1,721,058 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
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Understanding Human Behavior Using Language and BOLD Variability
Get PDFThis work consists of four projects that explore human behavior from two perspectives: language use and neural patterns.In my first and second projects, I focused on language, which can be used to categorize human behavior. In the first project, I used topic models to categorize the subjects and symptoms a patient discussed during psychotherapy treatment. The model functions by identifying topics that are representative of each subject or symptom. The model can predict the subjects and symptoms discussed in new therapy sessions with higher accuracy than discriminative techniques. Furthermore, the model can identify specific passages of text representative of a given subject or symptom.My second project developed an automated system for routing citizen requests to federal agencies within the Mexican government. The automated system functions by linking pat- terns in language and the appropriate federal agency. The automated system routes requests more efficiently than the current routing system.The third and fourth projects focused on neuroimaging, which is used to understand the underlying neural processes associated with human behavior. My neuroimaging work related blood-oxygen-level-dependent (BOLD) variability (BV) to experimental condition, behavior, and subject identity. The first phase of the neural work built on previous analyses showing that functional connectivity (FC) is predictive of the task a subject is performing and the identity of the subject performing a task. We extended these analyses to BV and compared its predictive accuracy with that of FC to assess whether some of the predictive power of FC is due to changes in BV. BV performed well compared to FC, suggesting that some of the predictive performance based on FC might be attributed to independent region-specific fluctuations.Given the predictive relationship between BV and task/subject, the second phase of my neu- ral work developed the Variance Design General Linear Model (VDGLM), a novel framework to facilitate the detection of BV effects. The framework models the mean and variance in the BOLD time course as functions of experimental design. This allows the VDGLM to i) simul- taneously make inferences about a mean or variance effect while controlling for the other and ii) test for variance effects that could be associated with multiple conditions and/or noise regressors. We demonstrated the use of the VDGLM in a working memory application and showed that engagement in a working memory task is associated with whole-brain decreases in BOLD variance
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Metacognitively Wise Crowds
Get PDFAggregates of many judgments tend to outperform each of the individual judgments that compose the aggregate, termed the Wisdom of Crowds effect. Metacognition has played an understudied role in the efficacy of these crowds and so in a series of experiments I explore how metacognition and self-direction can be used to improve crowd wisdom. I first demonstrate empirically that individuals can leverage their metacognitive abilities to improve the performance of crowds when they are allowed to opt-in to questions of their choosing. I develop a Bayesian framework wherein latent contextual knowledge describes how crowd members make opt-in decisions to elucidate the relationship between these cognitive and metacognitive processes. I then show that metacognitive ability can be estimated by asking questions with no correct response options and create metacognitively wise crowds which achieve more accurate responses despite incorporating fewer crowd members. I discuss my contributions to a geopolitical forecasting competition in which I developed models that combine human and algorithmic judgments to create highly accurate forecasts of the future. In this competition, I evaluated the effects of attenuating forecaster self-direction in an applied setting. These findings collectively demonstrate the importance of metacognition in forming accurate aggregate judgments and clarify the underlying metacognitive processes involved in self-direction
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
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Language-Based Learning: Cognitive and Computational Perspective
Get PDFThis thesis focuses on a challenging and long-standing problem of learning from language, in other words, how humans or machines may use language to share and acquire knowledge. The work has three distinct parts. First, I review how different disciplines define and approach the problem of learning from language and argue that a number of areas in Cognitive Science and Computer Science research have recently advanced enough to begin to tackle this challenge. Second, I present a series of three behavioral experiments studying the problem of learning from language in the context of pedagogical category communication. The experiments demonstrate the flexibility of verbal communication as a means for sharing category knowledge, as well as the advantage of mixing communication media (verbal and exemplar-based) as opposed to relying on any one isolated channel. In the last part of the dissertation, I focus on the question of how modern AI architectures can be adapted and applied to the problem of lifelong learning from language. In particular, I identify the types of operations that the model should be able to make, and propose a training procedure and an architecture that support learning such operations in an end-to-end fashion. I test the architecture on a number of simulated non-linguistic domains, leaving its NLP applications to future research. Although it is only a small step towards creating a fully functioning learning from language model, I still believe that this step is important
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Insights into Problem Solving, Algorithm Aversion, and Theory of Mind
Get PDFThis dissertation explores several important topics in the cognitive sciences: Insight, algorithm aversion, and theory of mind. First, I tackle the challenge of understanding insight, or the “aha!” experience. In Chapter 1, I use a well-defined class of problems (compound remote associates: Bowden and Jung-Beeman, 2003; Mednick, 1962) to test if various lexical and morphological properties affect solution retrieval and the likelihood of insight. While performance is only affected by one property (familiarity), other findings contest popular assumptions about insight. Namely, the reported magnitude of insight decreases with trial time (challenging the impasse hypothesis) and increases with the number of cues solved (challenging the all-or-none hypothesis). In Chapter 2, I introduce a new insight problem task: Joke completion. I find that performance and magnitude of insight within it correlate with an established task (rebus puzzles: MacGregor and Cunningham, 2009), though the distribution of reported insight is not bimodal, as was expected. Further, self-estimated and externally-rated joke funniness correlate with reported insight. Lastly, performance and reported insight decrease with trial time, again refuting impasse. In Chapter 3, I shift focus to a more recent concern: Algorithm aversion. As AI becomes an integral part of our daily lives, it is crucial to identify and anticipate biases regarding it. Since aversion mostly occurs in subjective contexts, I test whether people find jokes less humorous if they believe an AI created them. When joke source is ambiguous, people exhibit bias toward jokes they identify as human-created. However, this bias disappears when the (purported) source of jokes is stated. This demonstrates that such biases are weaker than proposed and are dependent on framing. In Chapter 4, I conclude by exploring another perennial topic: Theory of mind. Namely, I examine whether a few words provide an accurate estimate of another person's domain knowledge. This was done by having one group of people ("informants") describe images depicting various domains (e.g., video games, astronomy), then having a second group ("evaluators") make pairwise comparisons between these informants regarding who they believe is more knowledgeable, based on these descriptions. Strikingly, evaluators perform above chance at identifying the more knowledgeable informants when only one description is available (around seven words, on average). Further, the most knowledgeable informants produce the most specific facts and the most knowledgeable evaluators are the most sensitive to false information. However, less knowledgeable evaluators treat specific statements interchangeably, regardless of their factuality. These results show the inferential power a mere few words hold
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