The Persistence of Priming: Exploring Long-lasting Syntactic Priming Effects in Children and Adults

The implicit learning account of syntactic priming proposes that the same mechanism underlies syntactic priming and language development, providing a link between a child and adult language processing. The present experiment tested predictions of this account by comparing the persistence of syntactic priming effects in children and adults. Four-year-olds and adults first described transitive events after hearing transitive primes, constituting an exposure phase that established priming effects for passives. The persistence of this priming effect was measured in a test phase as participants described further transitive events but no longer heard primes. Their production of passives was compared to a baseline group who described the same pictures without any exposure to primes. Neither immediate nor long-term priming effects differed between children and adults but both children and adults showed significant immediate and persistent effects of the priming when the test phase occurred immediately after the exposure phase and when a short delay separated the exposure and test phase. The implications of these results for an implicit learning account of syntactic priming are discussed.     

States of curiosity and interest enhance memory differently in adolescents and in children

Curiosity – broadly defined as the desire to acquire new information – enhances learning and memory in adults. In addition, interest in the information (i.e., when the information is processed) can also facilitate later memory. To date, it is not known how states of pre‐information curiosity and post‐information interest enhance memory in childhood and adolescence. We used a trivia paradigm in which children and adolescents (N = 60, 10–14 years) encoded trivia questions and answers associated with high or low curiosity. States of high pre‐answer curiosity enhanced later memory for trivia answers in both children and adolescents. However, higher positive post‐answer interest enhanced memory for trivia answers beyond the effects of curiosity more strongly in adolescents than in children. These results suggest that curiosity and interest have positive effects on learning and memory in childhood and adolescence, but might need to be harnessed in differential ways across child development to optimize learning.     

Bridging ears and eyes when learning spoken words: On the effects of bilingual experience at 30 months

From the very first moments of their lives, infants selectively attend to the visible orofacial movements of their social partners and apply their exquisite speech perception skills to the service of lexical learning. Here we explore how early bilingual experience modulates children's ability to use visible speech as they form new lexical representations. Using a cross‐modal word‐learning task, bilingual children aged 30 months were tested on their ability to learn new lexical mappings in either the auditory or the visual modality. Lexical recognition was assessed either in the same modality as the one used at learning (‘same modality’ condition: auditory test after auditory learning, visual test after visual learning) or in the other modality (‘cross‐modality’ condition: visual test after auditory learning, auditory test after visual learning). The results revealed that like their monolingual peers, bilingual children successfully learn new words in either the auditory or the visual modality and show cross‐modal recognition of words following auditory learning. Interestingly, as opposed to monolinguals, they also demonstrate cross‐modal recognition of words upon visual learning. Collectively, these findings indicate a bilingual edge in visual word learning, expressed in the capacity to form a recoverable cross‐modal representation of visually learned words.     

Multilevel metric rank match for person re-identification

Metric learning is one of the important ways to improve the person re-identification (ReID) accurate, of which triplet loss is the most effect metric learning method. However, triplet loss only ranks the extracted feature at the end of the network, in this paper, we propose a multilevel metric rank match (MMRM) method, which ranks the extracted feature on multilevel of the network. At each rank level, the extracted features are ranked to find the hard sample pairs and the backward dissemination triplet loss. Each rank level has different penalize value to adjust the network, in which the value is bigger with the deeper level of the whole network. Experiment results on CUHK03, Market1501 and DukeMTMC datasets indicate that The MMRM algorithm can outperform the previous state-of-the-arts.     

De l’estime de soi et du sentiment d’auto-efficacité à la motivation au travail : le rôle médiateur du sens du travail chez les alternants en France

The purpose of this study was to test the links between self-esteem, the feeling of self-efficacy and the different forms of motivation. It also aimed to understand the mediating effect of the meaning of work between the variables. French work-integrated learning students (n = 471) completed a self-administered online questionnaire. The results showed significant links between self-esteem, perceived self-efficacy, work meaning, and different dimensions of motivation (extrinsic, intrinsic, and amotivation). In addition, the meaning of work played a mediating role in the relationship between self-esteem, perceived self-efficacy, and dimensions of motivation. This study showed the central mechanism of work meaning in work motivation.     

Detecting anonymous attacks in wireless communication medium using adaptive grasshopper optimization algorithm

Intrusion Detection Systems (IDSs) is a system that monitors network traffic for suspicious activity and issues alert when such activity is revealed. Moreover, the existing IDSs-based methods are based on outdated attacks that unable to identify modern attacks or malicious trends. For this reason, in this study we developed a new multi-swarm adaptive grasshopper optimization algorithm to utilize adaptation mechanism in a group of swarms based on fuzzy logic to protect against sophisticated attacks. The proposed (MSAGOA) technique has the capability of global optimization and rapid convergence that are used to attain optimal feature subsets to identify attack types on IDS datasets. In the MSAGOA technique, learning engine as Extreme learning Machine, Naive Bayes, Random Forest and Decision Tree is applied as a fitness function to select the highly discriminating features and to maximize classification performance. Afterward, select the best classifier which works as a fitness function in our approach to measure the performance in terms of accuracy, detection rate, and false alarm rate. The simulations are performed on three IDS datasets such as NSL-KDD, AWID-ATK-R, and NGIDS-DS. The experimental results demonstrated that MSAGOA method has performed better and obtained high detection rate of 99.86%, accuracy of 99.89% in NSL-KDD and high detection rate of 98.73%, accuracy of 99.67% in AWID-ATK-R and detection rate of 89.50%, accuracy of 90.23% in NGIDS-DS. In addition, the performance is compared with several other existing techniques to show the efficacy of the proposed approach.     

Doing it for the team: Soccer coaches' identity leadership predicts players' effort,turnover intentions,and performance

ObjectivesThe present research investigates how coaches' identity leadership predicts individual and team outcomes in soccer. Specifically, we tested hypotheses that coaches' identity leadership would be associated with players' perceptions of (a) higher team effort, (b) lower turnover intentions, (c) better individual performance, and (d) better team performance. In addition, we aimed to examine the relationship between coaches' identity leadership and increased team identification of players and the degree to which the associations of identity leadership with these various outcomes were mediated by players' strength of team identification.DesignWe conducted a cross-sectional study of male soccer players in Germany.MethodThe final sample consisted of 247 male soccer players nested in 24 teams that completed measures of their coaches' identity leadership, team identification, team effort, turnover intentions, and individual/team performance.ResultsAnalysis revealed a positive relationship between coaches' identity leadership and team effort, as well as individual and team performance. Moreover, coaches' identity leadership was associated with lower turnover intentions. There was also evidence that the relationships between identity leadership and the investigated outcomes were mediated by team identification.ConclusionsThese findings support claims that coaches' identity leadership is associated with better individual and team outcomes because it helps to build a sense of ‘we’ and ‘us’ in the team they lead.     

双眼视差的神经机制与知觉学习效应

双眼瞳距使得空间某物体在左右眼视网膜的成像存在微小位置差异, 这种差异被称为双眼视差(binocular disparity), 是立体视知觉的重要信息来源。对双眼视差的心理物理学研究始于18世纪初, 迄今已有接近两百年的历史。近年来, 双眼视差研究主要集中在两方面。其一是用电生理、脑成像技术考察双眼视差在视觉背、腹侧通路的模块化表征, 其脑区表征反映出视觉系统的层级式、平行式加工规律。其二是应用知觉学习范式研究双眼视差的可塑性。未来研究应综合脑成像和神经调控技术考察双眼视差的神经机制及其学习效应, 包括双眼视差与多种深度线索间的信息整合和交互作用。应用方向上, 可结合虚拟现实等技术优化训练范式, 实现立体视力的康复和增强。     

象似性和手语学习经验对中国手语-汉语跨模态语义启动效应的影响

采用启动条件下的词汇判断任务,考察熟练手语使用者和无手语经验成年听人的跨模态语义启动效应。结果发现：1)在象似词条件下,两组被试判断汉语语义相关词的反应时均快于语义无关词,说明手语象似词和汉语词之间存在跨模态语义启动效应。2)在非象似词条件下,仅手语熟练被试判断汉语语义相关词的反应时快于语义无关词,无手语经验被试判断汉语语义相关词和无关词的速度没有差异。这是由于前者心理词库中的手语词和口语词共享语义表征,而后者主要依赖手语象似词的视觉模拟性。整个研究表明,中国手语和汉语间存在跨模态语义启动效应,但该效应受到手语词象似性和手语学习经历的调节。     

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.