Such feedback may prevent internalisation of new, risky driving habits contributing to crash risk, potentially reducing deaths and injuries among young drivers as a result.     

Convolutional neural network for bio-medical image segmentation with hardware acceleration

Application of artificial intelligence in Bio-Medical image processing is gaining more and more importance in the field of Medical Science. The bio medical images, has to go through several steps before the diagnosis of the disease. Firstly, the images has to be acquired and preprocessing has to be done and the data has to be stored in memory. It requires huge amount of memory and processing time. Among the preprocessing steps, edge detection is one of the major step. Edge detection filters the unwanted details in the image, and preserves the edges of the image, which describe the boundary of the image. In biomedical application, for the detection of the diseases, it is very essential to have the boundary detail of the acquired image of the organ under observation. Thus it is very essential to extract the edges of the images. Power is one of the main parameters that have to be considered while dealing with biomedical instruments. The biomedical signal processing instruments should be capable of operating at low power and also at high speed. In order to segregate the images into different levels or stage, we use convolutional neural networks for classification. By having a hardware architecture for image edge detection, the computational time for pre-processing of the image can be reduced, and the hardware can be a part of acquisition device itself. In this paper a low-power architecture for edge detection to detect the biomedical images are presented. The edge detection output are given to the system, which will diagnose the diseases using image classification using convolutional neural network. In this paper, Sobel and Prewitt, algorithms are used for edge detection using 180 nm technology. The edge detection algorithms are implemented using VLSI, and digital IC design of the architecture is presented. The algorithms for edge detection is co-simulated using MATLAB and Modelsim. The architecture is first simulated using CMOS logic and new method using domino logic is presented for low power consumption.