14,161 research outputs found
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The Rumsfeld Effect: The unknown unknown
A set of studies tested whether people can use awareness of ignorance to provide enhanced test consistency over time if they are allowed to place uncertain items into a âdonât knowâ category. For factual knowledge this did occur, but for a range of other forms of knowledge relating to conceptual knowledge and personal identity, no such effect was seen. Known unknowns would appear to be largely restricted to factual kinds of knowledge
A cognitive approach to the study of ingroup bias: role of reasons
Despite considerable research on ingroup bias, there is a lack of theory to explain this phenomenon and thus a lack of understanding of the mechanism behind it. The purpose of this dissertation is to propose a new explanation of ingroup bias. In particular, the research is interested in the cognitive, rather than motivational, basis for ingroup bias. The most predominant theory of ingroup bias has been social identity theory. The theory is based on the assumption of existence of the need for a positive social identity, and is thus a motivationally oriented theory. As an alternative to social identity theory, the dissertation proposes a new approach drawn from persuasive arguments theory which is one of the major theories in the area of group polarization. A new cognitive approach contends that whilst social identity theory argues that ingroup bias originates from motivation for a positive social identity, it may not be based on such motivation or need, but rather on some cognitive process. It argues that ingroup bias may occur because of the reasoning or argumentation that people use as they think about the reasons they and others from both the ingroup and outgroup may have had for their choices, choices which were the basis for their categorization into groups. Based on persuasive arguments theory, the research investigates the effect of listing reasons, either in favor of the ingroup or outgroup\u27s choice, in producing ingroup bias and in reversing this bias using experimental method and questionnaire. The results showed that ingroup bias differed depending upon whether subjects had a chance to think again about their choice and to list reasons for the choice made either by the ingroup or the outgroup. Therefore, a new cognitive approach based on persuasive arguments theory is proved to be valid although further research is needed
Clustering by compression
We present a new method for clustering based on compression. The method
doesn't use subject-specific features or background knowledge, and works as
follows: First, we determine a universal similarity distance, the normalized
compression distance or NCD, computed from the lengths of compressed data files
(singly and in pairwise concatenation). Second, we apply a hierarchical
clustering method. The NCD is universal in that it is not restricted to a
specific application area, and works across application area boundaries. A
theoretical precursor, the normalized information distance, co-developed by one
of the authors, is provably optimal but uses the non-computable notion of
Kolmogorov complexity. We propose precise notions of similarity metric, normal
compressor, and show that the NCD based on a normal compressor is a similarity
metric that approximates universality. To extract a hierarchy of clusters from
the distance matrix, we determine a dendrogram (binary tree) by a new quartet
method and a fast heuristic to implement it. The method is implemented and
available as public software, and is robust under choice of different
compressors. To substantiate our claims of universality and robustness, we
report evidence of successful application in areas as diverse as genomics,
virology, languages, literature, music, handwritten digits, astronomy, and
combinations of objects from completely different domains, using statistical,
dictionary, and block sorting compressors. In genomics we presented new
evidence for major questions in Mammalian evolution, based on
whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta
hypothesis against the Theria hypothesis.Comment: LaTeX, 27 pages, 20 figure
The development of corpus-based computer assisted composition program and its application for instrumental music composition
In the last 20 years, we have seen the nourishing environment for the development of
music software using a corpus of audio data expanding significantly, namely that synthesis
techniques producing electronic sounds, and supportive tools for creative activities
are the driving forces to the growth. Some software produces a sequence of sounds by
means of synthesizing a chunk of source audio data retrieved from an audio database
according to a rule. Since the matching of sources is processed according to their descriptive
features extracted by FFT analysis, the quality of the result is significantly
influenced by the outcomes of the Audio Analysis, Segmentation, and Decomposition.
Also, the synthesis process often requires a considerable amount of sample data and
this can become an obstacle to establish easy, inexpensive, and user-friendly applications
on various kinds of devices. Therefore, it is crucial to consider how to treat the
data and construct an efficient database for the synthesis. We aim to apply corpusbased
synthesis techniques to develop a Computer Assisted Composition program, and
to investigate the actual application of the program on ensemble pieces. The goal of
this research is to apply the program to the instrumental music composition, refine its
function, and search new avenues for innovative compositional method
Dimensionality Reduction of very large document collections by Semantic Mapping
This paper describes improving in Semantic Mapping, a feature extraction method useful to dimensionality reduction of vectors representing documents of large text collections. This method may be viewed as a specialization of the Random Mapping, method proposed in WEBSOM project. Semantic Mapping, Random Mapping and Principal Component Analysis (PCA) are applied to categorization of document collections using Self-Organizing Maps (SOM). Semantic Mapping generated document representation as good as PCA and much better than Random Mapping
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Evaluating the psychometric properties of the multigroup ethnic identity measure (MEIM) within the United Kingdom
In the present study, we examined the psychometric properties of the Multigroup Ethnic Identity Measure (MEIM) (Phinney, 1992; Phinney & Alipuria, 1990) among an ethnically diverse sample within the United Kingdom. In initial analyses, we evaluated the goodness-of-fit of a one-factor model (i.e., global ethnic identity) and the goodness-of-fit of a two-factor model (i.e., correlated but distinct Exploration and Commitment components). Results of initial confirmatory factor analyses led us to reject both the one-factor and two-factor models. Results of subsequent exploratory and confirmatory factor analyses revealed a three-factor structure (i.e., correlated but distinct Behavioral, Cognitive, and Affective components of ethnic identity) among the sample as a whole (n = 234) and among Asian Indian persons (n = 88) in particular, though resulst were mixed among White U.K./Irish persons (n = 54) in particular. Implications for the study of ethnicity-related concepts in the incerasibgly multi-cultural U.K. are discussed
Down the (white) rabbit hole: the extreme right and online recommender systems
In addition to hosting user-generated video content, YouTube provides recommendation services, where sets of related and recommended videos are presented to users, based on factors such as co-visitation count and prior viewing history. This article is specifically concerned with extreme right (ER) video content, portions of which contravene hate laws and are thus illegal in certain countries, which are recommended by YouTube to some users. We develop a categorization of this content based on various schema found in a selection of academic literature on the ER, which is then used to demonstrate the political articulations of YouTubeâs recommender system, particularly the narrowing of the range of content to which users are exposed and the potential impacts of this. For this purpose, we use two data sets of English and German language ER YouTube channels, along with channels suggested by YouTubeâs related video service. A process is observable whereby users accessing an ER YouTube video are likely to be recommended further ER content, leading to immersion in an ideological bubble in just a few short clicks. The evidence presented in this article supports a shift of the almost exclusive focus on users as content creators and protagonists in extremist cyberspaces to also consider online platform providers as important actors in these same spaces
Online Social Networksâ Investigations of Individualsâ Healthy and Unhealthy Lifestyle Behaviors and Social Factors Influencing Them âThree Essays
More than half of U.S. adults suffer from one or more chronic diseases, which account for 86% of total U.S. healthcare costs. Major contributors to chronic diseases are unhealthy lifestyle behaviors, which include lack of physical activity, poor nutrition, tobacco use, and drinking too much alcohol. A reduction in the prevalence of health-risk behaviors could improve individualsâ longevity and quality of life and may halt the exponential growth of healthcare costs. Prior studies in the field have acknowledged that a comprehensive understanding of health behaviors requires the examination of individualâ behaviors in supra-dyadic social networks. In recent years, the growth of online social networks and popularity of location-based services have opened new research opportunities for observational studies on individualsâ healthy and unhealthy lifestyle behaviors. The goal of this three-essay dissertation is to examine the effect of various social factors, shared images, and communities of interest on healthy and unhealthy lifestyle behaviors of individuals. This dissertation makes novel contributions in terms of theoretical implications, data collection and analysis methods, and policy implications for promoting healthy lifestyle behaviors and inhibiting unhealthy behaviors.
Essay 1 draws on a synthesis of social cognitive and social network theories to conceptualize a causal model for healthy and unhealthy behaviors. To test the conceptualized model, we developed a new methodâdynamic sequential data extraction and integrationâto collect and integrate data over time from Twitter and Foursquare. The captured dataset was then combined with relevant data from the U.S. Census Bureau. The final dataset has more than 32,000 individuals from all states in the United States. Using this dataset, we derived variables to measure healthy and unhealthy lifestyle behaviors and metrics for factors representing individualsâ social support, social influence, and homophily, as well as the socioeconomic status of the communities where they live. To capture the impacts of social factors, we collected individualsâ behaviors in two separate time periods. We used zero-inflated negative binomial regression method for data analysis. The results of this study uncover factors that have significant impacts on healthy and unhealthy lifestyle behaviors.
Essay 2 focuses on embedded images in self-disclosed posts related to healthy and unhealthy lifestyle behaviors. While online photo-sharing has become widely popular, and neuroscience has reported the influence of images in brain activities, to our knowledge, there is no published research on the impacts of shared photos on health-related lifestyle behaviors. This study addresses this gap and examines the moderating role of shared images and the direct impacts of their contents. We relied on social learning and multimodality theories to argue that images can attract individualsâ attention and enhance the process of observational learning in online social networks. We developed a novel method for image analysis that involves the extraction, processing, dimensionality reduction, and categorization of images. The results show that the presence of photos in self-disclosed unhealthy lifestyle behaviors positively moderates friendsâ social influence. Moreover, the results indicate that the contents of shared photos influence individualsâ health-related behaviors.
Essay 3 focuses on the role of personal interests in individualsâ health-related lifestyle behaviors. Prior studies have demonstrated that health promotional programs can benefit from targeting individuals based on their interests. Specifically, prior studies have emphasized the role of interests as a factor influencing behaviors. However, current literature suffers from two major gaps. First, there is no systematic and comprehensive approach to capture individualsâ interests in online social networks. Second, to our knowledge, the role of interests in individualsâ healthy and unhealthy lifestyle behaviors as disclosed online has not been investigated. To address these gaps, we examine the role of individualsâ interests in their health-related behaviors. The theoretical foundation of this study is a synthesis of homophily and self-determination theories. We developed a novel methodâthe homophily-based interest detection methodâthat involves network simplification, network clustering, cluster labeling, and interest metrics. This method was applied to social networks of individuals in Essay 1 to measure individualsâ interests. The results show that health-related interests are associated with individualsâ healthy and unhealthy lifestyle behaviors. Our findings indicate that other forms of interest, such as music taste and political views, also play a role. Moreover, our results show that belonging to healthy (unhealthy) communities of interest has an inhibitive role that prevents postings of unhealthy (healthy) behaviors
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