Most theories of slurs fall into one of two families: those which understand slurring terms to involve special descriptive/informational content (however conveyed), and those which understand them to encode special emotive/expressive content. Our view is that both offer essential insights, but that part of what sets slurs apart is use-theoretic content. In particular, we urge that slurring words belong at the intersection of a number of categories in a sociolinguistic register taxonomy, one that usually includes [+slang] and [+vulgar] and always (...) includes [-polite] and [+derogatory]. Thus, e.g., what distinguishes ‘Chinese’ from ‘chink’ is neither a peculiar sort of descriptive nor emotional content, but rather the fact that ‘chink’ is lexically marked as belonging to different registers than ‘Chinese’. It is, moreover, partly such facts which makes slurring ethically unacceptable. (shrink)

The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented (...) to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method. (shrink)

In two experiments, subjects’ task was to decide whether a binocularly viewed target word was evaluatively good (e.g., fame, comedy, rescue) or bad (e.g., stress, detest, malaria) in meaning. Just prior to this target word, a priming word was presented to the nondominant eye, and masked by an immediately following presentation of a letter—fragment pattern to the dominant eye. (Masking effectiveness was demonstrated by subjects’ failure to discriminate the left vs. right position of a test series of words.) In Experiment (...) 1, which used evaluatively positive or negative words as priming stimuli, judgment latency for the evaluative decision task was facilitated by primes that agreed in evaluation with targets, and was retarded by primes that disagreed in evaluation with targets. This result demonstrated that the evaluative meaning of priming stimuli was processed under conditions that prevented subjects from detecting their presence. Primes in Experiment 2 were 2-word strings for which the evaluative meaning of individual words was orthogonal to the evaluative sentence meaning (e.g., the two evaluatively negative words, "enemy fails,“ make up an evaluatively positive sentence). Results of Experiment 2 indicated that masked priming effects were influenced by the evaluative meanings of individual words, but not by their sentence meanings. This result supported the conclusion that the processing of undetected, dichoptically masked words is limited to analyses that are not powerful enough to extract sentence meanings. (shrink)

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task‐specific knowledge acquired from a wealth of prior experience, or is it based on the domain‐general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three‐dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to (...) that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task‐specific knowledge to analogical reasoning. We also developed a new model using part‐based comparison (PCM) by applying a domain‐general mapping procedure to learned representations of cars and their component parts. Across four‐term analogies (Experiment 1) and open‐ended analogies (Experiment 2), the domain‐general PCM model, but not the task‐specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human‐like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity. (shrink)

The justice literature has coalesced around the notion that actors (e.g., supervisors) tend to utilize the norm of equity for resource allocation decisions because it is generally considered most fair when employees who contribute more to the organization receive more resources. Yet, actors might sometimes utilize a need norm to allocate resources to those most in need. Studies that have addressed need-based resource allocations have assumed a relatively straightforward conceptualization of need. However, research from related areas suggests that multiple characteristics (...) of the need itself could trigger actors’ use of a need norm to allocate resources. We advance a theoretical framework that outlines various need characteristics that drive actors’ use of a need norm. The framework draws on the processes outlined in attribution theory and integrates those with the content domains addressed in fairness theory. A discussion of the implications for justice, attribution, and fairness theory research follows. (shrink)

Cross-sector relationship building is an important strategy in corporate social responsibility initiatives, and communicating cross-sector relationships on social media can help raise the visibility of collaborative relationships. A noticeable gap in the literature is how social media enables and constrains the formation patterns of cross-sector connections. To understand how businesses communicate their relationships with government agencies and nonprofits about social issues on social media, we propose a theoretical framework that centers public attention as a critical resource and considers different sectors' (...) interests in the COVID-19 issue. We tested this framework with a nationally representative sample of 1,980 organizations on Twitter. The analysis reveals that the increase in public attention to the COVID-19 issue was accompanied by a greater likelihood of cross-sector ties. Specifically, firms severely affected by COVID-19 were more likely to build ties with government agencies, and the increase in public attention also drove firms to build more ties with nonprofits, especially advocacy nonprofits. Our findings suggest managers of social media communication should observe the volatile public attention and design communicative strategies accordingly. (shrink)

Online data collection methods are expanding the ease and access of developmental research for researchers and participants alike. While its popularity among developmental scientists has soared during the COVID-19 pandemic, its potential goes beyond just a means for safe, socially distanced data collection. In particular, advances in video conferencing software has enabled researchers to engage in face-to-face interactions with participants from nearly any location at any time. Due to the novelty of these methods, however, many researchers still remain uncertain about (...) the differences in available approaches as well as the validity of online methods more broadly. In this article, we aim to address both issues with a focus on moderated data collected using video-conferencing software. First, we review existing approaches for designing and executing moderated online studies with young children. We also present concrete examples of studies that implemented choice and verbal measures and looking time across both in-person and online moderated data collection methods. Direct comparison of the two methods within each study as well as a meta-analysis of all studies suggest that the results from the two methods are comparable, providing empirical support for the validity of moderated online data collection. Finally, we discuss current limitations of online data collection and possible solutions, as well as its potential to increase the accessibility, diversity, and replicability of developmental science. (shrink)