Contrarios y aprendizaje: preferencia por las diferencias salientes entre objetos

A partir de la hipótesis de que los contrarios son un elemento adecuado para las tareas de aprendizaje, en este estudio se ha investigado la respuesta de niños y adultos ante una serie de estímulos que se han agrupado en más opuestos y menos opuestos. La finalidad de este trabajo es investigar si los sujetos analizados se sienten más atraídos por los objetos que muestran una relación de oposición mayor que los que no la muestran. Los resultados evidencian que los niños escogen más los contrarios que los adultos. Estos resultados se discuten a la luz de las principales hipótesis que intentan explicar la dificultad de adquisición de los antónimos y también de las que los consideran un elemento adecuado para el aprendizaje

Did tool-use evolve with enhanced physical cognitive abilities?

The use and manufacture of tools have been considered to be cognitively demanding and thus a possible driving factor in the evolution of intelligence. In this study, we tested the hypothesis that enhanced physical cognitive abilities evolved in conjunction with the use of tools, by comparing the performance of naturally tool-using and non-tool-using species in a suite of physical and general learning tasks. We predicted that the habitually tool-using species, New Caledonian crows and Galápagos woodpecker finches, should outperform their non-tool-using relatives, the small tree finches and the carrion crows in a physical problem but not in general learning tasks. We only found a divergence in the predicted direction for corvids. That only one of our comparisons supports the predictions under this hypothesis might be attributable to different complexities of tool-use in the two tool-using species. A critical evaluation is offered of the conceptual and methodological problems inherent in comparative studies on tool-related cognitive abilities.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Engagement affectif, comportemental et cognitif des élèves du primaire dans un contexte pédagogique d’intégration des TIC : une étude multicas en milieux défavorisés

Réalisée en milieux défavorisés, cette étude porte sur l’engagement scolaire des élèves de troisième cycle du primaire (5e et 6e années au Québec) dans un contexte d’utilisation pédagogique des technologies de l’information et de la communication (TIC). L’objectif de cette recherche est d’analyser l’engagement d’élèves utilisant les TIC. Elle vise à décrire les pratiques pédagogiques d’intégration des TIC de dix enseignants, de relater la qualité de l’engagement de leurs 230 élèves lors de tâches TIC et de mesurer l’évolution et la qualité de leur engagement scolaire selon le degré de défavorisation de leur école. Pour ce faire, cette recherche s’est inspirée d’un cadre de référence traitant l’engagement scolaire selon les dimensions affective, comportementale et cognitive. De plus, cette étude multicas essentiellement de nature interprétative et descriptive a utilisé une méthodologie mixte de collecte et d’analyse des données. Les résultats montrent notamment que les enseignants accordent une valeur pédagogique importante aux TIC tant dans la fréquence de leur utilisation, dans les usages valorisés que dans la façon de les intégrer en classe. Les enseignants privilégient largement le traitement de texte et la recherche sur Internet, mais très peu d’autres usages pertinents sont mis de l’avant de manière soutenue. La majorité des enseignants interrogés préférerait se servir de quatre ordinateurs en classe pour utiliser les TIC plus facilement que d’avoir uniquement accès au laboratoire de leur école. De plus, ils perçoivent de manière unanime que l’utilisation des TIC exerce une influence importante sur la dimension affective de leurs élèves lors d’activités préparées, assez bonne sur la dimension comportementale et plus discutable sur la dimension cognitive. Plus globalement, les élèves eux-mêmes affichent en général un engagement scolaire relativement élevé. En six mois, la qualité de l’engagement affectif des élèves est restée stable, leur engagement comportemental a progressé, mais leur engagement cognitif a baissé légèrement. Les résultats montrent aussi que la qualité de l’engagement des élèves de milieux défavorisés évolue différemment selon le degré de défavorisation de leur école. Dans cette recherche, l’importance de l’utilisation des TIC sur la qualité de l’engagement est marquante et s’avère parfois meilleure que dans d’autres types de tâches. Sans pouvoir généraliser, cette étude permet aussi de saisir davantage la qualité et l’évolution de l’engagement scolaire des élèves de la fin du primaire en milieux défavorisés. Des recommandations pour le milieu et des pistes de recherches futures sont présentées en tenant compte des limites et des forces de cette thèse inédite réalisée par articles.

Impact des émotions sur les performances

Les émotions jouent un rôle primordial dans les processus cognitifs et plus particulièrement dans les tâches d’apprentissage. D’ailleurs, plusieurs recherches neurologiques ont montré l’interrelation qui existe entre la cognition et les émotions. Elles ont aussi déterminé plusieurs formes d’intelligence humaine autre que l’intelligence rationnelle parmi lesquelles nous distinguons la forme ayant comme dimension émotionnelle, à savoir l’intelligence émotionnelle, vu son impact sur les processus d’apprentissage. L’intelligence émotionnelle est alors un facteur significatif de réussite scolaire et professionnelle. Sous la lumière de ces constatations présentées, les informaticiens à leur tour, vont alors tenter de consentir de plus en plus de place au facteur émotionnel dans les systèmes informatiques et plus particulièrement dans ceux dédiés à l’apprentissage. L’intégration de l’intelligence émotionnelle dans ces systèmes et plus précisément, dans les Systèmes Tutoriels Intelligents (STI), va leur permettre de gérer les émotions de l’apprenant et par la suite améliorer ses performances. Dans ce mémoire, notre objectif principal est d’élaborer une stratégie d’apprentissage visant à favoriser et accentuer la mémorisation chez les enfants. Pour atteindre cet objectif, nous avons développé un cours d’anglais en ligne ainsi qu’un tuteur virtuel utilisant des ressources multimédia tels que le ton de la voix, la musique, les images et les gestes afin de susciter des émotions chez l’apprenant. Nous avons conduit une expérience pour tester l’efficacité de quelques stratégies émotionnelles ainsi qu’évaluer l’impact des émotions suscitées sur la capacité de mémorisation des connaissances à acquérir par l’apprenant. Les résultats de cette étude expérimentale ont prouvé que l’induction implicite des émotions chez ce dernier a une influence significative sur ses performances. Ils ont également montré qu’il n’existe pas une stratégie efficace pour tous les apprenants à la fois, cependant l’efficacité d’une telle stratégie par rapport à une autre dépend essentiellement du profil comportemental de l’apprenant déterminé à partir de son tempérament.

De la typicité des différentes mesures de tendance centrale dans la représentation mentale des catégories implicites

Quelle est la nature des représentations que se font les gens des catégories apprises? Il est généralement accepté que le type de tâche d’apprentissage a une influence sur la réponse à cette question. Ceci étant dit, la majorité des théories portant sur les processus de catégorisation élaborées durant les dernières décennies a porté presqu’exclusivement sur des tâches de classifications d’exemplaires. Le mémoire présenté ici avait quatre objectifs principaux. Le premier était de vérifier si une tâche d’apprentissage de catégories implicites par classifications mène davantage à l’intégration de dimensions diagnostiques qu’un apprentissage par inférences. Le deuxième était de vérifier si une tâche d’apprentissage de catégories implicites par inférences entraine davantage l’intégration de dimensions typiques qu’un apprentissage par classifications. Le troisième était d’évaluer si un effet de rehaussement du prototype (« prototype enhancement effect ») pouvait être observé dans le cadre d’un apprentissage par inférences. Le quatrième était de clarifier quelle est la mesure de tendance centrale qui présente réellement un effet de rehaussement du prototype : le mode, la médiane ou la moyenne. Suite aux résultats obtenus, les implications pour trois théories portant sur les processus de catégorisation sont discutées. Les trois théories sont celles des prototypes, des exemplaires et des frontières décisionnelles.

Capturing Emergent Semantics from Social Annotation Systems

The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to structure Web resources by metadata annotations. Interestingly, two major approaches which gained a considerable amount of attention are addressing the problem from nearly opposite directions: On the one hand, the idea of the Semantic Web suggests to formalize the knowledge within a particular domain by means of the "top-down" approach of defining ontologies. On the other hand, Social Annotation Systems as part of the so-called Web 2.0 movement implement a "bottom-up" style of categorization using arbitrary keywords. Experience as well as research in the characteristics of both systems has shown that their strengths and weaknesses seem to be inverse: While Social Annotation suffers from problems like, e. g., ambiguity or lack or precision, ontologies were especially designed to eliminate those. On the contrary, the latter suffer from a knowledge acquisition bottleneck, which is successfully overcome by the large user populations of Social Annotation Systems. Instead of being regarded as competing paradigms, the obvious potential synergies from a combination of both motivated approaches to "bridge the gap" between them. These were fostered by the evidence of emergent semantics, i. e., the self-organized evolution of implicit conceptual structures, within Social Annotation data. While several techniques to exploit the emergent patterns were proposed, a systematic analysis - especially regarding paradigms from the field of ontology learning - is still largely missing. This also includes a deeper understanding of the circumstances which affect the evolution processes. This work aims to address this gap by providing an in-depth study of methods and influencing factors to capture emergent semantics from Social Annotation Systems. We focus hereby on the acquisition of lexical semantics from the underlying networks of keywords, users and resources. Structured along different ontology learning tasks, we use a methodology of semantic grounding to characterize and evaluate the semantic relations captured by different methods. In all cases, our studies are based on datasets from several Social Annotation Systems. Specifically, we first analyze semantic relatedness among keywords, and identify measures which detect different notions of relatedness. These constitute the input of concept learning algorithms, which focus then on the discovery of synonymous and ambiguous keywords. Hereby, we assess the usefulness of various clustering techniques. As a prerequisite to induce hierarchical relationships, our next step is to study measures which quantify the level of generality of a particular keyword. We find that comparatively simple measures can approximate the generality information encoded in reference taxonomies. These insights are used to inform the final task, namely the creation of concept hierarchies. For this purpose, generality-based algorithms exhibit advantages compared to clustering approaches. In order to complement the identification of suitable methods to capture semantic structures, we analyze as a next step several factors which influence their emergence. Empirical evidence is provided that the amount of available data plays a crucial role for determining keyword meanings. From a different perspective, we examine pragmatic aspects by considering different annotation patterns among users. Based on a broad distinction between "categorizers" and "describers", we find that the latter produce more accurate results. This suggests a causal link between pragmatic and semantic aspects of keyword annotation. As a special kind of usage pattern, we then have a look at system abuse and spam. While observing a mixed picture, we suggest that an individual decision should be taken instead of disregarding spammers as a matter of principle. Finally, we discuss a set of applications which operationalize the results of our studies for enhancing both Social Annotation and semantic systems. These comprise on the one hand tools which foster the emergence of semantics, and on the one hand applications which exploit the socially induced relations to improve, e. g., searching, browsing, or user profiling facilities. In summary, the contributions of this work highlight viable methods and crucial aspects for designing enhanced knowledge-based services of a Social Semantic Web.

Regional response differences across the human amygdaloid complex during social conditioning

The amygdala is consistently implicated in biologically relevant learning tasks such as Pavlovian conditioning. In humans, the ability to identify individual faces based on the social outcomes they have predicted in the past constitutes a critical form of associative learning that can be likened to “social conditioning.” To capture such learning in a laboratory setting, participants learned about faces that predicted negative, positive, or neutral social outcomes. Participants reported liking or disliking the faces in accordance with their learned social value. During acquisition, we observed differential functional magnetic resonance imaging activation across the human amygdaloid complex consistent with previous lesion, electrophysiological, and functional neuroimaging data. A region of the medial ventral amygdala and a region of the dorsal amygdala/substantia innominata showed signal increases to both Negative and Positive faces, whereas a lateral ventral region displayed a linear representation of the valence of faces such that Negative > Positive > Neutral. This lateral ventral locus also differed from the dorsal and medial loci in that the magnitude of these responses was more resistant to habituation. These findings document a role for the human amygdala in social learning and reveal coarse regional dissociations in amygdala activity that are consistent with previous human and nonhuman animal data.

Working with Dyslexic Learners: Authoring in a Multimedia Environment

The fundamental principles of the teaching methodology followed for dyslexic learners evolve around the need for a multisensory approach, which would advocate repetition of learning tasks in an enjoyable way. The introduction of multimedia technologies in the field of education has supported the merging of new tools (digital camera, scanner) and techniques (sounds, graphics, animation) in a meaningful whole. Dyslexic learners are now given the opportunity to express their ideas using these alternative media and participate actively in the educational process. This paper discussed the preliminary findings of a single case study of two English monolingual dyslexic children working together to create an open-ended multimedia project on a laptop computer. The project aimed to examine whether and if the multimedia environment could enhance the dyslexic learners’ skills in composition. Analysis of the data has indicated that the technological facilities gave the children the opportunity to enhance the style and content of their work for a variety of audiences and to develop responsibilities connected to authorship.

Effects of pramipexole on the processing of rewarding and aversive taste stimuli

Rationale: Pramipexole, a D2/D3 dopamine receptor agonist, has been implicated in the development of impulse control disorders in patients with Parkinson's disease. Investigation of single doses of pramipexole in healthy participants in reward-based learning tasks has shown inhibition of the neural processing of reward, presumptively through stimulation of dopamine autoreceptors. Objectives: This study aims to examine the effects of pramipexole on the neural response to the passive receipt of rewarding and aversive sight and taste stimuli. Methods: We used functional magnetic resonance imaging to examine the neural responses to the sight and taste of pleasant (chocolate) and aversive (mouldy strawberry) stimuli in 16 healthy volunteers who received a single dose of pramipexole (0.25 mg) and placebo in a double-blind, within-subject, design. Results: Relative to placebo, pramipexole treatment reduced blood oxygen level-dependent activation to the chocolate stimuli in the areas known to play a key role in reward, including the ventromedial prefrontal cortex, the orbitofrontal cortex, striatum, thalamus and dorsal anterior cingulate cortex. Pramipexole also reduced activation to the aversive condition in the dorsal anterior cingulate cortex. There were no effects of pramipexole on the subjective ratings of the stimuli. Conclusions: Our results are consistent with an ability of acute, low-dose pramipexole to diminish dopamine-mediated responses to both rewarding and aversive taste stimuli, perhaps through an inhibitory action of D2/3 autoreceptors on phasic burst activity of midbrain dopamine neurones. The ability of pramipexole to inhibit aversive processing might potentiate its adverse behavioural effects and could also play a role in its proposed efficacy in treatment-resistant depression.

Inference From Aging Information

For many learning tasks the duration of the data collection can be greater than the time scale for changes of the underlying data distribution. The question we ask is how to include the information that data are aging. Ad hoc methods to achieve this include the use of validity windows that prevent the learning machine from making inferences based on old data. This introduces the problem of how to define the size of validity windows. In this brief, a new adaptive Bayesian inspired algorithm is presented for learning drifting concepts. It uses the analogy of validity windows in an adaptive Bayesian way to incorporate changes in the data distribution over time. We apply a theoretical approach based on information geometry to the classification problem and measure its performance in simulations. The uncertainty about the appropriate size of the memory windows is dealt with in a Bayesian manner by integrating over the distribution of the adaptive window size. Thus, the posterior distribution of the weights may develop algebraic tails. The learning algorithm results from tracking the mean and variance of the posterior distribution of the weights. It was found that the algebraic tails of this posterior distribution give the learning algorithm the ability to cope with an evolving environment by permitting the escape from local traps.

Inlärning och öppet-fält aktivitet hos förskolebarn

The purpose of this study was to analyze the impact of learning on open-field activity among pre-school children varying from 3 to 5 years old. Altogether 25 children, 13 girls and 12 boys, entered the test from three different preschools in Dalarna. Six of these children represented the control group. The children were asked to learn 2 tasks, 1 visual memory task and 1 spatial constructing-kit task. Before, between and after the tasks, the children were allowed to move freely in the open field. The control group did not solve any learning tasks and only entered the open field, which was divided into 18 equally large squares, where the children’s activities were observed. The children’s learning times as well as their spontaneous open-field activity and wall-seeking behaviour were registered. The result showed as a general rule that the learning time was reduced between each session. However, the visual memory task increased the children’s spontaneous open-field behaviour more and decreased their wall seeking to a greater extent than the spatial-construction learning task.

O impacto das tarefas de aprendizagem mediadas pela lousa digital interativa na motivação situacional de aprendizes de inglês

Some authors have suggested that learning tasks conducted in L2 classes can motivate learners in different ways. Similarly, Interactive Whiteboards (IWB) have already been linked as drivers to engagement and enthusiasm in L2 classes, which may cause some impact on affective variables that influence learning (e.g. motivation). This crosssectional mixed-methods study aims to understand how situational motivation caused by learning tasks mediated by the IWB impact participants. We seek to answer the following research questions: (1) How does motivation as a personality trait of the learner relate to his/her additional language learning performance?, (2) How does the type of learning task mediated by the IWB impact the learner s motivation?, (3) How does motivation vary along the learning task mediated by the IWB? and (4) What is the relation between the learning task motivation and the learners perception about the task mediated by the IWB? Data collection lasted four months with 29 learners from a private language school. The instruments used were the following: (a) an initial questionnaire (adapted from the Attitudes/Motivation Test Battery by GARDNER, 2004), (b) situation-specific on-line scales to assess learners motivation in three moments: before, during and after the task, and analyze how motivation varies along the task; (c) class observations and field notes resulting from these observations, (d) participants end-of-course grades to understand the connection between academic success and their motivational profiles and (e) a final questionnaire with the qualitative purpose to know learners perceptions about the tasks mediated by the IWB. Our theoretical framework is based on Task-Based Learning and cognitive aspects present in tasks (WILLIS, 1996; SKEHAN, 1996), theories on motivation and second language learning (GARDNER, 2001; DÖRNYEI e OTTÓ, 1998; DÖRNYEI, 2000; 2002) and conceptions about L2 learning mediated by technology (GIBSON, 2001; OLIVEIRA, 2001; MILLER et al, 2005). Our results do not point out to a significative correlation between learners end-of-course grades and their motivational profiles. However, they indicate that there is some variability in situational motivation along the tasks, even among learning tasks from the same type. Furthermore, they show that learners report different perceptions for each learning task and that the impact of the IWB on participants did not have a large proportion