Statistical learning of multiple speech streams: A challenge for monolingual infants

Language acquisition depends on the ability to detect and track the distributional properties of speech. Successful acquisition also necessitates detecting changes in those properties, which can occur when the learner encounters different speakers, topics, dialects, or languages. When encountering multiple speech streams with different underlying statistics but overlapping features, how do infants keep track of the properties of each speech stream separately? In four experiments, we tested whether 8‐month‐old monolingual infants (N = 144) can track the underlying statistics of two artificial speech streams that share a portion of their syllables. We first presented each stream individually. We then presented the two speech streams in sequence, without contextual cues signaling the different speech streams, and subsequently added pitch and accent cues to help learners track each stream separately. The results reveal that monolingual infants experience difficulty tracking the statistical regularities in two speech streams presented sequentially, even when provided with contextual cues intended to facilitate separation of the speech streams. We discuss the implications of our findings for understanding how infants learn and separate the input when confronted with multiple statistical structures.     

ELM-HTM guided bio-inspired unsupervised learning for anomalous trajectory classification

Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose a new bio-inspired learning model for a single-class classifier to detect abnormality in video object trajectories. The method uses a simple but dynamic extreme learning machine (ELM) and hierarchical temporal memory (HTM) together referred to as ELM-HTM in an unsupervised way to learn and classify time series patterns. The method has been tested on trajectory sequences in traffic surveillance to find abnormal behaviors such as high-speed, unusual stops, driving in wrong directions, loitering, etc. Experiments have also been performed with 3D air signatures captured using sensors and used for biometric authentication(forged/genuine). The results indicate a significant gain over training time and classification accuracy. The proposed method outperforms in predicting long-time patterns by observing small steps with an average accuracy gain of 15% as compared to the state-of-the-art HTM. The method has applications in detecting abnormal activities in videos by learning the movement patterns as well as in biometric authentication.     

Socially emotional brain-inspired cognitive architecture framework for artificial intelligence

Artificial Intelligence of the next generation needs to interact with users socially, convincing them in its ability to understand human minds, including emotions. For this to happen, an artificial emotional intelligence is needed, capable of adequate, believable behavior in social emotional interactions. Building on previous developments, the present work extends the general framework of emotional Biologically Inspired Cognitive Architecture (eBICA: Samsonovich, 2013, 2018), endowing it with fluents describing, in addition to appraisals, somatic markers, feelings, emotions, moods, emotional reactions and biases. Key building blocks that integrate them are moral schemas and semantic maps. The model describes interaction of three factors: plans and commitments, moral and ethical values, and somatic comfort. Learning in this framework includes self-organization of semantic maps that in turn may provide guidance for active humanlike learning. Implications for empirical studies and practical applications are discussed together with the expected impact.     

The role of instruction preference in analogy learning: Brain activity and motor performance

This study examined the role of verbal instruction preference when learning motor skills by analogy. During skill learning, analogies are a useful tool for providing knowledge about how to move. It has been argued that analogy instructions reduce reliance on verbal information processes during motor planning, compared to traditional forms of instruction (i.e., explicit rules about how to move). This may be reflected by reduced verbal activity in the brain, measured by EEG alpha power at the temporal region, as well as reduced verbal-motor cross-communication (EEG T7-Fz coherence) during the preparation phase of a movement. Preference for using verbal or visual instructions is likely to influence the efficacy of analogy instructions. This study investigated whether preference for verbal instructions was related to a) changes in performance and b) changes in verbal-cognitive information processing during performance of an adapted basketball task after instruction by analogy. Basketball novices with a high preference for verbal instructions (n = 15) showed significantly decreased activation of verbal brain regions when they used the analogy (high-alpha power), but their performance remained stable. Novices with a low preference for verbal instructions (n = 13) did not show a significant decrease in activation of verbal regions, and their performance deteriorated significantly after introduction of the analogy instruction. It is likely that both cognitive and performance changes after analogy instruction depend on personal aspects of information processing, such as verbal preference.     

Motivational climate,psychological needs,and personal and social responsibility in youth soccer: Comparisons by age group and competitive level

ObjectivesWe used a developmental lens to examine age group and competitive level differences on motivational climate dimensions, psychological needs satisfaction, and personal and social responsibility as well as relationships among variables, in line with basic psychological needs theory.DesignWe used a cross-sectional, correlational design.MethodGirls and boys age 8–13 on recreational and select soccer teams completed a survey.ResultsA 2 × 2 MANOVA showed main effects for age group and competitive level: early adolescents reported greater punishment for mistakes and autonomy compared to children; select players reported greater mastery climate dimensions and personal responsibility compared to recreational players. For all groups, path analyses revealed perceptions of cooperative learning were indirectly associated with personal and social responsibility, through coach and/or teammate relatedness.ConclusionsPatterns of relationships by age group and competitive level are discussed in reference to developmental literature. Results highlight the value of using a developmental approach to understand how coaches can promote youth soccer players’ psychological and social well-being.     

隐藏情绪分析与识别方法

隐藏情绪识别对公共安全防范与预警具有重要的意义。微表情是揭示隐藏情绪的一条重要通道。但目前隐藏情绪研究较少且微表情因其细微幅度与快速出现等特性难以识别, 其研究尚未在实际中广泛应用。因为, 隐藏情绪的认知与表达机理亟需系统的研究,采集实际场景中的微表情数据, 并以脑电信号辅助微表情的精确标注是提高微表情标注效率的有效途径。深入研究微表情识别方法, 并辅以人脸颜色、注视估计和非接触生理信号等多通道数据, 以检测与识别隐藏情绪。社会公共安全是隐藏情绪分析和识别的典型场景。面向精神疾病患者两害行为(即危害自身或他人的危险行为)风险评估和服刑人员会见场景隐藏情绪检测, 可以有效地对相应系统和方法进行验证和修正。     

Towards a new approach to predict business performance using machine learning

Financial ratio plays a crucial role in business performance prediction, but the ability of the decision maker to use this method in adjusting management strategy has been extensively ignored. In this paper we attempt to build a fuzzy chance constrained least squares twin support vector machine (FCC-LSTSVM) to predict the business performance through the financial ratios. Specifically, machine learning techniques are utilized to build the models and 796 listed companies in China are selected as the data set. We find that different efficiencies are performed for different models with the same industry and different effectiveness are shown for different predicting time periods with the same method. In addition, the predicting achievements of business performance depend on the types of industries. This paper has extent significance both in theoretical development and managerial practices.     

Social learning under the labeling effect: Exploring travellers’ behavior in social dilemmas

This paper presents results of a laboratory experiment, in which subjects made choices between public and private transportation when managing Personal Carbon Allowances (PCAs). The objectives of this study were twofold. Firstly, it was aimed at investigating the labeling effect under the social dilemma context. Secondly, it showed insights into the decision-making process when feedback about others’ choices was provided. A combination of the labeling effect and feedback helped individuals change their commuting pattern and increase the number of trips by public transportation under the PCAs regime. Feedback on others’ choices resulted in social learning and incentivized choice of public transportation more frequently in social dilemma situations.