957 resultados para Function Learning


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The encoding of goal-oriented motion events varies across different languages. Speakers of languages without grammatical aspect (e.g., Swedish) tend to mention motion endpoints when describing events, e.g., “two nuns walk to a house,”, and attach importance to event endpoints when matching scenes from memory. Speakers of aspect languages (e.g., English), on the other hand, are more prone to direct attention to the ongoingness of motion events, which is reflected both in their event descriptions, e.g., “two nuns are walking.”, and in their non-verbal similarity judgements. This study examines to what extent native speakers of Swedish (n = 82) with English as a foreign language (FL) restructure their categorisation of goal-oriented motion as a function of their English proficiency and experience with the English language (e.g., exposure, learning). Seventeen monolingual native English speakers from the United Kingdom (UK) were engaged for comparison purposes. Data on motion event cognition were collected through a memory-based triads matching task, in which a target scene with an intermediate degree of endpoint orientation was matched with two alternative scenes with low and high degrees of endpoint orientation, respectively. Results showed that the preference among the Swedish speakers of L2 English to base their similarity judgements on ongoingness rather than event endpoints was correlated with their use of English in their everyday lives, such that those who often watched television in English approximated the ongoingness preference of the English native speakers. These findings suggest that event cognition patterns may be restructured through the exposure to FL audio-visual media. The results thus add to the emerging picture that learning a new language entails learning new ways of observing and reasoning about reality.

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The aim of the current study is to investigate motion event cognition in second language learners in a higher education learning context. Based on recent findings showing that speakers of grammatical aspect languages like English attend less to the endpoint (goal) of events than speakers of non-aspect languages like Swedish in a nonverbal categorization task involving working memory (Athanasopoulos & Bylund, 2013; Bylund & Athanasopoulos, this issue), the current study asks whether native speakers of an aspect language start paying more attention to event endpoints when learning a non-aspect language. Native English and German (a non-aspect language) speakers, and English learners of L2 German, who were pursuing studies in German language and literature at an English university, were asked to match a target scene with intermediate degree of endpoint orientation with two alternate scenes with low and high degree of endpoint orientation, respectively. Results showed that, when compared to the native English speakers, the learners of German were more prone to base their similarity judgements on endpoint saliency, rather than ongoingness, primarily as a function of increasing L2 proficiency and year of university study. Further analyses revealed a non-linear relationship between length of L2 exposure and categorization patterns, subserved by a progressive strengthening of the relationship between L2 proficiency and categorization as length of exposure increased. These findings present evidence that cognitive restructuring may occur through increasing experience with an L2, but also suggest that this relationship may be complex, and unfold over a long period of time.

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The increase in incidence and prevalence of neurodegenerative diseases highlights the need for a more comprehensive understanding of how food components may affect neural systems. In particular, flavonoids have been recognized as promising agents capable of influencing different aspects of synaptic plasticity resulting in improvements in memory and learning in both animals and humans. Our previous studies highlight the efficacy of flavonoids in reversing memory impairments in aged rats, yet little is known about the effects of these compounds in healthy animals, particularly with respect to the molecular mechanisms by which flavonoids might alter the underlying synaptic modifications responsible for behavioral changes. We demonstrate that a 3-week intervention with two dietary doses of flavonoids (Dose I: 8.7 mg/day and Dose II: 17.4 mg/day) facilitates spatial memory acquisition and consolidation (24 recall) (p < 0.05) in young healthy rats. We show for the first time that these behavioral improvements are linked to increased levels in the polysialylated form of the neural adhesion molecule (PSA-NCAM) in the dentate gyrus (DG) of the hippocampus, which is known to be required for the establishment of durable memories. We observed parallel increases in hippocampal NMDA receptors containing the NR2B subunit for both 8.7 mg/day (p < 0.05) and 17.4 mg/day (p < 0.001) doses, suggesting an enhancement of glutamate signaling following flavonoid intervention. This is further strengthened by the simultaneous modulation of hippocampal ERK/CREB/BDNF signaling and the activation of the Akt/mTOR/Arc pathway, which are crucial in inducing changes in the strength of hippocampal synaptic connections that underlie learning. Collectively, the present data supports a new role for PSA-NCAM and NMDA-NR2B receptor on flavonoid-induced improvements in learning and memory, contributing further to the growing body of evidence suggesting beneficial effects of flavonoids in cognition and brain health.

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This paper explores the role of digital media and creativity in the processes of learning that occur in groups of urban skateboarders. In particular, it examines how the production and consumption of amateur videos contribute to both skaters’ mastery of the techniques of the sport and their integration into the culture of the sport. The data come from an ethnographic study of skateboarders in Hong Kong, which included in-depth interviews, participant observation and the collection of texts and artifacts like magazines, blog entries and amateur skating videos. Skateboarders use video in a number of ways that significantly impact their learning and integration into their communities. They use it to analyze tricks and techniques, to document the stages of their learning and socialization into the group, to set community standards, to build a sense of belonging with their ‘crews’ and to imagine ‘idealized futures’ for themselves and their communities. Understanding the value and function of such ‘semiotic mediation’ in learning and socialization into sport cultures, I suggest, can contribute to helping physical educators design tasks that integrate training in physical skills with opportunities for students to make meaning around their experiences of sport and physical education.

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The assertion of identity and power via computer-mediated communication in the context of distance or web-based learning presents challenges to both teachers and students. When regular, face-to-face classroom interaction is replaced by online chat or group discussion forums, participants must avail themselves of new techniques and tactics for contributing to and furthering interaction, discussion, and learning. During student-only chat sessions, the absence of teacher-led, face-to-face classroom activities requires the students to assume leadership roles and responsibilities normally associated with the teacher. This situation raises the questions of who teaches and who learns; how students discursively negotiate power roles; and whether power emerges as a function of displayed expertise and knowledge or rather the use of authoritative language. This descriptive study represents an examination of a corpus of task-based discussion logs among Vietnamese students of distance learning courses in English linguistics. The data reveal recurring discourse strategies for 1) negotiating the progression of the discussion sessions, 2) asserting and questioning knowledge, and 3) assuming or delegating responsibility. Power is defined ad hoc as the ability to successfully perform these strategies. The data analysis contributes to a better understanding of how working methods and materials can be tailored to students in distance learning courses, and how such students can be empowered by being afforded opportunities and effectively encouraged to assert their knowledge and authority.

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Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.

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On-line learning methods have been applied successfully in multi-agent systems to achieve coordination among agents. Learning in multi-agent systems implies in a non-stationary scenario perceived by the agents, since the behavior of other agents may change as they simultaneously learn how to improve their actions. Non-stationary scenarios can be modeled as Markov Games, which can be solved using the Minimax-Q algorithm a combination of Q-learning (a Reinforcement Learning (RL) algorithm which directly learns an optimal control policy) and the Minimax algorithm. However, finding optimal control policies using any RL algorithm (Q-learning and Minimax-Q included) can be very time consuming. Trying to improve the learning time of Q-learning, we considered the QS-algorithm. in which a single experience can update more than a single action value by using a spreading function. In this paper, we contribute a Minimax-QS algorithm which combines the Minimax-Q algorithm and the QS-algorithm. We conduct a series of empirical evaluation of the algorithm in a simplified simulator of the soccer domain. We show that even using a very simple domain-dependent spreading function, the performance of the learning algorithm can be improved.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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This four-experiment series sought to evaluate the potential of children with neurosensory deafness and cochlear implants to exhibit auditory-visual and visual-visual stimulus equivalence relations within a matching-to-sample format. Twelve children who became deaf prior to acquiring language (prelingual) and four who became deaf afterwards (postlingual) were studied. All children learned auditory-visual conditional discriminations and nearly all showed emergent equivalence relations. Naming tests, conducted with a subset of the: children, showed no consistent relationship to the equivalence-test outcomes.. This study makes several contributions: to the literature on stimulus equivalence. First; it demonstrates that both pre- and postlingually deaf children-can: acquire auditory-visual equivalence-relations after cochlear implantation, thus demonstrating symbolic functioning. Second, it directs attention to a population that may be especially interesting for researchers seeking to analyze the relationship. between speaker and listener repertoires. Third, it demonstrates the feasibility of conducting experimental studies of stimulus control processes within the limitations of a hospital, which these children must visit routinely for the maintenance of their cochlear implants.

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Studies have demonstrated that nutrient deficiency during pregnancy or in early postnatal life results in structural abnormalities in the offspring hippocampus and in cognitive impairment. In an attempt to analyze whether gestational protein restriction might induce learning and memory impairments associated with structural changes in the hippocampus, we carried out a detailed morphometric analysis of the hippocampus of male adult rats together with the behavioral characterization of these animals in the Morris water maze (MWM). Our results demonstrate that gestational protein restriction leads to a decrease in total basal dendritic length and in the number of intersections of CA3 pyramidal neurons whereas the cytoarchitecture of CA1 and dentate gyrus remained unchanged. Despite presenting significant structural rearrangements, we did not observe impairments in the MWM test. Considering the clear dissociation between the behavioral profile and the hippocampus neuronal changes, the functional significance of dendritic remodeling in fetal processing remains undisclosed. © 2012 ISDN.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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A self-learning simulated annealing algorithm is developed by combining the characteristics of simulated annealing and domain elimination methods. The algorithm is validated by using a standard mathematical function and by optimizing the end region of a practical power transformer. The numerical results show that the CPU time required by the proposed method is about one third of that using conventional simulated annealing algorithm.

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Because GABA(A) receptors containing alpha 2 subunits are highly represented in areas of the brain, such as nucleus accumbens (NAcc), frontal cortex, and amygdala, regions intimately involved in signaling motivation and reward, we hypothesized that manipulations of this receptor subtype would influence processing of rewards. Voltage-clamp recordings from NAcc medium spiny neurons of mice with alpha 2 gene deletion showed reduced synaptic GABA(A) receptor-mediated responses. Behaviorally, the deletion abolished cocaine`s ability to potentiate behaviors conditioned to rewards (conditioned reinforcement), and to support behavioral sensitization. In mice with a point mutation in the benzodiazepine binding pocket of alpha 2-GABA(A) receptors (alpha 2H101R), GABAergic neurotransmission in medium spiny neurons was identical to that of WT (i.e., the mutation was silent), but importantly, receptor function was now facilitated by the atypical benzodiazepine Ro 15-4513 (ethyl 8-amido-5,6-dihydro-5-methyl-6-oxo-4H-imidazo [1,5-a] [1,4] benzodiazepine-3-carboxylate). In alpha 2H101R, but not WT mice, Ro 15-4513 administered directly into the NAcc-stimulated locomotor activity, and when given systemically and repeatedly, induced behavioral sensitization. These data indicate that activation of alpha 2-GABA(A) receptors (most likely in NAcc) is both necessary and sufficient for behavioral sensitization. Consistent with a role of these receptors in addiction, we found specific markers and haplotypes of the GABRA2 gene to be associated with human cocaine addiction.

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The associationist account for early word learning is based on the co-occurrence between referents and words. Here we introduce a noisy cross-situational learning scenario in which the referent of the uttered word is eliminated from the context with probability gamma, thus modeling the noise produced by out-of-context words. We examine the performance of a simple associative learning algorithm and find a critical value of the noise parameter gamma(c) above which learning is impossible. We use finite-size scaling to show that the sharpness of the transition persists across a region of order tau(-1/2) about gamma(c), where tau is the number of learning trials, as well as to obtain the learning error (scaling function) in the critical region. In addition, we show that the distribution of durations of periods when the learning error is zero is a power law with exponent -3/2 at the critical point. Copyright (C) EPLA, 2012

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Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012