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.     

Word Senses as Clusters of Meaning Modulations: A Computational Model of Polysemy

Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one-to-many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic representations are linked to a single word form, which fails to capture important generalizations about how polysemous words are used; in particular, the graded nature of polysemous senses, and the flexibility and regularity of polysemy use. We provide a novel view of how polysemous words are represented and processed, focusing on how meaning is modulated by context. Our theory is implemented within a recurrent neural network that learns distributional information through exposure to a large and representative corpus of English. Clusters of meaning emerge from how the model processes individual word forms. In keeping with distributional theories of semantics, we suggest word meanings are generalized from contexts of different word tokens, with polysemy emerging as multiple clusters of contextually modulated meanings. We validate our results against a human-annotated corpus of polysemy focusing on the gradedness, flexibility, and regularity of polysemous sense individuation, as well as behavioral findings of offline sense relatedness ratings and online sentence processing. The results provide novel insights into how polysemy emerges from contextual processing of word meaning from both a theoretical and computational point of view.     

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9-year-old children. The neural network was trained and validated in the child semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed-effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed.     

人格特质及脑功能连接对社交网络的影响

采用网络分析的方法, 本研究从个体受欢迎程度和个体间亲密程度两方面探究了人格特质对社交网络的影响, 并在此基础上进一步探究了个体间大脑静息态功能连接相似性和社交网络的关系。结果发现：(1)高尽责性的个体在需要“值得信任”特质的社交网络中更受欢迎, 高宜人性的个体在需要“共享时光”的社交网络中更受欢迎; (2)在需要“相同兴趣”特质的社交网络中, 个体间人格相似性和社会距离呈显著负相关关系; (3)同样在需要“相同兴趣”特质的社交网络中, 个体间部分功能连接相似性与社会距离呈显著负相关关系, 这些功能连接主要集中在额顶控制网络以及背侧注意网络; 同时, 部分节点功能连接相似性与社会距离呈显著正相关关系, 这些功能连接主要集中在默认网络。研究结果揭示了人格特质对不同社交网络结构的影响, 以及个体间人格特质相似性和静息态脑网络相似性与社会距离的关系。本研究对理解社交网络的结构, 形成规律以及其中的信息传播规律有着重要启示意义。     

Attentional networks,vigilance, and distraction as a function of attention-deficit/hyperactivity disorder symptoms in an adult community sample

Attentional difficulties are a core axis in attention-deficit/hyperactivity disorder (ADHD). However, establishing a consistent and detailed pattern of these neurocognitive alterations has not been an easy endeavour. Based on a dimensional approach to ADHD, the present study aims at comprehensively characterizing three key attentional domains: the three attentional networks (alerting, orienting, and executive attention), two components of vigilance (executive and arousal vigilance), and distraction. To do so, we modified a single, fine-grained task (the ANTI-Vea) by adding irrelevant distractors. One hundred and twenty undergraduates completed three self-reports of ADHD symptoms in childhood and adulthood and performed the ANTI-Vea. Despite the low reliability of some ANTI-Vea indexes, the task worked successfully. While ADHD symptoms in childhood were related to alerting network and arousal vigilance, symptoms in adulthood were linked to executive vigilance. No association between ADHD symptom severity and executive attention and distraction was found. In general, our hypotheses about the relationships between ADHD symptoms and attentional processes were partially supported. We discuss our findings according to ADHD theories and attention measurement.     

Rule extraction using ensemble of neural network ensembles

Neural networks are well-known for their impressive classification performance, and the ensemble learning technique acts as a catalyst to improve this performance even further by integrating multiple networks.However, neural network ensembles, like neural networks, are regarded as a black box because they cannot explain their decision-making process. As a result, despite their high classification performance, neural networks and their ensembles are unsuitable for some applications that require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks the ability to justify their classification responses using explainable classification rules. There are several rule extraction algorithms for extracting classification rules from neural networks, but only a few of them use neural network ensembles to generate rules. As a result, this paper proposes a rule extraction algorithm called Rule Extraction Using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles.RE-E-NNES extracts classification rules by ensembling several neural network ensembles. The results demonstrate the efficacy of the proposed RE-E-NNES algorithm in comparison to other existing rule extraction algorithms.     

Bullying in Classrooms: Participant Roles From a Social Network Perspective

The aim of this research was to investigate if and how the group process of bullying can be examined using a social network perspective. In two studies, bullying was investigated using a social network version of the participant‐role questionnaire. Study 1 explored the social network structure of one classroom in detail. The findings provide evidence that ingroup and outgroup effects are important in explaining the group process of bullying, and shed new light on defending, suggesting that not only victims are defended. In line with Study 1, Study 2, using data from 494 children in 25 elementary school classes (M age = 10.5), revealed that victims as well as bullies were defended by their ingroup members. The social network perspective can be integrated in antibullying interventions by using it to inform teachers about the positive and negative relations among students, and the group structure of the classroom. Aggr. Behav. 38:494‐509, 2012. © 2012 Wiley Periodicals, Inc.     

A constructive neural-network approach to modeling psychological development

This article reviews a particular computational modeling approach to the study of psychological development – that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept learning, and theory of mind. Implications of this modeling for theoretical understanding of psychological development are considered.     

Motivated learning for the development of autonomous systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically-changing environments. In addition, this paper shows the basic neural network structures used to create abstract motivations, higher level goals, and subgoals. Finally, simulation results show comparisons between ML and RL in environments of gradually increasing sophistication and levels of difficulty.