The good and the successful - Examination of informal learning mechanisms

This thesis strived to find out which informal learning (IL) mechanisms are used the most by the respondents. Additionally, the goal was to know more about the respondents as informal learners and what could explain possible differences. The target was to resolve whether informal learning explains differences in individual performance or, do some other explanations for success exist. Informal learning was to be made more visible, since many are unaware of it. Relevant IL mechanisms that the interviewees could explain were selected for this thesis. The theory on informal learning was presented and some additional informal learning mechanisms were included: Underlying learning theories, internal and external learning resources, as well as some sport related informal learning mechanisms. Various scholars have explained these terms. The final results of this thesis relate to business context, but sport is at the scope of my research. The target group consisted of nine individuals in team sports that were considered as high performers (good/successful). Hence, also the concept of high performance was clarified with competence, expertise and talent literature. The study is qualitative and face-to-face interviews were chosen. The data was analyzed with Grounded Theory principles and theory elaboration. This thesis pointed out similarities and differences between the respondents´ answers (good/successful, inexperienced/experienced). Thus, the analysis clarified that there are different attitudes to learning and different learner profiles in sports context. Also, it became clear that some informal learning mechanisms are more used than others. Secondly, based on the most crucial differences, Typology of Talentum was formulated based on Le Deist & Winteron´s (2005, 39) Typology of holistic competence. Some variables of informal learning seemed to constitute the Meta-competence of Typology that ultimately causes the differences in individual performance and success. The results can be transferred to business context because meta-competence is transferable by nature.

Learning Mechanisms for Information Filtering Agents

Postprint

A influência do contexto familiar no aparecimento de dor espinal em crianças do concelho da Maia

A dor espinal em crianças e jovens constitui um problema cujas dimensões e prevalência tem vindo aumentar nas últimas décadas. Esta problemática parece ser tanto mais significativa se for tido em consideração que os episódios de dor espinal na idade escolar se perpetuam e estão relacionados com os que acontecem na idade adulta. Desta forma, os investigadores alertam para a necessidade de identificar potenciais factores de risco para o desenvolvimento de dor espinal em crianças e jovens de idade escolar. Assim, o presente estudo pretende identificar qual a importância assumida pelo contexto familiar na presença de queixas dolorosas espinais nas crianças em idade escolar do concelho da Maia. A amostra é constituída pelos pais de 1017 crianças do 1º ciclo do Ensino Básico do Concelho da Maia que foram avaliadas relativamente à postura, numa fase anterior do estudo. Desta população constituiu amostra 636 pais que concordaram participar no estudo, tendo obtido resposta de 226, obtendo uma percentagem de adesão de 35,5%. Para a obtenção dos dados qualitativos foi utilizado um método de amostragem estratégica de forma a seleccionar dez pais representativos de todas as categorias possíveis (pais com e sem dor e filhos com e sem dor). Esta amostra foi seleccionada tendo em conta os dados obtidos no questionário Parental Pain e Questionário de Bournemouth, sendo, posteriormente aplicado uma entrevista não estruturada. Os principais resultados obtidos permitiram concluir que o contexto tem uma influência determinante na criança com dor. De acordo com os dados extraídos das entrevistas, os comportamentos de manifestação de dor por parte das crianças podem ser um reflexo de comportamentos apreendidos através da observação do comportamento dos adultos e, consequentemente mimetizados, opinião esta igualmente expressa por alguns dos pais. Mas nem sempre os pais apontam a mimetização como causa para a dor referida pelos filhos, referindo igualmente o transporte da mochila, as posturas assumidas no dia-a-dia, o mobiliário escolar e as actividades de lazer realizadas pelas crianças.

On Neuron Mechanisms Used to Resolve Mental Problems of Identification and Learning in Sensorium

The paper considers some possible neuron mechanisms that do not contradict biological data. They are represented in terms of the notion of an elementary sensorium discussed in the previous authors’ works. Such mechanisms resolve problems of two large classes: when identification mechanisms are used and when sensory learning mechanisms are applied along with identification.

Parameter setting and statistical learning

Three main models of parameter setting have been proposed: the Variational model proposed by Yang (2002; 2004), the Structured Acquisition model endorsed by Baker (2001; 2005), and the Very Early Parameter Setting (VEPS) model advanced by Wexler (1998). The VEPS model contends that parameters are set early. The Variational model supposes that children employ statistical learning mechanisms to decide among competing parameter values, so this model anticipates delays in parameter setting when critical input is sparse, and gradual setting of parameters. On the Structured Acquisition model, delays occur because parameters form a hierarchy, with higher-level parameters set before lower-level parameters. Assuming that children freely choose the initial value, children sometimes will miss-set parameters. However when that happens, the input is expected to trigger a precipitous rise in one parameter value and a corresponding decline in the other value. We will point to the kind of child language data that is needed in order to adjudicate among these competing models.

Haptic robot-environment interaction for self-supervised learning in ground mobility

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de Computadores

Apprentissage automatique de la morphologie : le cas des structures racine-schème

RÉSUMÉ Cette thèse porte sur le développement de méthodes algorithmiques pour découvrir automatiquement la structure morphologique des mots d'un corpus. On considère en particulier le cas des langues s'approchant du type introflexionnel, comme l'arabe ou l'hébreu. La tradition linguistique décrit la morphologie de ces langues en termes d'unités discontinues : les racines consonantiques et les schèmes vocaliques. Ce genre de structure constitue un défi pour les systèmes actuels d'apprentissage automatique, qui opèrent généralement avec des unités continues. La stratégie adoptée ici consiste à traiter le problème comme une séquence de deux sous-problèmes. Le premier est d'ordre phonologique : il s'agit de diviser les symboles (phonèmes, lettres) du corpus en deux groupes correspondant autant que possible aux consonnes et voyelles phonétiques. Le second est de nature morphologique et repose sur les résultats du premier : il s'agit d'établir l'inventaire des racines et schèmes du corpus et de déterminer leurs règles de combinaison. On examine la portée et les limites d'une approche basée sur deux hypothèses : (i) la distinction entre consonnes et voyelles peut être inférée sur la base de leur tendance à alterner dans la chaîne parlée; (ii) les racines et les schèmes peuvent être identifiés respectivement aux séquences de consonnes et voyelles découvertes précédemment. L'algorithme proposé utilise une méthode purement distributionnelle pour partitionner les symboles du corpus. Puis il applique des principes analogiques pour identifier un ensemble de candidats sérieux au titre de racine ou de schème, et pour élargir progressivement cet ensemble. Cette extension est soumise à une procédure d'évaluation basée sur le principe de la longueur de description minimale, dans- l'esprit de LINGUISTICA (Goldsmith, 2001). L'algorithme est implémenté sous la forme d'un programme informatique nommé ARABICA, et évalué sur un corpus de noms arabes, du point de vue de sa capacité à décrire le système du pluriel. Cette étude montre que des structures linguistiques complexes peuvent être découvertes en ne faisant qu'un minimum d'hypothèses a priori sur les phénomènes considérés. Elle illustre la synergie possible entre des mécanismes d'apprentissage portant sur des niveaux de description linguistique distincts, et cherche à déterminer quand et pourquoi cette coopération échoue. Elle conclut que la tension entre l'universalité de la distinction consonnes-voyelles et la spécificité de la structuration racine-schème est cruciale pour expliquer les forces et les faiblesses d'une telle approche. ABSTRACT This dissertation is concerned with the development of algorithmic methods for the unsupervised learning of natural language morphology, using a symbolically transcribed wordlist. It focuses on the case of languages approaching the introflectional type, such as Arabic or Hebrew. The morphology of such languages is traditionally described in terms of discontinuous units: consonantal roots and vocalic patterns. Inferring this kind of structure is a challenging task for current unsupervised learning systems, which generally operate with continuous units. In this study, the problem of learning root-and-pattern morphology is divided into a phonological and a morphological subproblem. The phonological component of the analysis seeks to partition the symbols of a corpus (phonemes, letters) into two subsets that correspond well with the phonetic definition of consonants and vowels; building around this result, the morphological component attempts to establish the list of roots and patterns in the corpus, and to infer the rules that govern their combinations. We assess the extent to which this can be done on the basis of two hypotheses: (i) the distinction between consonants and vowels can be learned by observing their tendency to alternate in speech; (ii) roots and patterns can be identified as sequences of the previously discovered consonants and vowels respectively. The proposed algorithm uses a purely distributional method for partitioning symbols. Then it applies analogical principles to identify a preliminary set of reliable roots and patterns, and gradually enlarge it. This extension process is guided by an evaluation procedure based on the minimum description length principle, in line with the approach to morphological learning embodied in LINGUISTICA (Goldsmith, 2001). The algorithm is implemented as a computer program named ARABICA; it is evaluated with regard to its ability to account for the system of plural formation in a corpus of Arabic nouns. This thesis shows that complex linguistic structures can be discovered without recourse to a rich set of a priori hypotheses about the phenomena under consideration. It illustrates the possible synergy between learning mechanisms operating at distinct levels of linguistic description, and attempts to determine where and why such a cooperation fails. It concludes that the tension between the universality of the consonant-vowel distinction and the specificity of root-and-pattern structure is crucial for understanding the advantages and weaknesses of this approach.

De la perception à l'apprentissage [From perception to learning]

Dans le domaine de la perception, l'apprentissage est contraint par la présence d'une architecture fonctionnelle constituée d'aires corticales distribuées et très spécialisées. Dans le domaine des troubles visuels d'origine cérébrale, l'apprentissage d'un patient hémi-anopsique ou agnosique sera limité par ses capacités perceptives résiduelles, mais un déficit de reconnaissance visuelle de nature apparemment perceptive, peut également être associé à une altération des représentations en mémoire à long terme. Des réseaux neuronaux distincts pour la reconnaissance - cortex temporal - et pour la localisation des sons - cortex pariétal - ont été décrits chez l'homme. L'étude de patients cérébro-lésés confirme le rôle des indices spatiaux dans un traitement auditif explicite du « where » et dans la discrimination implicite du « what ». Cette organisation, similaire à ce qui a été décrit dans la modalité visuelle, faciliterait les apprentissages perceptifs. Plus généralement, l'apprentissage implicite fonde une grande partie de nos connaissances sur le monde en nous rendant sensible, à notre insu, aux règles et régularités de notre environnement. Il serait impliqué dans le développement cognitif, la formation des réactions émotionnelles ou encore l'apprentissage par le jeune enfant de sa langue maternelle. Le caractère inconscient de cet apprentissage est confirmé par l'étude des temps de réaction sériels de patients amnésiques dans l'acquisition d'une grammaire artificielle. Son évaluation pourrait être déterminante dans la prise en charge ré-adaptative. [In the field of perception, learning is formed by a distributed functional architecture of very specialized cortical areas. For example, capacities of learning in patients with visual deficits - hemianopia or visual agnosia - from cerebral lesions are limited by perceptual abilities. Moreover a visual deficit in link with abnormal perception may be associated with an alteration of representations in long term (semantic) memory. Furthermore, perception and memory traces rely on parallel processing. This has been recently demonstrated for human audition. Activation studies in normal subjects and psychophysical investigations in patients with focal hemispheric lesions have shown that auditory information relevant to sound recognition and that relevant to sound localisation are processed in parallel, anatomically distinct cortical networks, often referred to as the "What" and "Where" processing streams. Parallel processing may appear counterintuitive from the point of view of a unified perception of the auditory world, but there are advantages, such as rapidity of processing within a single stream, its adaptability in perceptual learning or facility of multisensory interactions. More generally, implicit learning mechanisms are responsible for the non-conscious acquisition of a great part of our knowledge about the world, using our sensitivity to the rules and regularities structuring our environment. Implicit learning is involved in cognitive development, in the generation of emotional processing and in the acquisition of natural language. Preserved implicit learning abilities have been shown in amnesic patients with paradigms like serial reaction time and artificial grammar learning tasks, confirming that implicit learning mechanisms are not sustained by the cognitive processes and the brain structures that are damaged in amnesia. In a clinical perspective, the assessment of implicit learning abilities in amnesic patients could be critical for building adapted neuropsychological rehabilitation programs.]

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Multimodal Integration in Statistical Learning: Evidence from the McGurk Illusion

Recent advances in the field of statistical learning have established that learners are able to track regularities of multimodal stimuli, yet it is unknown whether the statistical computations are performed on integrated representations or on separate, unimodal representations. In the present study, we investigated the ability of adults to integrate audio and visual input during statistical learning. We presented learners with a speech stream synchronized with a video of a speaker's face. In the critical condition, the visual (e.g., /gi/) and auditory (e.g., /mi/) signals were occasionally incongruent, which we predicted would produce the McGurk illusion, resulting in the perception of an audiovisual syllable (e.g., /ni/). In this way, we used the McGurk illusion to manipulate the underlying statistical structure of the speech streams, such that perception of these illusory syllables facilitated participants' ability to segment the speech stream. Our results therefore demonstrate that participants can integrate audio and visual input to perceive the McGurk illusion during statistical learning. We interpret our findings as support for modality-interactive accounts of statistical learning.

Learning Software-Maintenance Tasks in the Transition Phase of Offshore Outsourcing Projects: Two Learning-Theoretical Perspectives

The increasing practice of offshore outsourcing software maintenance has posed the challenge of effectively transferring knowledge to individual software engineers of the vendor. In this theoretical paper, we discuss the implications of two learning theories, the model of work-based learning (MWBL) and cognitive load theory (CLT), for knowledge transfer during the transition phase. Taken together, the theories suggest that learning mechanisms need to be aligned with the type of knowledge (tacit versus explicit), task characteristics (complexity and recurrence), and the recipients’ expertise. The MWBL proposes that learning mechanisms need to include conceptual and practical activities based on the relative importance of explicit and tacit knowledge. CLT explains how effective portfolios of learning mechanisms change over time. While jobshadowing, completion tasks, and supportive information may prevail at the outset of transition, they may be replaced by the work on conventional tasks towards the end of transition.

Learning in manufacturing organizations:what factors predict effectiveness?

This paper argues that it is possible to identify factors which pre-dispose organizations to adopt effective learning strategies and processes. It is hypothesized that effective OL is associated with: profitability, environmental uncertainty, structure, approach to HRM and quality orientation. The study focuses on forty-four manufacturing organizations, and draws on longitudinal data gathered through interviews. The findings suggest that two of these variables - approach to HRM and quality orientation - are particularly strongly correlated with measures of OL. It is concluded that effective learning mechanisms, with the potential to improve the quality of OL processes, are more likely to be established in businesses where HRM and quality initiatives are well established.

Investigating Turkey's national innovation and learning system

The national systems of innovation (NIS) approach focuses on the patterns and the determinants of innovation processes from the perspective of nation-states. This paper reports on continuing work on the application of an NIS model to the development of technological capability in Turkey. Initial assessment of the literature shows that there are a number of alternative conceptualisations of NIS. An attempt by the Government to identify a NIS for Turkey shows the main actors in the system but does not pay sufficient attention to the processes of interactions between agents within the system. An operational model should be capable of representing these processes and interactions and assessing the strengths and weaknesses of the NIS. For industrialising countries, it is also necessary to incorporate learning mechanisms into the model. Further, there are different levels of innovation and capability in different sectors which the national perspective may not reflect. This paper is arranged into three sections. The first briefly explains the basics of the national innovation and learning system. Although there is no single accepted definition of NIS, alternative definitions reviewed share some common characteristics. In the second section, an NIS model is applied to Turkey in order to identify the elements, which characterise the country’s NIS. This section explains knowledge flow and defines the relations between the actors within the system. The final section draws on the “from imitation to innovation” model apparently so successful in East Asia and assesses its applicability to Turkey. In assessing Turkey’s NIS, the focus is on the automotive and textile sectors.

Children's learning of anxiety sensitivity : a preliminary study using a sample of youths referred for anxiety disorders

The purpose of the present study was to examine the origins of anxiety sensitivity (AS) by assessing youths' learning experiences in relation to their AS symptoms and anxiety symptoms. Participants were 33 youths between 7 to 13 years old (M = 9.39 years, SD = 2.01). Youths were assessed using a structured interview and self-report measures. Chi-square analyses revealed no statistically significant differences in the proportions of boys vs. girls, Hispanic vs. non-Hispanic, and married vs. non-married. Pearson correlation analyses revealed that youths' AS learning experiences were significantly related to youths' AS and to youths' anxiety symptoms scores. Partial correlations between youths' learning experiences associated with AS symptoms in relation to AS scores controlling for anxiety symptoms effects were statistically significant. Findings were consistent with theory and suggest that learning mechanisms may be involved in AS acquisition and maintenance. The findings' implications are discussed regarding possible learning experiences' role in the development of AS.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.