Aggression Norms in the Classroom Social Network: Contexts of Aggressive Behavior and Social Preference in Middle Childhood

In a cross‐sectional sample of African‐American 2nd–4th grade students (N = 681), we examine the moderating effects of classroom overt and relational aggression norms on peers’ social acceptance of classmates who exhibit overt and relational aggression in urban schools. Extending theory and research on classroom norms, we integrate social network data to adjust aggression norms based on children’s direct and indirect connections in the classroom. Results of multilevel models indicate that network‐based classroom aggression norms moderated relations between children’s aggressive behavior and their social preference. Specifically, children benefited socially when their form of aggressive behavior fit with what was normative in the classroom social context. The moderating effect of classroom aggression norms was stronger for the association between overt aggression and social preference than relational aggression and social preference. Relationally aggressive youth were socially preferred by peers regardless of the classroom norm, although this positive association was magnified in classrooms with higher levels of relational aggression. Future research focused on aggression norms within classroom social networks are discussed and implications for school prevention efforts are considered.     

An Evolutionary Analysis of Learned Attention

Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments.     

Umwelt,food, and the limits of control

This paper draws on the work of the early 20th century ethologist Jakob von Uexküll to formulate a notion of food as a process of bodies becoming other bodies. I begin by situating my argument in relation to two strands of critical food research – feminist-inspired work on food and embodiment, and posthumanist approaches that focus on non-humans as mediators of food assemblages. I then discuss Uexküll's work, focusing on three key concepts: umwelt, “the island of the senses” that envelops each being; subjectivity as an intra- and intercorporeal phenomenon; and the variation among umwelten available to humans. These ideas, I contend, illustrate the inherently political nature of ‘food,’ which in turn calls for a realignment of food ethics and critical food scholarship. To illustrate this contention, I draw on my research with the Oklahoma Food Cooperative, considering this socio-ecological experiment as an effort to create a food system in better accord with the affective imperatives (or umwelten) of its constituent components. Throughout the paper, I develop the argument that food systems will always present limits to control by even those actors who seem to enjoy hegemonic positions. Uexküll's work helps us understand these limits in a way that allows critical analysis of dominant food systems and the alternatives proliferating in response to them, but does not prematurely foreclose the actual and virtual possibilities contained in the present heterogeneity of foodways.     

Exploring the dynamics of design fluency in children with and without ADHD using artificial neural networks

The neuropsychology of attention deficit/hyperactivity disorder (ADHD) has been extensively studied, with a general focus on global performance measures of executive function. In this study, we compared how global (i.e., endpoint) versus process (i.e., dynamic) measures of performance may help characterize children with and without ADHD using a design fluency task as a case study. The secondary goal was to compare the sensitivity of standard versus connectionist statistical models to group differences in cognitive data. Thirty-four children diagnosed with ADHD and 37 children without ADHD aged 8–11 years old were tested on the Five-Point Test. The continuous process measure of performance, indexed as the number of produced designs at each consecutive 1 minute interval during 5 minutes, was analyzed against the discrete process measure, that is, the number of designs between first and last intervals and the standard global performance measure of total number of produced designs. Results show that the continuous process measure distinguished the two groups better than the two other measures. The detailed observation of production patterns revealed a decreasing linear trajectory in children without ADHD that contrasts with the flat, but fluctuating productivity pattern of children with ADHD. With regards to the second goal, results show that the connectionist and standard methods are equally sensitive to group differences for the three types of measures. This illustrates the utility of quantitative process measures together with the connectionist method in neuropsychological research and suggests great potential for a dynamical approach to cognition.     

The dynamics of bidirectional thought

High-level judgement and decision-making tasks display dynamic bidirectional relationships in which salient cues determine how responses are evaluated by decision-makers, and these responses in turn determine the cues that are considered. In this paper, we propose Kosko's bidirectional associative memory (BAM) network, a minimal two-layer recurrent neural network, as a mathematically tractable toy model with which the properties of existing bidirectional models, and the behavioural implications of these properties, can be studied. We first derive results regarding the dynamics of the BAM network, and then show how these results can be used to provide an analytically sound explanation for a number of important findings, including coherence shifts in judgement and choice, anchoring effects, and reference point effects.     

Applying Evidence-Centered Design for the Development of Game-Based Assessments in Physics Playground

Game-based assessment (GBA) is a specific use of educational games that employs game activities to elicit evidence for educationally valuable skills and knowledge. While this approach can provide individualized and diagnostic information about students, the design and development of assessment mechanics for a GBA is a nontrivial task. In this article, we describe the 10-step procedure that the design team of Physics Playground (formerly known as Newton's Playground) has established by adapting evidence-centered design to address unique challenges of GBA. The scaling method used for Physics Playground was Bayesian networks; thus this article describes specific actions taken for the iterative process of constructing and revising Bayesian networks in the context of the game Physics Playground.     

社交网站使用能带来社会资本吗?

社交网站旨在维系和发展个体的人际关系网络,而社会资本是个体人际关系中潜在的、可获得的益处或资源,因此社交网站使用和社会资本存在密切关系。然而,有关一般性社交网站使用行为与社会资本关系的研究结果并不一致。针对不同类型使用行为的研究发现,特定类型的社交网站使用行为对特定类型社会资本有积极影响。社交网站中自我表露行为是获取社会资本的一种必要的途径,关系维持行为、资源动员请求行为、隐私保护行为等均是个体获取社会资本的有效行为策略。最后,就社会资本定义、线上线下社会资本存在覆盖等问题进行了探讨。     

Trans‐Congregational Triadic Closure: Churchgoers’ Networks Within and Beyond the Pews

Triadic closure is the common tendency for connections to emerge between people's social network ties. This phenomenon has clear implications for congregational networks and may underlie many of the social benefits associated with church involvement. Less documented in the sociology of religion, however, is the occurrence of triadic closure involving congregational and noncongregational relationships within people's close personal networks. To conceptualize this boundary‐spanning network overlap, we elaborate the concept of trans‐congregational triadic closure (TCTC). Using data from the Portraits of American Life Survey—a project that examines both general and congregation‐specific networks of U.S. adults—we consider how religious tradition, macro‐level context, and individual factors predict the occurrence of TCTC in churchgoers’ networks. Findings suggest pronounced differences between evangelicals and mainline Protestants, a considerably lower likelihood of TCTC in densely populated areas, and higher likelihoods of TCTC corresponding with long durations of congregational involvement. We conclude by noting some of the implications of TCTC for the lives of individual believers and for religious organizations, and suggest ways that this concept could elucidate further aspects of contemporary religious life.     

Advice seeking and loaning of money related to relapse in recovery homes

Recovery homes help individuals who have completed substance use treatment programs re‐integrate back into the community. However, it is unclear what factors determine who will succeed in these settings and how these factors may be reinforced or undermined by the social interactions and social networks between residents living in the Oxford House recovery homes. In an effort to better understand these factors, the current study evaluated (a) the extent to which the density of social networks (i.e., friendship, willingness to loan money, and advice‐seeking relationships) is associated with social capital (i.e., sense of community, quality of life, hopefulness, self‐efficacy), and (b) whether the density of social networks predicts relapse over time. Among the findings, willingness to loan money was positively associated with all four individual‐level social capital variables, suggesting that availability of instrumental resources may be important to ongoing recovery. To test whether these house‐level social network factors then support recovery, a survival analysis was conducted, finding associations between relapse risk and the network densities over a 28‐month span. In particular, more dense advice‐seeking networks were associated with higher rates of relapse, suggesting that the advice‐seeking might represent a sign of organisational house problems, with many residents unsure of issues related to their recovery. In contrast, more dense loaning networks were associated with less relapse, so willingness to lend money could be measuring a willingness to help those in need. The implications of these findings are discussed.     

Do Humans and Deep Convolutional Neural Networks Use Visual Information Similarly for the Categorization of Natural Scenes?

The investigation of visual categorization has recently been aided by the introduction of deep convolutional neural networks (CNNs), which achieve unprecedented accuracy in picture classification after extensive training. Even if the architecture of CNNs is inspired by the organization of the visual brain, the similarity between CNN and human visual processing remains unclear. Here, we investigated this issue by engaging humans and CNNs in a two-class visual categorization task. To this end, pictures containing animals or vehicles were modified to contain only low/high spatial frequency (HSF) information, or were scrambled in the phase of the spatial frequency spectrum. For all types of degradation, accuracy increased as degradation was reduced for both humans and CNNs; however, the thresholds for accurate categorization varied between humans and CNNs. More remarkable differences were observed for HSF information compared to the other two types of degradation, both in terms of overall accuracy and image-level agreement between humans and CNNs. The difficulty with which the CNNs were shown to categorize high-passed natural scenes was reduced by picture whitening, a procedure which is inspired by how visual systems process natural images. The results are discussed concerning the adaptation to regularities in the visual environment (scene statistics); if the visual characteristics of the environment are not learned by CNNs, their visual categorization may depend only on a subset of the visual information on which humans rely, for example, on low spatial frequency information.