寻找自我：自我相关物主代词的编码与theta节律的活动*

之前的相关研究表明相位锁定的脑电活动调节自我相关物主代词的认知加工。然而,非相位锁定的神经震荡是否参与自我相关物主代词的加工目前还不清楚。为了考察非相位锁定的神经震荡在自我相关物主代词加工中的功能性意义,我们要求被试完成一个标准/偏差刺激辨别任务,同时记录其脑电活动。小波分析的结果发现相对于非自我相关物主代词“他的”,自我相关物主代词“我的”引发了更大的 theta 频带(4~8 Hz)能量活动,这表明 theta 波的活动对自我相关信息加工中脑的大范围神经同步形成的功能性神经网络起着一个很重要的作用。     

Through neural stimulation to behavior manipulation: a novel method for analyzing dynamical cognitive models

The dynamical systems' approach to cognition (Dynamicism) promises computational models that effectively embed cognitive processing within its more natural behavioral context. Dynamical cognitive models also pose difficult, analytical challenges, which motivate the development of new analytical methodology. We start by illustrating the challenge by applying two conventional analytical methods to a well-known Dynamicist model of categorical perception. We then introduce our own analysis, which works by analogy with neural stimulation methods, and which yields some novel insights into the way the model works. We then extend and apply the method to a second Dynamicist model, which captures the key psychophysical trends that emerge when humans and animals compare two numbers. The results of the analysis-which reveals units with tuning functions that are monotonically related to the magnitudes of the numbers that the agents must compare-offer a clear contribution to the contentious debate concerning the way number information is encoded in the brain.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

Peer victimization in single-grade and multigrade classrooms

Although peer victimization mainly takes place within classrooms, little is known about the impact of the classroom context. To this end, we examined whether single-grade and multigrade classrooms (referring to classrooms with one and two grades in the same room) differ in victim–bully relationships in a sample of elementary school children (646 students; age 8–12 years; 50% boys). The occurrence of victim–bully relationships was similar in single-grade and multigrade classrooms formed for administrative reasons, but lower in multigrade classrooms formed for pedagogical reasons. Social network analyses did not provide evidence that peer victimization depended on age differences between children in any of the three classroom contexts. Moreover, in administrative multigrade classrooms, cross-grade victim–bully relationships were less likely than same-grade victim–bully relationships. The findings did not indicate that children in administrative multigrade classrooms are better or worse off in terms of victim–bully relationships than are children in single-grade classrooms.     

Is it worth generating rules from neural network ensembles?

Although many authors generated comprehensible models from individual networks, much less work has been done in the explanation of ensembles. DIMLP is a special neural network model from which rules are generated at the level of a single network and also at the level of an ensemble of networks. We applied ensembles of 25 DIMLP networks to several datasets of the public domain and a classification problem related to post-translational modifications of proteins. For the classification problems of the public domain, the average predictive accuracy of rulesets extracted from ensembles of neural networks was significantly better than the average predictive accuracy of rulesets generated from ensembles of decision trees. By varying the architectures of DIMLP networks we found that the average predictive accuracy of rules, as well as their complexity were quite stable. The comparison to other rule extraction techniques applied to neural networks showed that rules generated from DIMLP ensembles gave very good results. In the last problem related to bioinformatics, the best result obtained by ensembles of DIMLP networks was also significantly better than the best result obtained by ensembles of decision trees. Thus, although neural networks take much longer to train than decision trees and also rules are generated at a greater computational cost (however, still polynomial), at least for several classification problems it was worth using neural network ensembles, as extracted rules were more accurate, on average. The DIMLP software is available for PC-Linux under http://us.expasy.org/people/Guido.Bologna.html.     

内隐记忆中通道效应的认知与神经机制

通道转换是使内隐和外显记忆任务之间产生功能性分离经常操纵的典型变量之一,被看作是内隐记忆主要依赖刺激知觉住处加工的有利语气。该文回顾了近些年通道效应实验和理论研究的有关文献。对产生通道效应的认知机制和神经解剖基础进行了总结和分析,并提出了有待于进一步验证的问题。     

Sober social networks: The role of online support groups in recovery from alcohol addiction

In the context of addiction research, positive recovery outcomes are affected by the quality of people's social interactions and perhaps to an even greater extent, by the defining norms of the groups they identify with—that is, using versus recovery groups. Here, we examine the role of online supportive networks in the process of recovery from alcohol addiction. We analyse the relationship between negative and positive aspects of recovery capital (i.e., self‐stigma, and respectively, positive recovery identity and self‐efficacy) as they relate to well‐being outcomes among alcohol users in an online recovery support group. Based on a computerised linguistic analysis of the naturally occurring data extracted from an online recovery support group (i.e., 257 posts made by 237 group members), we found that self‐stigma negatively predicts self‐efficacy and well‐being, and social identification with a recovery identity mediates these relationships. Overall, these findings highlight that positive engagement with supportive recovery networks is central to an effective and sustainable recovery.     

The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness

How can we explain consciousness? This question has become a vibrant topic of neuroscience research in recent decades. A large body of empirical results has been accumulated, and many theories have been proposed. Certain theories suggest that consciousness should be explained in terms of brain functions, such as accessing information in a global workspace, applying higher order to lower order representations, or predictive coding. These functions could be realized by a variety of patterns of brain connectivity. Other theories, such as Information Integration Theory (IIT) and Recurrent Processing Theory (RPT), identify causal structure with consciousness. For example, according to these theories, feedforward systems are never conscious, and feedback systems always are. Here, using theorems from the theory of computation, we show that causal structure theories are either false or outside the realm of science.     

Social network members' responses to friends' breakup false alarms

Social networks influence romantic relationships through friends' support or lack of support of the union. However, the consequences of friends repeatedly telling other friends their relationship was going to end but failing to actually terminate the relationship are unclear. Guided by the false alarm effect (FAE), and data from both student (N = 273) and noncollege (N = 226) samples, it was found that breakup false alarms were associated with increases in both positive and negative responses. In addition, when participants were friends with both partners, breakup false alarms were associated with “feeling caught” between the friends. Findings generally support the FAE and paint a complex picture of how social network members react to their close friends' tumultuous relationships.     

An improved energy efficient cluster head selection protocol using the double cluster heads and data fusion methods for IoT applications

The more energy consumption is the major issue for wireless sensor network. In wireless sensor network (WSN), sensor nodes are fixed in various routing algorithms. In the same networks, mobile sensor and fixed sensor nodes are combine and its used in few applications. The function is corruption while mobility was achieved, since these nodes have minimum battery power, lower range of communication and a lessor amount of memory. To overcome this issue, Improved Energy Efficient Cluster Head Selection protocol (IEECHS-WSN) is proposed, in these technique is used to transfer the received information by using energy efficient routing protocol. In the CH election method, two cluster heads are selected in a separated cluster and its work in various functions, this can be prolong the network lifetime and decrease the energy consumption of IoT applications. Proposed technique is described on clustering of dual CHs in the method of data fusion for data entropy. This information entropy is used for fusion and classification, the result of fusion and classification are accurate and efficient for data transmission. Our proposed IEECHS protocol has better throughput, lifetime of network and energy consumption compared than the existing technique.