Attentional breadth and proximity seeking in romantic attachment relationships

The present study provides first evidence that attentional breadth responses can be influenced by proximity-distance goals in adult attachment relationships. In a sample of young couples, we measured attachment differences in the breadth of attentional focus in response to attachment-related cues. Results showed that priming with a negative attachment scenario broadens attention when confronted with pictures of the attachment figure in highly avoidant men. In women, we found that attachment anxiety was associated with a more narrow attentional focus on the attachment figure, yet only at an early stage of information processing. We also found that women showed a broader attentional focus around the attachment figure when their partner was more avoidantly attached. This pattern of results reflects the underlying action of attachment strategies and provides insight into the complex and dynamic influence of attachment on attentional processing in a dyadic context

Size and depth of monotone neural networks: interpolation and approximation

Monotone functions and data sets arise in a variety of applications. We study the interpolation problem for monotone data sets: The input is a monotone data set with $n$ points, and the goal is to find a size and depth efficient monotone neural network, with non negative parameters and threshold units, that interpolates the data set. We show that there are monotone data sets that cannot be interpolated by a monotone network of depth $2$. On the other hand, we prove that for every monotone data set with $n$ points in $\mathbb{R}^d$, there exists an interpolating monotone network of depth $4$ and size $O(nd)$. Our interpolation result implies that every monotone function over $[0,1]^d$ can be approximated arbitrarily well by a depth-4 monotone network, improving the previous best-known construction of depth $d+1$. Finally, building on results from Boolean circuit complexity, we show that the inductive bias of having positive parameters can lead to a super-polynomial blow-up in the number of neurons when approximating monotone functions.Comment: 19 page

Attachment style Is related to quality of life for assistance dog owners

Attachment styles have been shown to affect quality of life. Growing interest in the value of companion animals highlights that owning a dog can also affect quality of life, yet little research has explored the role of the attachment bond in affecting the relationship between dog ownership and quality of life. Given that the impact of dog ownership on quality of life may be greater for assistance dog owners than pet dog owners, we explored how anxious attachment and avoidance attachment styles to an assistance dog affected owner quality of life (n = 73). Regression analysis revealed that higher anxious attachment to the dog predicted enhanced quality of life. It is suggested that the unique, interdependent relationship between an individual and their assistance dog may mean that an anxious attachment style is not necessarily detrimental. Feelings that indicate attachment insecurity in other relationships may reflect more positive aspects of the assistance dog owner relationship, such as the level of support that the dog provides its owner

Community detection and percolation of information in a geometric setting

We make the first steps towards generalizing the theory of stochastic block models, in the sparse regime, towards a model where the discrete community structure is replaced by an underlying geometry. We consider a geometric random graph over a homogeneous metric space where the probability of two vertices to be connected is an arbitrary function of the distance. We give sufficient conditions under which the locations can be recovered (up to an isomorphism of the space) in the sparse regime. Moreover, we define a geometric counterpart of the model of flow of information on trees, due to Mossel and Peres, in which one considers a branching random walk on a sphere and the goal is to recover the location of the root based on the locations of leaves. We give some sufficient conditions for percolation and for non-percolation of information in this model.Comment: 21 page