Cultural Influences on Categorization Processes
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Chiu (1972) reported that in a categorization task, Chinese children were more likely to categorize objects based on shared relationships, whereas American children were more likely to categorize objects based on similarity. This research examines whether such findings generalize to adults and whether cultural differences would also be observed in the activation of semantic concepts. In Experiment 1, Chinese adults were equally likely to categorize based on relationships and similarity, whereas Western adults were more likely to categorize based on similarity. Analogous differences in response latencies were observed in a timed task that reflected semantic processing in Experiment 2, and to some extent in a slightly different task in Experiment 3, although differences between the two experiments suggest that the nature of the categorization task determines the extent to which cultural differences are observed. Overall, results suggest that differences in categorization styles are associated with differences in semantic activation.Keywords:
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Abstract Cognitive science invokes semantic networks to explain diverse phenomena, from memory retrieval to creativity. Research in these areas often assumes a single underlying semantic network that is shared across individuals. Yet, recent evidence suggests that content, size, and connectivity of semantic networks are experience-dependent, implying sizable individual and age-related differences. Here, we investigate individual and age differences in the semantic networks of younger and older adults by deriving semantic networks from both fluency and similarity rating tasks. Crucially, we use a megastudy approach to obtain thousands of similarity ratings per individual to allow us to capture the characteristics of individual semantic networks. We find that older adults possess lexical networks with smaller average degree and longer path lengths relative to those of younger adults, with older adults showing less interindividual agreement and thus more unique lexical representations relative to younger adults. Furthermore, this approach shows that individual and age differences are not evenly distributed but, rather, are related to weakly connected, peripheral parts of the networks. All in all, these results reveal the interindividual differences in both the content and the structure of semantic networks that may accumulate across the life span as a function of idiosyncratic experiences.
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Categorization is the act of responding differently to objects or events in separate classes or categories. It is a vitally important skill that allows us to approach friends and escape foes, to find food, and avoid toxins. The scientific study of categorization has a long history. For most of this time, the focus was on the cognitive processes that mediate categorization. Within the past decade, however, considerable attention has shifted to the study of the neural basis of categorization. This chapter reviews that work. It begins with a brief overview of the basal ganglia, which are a collection of subcortical nuclei that are especially important in categorization. It then focuses on initial category learning and considers the neural basis of automatic categorization judgements.
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Several researchers have reported that learning a particular categorization leads to compatible changes in the similarity structure of the categorized stimuli. The purpose of this study is to examine whether different category structures may lead to greater or less corresponding similarity change. We created six category structures and examined changes in similarity within categories or between categories, as a result of categorization, in between-participant conditions. The best supported hypothesis was that the ease of learning a categorization affects change in within-categories similarity, so that greater (within-categories) similarity change was observed for category structures that were harder to learn.
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Word similarity computing is a crucial question in information processing technology. In this paper, an integrated word similarity computing method is proposed by analyzed morpheme's similarity, word order's similarity and word length's similarity, and parameters of the method are decided by experiments. The experiments show that this method has high efficiency.
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This paper explains about similarity measure and the relationship between the knowledge repositories. This paper also describes the significance of document similarity measures, algorithms and to which type of text it can be applied Document similarity measures are of full text similarity, paragraph similarity, sentence similarity, semantic similarity, structural similarity and statistical measures. Two different frameworks had been proposed in this paper, one for measuring document to document similarity and the other model which measures similarity between documents to multiple documents. These two proposed models can use any one of the similarity measures in implementation aspect, which is been put forth for further research.
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In recent years, research on measuring trajectory similarity has attracted a lot of attentions. Most of similarities are defined based on the geographic features of mobile users' trajectories. However, trajectories geographically close may not necessarily be similar because the activities implied by nearby landmarks they pass through may be different. In this paper, we argue that a better similarity measurement should have taken into account the semantics of trajectories. In this paper, we propose a novel approach for recommending potential friends based on users' semantic trajectories for location-based social networks. The core of our proposal is a novel trajectory similarity measurement, namely, Maximal Semantic Trajectory Pattern Similarity (MSTP-Similarity), which measures the semantic similarity between trajectories. Accordingly, we propose a user similarity measurement based on MSTP-Similarity of user trajectories and use it as the basis for recommending potential friends to a user. Through experimental evaluation, the proposed friend recommendation approach is shown to deliver excellent performance.
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The article looks at idioms as categorization means. On the basis of linguistic analysis of semantic organization of idioms two patterns of idiomatic categorization are argued — general categorization and relevant property based categorization. Cognitive functions of idioms differ with regard to their role as categorization means, idioms can serve different categorization purposes according to two general cognitive processes — static and dynamic — including in a category or considering the given qualities as the reasons for categorization. Moreover, the purpose of categorization was investigated with defining the specificity of the phenomena and its types. The categorization purpose was conceived as different types of information e.g. behavioral expectations or interaction models with the object. The cause-effect relationship between the category and the categorization purpose was claimed.
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