Breaking Bread: the Functions of Social Eating

Communal eating, whether in feasts or everyday meals with family or friends, is a human universal, yet it has attracted surprisingly little evolutionary attention. I use data from a UK national stratified survey to test the hypothesis that eating with others provides both social and individual benefits. I show that those who eat socially more often feel happier and are more satisfied with life, are more trusting of others, are more engaged with their local communities, and have more friends they can depend on for support. Evening meals that result in respondents feeling closer to those with whom they eat involve more people, more laughter and reminiscing, as well as alcohol. A path analysis suggests that the causal direction runs from eating together to bondedness rather than the other way around. I suggest that social eating may have evolved as a mechanism for facilitating social bonding

A dynamic annotation tool to support in-home autism intervention

The Congress sets out to be an international forum for the exchange of ideas and opinions in which different parties (people with autism, families, professionals of the intervention, investigators and developers) are able to debate on the subject of applied technologies in the world of autism

Spatial Filtering and Eigenvector Stability: Space-time Models for German Unemployment Data

Regions, independent of their geographic level of aggregation, are known to be interrelated partly due to their relative locations. Similar economic performance among regions can be attributed to proximity. Consequently, a proper understanding, and accounting, of spatial liaisons is needed in order to effectively forecast regional economic variables. Several spatial econometric techniques are available in the literature, which deal with the spatial autocorrelation (SAC) in geographically referenced data. The experiments carried out in this article are concerned with the analysis of the SAC observed for unemployment rates in 439 NUTS-3 German districts. The authors employ a semiparametric approach-spatial filtering-in order to uncover spatial patterns that are consistently significant over time. The authors first provide a brief overview of the spatial filtering method and illustrate the data set. Subsequently, they describe the empirical application carried out: that is, the spatial filtering analysis of regional unemployment rates in Germany. Furthermore, the authors exploit the resulting spatial filter as an explanatory variable in a panel modeling framework. Additional explanatory variables, such as average daily wages, are used in concurrence with the spatial filter. Their experiments show that the computed spatial filters account for most of the residual SAC in the data. © 2011 SAGE Publications