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 over 1 year 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.
Categories can be grouped by shared sensory attributes (i.e., cats) or a more abstract rule (i.e., animals). We explored the neural basis of abstraction by recording from multi-electrode arrays in prefrontal cortex (PFC) while monkeys performed a dot-pattern categorization task. Category abstraction was varied by the degree of exemplar distortion from the prototype pattern. Different dynamics in different PFC regions processed different levels of category abstraction. Bottom-up dynamics (stimulus-locked gamma power and spiking) in the ventral PFC processed more low-level abstractions, whereas top-down dynamics (beta power and beta spike-LFP coherence) in the dorsal PFC processed more high-level abstractions. Our results suggest a two-stage, rhythm-based model for abstracting categories.
The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecular function, biological process and cellular component. For better biological reasoning, identifying the biological relationships between terms in different categories are important. However, the existing measurements to calculate similarity between terms in different categories are either developed by using the GO data only or only take part of combined gene co-function network information.
We compared the change in joint inflammation and the proportion of subjects achieving threshold levels of improvement using the existing methods employing ultrasonography on pre-determined joint sites versus novel methods. These novel methods select the most affected joints based on (i) ultrasonography-the Individualized-Ultrasound (IUS) method, or (ii) ultrasonography and clinical joint assessment-the individualized-Composite-Ultrasound (ICUS) method. Mean 3-month change in total inflammation score (ΔTIS) and 95% CI was computed for each method on 24 RA subjects initiated or escalated on treatment. Individual improvement in TIS per subject, calculated as the 3-month ΔTIS divided by the maximum possible TIS score expressed as a percentage, was used to obtain the proportion of subjects achieving response across improvement categories. Mean 3-month ΔTIS was significantly greater (p values ranging from 0.0003 to 0.0026) for novel versus existing methods using 12- and 7-joint approaches. Using 12-joint approach, percentages of subjects in improvement categories ≥5%, ≥10%, ≥15% and ≥20% were, respectively, 50, 37.5, 12.5 and 8.3% for IUS; 58.3, 37.5, 12.5 and 8.3% for ICUS; and 16.7, 0, 0 and 0% for the existing method. Using 7-joint approach, the respective category percentages were 62.5, 37.5, 25 and 12.5% for IUS; 62.5, 41.7, 16.7 and 8.3% for ICUS; and 12.5, 4.2, 4.2 and 0% for the existing method. Novel ultrasound methods are more likely to detect improvement in joint inflammation, with more subjects achieving response across improvement categories, thereby representing a substantial advantage over the existing methods. However, this requires confirmation in larger RA cohorts.
A key function of categories is to help predictions about unobserved features of objects. At the same time, humans are often in situations where the categories of the objects they perceive are uncertain. In an influential paper, Anderson (Psychological Review, 98(3), 409-429, 1991) proposed a rational model for feature inferences with uncertain categorization. A crucial feature of this model is the conditional independence assumption-it assumes that the within category feature correlation is zero. In prior research, this model has been found to provide a poor fit to participants' inferences. This evidence is restricted to task environments inconsistent with the conditional independence assumption. Currently available evidence thus provides little information about how this model would fit participants' inferences in a setting with conditional independence. In four experiments based on a novel paradigm and one experiment based on an existing paradigm, we assess the performance of Anderson’s model under conditional independence. We find that this model predicts participants' inferences better than competing models. One model assumes that inferences are based on just the most likely category. The second model is insensitive to categories but sensitive to overall feature correlation. The performance of Anderson’s model is evidence that inferences were influenced not only by the more likely category but also by the other candidate category. Our findings suggest that a version of Anderson’s model which relaxes the conditional independence assumption will likely perform well in environments characterized by within-category feature correlation.