The "Snacking Child" and its social network: some insights from an italian survey

<p>Abstract</p> <p>Background</p> <p>The hypothesis underlying this work is that the social network of a child might have an impact on the alimentary behaviors, in particular for what concerns snack consumption patterns.</p> <p>Methods</p> <p>1215 Italian children 6-10 ys old were interviewed using a CATI facility in January 2010. 608 "snackers" and 607 "no-snackers" were identified. Information regarding family composition, child and relatives BMI, mother perception of child weight, child, father and mother physical activity, TV watching, social network, leisure time habits and dietary habits of peers, were collected. Association of variables with the status of snacker was investigated using a multivariable logistic regression model.</p> <p>Results</p> <p>Snackers children seem to be part of more numerous social network (1.40 friends vs 1.14, p = 0.042) where the majority of peers are also eating snacks, this percentage being significantly higher (89.5 vs 76.3, p < 0.001) than in the "no-snacker" group. The snacking group is identified by the fact that it tends to practice at least 4 hours per week of physical activity (OR: 1.36, CI: 1.03-1.9). No evidence of an association between snacking consumption and overweight status has been shown by our study.</p> <p>Conclusions</p> <p>The snacking child has more active peer-to-peer social relationships, mostly related with sport activities. However, spending leisure time in sportive activities implies being part of a social environment which is definitely a positive one from the point of view of obesity control, and indeed, no increase of overweight/obesity is seen in relation to snack consumption.</p

Effort and performance in a cooperative activity are boosted by perception of a partner’s effort

In everyday life, people must often determine how much time and effort to allocate to cooperative activities. In the current study, we tested the hypothesis that the perception of others’ effort investment in a cooperative activity may elicit a sense of commitment, leading people to allocate more time and effort to the activity themselves. We developed an effortful task in which participants were required to move an increasingly difficult bar slider on a screen while simultaneously reacting to the appearance of virtual coins and earn points to share between themselves and their partner. This design allowed us to operationalize commitment in terms of participants’ investment of time and effort. Crucially, the cooperative activity could only be performed after a partner had completed a complementary activity which we manipulated to be either easy (Low Effort condition) or difficult (High Effort condition). Our results revealed participants invested more effort, persisted longer and performed better in the High Effort condition, i.e. when they perceived their partner to have invested more effort. These results support the hypothesis that the perception of a partner’s effort boosts one’s own sense of commitment to a cooperative activity, and consequently also one’s willingness to invest time and effort

Privileged information for data clustering

Many machine learning algorithms assume that all input samples are independently and identically distributed from some common distribution on either the input space X, in the case of unsupervised learning, or the input and output space X Y in the case of supervised and semi-supervised learning. In the last number of years the relaxation of this assumption has been explored and the importance of incorporation of additional information within machine learning algorithms became more apparent. Traditionally such fusion of information was the domain of semi-supervised learning. More recently the inclusion of knowledge from separate hypothetical spaces has been proposed by Vapnik as part of the supervised setting. In this work we are interested in exploring Vapnik’s idea of ‘master-class’ learning and the associated learning using ‘privileged’ information, however within the unsupervised setting. Adoption of the advanced supervised learning paradigm for the unsupervised setting instigates investigation into the dierence between privileged and technical data. By means of our proposed aRi-MAX method stability of the K-Means algorithm is improved and identification of the best clustering solution is achieved on an artificial dataset. Subsequently an information theoretic dot product based algorithm called P-Dot is proposed. This method has the ability to utilize a wide variety of clustering techniques, individually or in combination, while fusing privileged and technical data for improved clustering. Application of the P-Dot method to the task of digit recognition confirms our findings in a real-world scenario