Eighteen-month-olds integrate verbal cues into their action processing: Evidence from ERPs and mu power

Behavioral research has shown that infants use both behavioral cues and verbal cues when processing the goals of others’ actions. For instance, 18-month-olds selectively imitate an observed goal-directed action depending on its (in)congruence with a model’s previous verbal announcement of a desired action goal. This EEG-study analyzed the electrophysiological underpinnings of these behavioral findings on the two functional levels of conceptual action processing and motor activation. Mid-latency mean negative ERP amplitude and mu-frequency band power were analyzed while 18-month-olds (N = 38) watched videos of an adult who performed one out of two potential actions on a novel object. In a within-subjects design, the action demonstration was preceded by either a congruent or an incongruent verbally announced action goal (e.g., “up” or “down” and upward movement). Overall, ERP negativity did not differ between conditions, but a closer inspection revealed that in two subgroups, about half of the infants showed a broadly distributed increased mid-latency ERP negativity (indicating enhanced conceptual action processing) for either the congruent or the incongruent stimuli, respectively. As expected, mu power at sensorimotor sites was reduced (indicating enhanced motor activation) for congruent relative to incongruent stimuli in the entire sample. Both EEG correlates were related to infants’ language skills. Hence, 18-month-olds integrate action-goal-related verbal cues into their processing of others’ actions, at the functional levels of both conceptual processing and motor activation. Further, cue integration when inferring others’ action goals is related to infants’ language proficiency.     

The role of TPH2 variant rs4570625 in shaping infant attention to social signals

TPH2, the rate-limiting enzyme in the synthesis of serotonin, has been connected to several psychiatric outcomes. Its allelic variant, rs4570625, has been found to relate to individual differences in cognitive and emotion regulation during infancy with T-carriers of rs4570625 showing a relatively heightened attention bias for fearful faces. A significant gene-environment interaction was also reported with the T-carriers of mothers with depressive symptoms showing the highest fear bias.We investigated these associations in a sample of 8-month old infants (N = 330), whose mothers were prescreened for low/high levels of prenatal depressive and/or anxiety symptoms. Attention disengagement from emotional faces (neutral, happy, fearful, and phase-scrambled control faces) to distractors was assessed with eye tracking and an overlap paradigm. Maternal depressive symptoms were assessed at several time points during pregnancy and postpartum. The mean levels of symptoms at six months postpartum and the trajectories of symptoms from early pregnancy until six months postpartum were used in the analyses (N = 274).No main effect of the rs4570625 genotype on attention disengagement was found. The difference in fear bias between the genotypes was significant but in an opposite direction compared to a previous study. The results regarding the interaction of the genotype and maternal depression were not in accordance with the previous studies.These results show inconsistencies in the effects of the rs4570625 genotype on attention biases in separate samples of infants from the same population with only slight differences in age.     

Safe on the road – Does Advanced Driver-Assistance Systems Use affect Road Risk Perception?

A major goal of advanced driver-assistance systems (ADAS) deployment is higher road safety. However, these systems are only as safe as users handle them. This article presents a study evaluating the links between the use of ADAS and road risk perception. 101 drivers were invited to evaluate accident probability and risk controllability and to indicate whether they were using ADAS or not. Results show that the use of ADAS decreases perceived accident probability and increases perceived controllability for risky driving situations. Since the degree of automation in ADAS is still low nowadays, it is important to discuss the importance of human factors for the purpose of a safer implementation.     

Brain tumor detection using fusion of hand crafted and deep learning features

The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.     

Vers une évolution du rapport au travail et au chômage ? Points de vue de demandeurs d’emploi et discours de salariés

Professional career paths are nowadays marked by multiple transitions. Job loss is one of the most frequent causes of professional transitions. Given this emphasis, recent studies questioned a possible evolution of the “work” norm, the emergence of an “unemployment norm” and its effects on job seekers. This paper proposes to contribute to the discussion on the current evolution of the relation to work and unemployment. At first, the results of a study with 500 unemployed people, who completed a questionnaire on work centrality and on the perception of work, will be presented. Then, a discourse analysis on the meaning and meaningfulness of work of 15 working people will be discussed. The results show the central function of work and the relative normalization of unemployment.     

Segment routing based energy aware routing for software defined data center

Despite the fact that most of the data centers are software-defined, the multifaceted network architecture and increase in network traffic make data centers to suffer from overhead. Multipath TCP supports multiple paths for a single routing session and ensures proper utilization of bandwidth over all available links. As rise in number of nodes in data center is frequent and drastic, scalability issue limits the performance of many existing techniques. Segment Routing is vibrant in reducing scalability disputes and routing overhead. Segment routing approach combined with MPTCP traffic result in efficient routing approach. The downfall of the link capacity due to drastic incoming traffic remains as a major concern in data center network which enforces preventing link energy depletion due to high network traffic. Our proposed work, segment routing based energy aware routing approach for software defined data center aims to achieve throughput maximization through preserving link residual capacity and proper utilization of links. As well, our approach shows a decrease in length of segment label stack with respect to maximum segment label depth. Analysis is done by comparing the executions of other existing approaches in a single-controller environment with our energy-aware routing approach in a distributed environment. Distributed controller setup prevents network from single point of failure. It helps to prevent controller overhead and provides improved network performance through throughput.     

Modeling influence of change readiness on knowledge acquisition process: A case study

Change readiness (CR) has important impact on the success of knowledge acquisition (KA). So it is necessary for managers to know how KA is shaped by CR elements. Review of the extant literature shows a paucity in this regard specially regarding intra-organizational rather than inter-organizational level. Therefore, to bridge this gap, this study aims to present a fuzzy cognitive map (FCM) model in which interactions among CR elements for KA are identified. To do so, first, elements for measuring CR for KA were extracted from the relevant literature. Then, the identified elements were screened through distributing the first designed questionnaire among the select sample of survey organization. Then, the second questionnaire was used to measure the select elements and find the relation between them, using FCM and Mental Modeler Software. To improve CR for KA, some scenarios are suggested by the managers and the impact of each scenario on the whole CR elements is identified using sensitive analysis of the FCM model. After comparing the scenarios, it is concluded improving more elements in one scenario does not necessarily result in better impact on the whole CR for KA. This is due to the interaction among the elements which sometimes could be negative. The proposed model might help managers of the survey organization evaluate their CR improvement plans for KA before taking any actions. In addition, this study can give an idea to other organizations and industries to apply this approach according to their own relevant criteria. This study is among the first in its kind which presents a model using FCM method in which the interactions among the CR elements influencing KA are considered.     

PSO-GA based hybrid with Adam Optimization for ANN training with application in Medical Diagnosis

This paper introduces a novel PSO-GA based hybrid training algorithm with Adam Optimization and contrasts performance with the generic Gradient Descent based Backpropagation algorithm with Adam Optimization for training Artificial Neural Networks. We aim to overcome the shortcomings of the traditional algorithm, such as slower convergence rate and frequent convergence to local minima, by employing the characteristics of evolutionary algorithms. PSO has a property of faster convergence rate, which can be exploited to account for the slower pace of convergence of the traditional BP (which is due to low values of gradients). In contrast, the integration with GA complements the drawback of convergence to local minima as GA, possesses the capability of efficient global search. So by this integration of these algorithms, we propose our new hybrid algorithm for training ANNs. We compare both the algorithms for the application of medical diagnosis. Results display that the proposed hybrid training algorithm, significantly outperforms the traditional training algorithm, by enhancing the accuracies of the ANNs with an increase of 20% in the average testing accuracy and 0.7% increase in the best testing accuracy.     

人声加工的神经机制

人声是人类听觉环境中最熟知和重要的声音, 传递着大量社会相关信息。与视觉人脸加工类似, 大脑对人声也有着特异性加工。研究者使用电生理、脑成像等手段找到了对人声有特异性反应的脑区, 即颞叶人声加工区(TVA), 并发现非人类动物也有类似的特异性加工区域。人声加工主要涉及言语、情绪和身份信息的加工, 分别对应于三条既相互独立又相互作用的神经通路。研究者提出了双通路模型、多阶段模型和整合模型分别对人声的言语、情绪和身份加工进行解释。未来研究需要进一步讨论人声加工的特异性能否由特定声学特征的选择性加工来解释, 并深入探究特殊人群(如自闭症和精神分裂症患者)的人声加工的神经机制。     

多感觉整合中的声音诱发闪光错觉效应

声音诱发闪光错觉效应是典型的视听整合错觉现象, 是指当视觉闪光刺激与间隔100 ms内的听觉声音刺激不等数量呈现时, 被试知觉视觉闪光的数量与听觉声音的数量相等。声音诱发闪光错觉的影响因素既包括自下而上和自上而下的被试内差异因素, 也包括视听刺激依赖程度、视听整合的发展和视听刺激知觉敏感性等被试间差异因素。该效应的产生在时程上主要体现在早期加工阶段, 在脑区上主要涉及多处皮层及皮层下相关脑区。未来研究应进一步考察注意、奖赏和视听整合方式等认知加工对声音诱发闪光错觉的影响, 同时也应该关注声音诱发闪光错觉对记忆和学习的影响以及结合计算模型和神经科学的手段进一步探讨其认知神经机制。