Mapping differential responses to cognitive training using machine learning

We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self‐organizing maps (SOMs)—a type of simple artificial neural network—to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K‐means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K‐means clustering was applied to an independent large sample (N = 616, M_age = 9.16 years, range = 5.16–17.91 years) to identify subgroups. We then allocated children who had been through cognitive training (N = 179, M_age = 9.00 years, range = 7.08–11.50 years) to these same four subgroups, both before and after their training. In doing so, we were able to map their improvement trajectories. Scores on a separate measure of fluid intelligence were predictive of a child's improvement trajectory. This paper provides an alternative approach to analysing cognitive training data that go beyond considering changes in individual tasks. This proof‐of‐principle demonstrates a potentially powerful way of distinguishing task‐specific from domain‐general changes following training and of establishing different profiles of response to training.     

Investigating route learning,metacognition, and beacon-based strategies using virtual environments

IntroductionDominant theories of wayfinding may have underestimated the ease with which adults can learn a route, even with minimal exposure to that route.ObjectivesIn this paper we present three different experiments to provide a comprehensive picture of whether adults can learn complex routes consisting of a number of choicepoints, and if so, what strategies they might be employing to do so.Methodand results We found that adults could learn a 15-junction route after only a single experience of the route (Experiment 1) but that they underestimated how good they would be at learning the 15-junction route (Experiment 2). In Experiment 3, we found that when learning a route made up of “T” shaped junctions, participants relied on a “beacon” strategy based on visual matching.ConclusionsCollectively, these findings suggest that adults can learn complex routes, even with as many as 15 choicepoints, very quickly and without the need for repeated exposure. These findings have implications for theories of wayfinding and call into question the need for repeated exposure.     

Irrelevant‐action imitation is short‐term and contextual: Evidence from two under‐studied populations

Across the lifespan and across populations, humans ‘overimitate’ causally unnecessary behaviors. Such irrelevant‐action imitation facilitates faithful cultural transmission, but its immediate benefits to the imitator are controversial. Over short time scales, irrelevant‐action imitation may bootstrap artifact exploration or interpersonal affiliation, and over longer time scales it may facilitate acquisition of either causal models or social conventions. To investigate these putative functions, we recruited community samples from two under‐studied populations: Yasawa, Fiji, and Huatasani, Peru. We use a two‐action puzzle box: first after a video demonstration, and again one month later. Treating age as a continuous variable, we reveal divergent developmental trajectories across sites. Yasawans (44 adults, M = 39.9 years, 23 women; 42 children, M = 9.8 years, 26 girls) resemble documented patterns, with irrelevant‐action imitation increasing across childhood and plateauing in adulthood. In contrast, Huatasaneños (48 adults, M = 37.6 years, 33 women; 47 children, M = 9.3 years, 13 girls) evince a parabolic trajectory: adults at the site show the lowest irrelevant‐action imitation of any demographic set in our sample. In addition, all age sets in both populations reduce their irrelevant actions at Time 2, but do not reduce their relevant‐action imitation or goal attainment. Taken together, and considering the local cultural contexts, our results suggest that irrelevant‐action imitation serves a short‐term function and is sensitive to the social context of the demonstration.     

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.     

Life skills development in physical education: A self-determination theory-based investigation across the school term

ObjectivesGrounded in self-determination theory (SDT), the main aim of this study was to examine the longitudinal associations between teacher autonomy support, students’ basic psychological need satisfaction and life skills development in physical education (PE).DesignThis study employed a two-wave longitudinal research design.MethodStudents (N = 266, Mage = 12.94 years, SD = 0.70) completed measures assessing perceived autonomy-supportive teaching, need satisfaction (autonomy, competence, and relatedness), and life skills development in PE (teamwork, goal setting, social skills, problem solving and decision making, emotional skills, leadership, time management, and interpersonal communication). Data collections took place during week 6 (timepoint 1; T1) and week 15 (timepoint 2; T2) of the autumn school term.ResultsCross-lagged panel analyses showed that T1 teacher autonomy support did not significantly predict students’ three basic psychological needs, total need satisfaction or life skills development at T2. Students’ T1 total need satisfaction positively predicted their development of all eight life skills at T2. Additionally, students’ T1 autonomy satisfaction positively predicted their teamwork, social skills, emotional skills, leadership, and interpersonal communication skills at T2, T1 competence satisfaction positively predicted students’ teamwork skills at T2, and students’ T1 relatedness satisfaction positively predicted their social skills at T2.ConclusionsProviding partial support for SDT, the findings highlighted that satisfaction of students’ three basic psychological needs had some positive effects on students’ life skills development in PE. As such, a climate that satisfies students’ basic psychological needs should help to develop their life skills in PE.     

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.     

指尖上的学习：触屏学习的作用

触屏学习是通过触屏软硬件设备呈现学习内容,并以手势触屏交互方式获取知识或技能的过程。目前触屏学习基础性研究处于探索阶段,在有效性上,研究发现触屏学习本身可能是有效的,但在相对优势上结果具有较大异质性;在学习后效上,触屏学习有助于提高学习动机,但没有稳定地促进知识保持、知识理解以及二维到三维的学习迁移。针对触屏学习的促进或阻碍作用,以往研究分别从具身认知或认知负荷等理论视角给予解释。学习者、学习材料、学习环境等可能是影响触屏学习效果的重要因素。广泛地将触屏设备应用于学习或课堂场景为时尚早,呼吁未来研究从理论建构、影响因素、特点分析及行为/神经机制角度考察触屏学习的作用。