When Code Words Aren’t Coded

According to the standard framing of racial appeals in political speech, politicians generally rely on coded language to communicate racial messages. Yet recent years have demonstrated that politicians often express quite explicit forms of racism in mainstream political discourse. The standard framing can explain neither why these appeals work politically nor how they work semantically. This paper moves beyond the standard framing, focusing on the politics and semantics of one type of explicit appeal, candid racial communication (CRC). The linguistic vehicles of CRC are neither true code words, nor slurs, but a conventionally defined class of “racialized terms.

Beyond happiness: Building a science of discrete positive emotions.

While trait positive emotionality and state positive-valence affect have long been the subject of intense study, the importance of differentiating among several "discrete" positive emotions has only recently begun to receive serious attention. In this article, we synthesize existing literature on positive emotion differentiation, proposing that the positive emotions are best described as branches of a "family tree" emerging from a common ancestor mediating adaptive management of fitness-critical resources (e.g., food). Examples are presented of research indicating the importance of differentiating several positive emotion constructs. We then offer a new theoretical framework, built upon a foundation of phylogenetic, neuroscience, and behavioral evidence, that accounts for core features as well as mechanisms for differentiation. We propose several directions for future research suggested by this framework and develop implications for the application of positive emotion research to translational issues in clinical psychology and the science of behavior change. (PsycINFO Database Recor

Time Perception in Mesial Temporal Lobe Epilepsy Patients

openObjectives: The present study investigates the explicit and implicit timing abilities of patients with mesial temporal lobe epilepsy (MTLE). Based on previous studies, it was hypothesized that timing abilities were decreased in MTLE patients. Methods: The performance of 21 MTLE patients and 20 neurologically healthy probands was tested on two separate tasks. The time bisection task was used to investigate explicit timing and the foreperiod task to test implicit timing. Results: For the time bisection task, a flatter psychophysical curve, indicative of less precise temporal judgements, was found in the patients compared to controls (RR = 0.64, 95% CI [0.57, 0.72], p = <.001). Also, patients with a higher IQ demonstrated less precise temporal judgments than patients with a lower IQ (RR = 0.76, 95% CI [0.67, 0.86], p = <.001). Moreover, the Weber Ratio (WR) of the patient group was higher than the control group’s WR (patient’s WR = 0.44, SD = 0.22; control’s WR = 0.31, SD = 0.14), indicating a significantly lower temporal sensitivity in the patient group (t(37) = -2.24, p = .031). In the foreperiod task, the RTs of the participants became shorter with longer durations which demonstrated the foreperiod effect (F(10,4992)= -0.19, p = <0.001). There was no statistical difference between the performance of the control and the patient group in the implicit timing task. Conclusions: MTLE patients showed less precise temporal judgments in explicit timing, while their implicit timing was largely preserved. This finding suggests that explicit time perception should be routinely investigated in MTLE patients

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

A survey on sentiment analysis in Urdu: A resource-poor language

© 2020 Background/introduction: The dawn of the internet opened the doors to the easy and widespread sharing of information on subject matters such as products, services, events and political opinions. While the volume of studies conducted on sentiment analysis is rapidly expanding, these studies mostly address English language concerns. The primary goal of this study is to present state-of-art survey for identifying the progress and shortcomings saddling Urdu sentiment analysis and propose rectifications. Methods: We described the advancements made thus far in this area by categorising the studies along three dimensions, namely: text pre-processing lexical resources and sentiment classification. These pre-processing operations include word segmentation, text cleaning, spell checking and part-of-speech tagging. An evaluation of sophisticated lexical resources including corpuses and lexicons was carried out, and investigations were conducted on sentiment analysis constructs such as opinion words, modifiers, negations. Results and conclusions: Performance is reported for each of the reviewed study. Based on experimental results and proposals forwarded through this paper provides the groundwork for further studies on Urdu sentiment analysis

We usually don't like going to the dentist : using common sense to detect irony on Twitter

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