Concept: Category of being
Audience segmentation is a useful tool for designing effective campaigns. Further, the efficiency promised in diffusion science rests to some degree on the existence of adopter categories that can be identified and used to strategically disseminate prevention innovations. This study investigates the potential to identify adopter categories in potential recipients (n = 127) of an innovation to prevent food shortages in Mozambique. A 5-class model was found using latent class analysis, but it showed important differences from existing descriptions of adopter categories. Implications for theory and practice are discussed.
We introduce the ReAL model for the Implicit Association Test (IAT), a multinomial processing tree model that allows one to mathematically separate the contributions of attitude-based evaluative associations and recoding processes in a specific IAT. The ReAL model explains the observed pattern of erroneous and correct responses in the IAT via 3 underlying processes: Recoding of target and attribute categories into a binary representation in the compatible block (Re), evaluative associations of the target categories (A), and label-based identification of the response that is assigned to the respective nominal category (L). In 7 validation studies, using an adaptive response deadline procedure in order to increase the amount of erroneous responses in the IAT, we demonstrated that the ReAL model fits IAT data and that the model parameters vary independently in response to corresponding experimental manipulations. Further studies yielded evidence for the specific predictive validity of the model parameters in the domain of consumer behavior. The ReAL model allows one to disentangle different sources of IAT effects where global effect measures based on response times lead to equivocal interpretations. Possible applications and implications for future IAT research are discussed. (PsycINFO Database Record © 2012 APA, all rights reserved).
After reviewing the pertinent philosophical and psychoanalytic writings on the concept of dignity, this paper proposes three categories of dignity. Conceptualized as phenomenological clusters, heuristic viewpoints, and levels of abstraction, these include (i) metaphysical dignity which extends the concept of dignity beyond the human species to all that exists in this world, (ii) existential dignity which applies to human beings alone and rests upon their inherent capacity for moral transcendence, and (iii) characterological dignity which applies more to some human beings than others since they possess a certain set of personality traits that are developmentally derived. The paper discusses the pros and cons of each category and acknowledges the limitations of such classification. It also discusses the multiple ways in which these concepts impact upon clinical work and concludes with some remarks on the relationship of dignity to choice, narcissism, and suicide.
We examine developmental interactions between context, exploration, and word learning. Infants show an understanding of how nonsolid substances are categorized that does not reliably transfer to learning how these categories are named in laboratory tasks. We argue that what infants learn about naming nonsolid substances is contextually bound - most nonsolids that toddlers are familiar with are foods and thus, typically experienced when sitting in a highchair. We asked whether 16-month-old children’s naming of nonsolids would improve if they were tested in that typical context. Children tested in the highchair demonstrated better understanding of how nonsolids are named. Furthermore, context-based differences in exploration drove differences in the properties attended to in real-time. We discuss what implications this context-dependency has for understanding the development of an ontological distinction between solids and nonsolids. Together, these results demonstrate a developmental cascade between context, exploration, and word learning.
- Proceedings of the National Academy of Sciences of the United States of America
- Published 12 months ago
Focal colors, or best examples of color terms, have traditionally been viewed as either the underlying source of cross-language color-naming universals or derived from category boundaries that vary widely across languages. Existing data partially support and partially challenge each of these views. Here, we advance a position that synthesizes aspects of these two traditionally opposed positions and accounts for existing data. We do so by linking this debate to more general principles. We show that best examples of named color categories across 112 languages are well-predicted from category extensions by a statistical model of how representative a sample is of a distribution, independently shown to account for patterns of human inference. This model accounts for both universal tendencies and variation in focal colors across languages. We conclude that categorization in the contested semantic domain of color may be governed by principles that apply more broadly in cognition and that these principles clarify the interplay of universal and language-specific forces in color naming.
Network alignment (NA) aims to find regions of similarities between species' molecular networks. There exist two NA categories: local (LNA) and global (GNA). LNA finds small highly conserved network regions and produces a many-to-many node mapping. GNA finds large conserved regions and produces a one-to-one node mapping. Given the different outputs of LNA and GNA, when a new NA method is proposed, it is compared against existing methods from the same category. However, both NA categories have the same goal: to allow for transferring functional knowledge from well- to poorly-studied species between conserved network regions. So, which one to choose, LNA or GNA? To answer this, we introduce the first systematic evaluation of the two NA categories.
To get a better fit performance of filtering facepieces, a tight fitting net (TFN) was invented. This study was carried out to evaluate whether the TFN improves fit performance using a quantitative fit test (QNFT). The existing mask was of cup type with an aluminum clip on the nose bridge. The TFN mask was the same as the existing mask, but attached a TFN instead of aluminum clip. One hundred subjects (male 52, female 48) were selected to match fourfold in Korean 25-member facial size category for half-mask (KFCH). Fit factors (FFs) were measured using a QNFT by a Portacount(®)Pro+8038. Three QNFTs for each mask on the same subject was conducted and geometric mean FF (GMFF) was determined. The mean and median GMFFs of the TFN masks had higher than those of the existing mask (p = <0.001). The existing masks had tendency to have higher GMFFs with common facial size categories, while the TFN masks were regardless of facial size. The result indicates that putting even pressure on the entire parts of filter media would improve fit performance. In conclusion, to get a good fit when wearing filtering facepieces, a TFN would be an alternative to mask designing.
This paper argues that grammatical constructions, specifically argument structure constructions that determine the “who did what to whom” part of sentence meaning and how this meaning is expressed syntactically, can be considered a kind of relational category. That is, grammatical constructions are represented as the abstraction of the syntactic and semantic relations of the exemplar utterances that are expressed in that construction, and it enables the generation of novel exemplars. To support this argument, I review evidence that there are parallel behavioral patterns between how children learn relational categories generally and how they learn grammatical constructions specifically. Then, I discuss computational simulations of how grammatical constructions are abstracted from exemplar sentences using a domain-general relational cognitive architecture. Last, I review evidence from adult language processing that shows parallel behavioral patterns with expert behavior from other cognitive domains. After reviewing the evidence, I consider how to integrate this account with other theories of language development.
The present study investigates category intension in school-aged children and adults at two different levels of abstraction (i.e., superordinate and basic level) for two category types (i.e., artefacts and natural kinds). We addressed two critical questions: what kind of features do children and adults generate to define semantic categories and which features predict category membership judgment best at each abstraction level? Overall, participants generated relatively more entity features for natural kinds categories, compared to artefact categories, as well as for basic level categories, compared to superordinate categories. Furthermore, the results showed that older children and adults generated relatively more entity features than younger children. Finally, situation features play the most important role in the prediction of category judgments at both levels of abstraction. Theoretical implications and comparable results from previous studies are described in detail.
Using online consumer reviews as electronic word of mouth to assist purchase-decision making has become increasingly popular. The Web provides an extensive source of consumer reviews, but one can hardly read all reviews to obtain a fair evaluation of a product or service. A text processing framework that can summarize reviews, would therefore be desirable. A subtask to be performed by such a framework would be to find the general aspect categories addressed in review sentences, for which this paper presents two methods. In contrast to most existing approaches, the first method presented is an unsupervised method that applies association rule mining on co-occurrence frequency data obtained from a corpus to find these aspect categories. While not on par with state-of-the-art supervised methods, the proposed unsupervised method performs better than several simple baselines, a similar but supervised method, and a supervised baseline, with an ₁-score of 67%. The second method is a supervised variant that outperforms existing methods with an F₁-score of 84%.