Teoria, proposta e aplicação de uma sequência didática em Educação Linguística: trabalhando os conceitos de língua, gramáticas e normas linguísticas no nono ano do Ensino Fundamental

O ensino-aprendizagem da disciplina Língua Portuguesa se apresenta como um desafio para professores e alunos; muito disso se deve à falta de reconhecimento da diversidade linguística (LIMA, 2014). Há uma tendência no ambiente escolar em se valorizar apenas uma norma linguística, ignorando as diferentes normas que fazem parte do convívio em sociedade. Com isso, os discentes entram em conflito, pois aquilo que é apresentado para eles como a Língua Portuguesa está bem distante da sua experiência pessoal com a língua. Em via de eliminar esse conflito, os PCNs e o PNLD estabelecem a inserção do tema variação linguística no conteúdo programático da disciplina Língua Portuguesa. Entretanto, ainda perdura uma lacuna no tratamento desse tema. A preocupação em preencher essa lacuna motivou este trabalho, que teve como objetivo principal propor e aplicar uma sequência didática que abordasse os seguintes temas: língua, gramáticas e normas linguísticas, a fim de contribuir com a realização de uma Educação Linguística eficaz (BAGNO; RANGEL, 2005). Esses três conteúdos principais geraram desdobramentos que proporcionaram a discussão dos seguintes tópicos: variação e preconceito linguísticos. A sequência didática elaborada é composta por oito etapas em que são intercalados momentos de aula expositiva e de atividades lúdicas. A aplicação da proposta de sequência didática ocorreu em uma turma de nono ano do Ensino Fundamental de um CIEP (Centro Integrado de Educação Pública) localizado na Zona Oeste da cidade do Rio de Janeiro. As ferramentas utilizadas para identificar os conteúdos assimilados pelos alunos foram a avaliação da participação da turma e os resultados de quatro atividades específicas. Considerando a complexidade e o ineditismo dos conceitos apresentados na sequência didática, o desempenho da classe foi satisfatório. Os alunos também responderam a dois questionários, um no início da sequência e outro ao final, que tinham o propósito de avaliar se houve mudança com relação ao entendimento da existência de variação linguística e do preconceito linguístico. O primeiro questionário revelou que a maioria dos alunos já havia sofrido e praticado preconceito linguístico; já o segundo mostrou que a maioria dos discentes assimilou a noção de preconceito linguístico e rompeu com a ideia de certo e errado na língua. Como objetivo secundário, este trabalho intentou deixar um legado para professores, pois com esta dissertação a ação não ficará limitada apenas ao nono ano daquele CIEP, mas poderá contribuir com o ensino linguístico de diversas escolas; e para pesquisadores, pois este registro descreve um modelo de trabalho acadêmico em que se pretendeu aplicar o princípio de indissociabilidade entre ensino, pesquisa e extensão. A expectativa é que, somados a trabalhos similares, seja possível abrir um precedente de trabalhos acadêmicos fundamentados teoricamente e desenvolvidos de forma prática, além de realizados com preocupações sociais e educacionais

Inferring visuomotor priors for sensorimotor learning.

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations--the mapping between actual and visual location of the hand--during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior.

Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG.

In the field of motor control, two hypotheses have been controversial: whether the brain acquires internal models that generate accurate motor commands, or whether the brain avoids this by using the viscoelasticity of musculoskeletal system. Recent observations on relatively low stiffness during trained movements support the existence of internal models. However, no study has revealed the decrease in viscoelasticity associated with learning that would imply improvement of internal models as well as synergy between the two hypothetical mechanisms. Previously observed decreases in electromyogram (EMG) might have other explanations, such as trajectory modifications that reduce joint torques. To circumvent such complications, we required strict trajectory control and examined only successful trials having identical trajectory and torque profiles. Subjects were asked to perform a hand movement in unison with a target moving along a specified and unusual trajectory, with shoulder and elbow in the horizontal plane at the shoulder level. To evaluate joint viscoelasticity during the learning of this movement, we proposed an index of muscle co-contraction around the joint (IMCJ). The IMCJ was defined as the summation of the absolute values of antagonistic muscle torques around the joint and computed from the linear relation between surface EMG and joint torque. The IMCJ during isometric contraction, as well as during movements, was confirmed to correlate well with joint stiffness estimated using the conventional method, i.e., applying mechanical perturbations. Accordingly, the IMCJ during the learning of the movement was computed for each joint of each trial using estimated EMG-torque relationship. At the same time, the performance error for each trial was specified as the root mean square of the distance between the target and hand at each time step over the entire trajectory. The time-series data of IMCJ and performance error were decomposed into long-term components that showed decreases in IMCJ in accordance with learning with little change in the trajectory and short-term interactions between the IMCJ and performance error. A cross-correlation analysis and impulse responses both suggested that higher IMCJs follow poor performances, and lower IMCJs follow good performances within a few successive trials. Our results support the hypothesis that viscoelasticity contributes more when internal models are inaccurate, while internal models contribute more after the completion of learning. It is demonstrated that the CNS regulates viscoelasticity on a short- and long-term basis depending on performance error and finally acquires smooth and accurate movements while maintaining stability during the entire learning process.

Facilitation of learning induced by both random and gradual visuomotor task variation.

Motor task variation has been shown to be a key ingredient in skill transfer, retention, and structural learning. However, many studies only compare training of randomly varying tasks to either blocked or null training, and it is not clear how experiencing different nonrandom temporal orderings of tasks might affect the learning process. Here we study learning in human subjects who experience the same set of visuomotor rotations, evenly spaced between -60° and +60°, either in a random order or in an order in which the rotation angle changed gradually. We compared subsequent learning of three test blocks of +30°→-30°→+30° rotations. The groups that underwent either random or gradual training showed significant (P < 0.01) facilitation of learning in the test blocks compared with a control group who had not experienced any visuomotor rotations before. We also found that movement initiation times in the random group during the test blocks were significantly (P < 0.05) lower than for the gradual or the control group. When we fit a state-space model with fast and slow learning processes to our data, we found that the differences in performance in the test block were consistent with the gradual or random task variation changing the learning and retention rates of only the fast learning process. Such adaptation of learning rates may be a key feature of ongoing meta-learning processes. Our results therefore suggest that both gradual and random task variation can induce meta-learning and that random learning has an advantage in terms of shorter initiation times, suggesting less reliance on cognitive processes.

Reinforcement learning with reference tracking control in continuous state spaces

The contribution described in this paper is an algorithm for learning nonlinear, reference tracking, control policies given no prior knowledge of the dynamical system and limited interaction with the system through the learning process. Concepts from the field of reinforcement learning, Bayesian statistics and classical control have been brought together in the formulation of this algorithm which can be viewed as a form of indirect self tuning regulator. On the task of reference tracking using a simulated inverted pendulum it was shown to yield generally improved performance on the best controller derived from the standard linear quadratic method using only 30 s of total interaction with the system. Finally, the algorithm was shown to work on the simulated double pendulum proving its ability to solve nontrivial control tasks. © 2011 IEEE.

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.

Fast Perceptual Learning in Visual Hyperacuity

In many different spatial discrimination tasks, such as in determining the sign of the offset in a vernier stimulus, the human visual system exhibits hyperacuity-level performance by evaluating spatial relations with the precision of a fraction of a photoreceptor"s diameter. We propose that this impressive performance depends in part on a fast learning process that uses relatively few examples and occurs at an early processing stage in the visual pathway. We show that this hypothesis is plausible by demonstrating that it is possible to synthesize, from a small number of examples of a given task, a simple (HyperBF) network that attains the required performance level. We then verify with psychophysical experiments some of the key predictions of our conjecture. In particular, we show that fast timulus-specific learning indeed takes place in the human visual system and that this learning does not transfer between two slightly different hyperacuity tasks.

Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics

Meng, Q., & Lee, M. (2005). Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics. Wermter, S., Palm, G., & Elshaw, M. (Eds.), In: Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience. (pp. 315-332). (Lecture Notes in Computer Science). Springer Berlin Heidelberg.

Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics

Q. Meng and M. H. Lee, Novelty and Habituation: the Driving Forces in Early Stage Learning for Developmental Robotics, AI-Workshop on NeuroBotics, University of Ulm, Germany. September 2004.

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

Q. Meng and M. H. Lee, 'Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning', Journal of Intelligent and Robotic Systems, 48(1), pp 97-114, 2007.

Total quality leadership : Employing organisational learning as a conduit

Oxtoby, B.; Morgan, R.; McGuinness, T.; and Jones, M. (2001). Total quality leadership: Employing organisational learning as a conduit. International Journal of Management. 18(2), pp.245-251 RAE2008

Robert Heinich, Michael Molenda, James D. Russell, Sharon E. Smaldino, Instructional Media and Technologies for Learning, Prentice-Hall, Inc., Englewood Cliffs, New Jersey 1996, ss. 446. Steven Hackbarth, The Educational Technology Handbook. A Comprehensive Guide, Educational Technology Publications Inc., Englewood Cliffs, New Jersey 1996, ss. 351

Recenzje i sprawozdania z książek

Neural Representations for Sensory-Motor Control, III: Learning a Body-Centered Representation of 3-D Target Position

A neural model is described of how the brain may autonomously learn a body-centered representation of 3-D target position by combining information about retinal target position, eye position, and head position in real time. Such a body-centered spatial representation enables accurate movement commands to the limbs to be generated despite changes in the spatial relationships between the eyes, head, body, and limbs through time. The model learns a vector representation--otherwise known as a parcellated distributed representation--of target vergence with respect to the two eyes, and of the horizontal and vertical spherical angles of the target with respect to a cyclopean egocenter. Such a vergence-spherical representation has been reported in the caudal midbrain and medulla of the frog, as well as in psychophysical movement studies in humans. A head-centered vergence-spherical representation of foveated target position can be generated by two stages of opponent processing that combine corollary discharges of outflow movement signals to the two eyes. Sums and differences of opponent signals define angular and vergence coordinates, respectively. The head-centered representation interacts with a binocular visual representation of non-foveated target position to learn a visuomotor representation of both foveated and non-foveated target position that is capable of commanding yoked eye movementes. This head-centered vector representation also interacts with representations of neck movement commands to learn a body-centered estimate of target position that is capable of commanding coordinated arm movements. Learning occurs during head movements made while gaze remains fixed on a foveated target. An initial estimate is stored and a VOR-mediated gating signal prevents the stored estimate from being reset during a gaze-maintaining head movement. As the head moves, new estimates arc compared with the stored estimate to compute difference vectors which act as error signals that drive the learning process, as well as control the on-line merging of multimodal information.

Space, Time and Learning in the Hippocampus: How Fine Spatial and Temporal Scales Are Expanded into Population Codes for Behavioral Control

The hippocampus participates in multiple functions, including spatial navigation, adaptive timing, and declarative (notably, episodic) memory. How does it carry out these particular functions? The present article proposes that hippocampal spatial and temporal processing are carried out by parallel circuits within entorhinal cortex, dentate gyrus, and CA3 that are variations of the same circuit design. In particular, interactions between these brain regions transform fine spatial and temporal scales into population codes that are capable of representing the much larger spatial and temporal scales that are needed to control adaptive behaviors. Previous models of adaptively timed learning propose how a spectrum of cells tuned to brief but different delays are combined and modulated by learning to create a population code for controlling goal-oriented behaviors that span hundreds of milliseconds or even seconds. Here it is proposed how projections from entorhinal grid cells can undergo a similar learning process to create hippocampal place cells that can cover a space of many meters that are needed to control navigational behaviors. The suggested homology between spatial and temporal processing may clarify how spatial and temporal information may be integrated into an episodic memory.

Was ist aus dem Integrierten Handlungsmodell geworden?

In diesem Beitrag wird das von Thomas Martens und Jürgen Rost entwickelte Integrierte Handlungsmodell reflektiert, auf Lernprozesse übertragen, theoretische erweitert und beispielhaft empirisch überprüft. Zunächst werden die theoretischen Potenziale des Modells aufgezeigt, indem es auf Lernprozesse angewendet wird. Danach werden die typischen Prozesse der drei Handlungsphasen dargestellt, insbesondere die funktionslogische Integration von bewussten und unbewussten sowie kognitiven und affektiven Prozessen. Abschließend wird eine empirische Studie vorgestellt, die das Lernen von Statistik im Hochschulbereich untersucht. Mit Hilfe von Mischverteilungsmodellen werden sieben Lerntypen identifiziert, die den entsprechenden motivationalen, intentionalen und volitionalen Mustern der Lern- und Handlungsgenese entsprechen. (DIPF/Orig.)