Guanxi networks and members' effectiveness in Chinese work teams: Mediating effects of trust networks

The present study examined 33 teams with a total of 206 team members (excluding team leaders) to explore the effects of intrateam guanxi and trust networks on individual effectiveness in Taiwan. Guanxi networks included non-job, departmental and past team networks, while trust networks included affective and cognitive trust networks. Results showed that: (i) guanxi networks could better explain members' effectiveness compared with relational demography, and different guanxi networks had different effects; (ii) the more central an individual's position was in the cognitive trust network, the higher the individual's effectiveness; (iii) the past team guanxi network of a team member displayed a positive effect on the centrality of cognitive trust network, while non-job and departmental guanxi networks showed negative effects; and (iv) the effects of members' guanxi networks on individual effectiveness were mediated by the centrality of trust networks.     

万拉法新对抑郁症注意网络功能的影响

目的 研究抑郁症的注意网络状况及抑制神经递质再摄取的药物万拉法新对其的影响,探索神经递质与注意网络的关系。方法 对32例应用万拉法新治疗的抑郁症患者治疗前及疗后6周进行注意网络测定和临床症状评分。结果 与正常组比较,抑郁组的警觉网络和执行控制网络效率差异有显著性,定向网络效率差别无显著性。警觉网络效率与HAMD评分的睡眠障碍因子分呈负相关。治疗后,抑郁组的警觉效率显著增加,其改善与睡眠障碍的好转呈正相关。执行控制网络效率,平均反应时和临床症状评分均较治疗前显著降低,定向网络效率无变化。结论 抑郁症患者存在警觉和执行控制注意网络的异常,万拉法新可选择性地改善警觉和执行控制注意网络的异常而不影响其定向网络     

An improved learning algorithm for random neural networks based on particle swarm optimization and input-to-output sensitivity

Extreme learning machine (ELM) for random single-hidden-layer feedforward neural networks (RSLFN) has been widely applied in many fields in the past ten years because of its fast learning speed and good generalization performance. But because traditional ELM randomly selects the input weights and hidden biases, it typically requires high number of hidden neurons and thus decreases its convergence performance. It is necessary to select optimal input weights and hidden biases to improve the convergence performance of the traditional ELM. Generally, the single-hidden-layer feedforward neural networks (SLFN) with low input-to-output sensitivity will cause good robustness of the network, which may further lead into good generalization performance. Moreover, particle swarm optimization (PSO) has no complicated evolutionary operators and fewer parameters need to adjust, and is easy to implement. In this study, an improved ELM based on PSO and input-to-output sensitivity information is proposed to improve RSLFN’s convergence performance. In the improved ELM, PSO encoding the input to output sensitivity information of the SLFN is used to optimize the input weights and hidden biases. The improved ELM could obtain better generalization performance as well as improve the conditioning of the SLFN by decreasing the input-to-output sensitivity of the network. Finally, experiment results on the regression and classification problems verify the improved performance the proposed ELM.     

A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images

Computer Aided Decision (CAD) systems, based on 3D tomosynthesis imaging, could support radiologists in classifying different kinds of breast lesions and then improve the diagnosis of breast cancer (BC) with a lower X-ray dose than in Computer Tomography (CT) systems.In previous work, several Convolutional Neural Network (CNN) architectures were evaluated to discriminate four different classes of lesions considering high-resolution images automatically segmented: (a) irregular opacity lesions, (b) regular opacity lesions, (c) stellar opacity lesions and (d) no-lesions. In this paper, instead, we use the same previously extracted relevant Regions of Interest (ROIs) containing the lesions, but we propose and evaluate two different approaches to better discriminate among the four classes.In this work, we evaluate and compare the performance of two different frameworks both considering supervised classifiers topologies. The first framework is feature-based, and consider morphological and textural hand-crafted features, extracted from each ROI, as input to optimised Artificial Neural Network (ANN) classifiers. The second framework, instead, considers non-neural classifiers based on automatically computed features evaluating the classification performance extracting several sets of features using different Convolutional Neural Network models.Final results show that the second framework, based on features computed automatically by CNN architectures performs better than the first approach, in terms of accuracy, specificity, and sensitivity.     

Three-channel convolutional neural networks for vegetable leaf disease recognition

The color information of diseased leaf is the main basis for leaf based plant disease recognition. To make use of color information, a novel three-channel convolutional neural networks (TCCNN) model is constructed by combining three color components for vegetable leaf disease recognition. In the model, each channel of TCCNN is fed by one of three color components of RGB diseased leaf image, the convolutional feature in each CNN is learned and transmitted to the next convolutional layer and pooling layer in turn, then the features are fused through a fully connected fusion layer to get a deep-level disease recognition feature vector. Finally, a softmax layer makes use of the feature vector to classify the input images into the predefined classes. The proposed method can automatically learn the representative features from the complex diseased leaf images, and effectively recognize vegetable diseases. The experimental results validate that the proposed method outperforms the state-of-the-art methods of the vegetable leaf disease recognition.     

Exploratory study of the effect of binaural beat stimulation on the EEG activity pattern in resting state using artificial neural networks

Anxiety disorders afflict almost 7.3 percent of the world’s population. One in 14 people will experience anxiety disorder at the given year. When associated with mood disorders, anxiety can also trigger or increase other diseases’ symptoms and effects, like depression and suicidal behavior. Binaural beats are a low-frequency type of acoustic stimulation perceived when the individual is subjected to two slightly different wave frequencies, from 200 to 900 Hz. Binaural beats can contribute to anxiety reduction and modification of other psychological conditions and states, modifying cognitive processes and mood states. In this work, we applied a 5 Hz binaural beat to 6 different subjects, to detect a relevant change in their brainwaves before and after the stimuli. We applied 20 min stimuli in 10 separated sessions. We assessed the differences using a Multi-Layer Perceptron classifier in comparison with non-parametric tests and Low-Resolution Brain Electromagnetic Tomography (eLORETA). eLORETA showed remarkable changes in High Alpha. Both eLORETA and MLP approaches revealed outstanding modifications in high Beta. MLP evinced significant changes in Theta brainwaves. Our study evidenced high Alpha modulation at the limbic lobe, implicating in a possible reduction of sympathetic system activation in the studied sample. Our main results on eLORETA suggest a strong increase in the current distribution, mostly in Alpha 2, at the Anterior Cingulate, which is related to the monitoring of mistakes regarding social conduct, recognition and expression of emotions. We also found that MLPs are able of evincing the main differences with high separability in Delta and Theta.     

社会性信息对Flanker效应的影响

研究显示,面孔Flanker任务中,经典Flanker效应会消失,但其机制还不明确。本文在ANT-I范式的基础上,除常规的箭头Flanker,增加面孔Flanker、两侧为箭头中间为面孔和两侧为面孔中间为箭头的混合Flanker,探究造成该现象的可能原因。结果发现,当Flanker任务中的干扰刺激为箭头时,Flanker效应存在;而干扰刺激为面孔时,Flanker效应则消失了。提示,Flanker任务中干扰刺激的社会性可能是造成Flanker效应消失的原因。这为冲突信息加工中社会性与非社会性信息的控制机制提供了新的视角。     

SIMPLIFYING HEURISTICS VERSUS CAREFUL THINKING: SCIENTIFIC ANALYSIS OF MILLENNIAL SPIRITUAL ISSUES

There is ample evidence that humans (and other primates) possess a knowledge instinct—a biologically driven impulse to make coherent sense of the world at the highest level possible. Yet behavioral decision‐making data suggest a contrary biological drive to minimize cognitive effort by solving problems using simplifying heuristics. Individuals differ, and the same person varies over time, in the strength of the knowledge instinct. Neuroimaging studies suggest which brain regions might mediate the balance between knowledge expansion and heuristic simplification. One region implicated in primary emotional experience is more activated in individuals who use primitive heuristics, whereas two areas of the cortex are more activated in individuals with a strong knowledge drive: one region implicated in detecting risk or conflict and another implicated in generating creative ideas. Knowledge maximization and effort minimization are both evolutionary adaptations, and both are valuable in different contexts. Effort minimization helps us make minor and routine decisions efficiently, whereas knowledge maximization connects us to the beautiful, to the sublime, and to our highest aspirations. We relate the opposition between the knowledge instinct and heuristics to the biblical story of the fall, and argue that the causal scientific worldview is mathematically equivalent to teleological arguments from final causes. Elements of a scientific program are formulated to address unresolved issues.     

Willingness to pay for advice: The role of rational and experiential processing

Everyday, millions of decision makers receive advice from one or more sources. Although research has addressed some of the issues concerning how people take and use advice that they are given, less is known about the psychological processes that underlie decision makers’ willingness to pay for advice. In the present research, we explore the important role that mode of information processing and decision-specific knowledge have on willingness to pay for advice. In a pretest and two experiments, we use a priming procedure to induce either a rational or experiential mode of processing. We find that people processing information rationally are willing to pay substantially more for advice than those who are processing information experientially, and that this effect is moderated by the individual’s decision-specific knowledge.