How the size of our social network influences our semantic skills

People differ in the size of their social network, and thus in the properties of the linguistic input they receive. This article examines whether differences in social network size influence individuals’ linguistic skills in their native language, focusing on global comprehension of evaluative language. Study 1 exploits the natural variation in social network size and shows that individuals with larger social networks are better at understanding the valence of restaurant reviews. Study 2 manipulated social network size by randomly assigning participants to learn novel evaluative words as used by two (small network) versus eight (large network) speakers. It replicated the finding from Study 1, showing that those exposed to a larger social network were better at comprehending the valence of product reviews containing the novel words that were written by novel speakers. Together, these studies show that the size of one's social network can influence success at language comprehension. They thus open the door to research on how individuals’ lifestyle and the nature of their social interactions can influence linguistic skills

People with larger social networks show poorer voice recognition

The way we process language is influenced by our experience. We are more likely to attend to features that proved to be useful in the past. Importantly, the size of individuals’ social network can influence their experience, and consequently, how they process language. In the case of voice recognition, having a larger social network might provide more variable input and thus enhance the ability to recognise new voices. On the other hand, learning to recognise voices is more demanding and less beneficial for people with a larger social network as they have more speakers to learn yet spend less time with each. This paper tests whether social network size influences voice recognition, and if so, in which direction. Native Dutch speakers listed their social network and performed a voice recognition task. Results showed that people with larger social networks were poorer at learning to recognise voices. Experiment 2 replicated the results with a British sample and English stimuli. Experiment 3 showed that the effect does not generalise to voice recognition in an unfamiliar language suggesting that social network size influences attention to the linguistic rather than non-linguistic markers that differentiate speakers. The studies thus show that our social network size influences our inclination to learn speaker-specific patterns in our environment, and consequently, the development of skills that rely on such learned patterns, such as voice recognition

FairLedger: A Fair Blockchain Protocol for Financial Institutions

Financial institutions are currently looking into technologies for permissioned blockchains. A major effort in this direction is Hyperledger, an open source project hosted by the Linux Foundation and backed by a consortium of over a hundred companies. A key component in permissioned blockchain protocols is a byzantine fault tolerant (BFT) consensus engine that orders transactions. However, currently available BFT solutions in Hyperledger (as well as in the literature at large) are inadequate for financial settings; they are not designed to ensure fairness or to tolerate selfish behavior that arises when financial institutions strive to maximize their own profit. We present FairLedger, a permissioned blockchain BFT protocol, which is fair, designed to deal with rational behavior, and, no less important, easy to understand and implement. The secret sauce of our protocol is a new communication abstraction, called detectable all-to-all (DA2A), which allows us to detect participants (byzantine or rational) that deviate from the protocol, and punish them. We implement FairLedger in the Hyperledger open source project, using Iroha framework, one of the biggest projects therein. To evaluate FairLegder's performance, we also implement it in the PBFT framework and compare the two protocols. Our results show that in failure-free scenarios FairLedger achieves better throughput than both Iroha's implementation and PBFT in wide-area settings

A bad feeling or a bad filling? The influence of social network size on speech perception

Infants and adults learn new phonological varieties better when exposed to multiple rather than a single speaker. Does having a larger social network similarly facilitate phonological performance? Study 1 shows that people with larger social networks are indeed better at speech perception in noise, indicating that the benefit of exposure to multiple speakers extends to real life experience and to adult native speakers. Furthermore, the study shows that this association is not due to differences in amount of input or to cognitive differences between people with different social network sizes. Using computational simulations, Study 2 reveals that the effect of social network size on speech perception is fully mediated by the fact that having a larger social network leads to smoother sampling of the central areas of the phonemes. Furthermore, the simulations reveal that in contrast to previous assumptions, variability itself does not boost performance. The simulations also show that the effect of social network size is independent of amount of input but is modulated by the ratio of intra- to inter-individual variability. Together, these studies show how properties of our social network influence our speech perception. They thus show how aspects of our life-style can influence our linguistic performance

How social network heterogeneity facilitates lexical access and lexical prediction

People learn language from their social environment. As individuals differ in their social networks, they might be exposed to input with different lexical distributions, and these might influence their linguistic representations and lexical choices. In this article we test the relation between linguistic performance and 3 social network properties that should influence input variability, namely, network size, network heterogeneity, and network density. In particular, we examine how these social network properties influence lexical prediction, lexical access, and lexical use. To do so, in Study 1, participants predicted how people of different ages would name pictures, and in Study 2 participants named the pictures themselves. In both studies, we examined how participants’ social network properties related to their performance. In Study 3, we ran simulations on norms we collected to see how age variability in one’s network influences the distribution of different names in the input. In all studies, network age heterogeneity influenced performance leading to better prediction, faster response times for difficult-to-name items, and less entropy in input distribution. These results suggest that individual differences in social network properties can influence linguistic behavior. Specifically, they show that having a more heterogeneous network is associated with better performance. These results also show that the same factors influence lexical prediction and lexical production, suggesting the two might be related

Executive control influences linguistic representations

Although it is known that words acquire their meanings partly from the contexts in which they are used, we proposed that the way in which words are processed can also influence their representation. We further propose that individual differences in the way that words are processed can consequently lead to individual differences in the way that they are represented. Specifically, we showed that executive control influences linguistic representations by influencing the coactivation of competing and reinforcing terms. Consequently, people with poorer executive control perceive the meanings of homonymous terms as being more similar to one another, and those of polysemous terms as being less similar to one another, than do people with better executive control. We also showed that bilinguals with poorer executive control experience greater cross-linguistic interference than do bilinguals with better executive control. These results have implications for theories of linguistic representation and language organization

artsKSU Presents: Anat Cohen Tentet, Musical Director Oded Lev-Ari

Ever charismatic, prolific, and inspired, GRAMMY-nominated clarinetist-saxophonist Anat Cohen has won hearts and minds the world over with her expressive virtuosity and delightful stage presence. Anat has been declared Clarinetist of the Year by the Jazz Journalists Association every year since 2007 and has also been named the Top Clarinetist, Rising Star, and Jazz Artist of the Year by Downbeat Magazine. The tentet(rhythm section, horns, vibraphone, cello, and accordion) performs tunes from their recent album Happy Song which draws influence from Brazilian music and African grooves to vintage swing and touching ballads.https://digitalcommons.kennesaw.edu/musicprograms/2261/thumbnail.jp