O desenvolvimento do pensamento abstrato através de game-based learning e do Scratch

Relatório de estágio de mestrado em Ensino de Informática

Aprendizaje durante la ontogenia temprana: Estudio de la participación del sistema opioide en la modulación de aprendizajes mediados por el etanol. LEARNING AND EARLY ONTOGENY: ANALYSIS OF THE INVOLVEMENT OF OPIOID SYSTEM IN FETAL AND INFANTILE ETHANOL-MEDIATED EXPERIENCIES.

Este plan de trabajos es continuidad de una línea de investigación centrada en evaluar los mecanismos responsables de la adquisición, expresión y persistencia de experiencias con el etanol. A partir de ello, indagar acerca de los efectos de esta experiencia sobre comportamientos de búsqueda y autoadministración de etanol en neonatos e infantes de rata. Se pretende analizar la participación del sistema opiáceo en los mecanismos implicados en una memoria fetal y/o infantil, generada como consecuencia de la exposición etílica. En una primera etapa, nos proponemos establecer de qué manera experiencias prenatales con la droga modulan el patrón de auto-administración de alcohol y otros reforzadores, como sacarosa. En este primer bloque de experimentos realizaremos manipulaciones fetales para determinar con mayor grado de especificidad la posible acción del sistema opiáceo en los mecanismos de adquisición de una memoria etílica prenatal. Se realizarán administraciones de etanol y el antagonista opiáceo, directamente a nivel fetal, y se evaluará esta experiencia en un paradigma de condicionamiento neonatal positivo, mediado por la droga. De acuerdo a la evidencia previa, esperamos que la exposición prenatal con la droga facilite la expresión de conductas de consumo y búsqueda del etanol o hacia las claves que señalizan al psicotrópico, tanto durante la infancia como en el neonato. A su vez, cuando la droga es presentada bajo los efectos de un antagonista opiáceo esperamos que estas conductas muestren un perfil similar a las desplegadas por sujetos controles. El segundo bloque de experimentos ha sido ideado con el objeto de indagar acerca de la posible participación del sistema opiáceo en la modulación de los aspectos reforzantes de la droga, a través de un esquema de auto-administración etílica infantil. Se utilizará un paradigma de condicionamiento instrumental adaptado para ratas infantes que consta de dos instancias, una de adquisición de la conducta instrumental (DPs 14-17) en la cual los animales reciben un pulso de refuerzo, como consecuencia de la ejecución de la conducta operante. En una segunda fase se analiza el patrón de búsqueda del reforzador ya que se registra la respuesta instrumental, sin que ocurra el refuerzo por la misma. Para analizar la participación del sistema opiáceo, durante la fase de adquisición de la conducta operante (DPs 16 y 17) los animales serán re-expuestos a mínimas cantidades del reforzador, bajo los efectos de un antagonista opiáceo, momentos previos al ensayo instrumental correspondiente para cada uno de estos días (Exp. 3). Esperamos que el bloqueo del sistema opiáceo, durante esta re-exposición al etanol, sea suficiente para disminuir el patrón de respuesta instrumental hacia el refuerzo etílico. Un último experimento incorporará un tercer evento de re-exposición al etanol -bajo los efectos del antagonista- previo al ensayo de extinción de la conducta instrumental (DP 18). Este nuevo evento tiene por objeto analizar la participación de este sistema neurobiológico en los mecanismos de búsqueda de etanol. Si el sistema opiáceo participa en la modulación de patrones tanto de búsqueda como consumatorios del reforzamiento por etanol, se espera que la re-exposición a la droga bajo los efectos del antagonista, inhiba estas respuestas tanto durante la sesión de adquisición, como de extinción de la conducta operante. Este proyecto intenta profundizar en el conocimiento de los mecanismos que regulan reconocimiento, aceptación, búsqueda y consumo de etanol, como consecuencia de experiencias tempranas con la droga. A su vez, es importante identificar y estudiar los sistemas neurobiológicos involucrados en estos mecanismos. Es por ello que se intenta determinar el rol que ejerce el sistema opiáceo en la adquisición de estas experiencias etílicas a nivel fetal e infantil, que se conoce promueven la búsqueda y el consumo de la droga. Our work is directed to analyze the involvement of the opioid system in the generation of pre- and early postnatal ethanol-related memories. As a first step, maternal manipulations with ethanol will be done. Infants will be evaluated in a paradigm of infantile self-administration of different reinforcers (ethanol, sucrose or water), employing a model of operant conditioning adapted to infant rats. A second experiment will be conducted in order to analyze if a central administration of ethanol, directly to the fetus, modifies subsequent patterns of neonatal conditioned responses to an artificial nipple, mediated by ethanol reinforcing effects. Fetal presentation of ethanol will be accompanied with the injection of an opioid antagonist in order to analyze the involvement of this system in acquisition processes of a fetal ethanol-mediated memory. A second set of studies will be conducted to analyze appetitive and consummatory behaviors in an infant model of ethanol self-administration. Involvement of opioid system in the acquisition or expression of this experience will be also inquired. Infant rats (PDs14-17) have to display a target behavior (nose-poke) to gain access to 5% sucrose or 3.75% ethanol. On PD18 an extinction session will be included. At PDs16-17, 6-hr before training, pups will be re-exposed to ethanol under opioid antagonism effects (naloxone). In a follow up experiment, a re-exposure trial will be included at PD18. Prior extinction, pups will receive naloxone and will be re-exposed to ethanol. We aim to observe if opioid system is modulating etha¬nol reinforcing effects, in terms of both appetitive and consummatory behaviors.

When Do We Learn to Cooperate? The Role of Social Learning in Social Dilemmas

In this paper, I look at the interaction between social learning and cooperative behavior. I model this using a social dilemma game with publicly observed sequential actions and asymmetric information about pay offs. I find that some informed agents in this model act, individually and without collusion, to conceal the privately optimal action. Because the privately optimal action is socially costly the behavior of informed agents can lead to a Pareto improvement in a social dilemma. In my model I show that it is possible to get cooperative behavior if information is restricted to a small but non-zero proportion of the population. Moreover, such cooperative behavior occurs in a finite setting where it is public knowledge which agent will act last. The proportion of cooperative agents within the population can be made arbitrarily close to 1 by increasing the finite number of agents playing the game. Finally, I show that under a broad set of conditions that it is a Pareto improvement on a corner value, in the ex-ante welfare sense, for an interior proportion of the population to be informed.

What is the Causal Effect of Information and Learning about a Public Good on Willingness to Pay?

In this study we elicit agents’ prior information set regarding a public good, exogenously give information treatments to survey respondents and subsequently elicit willingness to pay for the good and posterior information sets. The design of this field experiment allows us to perform theoretically motivated hypothesis testing between different updating rules: non-informative updating, Bayesian updating, and incomplete updating. We find causal evidence that agents imperfectly update their information sets. We also field causal evidence that the amount of additional information provided to subjects relative to their pre-existing information levels can affect stated WTP in ways consistent overload from too much learning. This result raises important (though familiar) issues for the use of stated preference methods in policy analysis.

Learning-induced plasticity in human audition: Objects, time, and space.

The human auditory system is comprised of specialized but interacting anatomic and functional pathways encoding object, spatial, and temporal information. We review how learning-induced plasticity manifests along these pathways and to what extent there are common mechanisms subserving such plasticity. A first series of experiments establishes a temporal hierarchy along which sounds of objects are discriminated along basic to fine-grained categorical boundaries and learned representations. A widespread network of temporal and (pre)frontal brain regions contributes to object discrimination via recursive processing. Learning-induced plasticity typically manifested as repetition suppression within a common set of brain regions. A second series considered how the temporal sequence of sound sources is represented. We show that lateralized responsiveness during the initial encoding phase of pairs of auditory spatial stimuli is critical for their accurate ordered perception. Finally, we consider how spatial representations are formed and modified through training-induced learning. A population-based model of spatial processing is supported wherein temporal and parietal structures interact in the encoding of relative and absolute spatial information over the initial ∼300ms post-stimulus onset. Collectively, these data provide insights into the functional organization of human audition and open directions for new developments in targeted diagnostic and neurorehabilitation strategies.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.

Evolutionary ecology of learning: insights from fruit flies

Ecologically and evolutionarily oriented research on learning has traditionally been carried out on vertebrates and bees. While less sophisticated than those animals, fruit flies (Drosophila) are capable of several forms of learning, and have an advantage of a short generation time, which makes them an ideal system for experimental evolution studies. This review summarizes the insights into evolutionary questions about learning gained in the last decade from evolutionary experiments on Drosophila. These experiments demonstrate that Drosophila have the genetic potential to evolve substantially improved learning performance in ecologically relevant learning tasks. In at least one set of selected populations the improved learning generalized to another task than that used to impose selection, involving a different behavior, different stimuli, and a different sensory channel for the aversive reinforcement. This improvement in learning ability was associated with reduction in other fitness-related traits, such as larval competitive ability and lifespan, pointing out to evolutionary trade-offs of improved learning. These trade-offs were confirmed by other evolutionary experiments where reduction in learning performance was observed as a correlated response to selection for tolerance to larval nutritional stress or for delayed aging. Such trade-offs could be one reason why fruit flies have not fully used up their evolutionary potential for learning ability. Finally, another evolutionary experiment with Drosophila provided the first direct evidence for the long-standing ideas that learning can under some circumstances accelerate and in other slow down genetically-based evolutionary change. These results demonstrate the usefulness of fruit flies as a model system to address evolutionary questions about learning.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method

A Formal Framework for Specifying, Reusing and Implementing Learning Processes. First level

This file contains the ontology of patterns of educational settings, as part of the formal framework for specifying, reusing and implementing educational settings. Furthermore, it includes the set of rules that extend the ontology of educational scenarios as well as a brief description of the level of patters of such ontological framework.

Implementing statistical learning methods through Bayesian networks (Part 2): bayesian evaluations for results of black toner analyses in forensic document examination

This paper presents and discusses the use of Bayesian procedures - introduced through the use of Bayesian networks in Part I of this series of papers - for 'learning' probabilities from data. The discussion will relate to a set of real data on characteristics of black toners commonly used in printing and copying devices. Particular attention is drawn to the incorporation of the proposed procedures as an integral part in probabilistic inference schemes (notably in the form of Bayesian networks) that are intended to address uncertainties related to particular propositions of interest (e.g., whether or not a sample originates from a particular source). The conceptual tenets of the proposed methodologies are presented along with aspects of their practical implementation using currently available Bayesian network software.

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.

Learning design twofold strategies for teacher-led inquiry andstudent active learning

This workshop paper states that fostering active student participation both in face-to-face lectures / seminars and outside the classroom (personal and group study at home, the library, etc.) requires a certain level of teacher-led inquiry. The paper presents a set of strategies drawn from real practice in higher education with teacher-led inquiry ingredients that promote active learning. Thesepractices highlight the role of the syllabus, the importance of iterative learning designs, explicit teacher-led inquiry, and the implications of the context, sustainability and practitioners’ creativity. The strategies discussed in this paper can serve as input to the workshop as real cases that need to be represented in design and supported in enactment (with and without technologies).

Learning ability and longevity: a symmetrical evolutionary trade-off in Drosophila.

Learning ability can be substantially improved by artificial selection in animals ranging from Drosophila to rats. Thus these species have not used their evolutionary potential with respect to learning ability, despite intuitively expected and experimentally demonstrated adaptive advantages of learning. This suggests that learning is costly, but this notion has rarely been tested. Here we report correlated responses of life-history traits to selection for improved learning in Drosophila melanogaster. Replicate populations selected for improved learning lived on average 15% shorter than the corresponding unselected control populations. They also showed a minor reduction in fecundity late in life and possibly a minor increase in dry adult mass. Selection for improved learning had no effect on egg-to-adult viability, development rate, or desiccation resistance. Because shortened longevity was the strongest correlated response to selection for improved learning, we also measured learning ability in another set of replicate populations that had been selected for extended longevity. In a classical olfactory conditioning assay, these long-lived flies showed an almost 40% reduction in learning ability early in life. This effect disappeared with age. Our results suggest a symmetrical evolutionary trade-off between learning ability and longevity in Drosophila.

A influência do e-learning no capital humano

Na Europa e nas últimas décadas do Século XX, a emergência da Sociedade de Informação veio impor às organizações a necessidade de que, para além das inovações tecnológicas, haja uma preocupação relativamente aos bens intangíveis como a informação, as novas metodologias de trabalho e o know how (Batista, 2002). Paralelamente a estas inovações, as Instituições de Ensino Superior têm contribuído para a evolução do Capital Humano, como ativo intangível intrínseco ao Homem. Em Portugal e no contexto do Ensino/Formação a Distância parecem continuar a existir, ainda, em algumas instituições, problemas de identificação, e de descriminação das vantagens no que concerne à estrutura aberta e flexível, com o estudante/formando a ter algumas dificuldades em adaptar o seu perfil e interesses profissionais ao tipo de aprendizagem que mais se lhe adequa. O e-learning surge como um método de Ensino/Formação a Distância, só possível com a especificidade dos processos pedagógicos e em complementaridade com as Tecnologias de Informação e Comunicação (TIC), uma vez que são estas que lhe dão o suporte necessário à sua concretização. O e-learning ao proporcionar novas formas de comunicação, de interação e de confronto de ideias, permite uma aprendizagem baseada na partilha de saberes, tendo em consideração as experiências e os objetivos profissionais dos formandos. Dentro destes pressupostos, achámos importante fazer uma investigação a partir de Instituições de Ensino Superior Portuguesas, de modo a percebermos qual o papel e a influência que o e-learning desempenha nos objetivos das organizações académicas em geral e no Capital Humano dos seus Estudantes/Formandos em particular. A partir da questão da investigação foram definidos os objetivos e hipóteses de investigação de modo a que ao ser enunciada uma metodologia esta englobe fatores que foquem os elementos necessários à confirmação, ou não, dos pressupostos enunciados. Foi analisada documentação diversa, criado um questionário e conduzidas entrevistas, de modo a obter e potenciar a informação necessária e suficiente para o efeito. A recolha de dados para posterior análise e os resultados depois de interpretados, permitirão responder aos propósitos expressos desde o início da investigação.

Learning and adaptation in physical heterogeneous teams of robots

In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems