1,721,020 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Learning Algorithms For Biologically Plausible Recurrent Neural Networks
Get PDFNeural networks underlie a wide range of ongoing research in the fields of Applied Mathe- matics, Statistics, and Computer Science. Originally inspired by early observations of biological neurons, these structures are capable of performing complex tasks despite having fairly simple in- dividual components. Since their original conception, the study of neural networks has revealed many categories of relevant network structures for modeling and solving statistical problems, some of which have been found to excel at particular tasks like classification and prediction once appropri- ate weights have been learned. Accordingly, determining ideal network architecture and methods by which weights (network connections) can be trained are fundamental problems in this field. Many have observed that the similarities between artificial neural networks and biological neural networks essentially end with biology inspiring the artificial, but this need not be the case. By considering artificial neural networks that share topological network features with their biological counterparts, we work incrementally towards development of models of how biological learning might occur. In this work, we reproduce several prior results of novel learning techniques applied to recurrent neural networks, demonstrating the ability of FORCE learning to reproduce patterns in both input driven and input-free environments and the EMPJ method of translating low dimensional dynamics into a higher dimensional recurrent neural network. We then extend those results, exploring whether FORCE can learn to perform a simple working memory task, if EMPJ works for one dimensional dynamics, and consider how these structures and methods fit into the overall goal of achieving biologically plausible recurrent neural networks, with features that are entirely in agreement with biological neural networks. That is to say, networks that respect Dale’s law, are highly recurrent, do not rely on global knowledge, and are able to develop coherent patterns amidst the time delayed chaotic instabilities previously observed in highly recurrently connected networks.</p
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Optimal Decision Making Models in Changing Environments
No full textMathematical decision making theory has been successfully applied to the neuroscience of sensation, behavior, and cognition, for more than fifty years. Classical models rely on the assumption that the environment doesn't change during the period of observation. This assumption has been relaxed in more recent studies of adaptive decision making. We develop new ideal observer -- Bayes-optimal -- models for this latter setting; and more specifically for the case in which temporal integration of noisy evidence improves choice accuracy. The generative model of the stimulus is a Hidden Markov Model that the ideal observer must filter, and more generally learn. In a first part, we derive and study models tailored to pulsatile evidence with Poisson-distributed timing. We characterize the model parameters that determine choice accuracy, and compare the ideal observer to a finely tuned linear-leak model. We show that the linear model is both more sensitive to parameter perturbation and easier to fit to choice data. In a second part, we derive Bayes-optimal models that learn the change rates of their environment. We do so in several configurations: in discrete time, in continuous time, when more than one change rate must be learned, and for both pulsatile and continuously arriving, drift-diffusion type evidence. We find that such learning models may outperform wrongly tuned known-hazard rate models, but are hard to implement computationally. We conclude that the mathematical study of optimal decision making is crucial for at least three reasons. First, it helps develop an intuition about the various computations required to perform a task. Second, Bayes-optimal models allow benchmarking accuracy and other dependent variables from experiments. Finally, from them, approximate schemes may be built, hopefully taking us one step closer to understanding the human brain.Mathematics, Department o
How trial correlations and feedback shape sequential decision-making
No full textTo make the best decisions, organisms must flexibly accumulate information, accounting for what is relevant and ignoring what is not. Many decision-making studies focus on sequences of independent trials in which the evidence gathered to make a choice, as well as the resulting actions and feedback, are irrelevant to future decisions. Two-alternative forced choice tasks (2AFC) are often used to characterize strategies subjects use to make decisions. Normative theories, which model ideal observers, have been developed for such tasks when rewards provide the sole evidence (e.g., two-armed bandit tasks). Less is known about how observers should integrate probabilistic rewards interspersed with noisy evidence to inform their decisions in future correlated trials. To understand decision-making under more natural conditions, we extend drift-diffusion models to obtain the normative form of evidence accumulation in a series of 2AFC trials with the correct choice evolving as a two-state Markov process. We analyze 3 different feedback cases: withholding trial-to-trial feedback, giving probabilistic trial-to-trial signal, and giving probabilistic trial-to-trial reward. Ideal observers integrate noisy evidence within a trial until reaching a decision threshold and bias their initial belief depending on the evidence accumulated and feedback received on previous trials. Optimal observers accumulate more evidence on early trials and make faster decisions on later trials. Gains in performance are primarily due to biases in initial beliefs that lead to faster decisions even when feedback is lacking. Feedback shapes trial-to-trial decision strategies determining whether decisions are immediate, or a result of past and present evidence, depending on whether the feedback is strong enough to overcome the volatility of changes between trials. Our findings are also consistent with experimentally observed response trends, showing decreased reaction times when correct choices are repeated and in response to prior trial rewards.Mathematics, Department o
Learning Algorithms For Biologically Plausible Recurrent Neural Networks
Get PDFNeural networks underlie a wide range of ongoing research in the fields of Applied Mathe- matics, Statistics, and Computer Science. Originally inspired by early observations of biological neurons, these structures are capable of performing complex tasks despite having fairly simple in- dividual components. Since their original conception, the study of neural networks has revealed many categories of relevant network structures for modeling and solving statistical problems, some of which have been found to excel at particular tasks like classification and prediction once appropri- ate weights have been learned. Accordingly, determining ideal network architecture and methods by which weights (network connections) can be trained are fundamental problems in this field. Many have observed that the similarities between artificial neural networks and biological neural networks essentially end with biology inspiring the artificial, but this need not be the case. By considering artificial neural networks that share topological network features with their biological counterparts, we work incrementally towards development of models of how biological learning might occur. In this work, we reproduce several prior results of novel learning techniques applied to recurrent neural networks, demonstrating the ability of FORCE learning to reproduce patterns in both input driven and input-free environments and the EMPJ method of translating low dimensional dynamics into a higher dimensional recurrent neural network. We then extend those results, exploring whether FORCE can learn to perform a simple working memory task, if EMPJ works for one dimensional dynamics, and consider how these structures and methods fit into the overall goal of achieving biologically plausible recurrent neural networks, with features that are entirely in agreement with biological neural networks. That is to say, networks that respect Dale’s law, are highly recurrent, do not rely on global knowledge, and are able to develop coherent patterns amidst the time delayed chaotic instabilities previously observed in highly recurrently connected networks.</p
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