Downloads & Free Reading Options - Results

The aim of the research is to determine the peculiarities of the evaluative categorization of human intelligence in linguistic world images. The study describes the interdisciplinary approach to studying evaluative categorization, which assumes the use of complex methodology including the anthropocentric, the interdisciplinary, and the cognitive principles. The paper suggests a modified cognitive procedure of evaluation, based on determining and using cognitive classification features, and differential abilities of subjects, based on the consideration of their ethnic experience. Research findings can be used in studies on the evaluative categorization of any objects of reality and during the systematization of evaluative linguistic phenomena as a modus representation of knowledge of objects and phenomena in the world.

Generic language (e.g., “Bears hibernate in the winter”, “Dogs bark”) makes broad statements about category properties that need not be true for all category members, although they are often interpreted as so. This form of language has important implications for conceptualizing social categories (e.g., Gelman, Ware, & Kleinberg, 2010; Rhodes, Leslie, & Tworek, 2012; Rhodes, Leslie, Bianchi, & Chalik, 2018; Noyes & Keil, 2019). For example, hearing generic statements highlights features as central to the category (Cimpian & Markman, 2009; Hollander, Gelman, & Raman, 2009), inducing essentialist assumptions about categories as richly structured and inductive (Rhodes, Leslie, & Tworek, 2012; but also see Noyes & Keil, 2019). Additionally--and most relevant for the present study--generic language facilitates toddlers’ learning about novel social groups in cases where other forms of language have not been enough (Rhodes et al., 2018). However, most previous studies were conducted with English-speaking participants, leaving the question open whether the effect of generics on social categorization generalize broadly to other linguistic contexts. To address this issue, we will test the role of generic language on social categorization in Chinese-speaking children. The Chinese language provides a theoretically interesting comparison with the English language due to their different linguistic formats of generics. In English, generics are often expressed in bare plural noun phrases (e.g., “Bunnies eat carrots”) or indefinite singular noun phrases (e.g., “A bunny eats carrots”). However, some linguistic features of the Chinese language, such as the lack of plurality and definite/indefinite articles, make the linguistic distinctions between generics and non-generic noun phrases much more ambiguous (Tardif et al., 2012). This linguistic ambiguity may delay the development of sensitivity to generic languages in Chinese-speaking children (Tardif et al., 2012). The current study aims to investigate the role of generics on social category learning in Chinese-speaking toddlers. First, we will replicate results from Rhodes and colleagues (2018) by testing an online sample of American children aged between 28 to 36 months to validate the online testing method. Additionally, we will recruit a sample of native Chinese-speaking children aged between 28 to 36 months to compare with the English-speaking sample.

Generic language (e.g., “Bears hibernate in the winter”, “Dogs bark”) makes broad statements about category properties that need not be true for all category members, although they are often interpreted as so. This form of language has important implications for conceptualizing social categories (e.g., Gelman, Ware, & Kleinberg, 2010; Rhodes, Leslie, & Tworek, 2012; Rhodes, Leslie, Bianchi, & Chalik, 2018; Noyes & Keil, 2019). For example, hearing generic statements highlights features as central to the category (Cimpian & Markman, 2009; Hollander, Gelman, & Raman, 2009), inducing essentialist assumptions about categories as richly structured and inductive (Rhodes, Leslie, & Tworek, 2012; but also see Noyes & Keil, 2019). Additionally--and most relevant for the present study--generic language facilitates toddlers’ learning about novel social groups in cases where other forms of language have not been enough (Rhodes et al., 2018). However, most previous studies were conducted with English-speaking participants, leaving the question open whether the effect of generics on social categorization generalize broadly to other linguistic contexts. To address this issue, we will test the role of generic language on social categorization in Chinese-speaking children. The Chinese language provides a theoretically interesting comparison with the English language due to their different linguistic formats of generics. In English, generics are often expressed in bare plural noun phrases (e.g., “Bunnies eat carrots”) or indefinite singular noun phrases (e.g., “A bunny eats carrots”). However, some linguistic features of the Chinese language, such as the lack of plurality and definite/indefinite articles, make the linguistic distinctions between generics and non-generic noun phrases much more ambiguous (Tardif et al., 2012). This linguistic ambiguity may delay the development of sensitivity to generic languages in Chinese-speaking children (Tardif et al., 2012). The current study aims to investigate the role of generics on social category learning in Chinese-speaking toddlers. First, we will replicate results from Rhodes and colleagues (2018) by testing an online sample of American children aged between 28 to 36 months to validate the online testing method. Additionally, we will recruit a sample of native Chinese-speaking children aged between 28 to 36 months to compare with the English-speaking sample.

Influencing one's adversary has always been an objective in warfare. However, to date the majority of influence operations have been geared toward the masses or to very small numbers of individuals. Although marginally effective, this approach is inadequate with respect to larger numbers of high value targets and to specific subsets of the population. Limited human resources have prevented a more tailored approach, which would focus on segmentation, because individual targeting demands significant time from psychological analysts. This research examined whether or not Information Technology (IT) tools, specializing in text mining, are robust enough to automate the categorization/segmentation of individual profiles for the purpose of psychological operations (PSYOP). Research indicated that only a handful of software applications claimed to provide adequate functionality to perform these tasks. Text mining via neural networks was determined to be the best approach given the constraints of the profile data and the desired output. Five software applications were tested and evaluated for their ability to reproduce the results of a social psychologist. Through statistical analysis, it was concluded that the tested applications are not currently mature enough to produce accurate results that would enable automated segmentation of individual profiles based on supervised linguistic processing.

Influencing one's adversary has always been an objective in warfare. However, to date the majority of influence operations have been geared toward the masses or to very small numbers of individuals. Although marginally effective, this approach is inadequate with respect to larger numbers of high value targets and to specific subsets of the population. Limited human resources have prevented a more tailored approach, which would focus on segmentation, because individual targeting demands significant time from psychological analysts. This research examined whether or not Information Technology (IT) tools, specializing in text mining, are robust enough to automate the categorization/segmentation of individual profiles for the purpose of psychological operations (PSYOP). Research indicated that only a handful of software applications claimed to provide adequate functionality to perform these tasks. Text mining via neural networks was determined to be the best approach given the constraints of the profile data and the desired output. Five software applications were tested and evaluated for their ability to reproduce the results of a social psychologist. Through statistical analysis, it was concluded that the tested applications are not currently mature enough to produce accurate results that would enable automated segmentation of individual profiles based on supervised linguistic processing.

Influencing one's adversary has always been an objective in warfare. However, to date the majority of influence operations have been geared toward the masses or to very small numbers of individuals. Although marginally effective, this approach is inadequate with respect to larger numbers of high value targets and to specific subsets of the population. Limited human resources have prevented a more tailored approach, which would focus on segmentation, because individual targeting demands significant time from psychological analysts. This research examined whether or not Information Technology (IT) tools, specializing in text mining, are robust enough to automate the categorization/segmentation of individual profiles for the purpose of psychological operations (PSYOP). Research indicated that only a handful of software applications claimed to provide adequate functionality to perform these tasks. Text mining via neural networks was determined to be the best approach given the constraints of the profile data and the desired output. Five software applications were tested and evaluated for their ability to reproduce the results of a social psychologist. Through statistical analysis, it was concluded that the tested applications are not currently mature enough to produce accurate results that would enable automated segmentation of individual profiles based on supervised linguistic processing.

Influencing one's adversary has always been an objective in warfare. However, to date the majority of influence operations have been geared toward the masses or to very small numbers of individuals. Although marginally effective, this approach is inadequate with respect to larger numbers of high value targets and to specific subsets of the population. Limited human resources have prevented a more tailored approach, which would focus on segmentation, because individual targeting demands significant time from psychological analysts. This research examined whether or not Information Technology (IT) tools, specializing in text mining, are robust enough to automate the categorization/segmentation of individual profiles for the purpose of psychological operations (PSYOP). Research indicated that only a handful of software applications claimed to provide adequate functionality to perform these tasks. Text mining via neural networks was determined to be the best approach given the constraints of the profile data and the desired output. Five software applications were tested and evaluated for their ability to reproduce the results of a social psychologist. Through statistical analysis, it was concluded that the tested applications are not currently mature enough to produce accurate results that would enable automated segmentation of individual profiles based on supervised linguistic processing.