Gossip as a Burdened Virtue

Gossip is often serious business, not idle chitchat. Gossip allows those oppressed to privately name their oppressors as a warning to others. Of course, gossip can be in error. The speaker may be lying or merely have lacked sufficient evidence. Bias can also make those who hear the gossip more or less likely to believe the gossip. By examining the social functions of gossip and considering the differences in power dynamics in which gossip can occur, we contend that gossip may be not only permissible but virtuous, both as the only reasonable recourse available and as a means of resistance against oppression

Judging personality from a brief sample of behaviour: detecting where others stand on trait continua

Trait inferences occur routinely and rapidly during social interaction, sometimes based on scant or fleeting information. In this research, participants (perceivers) made inferences of targets’ big-five traits after briefly watching or listening to an unfamiliar target (a third party) performing various mundane activities (telling a scripted joke or answering questions about him/herself or reading aloud a paragraph of promotional material). Across three studies, when perceivers judged targets to be either low or high in one or more dimensions of the big-five traits they tended to be correct, but they did not tend to be correct when they judged targets as average. Such inferences seemed to vary in effectiveness across different trait dimensions and depending on whether the target’s behavior was presented either in a video with audio, a silent video or just in an audio track – perceivers generally were less often correct when they judged targets as average in each of the big-five traits across various information channels (videos with audio, silent videos and audios). Study 3 replicated these findings in a different culture. We conclude with discussion of the scope and the adaptive value of this trait inferential ability

Mapping the multicausality of Alzheimer's disease through group model building

Alzheimer's disease (AD) is a complex, multicausal disorder involving several spatiotemporal scales and scientific domains. While many studies focus on specific parts of this system, the complexity of AD is rarely studied as a whole. In this work, we apply systems thinking to map out known causal mechanisms and risk factors ranging from intracellular to psychosocial scales in sporadic AD. We report on the first systemic causal loop diagram (CLD) for AD, which is the result of an interdisciplinary group model building (GMB) process. The GMB was based on the input of experts from multiple domains and all proposed mechanisms were supported by scientific literature. The CLD elucidates interaction and feedback mechanisms that contribute to cognitive decline from midlife onward as described by the experts. As an immediate outcome, we observed several non-trivial reinforcing feedback loops involving factors at multiple spatial scales, which are rarely considered within the same theoretical framework. We also observed high centrality for modifiable risk factors such as social relationships and physical activity, which suggests they may be promising leverage points for interventions. This illustrates how a CLD from an interdisciplinary GMB process may lead to novel insights into complex disorders. Furthermore, the CLD is the first step in the development of a computational model for simulating the effects of risk factors on AD.Neuro Imaging Researc

Mobile Phones and Social Signal Processing for Analysis and Understanding of Dyadic Conversations

Social Signal Processing is the domain aimed at bridging the social intelligence gap between humans and machines via modeling, analysis and synthesis of nonverbal behavior in social interactions. One of the main challenges of the domain is to sense unobtrusively the behavior of social interaction participants, one of the key conditions to preserve the spontaneity and naturalness of the interactions under exam. In this respect, mobile devices offer a major opportunity because they are equipped with a wide array of sensors that, while capturing the behavior of their users with an unprecedented depth, are still invisible. This is particularly important because mobile devices are part of the everyday life of a large number of individuals and, hence, they can be used to investigate and sense natural and spontaneous scenarios