Statistical learning of random probability measures

The study of random probability measures is a lively research topic that has attracted interest from different fields in recent years. In this thesis, we consider random probability measures in the context of Bayesian nonparametrics, where the law of a random probability measure is used as prior distribution, and in the context of distributional data analysis, where the goal is to perform inference given avsample from the law of a random probability measure. The contributions contained in this thesis can be subdivided according to three different topics: (i) the use of almost surely discrete repulsive random measures (i.e., whose support points are well separated) for Bayesian model-based clustering, (ii) the proposal of new laws for collections of random probability measures for Bayesian density estimation of partially exchangeable data subdivided into different groups, and (iii) the study of principal component analysis and regression models for probability distributions seen as elements of the 2-Wasserstein space. Specifically, for point (i) above we propose an efficient Markov chain Monte Carlo algorithm for posterior inference, which sidesteps the need of split-merge reversible jump moves typically associated with poor performance, we propose a model for clustering high-dimensional data by introducing a novel class of anisotropic determinantal point processes, and study the distributional properties of the repulsive measures, shedding light on important theoretical results which enable more principled prior elicitation and more efficient posterior simulation algorithms. For point (ii) above, we consider several models suitable for clustering homogeneous populations, inducing spatial dependence across groups of data, extracting the characteristic traits common to all the data-groups, and propose a novel vector autoregressive model to study of growth curves of Singaporean kids. Finally, for point (iii), we propose a novel class of projected statistical methods for distributional data analysis for measures on the real line and on the unit-circle.

Analysis of human posterior EEG alpha frequency as a function of implicit learning of probabilistic lateralized stimuli in a visual detection task

Alpha oscillatory activity has long been associated with perceptual and cognitive processes related to attention control. The aim of this study is to explore the task-dependent role of alpha frequency in a lateralized visuo-spatial detection task. Specifically, the thesis focuses on consolidating the scientific literature's knowledge about the role of alpha frequency in perceptual accuracy, and deepening the understanding of what determines trial-by-trial fluctuations of alpha parameters and how these fluctuations influence overall task performance. The hypotheses, confirmed empirically, were that different implicit strategies are put in place based on the task context, in order to maximize performance with optimal resource distribution (namely alpha frequency, associated positively with performance): “Lateralization” of the attentive resources towards one hemifield should be associated with higher alpha frequency difference between contralateral and ipsilateral hemisphere; “Distribution” of the attentive resources across hemifields should be associated with lower alpha frequency difference between hemispheres; These strategies, used by the participants according to their brain capabilities, have proven themselves adaptive or maladaptive depending on the different tasks to which they have been set: "Distribution" of the attentive resources seemed to be the best strategy when the distribution probability between hemifields was balanced: i.e. the neutral condition task. "Lateralization" of the attentive resources seemed to be more effective when the distribution probability between hemifields was biased towards one hemifield: i.e., the biased condition task.

A improvisação ao órgão no desenvolvimento da leitura à primeira vista

Resumo I (Prática Pedagógica) - A Secção I - Prática Pedagógica - refere-se ao Estágio que a mestranda realizou no Conservatório de Música de Ourém e Fátima durante o ano lectivo 2013-2014. No âmbito das suas funções de docente, a mestranda realizou o estágio com um aluno do I Grau – 5º ano do Regime Articulado, e com um aluno do III Grau - 7º ano do Regime Articulado e assistiu, ainda, às aulas de um aluno do V Grau - 9º ano do Regime Articulado, sob a orientação da professora cooperante, docente de órgão dessa mesma escola. Nesta secção é feita uma breve caracterização da escola onde se realizou o estágio bem como dos alunos envolvidos no estágio. Por último, são mencionadas as práticas educativas desenvolvidas, os objectivos gerais para o ano lectivo, os métodos utilizados, os objectivos pedagógicos implícitos para cada período e, a finalizar, é apresentada uma análise crítica da actividade docente.

Does affective learning exist in the absence of contingency awareness?

The proposal that affective learning, the learning of likes and dislikes, can exist in the absence of contingency awareness, whereas signal learning, the learning of stimulus relationships, cannot, was investigated in a differential conditioning paradigm that was embedded in a visual masking task. Startle magnitude modulation and changes in verbal ratings served as measures of affective learning, whereas skin conductance was taken to reflect signal learning. Awareness was assessed online with an expectancy dial and in a postexperimental questionnaire. Both between-subject comparisons of verbalizers and nonverbalizers and within-subject comparisons of verbalizers before and after verbalization failed to reveal any evidence for learning, whether affective or otherwise, in the absence of knowledge of the stimulus contingencies. (C) 2001 Academic Press.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Expectations, Learning and Monetary Policy: An Overview of Recent Research

Expectations about the future are central for determination of current macroeconomic outcomes and the formulation of monetary policy. Recent literature has explored ways for supplementing the benchmark of rational expectations with explicit models of expectations formation that rely on econometric learning. Some apparently natural policy rules turn out to imply expectational instability of private agents’ learning. We use the standard New Keynesian model to illustrate this problem and survey the key results about interest-rate rules that deliver both uniqueness and stability of equilibrium under econometric learning. We then consider some practical concerns such as measurement errors in private expectations, observability of variables and learning of structural parameters required for policy. We also discuss some recent applications including policy design under perpetual learning, estimated models with learning, recurrent hyperinflations, and macroeconomic policy to combat liquidity traps and deflation.

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Learning through Computer Model Improvisations

It has been convincingly argued that computer simulation modeling differs from traditional science. If we understand simulation modeling as a new way of doing science, the manner in which scientists learn about the world through models must also be considered differently. This article examines how researchers learn about environmental processes through computer simulation modeling. Suggesting a conceptual framework anchored in a performative philosophical approach, we examine two modeling projects undertaken by research teams in England, both aiming to inform flood risk management. One of the modeling teams operated in the research wing of a consultancy firm, the other were university scientists taking part in an interdisciplinary project experimenting with public engagement. We found that in the first context the use of standardized software was critical to the process of improvisation, the obstacles emerging in the process concerned data and were resolved through exploiting affordances for generating, organizing, and combining scientific information in new ways. In the second context, an environmental competency group, obstacles were related to the computer program and affordances emerged in the combination of experience-based knowledge with the scientists' skill enabling a reconfiguration of the mathematical structure of the model, allowing the group to learn about local flooding.

Feedback-related Brain Potential Activity Complies with Basic Assumptions of Associative Learning Theory

Feedback-related negativity (FRN) is an ERP component that distinguishes positive from negative feedback. FRN has been hypothesized to be the product of an error signal that may be used to adjust future behavior. In addition, associative learning models assume that the trial-to-trial learning of cueoutcome mappings involves the minimization of an error term. This study evaluated whether FRN is a possible electrophysiological correlate of this error term in a predictive learning task where human subjects were asked to learn different cueoutcome relationships. Specifically, we evaluated the sensitivity of the FRN to the course of learning when different stimuli interact or compete to become a predictor of certain outcomes. Importantly, some of these cues were blocked by more informative or predictive cues (i.e., the blocking effect). Interestingly, the present results show that both learning and blocking affect the amplitude of the FRN component. Furthermore, independent analyses of positive and negative feedback event-related signals showed that the learning effect was restricted to the ERP component elicited by positive feedback. The blocking test showed differences in the FRN magnitude between a predictive and a blocked cue. Overall, the present results show that ERPs that are related to feedback processing correspond to the main predictions of associative learning models. ■

Neural correlates of language learning in adults

The human language-learning ability persists throughout life, indicating considerable flexibility at the cognitive and neural level. This ability spans from expanding the vocabulary in the mother tongue to acquisition of a new language with its lexicon and grammar. The present thesis consists of five studies that tap both of these aspects of adult language learning by using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) during language processing and language learning tasks. The thesis shows that learning novel phonological word forms, either in the native tongue or when exposed to a foreign phonology, activates the brain in similar ways. The results also show that novel native words readily become integrated in the mental lexicon. Several studies in the thesis highlight the left temporal cortex as an important brain region in learning and accessing phonological forms. Incidental learning of foreign phonological word forms was reflected in functionally distinct temporal lobe areas that, respectively, reflected short-term memory processes and more stable learning that persisted to the next day. In a study where explicitly trained items were tracked for ten months, it was found that enhanced naming-related temporal and frontal activation one week after learning was predictive of good long-term memory. The results suggest that memory maintenance is an active process that depends on mechanisms of reconsolidation, and that these process vary considerably between individuals. The thesis put special emphasis on studying language learning in the context of language production. The neural foundation of language production has been studied considerably less than that of perceptive language, especially on the sentence level. A well-known paradigm in language production studies is picture naming, also used as a clinical tool in neuropsychology. This thesis shows that accessing the meaning and phonological form of a depicted object are subserved by different neural implementations. Moreover, a comparison between action and object naming from identical images indicated that the grammatical class of the retrieved word (verb, noun) is less important than the visual content of the image. In the present thesis, the picture naming was further modified into a novel paradigm in order to probe sentence-level speech production in a newly learned miniature language. Neural activity related to grammatical processing did not differ between the novel language and the mother tongue, but stronger neural activation for the novel language was observed during the planning of the upcoming output, likely related to more demanding lexical retrieval and short-term memory. In sum, the thesis aimed at examining language learning by combining different linguistic domains, such as phonology, semantics, and grammar, in a dynamic description of language processing in the human brain.

A multimethod examination of contributors to successful on-the-job learning for vocational students

Rapid changes in working life and competence requirements of different professions have increased interest in workplace learning. It is considered an effective way to learn and update professional skills by performing daily tasks in an authentic environment. Especially, ensuring a supply of skilled future workers is a crucial issue for firms facing tight competition and a shortage of competent employees due to the retirement of current professionals. In order to develop and make the most of workplace learning, it is important to focus on workplace learning environments and the individual characteristics of those participating in workplace learning. The literature has suggested various factors that influence adults' and professionals’ workplace learning of profession-related skills, but lacks empirical studies on contextual and individual-related factors that positively affect students' workplace learning. Workers with vocational education form a large group in modern firms. Therefore, elements of vocational students’ successful workplace learning during their studies, before starting their career paths, need to be examined. To fill this gap in the literature, this dissertation examines contributors to vocational students’ workplace learning in Finland, where students’ workplace learning is included in the vocational education and training system. The study is divided into two parts: the introduction, comprised of the overview of the relevant literature and the conclusion of the entire study, and five separate articles. Three of the articles utilize quantitative methods and two use qualitative methods to examine factors that contribute to vocational students’ workplace learning. The results show that, from the students’ perspective, attitudinal, motivational, and organizationrelated factors enhance the student’s development of professionalism during the on-the-job learning period. Specifically, the organization-related factors such as innovative climate, guidance, and interactions with seniors have a strong positive impact on the students’ perceived development of professional skills because, for example, the seniors’ guidance and provision of new viewpoints for the tasks helps the vocational students to gain autonomy at work performance. A multilevel analysis shows that of those factors enhancing workplace learning from the student perspective, innovative climate, knowledge transfer accuracy, and the students’ performance orientation were significantly related to the workplace instructors’ assessment regarding the students’ professional performance. Furthermore, support from senior colleagues and the students’ self-efficacy were both significantly associated with the formal grades measuring how well the students managed to learn necessary professional skills. In addition, the results suggest that the students’ on-the-job learning can be divided into three main phases, of which two require efforts from both the student and the on-the-job learning organization. The first phase includes the student’s application of basic professional skills, demonstration of potential in performing daily tasks, and orientation provided by the organization at the beginning of the on-the-job learning period. In the second phase, the student actively develops profession-related skills by performing daily tasks, thus learning a fluent working style while observing the seniors’ performance. The organization offers relevant tasks and follows the student’s development. The third level indicates a student who has reached the professional level described as a full occupation. The results suggest that constructing the vocational students’ successful on-the-job learning period requires feedback from seniors, opportunities to learn to manage entire work processes, self-efficacy on the part of the students, proactive behavior, and initiative in learning. The study contributes to research on workplace learning in three ways: firstly, it identifies the key individual- and organization-based factors that influence the vocational students’ successful on-the-job learning from their perspective and examines mutual relationships between these factors. Second, the study provides knowledge of how the factors related to the students’ view of successful workplace learning are associated with the workplace instructors’ perspective and the formal grades. Third, the present study finds elements needed to construct a successful on-the-job learning for the students.

Displaced women professionals: an exploration of perceived learning processes during workplace transition /

This study focused on obtaining a deeper understanding of the perceived learning of female professionals during workplace transition. The women's lived experiences were explored through a feminist interpretive lens (Bloom, 1998). The study also drew upon concepts from adult learning such as barriers and facilitating factors to learning, resistance, transformative learning, and multiple ways of knowing. Five women participated in a 1 -hour interview and a focus group activity. The findings are presented under the 2 broad themes of perceived learning and factors affecting learning. The most common theme of perceived learning was participants' experience of increased self-knowledge. Additionally, while learning was thought of as a struggle, it provided either an opportunity for a reexamination of goals or a reexamination of self. Reflection by participants seemed to follow two orientations and other types of perceived learning included experiential, formal, and informal learning. In the broad theme of factors affecting learning, contradictions and conflict emerged through the examination of participants' multiple subjectivities, and within their naming of many factors as both facilitating factors and barriers to learning. The factors affecting learning themes included personal relationships, professional communities, selfesteem, attitude and emotion, the gendered experience of transition, time, and finances. The final theme explored participants' view of work and their orientations to the future. A proposed model of learning during workplace transition is presented (Figure 1 ) and the findings discussed within this proposed model's framework. Additional developmental theories of women (Josselson, 1987; Levinson & Levinson, 1996), communities of practice theories (Wenger, 1998), and career resilience theories (Pulley, 1995) are discussed within the context of the proposed model. Implications to practice for career counsellors, people going through workplace transition, human resource managers and career coaches were explored. Additionally implications to theory and future areas of research are also discussed.

Teachers' professional learning within communities of mathematics-for-teaching practice

Ontario bansho is an emergent mathematics instructional strategy used by teachers working within communities of practice that has been deemed to have a transformational effect on teachers' professional learning of mathematics. This study sought to answer the following question: How does teachers' implementation of Ontario bansho within their communities of practice inform their professional learning process concerning mathematics-for-teaching? Two other key questions also guided the study: What processes support teachers' professional learning of content-for-teaching? What conditions support teachers' professional learning of content-for-teaching? The study followed an interpretive phenomenological approach to collect data using a purposive sampling of teachers as participants. The researcher conducted interviews and followed an interpretive approach to data analysis to investigate how teachers construct meaning and create interpretations through their social interactions. The study developed a model of professional learning made up of 3 processes, informing with resources, engaging with students, and visualizing and schematizing in which the participants engaged and 2 conditions, ownership and community that supported the 3 processes. The 3 processes occur in ways that are complex, recursive, nonpredictable, and contextual. This model provides a framework for facilitators and leaders to plan for effective, content-relevant professional learning by placing teachers, students, and their learning at the heart of professional learning.