现实利他行为与网络利他行为对大学生主观幸福感的影响：多重中介模型

为探讨现实利他行为与网络利他行为对主观幸福感的影响,并着力考察现实社会支持、网络社会支持与自尊在其中所起的作用,以1064名在读大学生为被试进行问卷调查,研究结果显示：（1）现实利他行为与网络利他行为均能在一定程度上预测主观幸福感。（2）现实、网络社会支持在现实、网络利他行为对主观幸福感的影响中起中介作用。（3）现实社会支持和自尊、网络社会支持和自尊在现实、网络利他行为对主观幸福感的影响中起多重中介作用。     

Improved Insight into and Prediction of Network Dynamics by Combining VAR and Dimension Reduction

To understand within-person psychological processes, one may fit VAR(1) models (or continuous-time variants thereof) to multivariate time series and display the VAR(1) coefficients as a network. This approach has two major problems. First, the contemporaneous correlations between the variables will frequently be substantial, yielding multicollinearity issues. In addition, the shared effects of the variables are not included in the network. Consequently, VAR(1) networks can be hard to interpret. Second, crossvalidation results show that the highly parametrized VAR(1) model is prone to overfitting. In this article, we compare the pros and cons of two potential solutions to both problems. The first is to impose a lasso penalty on the VAR(1) coefficients, setting some of them to zero. The second, which has not yet been pursued in psychological network analysis, uses principal component VAR(1) (termed PC-VAR(1)). In this approach, the variables are first reduced to a few principal components, which are rotated toward simple structure; then VAR(1) analysis (or a continuous-time analog) is applied to the rotated components. Reanalyzing the data of a single participant of the COGITO study, we show that PC-VAR(1) has the better predictive performance and that networks based on PC-VAR(1) clearly represent both the lagged and the contemporaneous variable relations.     

A social network analysis of student retention using archival data

This study attempts to determine if a relationship exists between first-to-second-year retention and social network variables for a cohort of first-year students at a small liberal arts college. The social network is reconstructed using not survey data as is most common, but rather using archival data from a student information system. Each student is given a retention score and an attrition score based on the behavior of their immediate relationships in the network. Those scores are then entered into a logistic regression that includes tradition background and performance variables that are traditionally significantly related to retention. Students?? friends?? retention and attrition behaviors are found to have a greater impact on retention that any background or performance variable.     

Detecting “cracks” in mental health service systems: Application of network analytic techniques

This paper argued that problems of coordination and integration of community mental health systems are best approached from a network perspective in which all linkages between agencies are considered simultaneously. Structural coordination and integration can be assessed through the analysis of these linkages. The utility of this approach was demonstrated by deriving a typology of system "cracks" from network analytic constructs. A network analysis of a community mental health service network was then generated to illustrate how these cracks can be empirically identified. It is suggested that mental health planners will find both the network analytic approach and the typology of cracks useful for addressing problems of coordination and integration.     

Conditional estimation of exponential random graph models from snowball sampling designs

A complete survey of a network in a large population may be prohibitively difficult and costly. So it is important to estimate models for networks using data from various network sampling designs, such as link-tracing designs. We focus here on snowball sampling designs, designs in which the members of an initial sample of network members are asked to nominate their network partners, their network partners are then traced and asked to nominate their network partners, and so on. We assume an exponential random graph model (ERGM) of a particular parametric form and outline a conditional maximum likelihood estimation procedure for obtaining estimates of ERGM parameters. This procedure is intended to complement the likelihood approach developed by  Handcock and Gile (2010) by providing a practical means of estimation when the size of the complete network is unknown and/or the complete network is very large. We report the outcome of a simulation study with a known model designed to assess the impact of initial sample size, population size, and number of sampling waves on properties of the estimates. We conclude with a discussion of the potential applications and further developments of the approach.     

An Attractor Model of Lexical Conceptual Processing: Simulating Semantic Priming

An attractor network was trained to compute from word form to semantic representations that were based on subject-generated features. The model was driven largely by higher-order semantic structure. The network simulated two recent experiments that employed items included in its training set (McRae and Boisvert, 1998). In Simulation 1, short stimulus onset asynchrony priming was demonstrated for semantically similar items. Simulation 2 reproduced subtle effects obtained by varying degree of similarity. Two predictions from the model were then tested on human subjects. In Simulation 3 and Experiment 1, the items from Simulation 1 were reversed, and both the network and subjects showed minimally different priming effects in the two directions. In Experiment 2, consistent with attractor networks but contrary to a key aspect of hierarchical spreading activation accounts priming was determined by featural similarity rather than shared superordinate category. It is concluded that semantic-similarity priming is due to featural overlap that is a natural consequence of distributed representations of word meaning.     

Modeling the emergence of informational content by adaptive networks for temporal factorisation and criterial causation

Propagated activation of neurons through their network is an important process in the brain. Another crucial part of neural processing concerns adaptation over time of characteristics of this network such as connection strengths or excitability thresholds. This adaptation can be slow, as in learning from a multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics can be considered informational criteria for activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is determined by such information via a process termed criterial causation. In the current paper, the relationship of criterial causation with the principle of temporal factorisation for the dynamics of the world in general is explored. Temporal factorisation describes how the world represents information about its past in its present state, which then in turn determines the world’s future. In the paper, it is shown how these processes are analysed in more detail and modeled by (adaptive) network models.     

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means—the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.     

Familias Unidas: a family-centered ecodevelopmental intervention to reduce risk for problem behavior among Hispanic adolescents

This paper describes the theoretical and empirical foundations of Familias Unidas, a multilevel, family-centered intervention designed to prevent problem behavior in Hispanic adolescents. The main theoretical tenets for the intervention model; an ecological-developmental perspective, the centrality of ethnic and cultural themes, application of empowerment principles, and a family focus are reviewed. The literature on the risk and protective factors that provided the justification for the intervention's targeted mediators and the core clinical applications that are intended to alter them are discussed. Familias Unidas engages Hispanic immigrant parents into an empowerment process in which they first build a strong parent-support network and then use the network to increase knowledge of culturally relevant parenting, strengthen parenting skills, and then apply these new skills in a series of activities designed to reduce risks frequently found in poor, urban environments. The available evidence supporting the efficacy of Familias Unidas is summarized, as are future goals and a current, second-generation application of the intervention.     

A hybrid model using neural networks and ACT-R

How do people make decisions given contradictory information? This paper presents a model of how expert DSOs (defensive system operators) on a B1 bomber examine a complex series of signals, categorize whether those signals are dangerous or not, and then make a decision on the basis of those signals. This decision is made more difficult because an automatic on-board computer sometimes identifies the signal incorrectly. Therefore, the DSO must compare the actual signal to the system ID “guess.” The proposed model is a hybrid model, combining a standard neural network and ACT-R, a production system, which achieves a high degree of success.     

Brand names on packaging and their impact on purchase preference

This study examines whether appearance of corporate, product and dual brand names (or a combination of brand names used together) on packaging influence consumer purchase preference. The face‐to‐face survey consists of consumers rating their purchase preference on a 7‐point scale sixteen random brand names, and combinations of brand names on packaging along with three different prices, each for two product categories: chocolate and cereals – a total of 4032 observations are examined. Hypotheses are tested using multiple linear regression models. The findings show corporate, product and dual brand names have little impact on purchase preference per se, instead brand category dominance influence consumer choice, and corporate names do not add value as previously thought. The study suggests trends and contexts in which, a corporate name and a product brand name may be extended to optimise consumer brand associations and influence purchase, as well as strategies for extending in remote product categories. Future research needs focusing on international consumers' response on brand linkages in the international arena, and the issues of brand building and brand equity maintenance in international markets. The study makes contribution to brand extension, brand portfolio management and strategic brand management research. Copyright © 2011 John Wiley & Sons, Ltd.     

Structural Equation Modeling of Social Networks: Specification,Estimation, and Application

Psychologists are interested in whether friends and couples share similar personalities or not. However, no statistical models are readily available to test the association between personalities and social relations in the literature. In this study, we develop a statistical model for analyzing social network data with the latent personality traits as covariates. Because the model contains a measurement model for the latent traits and a structural model for the relationship between the network and latent traits, we discuss it under the general framework of structural equation modeling (SEM). In our model, the structural relation between the latent variable(s) and the outcome variable is no longer linear or generalized linear. To obtain model parameter estimates, we propose to use a two-stage maximum likelihood (ML) procedure. This modeling framework is evaluated through a simulation study under representative conditions that would be found in social network data. Its usefulness is then demonstrated through an empirical application to a college friendship network.     

Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network

The risk assessment of knowledge fusion in innovation ecosystems is directly related to these ecosystems’ success or failure. A back-propagation (BP) neural network optimized by a genetic algorithm (GA) is thus proposed to evaluate the risk of knowledge fusion in innovation ecosystems. First, an index system is constructed for evaluating the risk of knowledge fusion in innovation ecosystems, and data are collected by questionnaire for use as training data for the neural networks. To realize machine learning, 84 datasets were generated, of which 60 were used to train the network, and 24 were used to test the network in MATLAB (R2014b). Evaluation models were then constructed by the BP neural network and GA-BP neural network, and their accuracy was judged by comparing the evaluation value with the target value. The comparison shows that the GA-BP neural network has faster convergence speed and higher stability, can achieve the goal more often, and reduces the possibility of the BP neural network falling into a local optimum instead of reaching global optimization. The GA-BP neural network model for the knowledge fusion risk assessment of innovation ecosystems provides a new method for practice.     

Effects of centrality on retrieval of text-based concepts

Four experiments examined the effect of centrality on retrieval time using a wide range of testing conditions. Subjects read passages containing concepts that were rated as central, moderately central, or peripheral to a passage, and then were asked to retrieve those concepts. Experiment 1 used a probe recognition task that contained foils that were unrelated to the passage, and it was found that central concepts were recognized more quickly than peripheral concepts. In Experiment 2, the foils were passage related, resulting in a much larger recognition advantage of central concepts over peripheral concepts. Experiment 3 used a speeded-recall task. Again central concepts were recalled more quickly than less central concepts. Experiment 4 ruled out a simple strength argument. The results of all four experiments are discussed in terms of text being represented as an integrated network. Within this network, central concepts are more interconnected, providing additional access routes that facilitate retrieval.     

A Network Perspective on the Processes of Empowered Organizations

Organizational empowerment is a multi-faceted concept that involves processes occurring both within and between organizations that facilitate achievement of their goals. This paper takes a closer look at three interorganizational processes that lead to empowered organizations: building alliances, getting the word out, and capturing others’ attention. These processes are located within the broader nomological network of empowerment and organizational empowerment, and are linked to particular patterns of interorganizational relationships that facilitate organizations’ ability to engage in them. A new network-based measure, γ-centrality, is introduced to capture the particular network structure associated with each process to be assessed. It is demonstrated first in a hypothetical organizational network, then applied to take a closer look at organizational empowerment in the context of a coordinating council composed of human service agencies. The paper concludes with a discussion of the implications of relationships between these processes, and the potential for unintended consequences in the empowerment of organizations.     

Situation models and memory: the effects of temporal and causal information on recall sequence

Participants watched an episode of the television show Cheers on video and then reported free recall. Recall sequence followed the sequence of events in the story; if one concept was observed immediately after another, it was recalled immediately after it. We also made a causal network of the show's story and found that recall sequence followed causal links; effects were recalled immediately after their causes. Recall sequence was more likely to follow causal links than temporal sequence, and most likely to follow causal links that were temporally sequential. Results were similar at 10-minute and 1-week delayed recall. This is the most direct and detailed evidence reported on sequential effects in recall. The causal network also predicted probability of recall; concepts with more links and concepts on the main causal chain were most likely to be recalled. This extends the causal network model to more complex materials than previous research.     

Situation models and memory: The effects of temporal and causal information on recall sequence

Participants watched an episode of the television show Cheers on video and then reported free recall. Recall sequence followed the sequence of events in the story; if one concept was observed immediately after another, it was recalled immediately after it. We also made a causal network of the show's story and found that recall sequence followed causal links; effects were recalled immediately after their causes. Recall sequence was more likely to follow causal links than temporal sequence, and most likely to follow causal links that were temporally sequential. Results were similar at 10-minute and 1-week delayed recall. This is the most direct and detailed evidence reported on sequential effects in recall. The causal network also predicted probability of recall; concepts with more links and concepts on the main causal chain were most likely to be recalled. This extends the causal network model to more complex materials than previous research.     

A small-world network model of facial emotion recognition

Various models have been proposed to increase understanding of the cognitive basis of facial emotions. Despite those efforts, interactions between facial emotions have received minimal attention. If collective behaviours relating to each facial emotion in the comprehensive cognitive system could be assumed, specific facial emotion relationship patterns might emerge. In this study, we demonstrate that the frameworks of complex networks can effectively capture those patterns. We generate 81 facial emotion images (6 prototypes and 75 morphs) and then ask participants to rate degrees of similarity in 3240 facial emotion pairs in a paired comparison task. A facial emotion network constructed on the basis of similarity clearly forms a small-world network, which features an extremely short average network distance and close connectivity. Further, even if two facial emotions have opposing valences, they are connected within only two steps. In addition, we show that intermediary morphs are crucial for maintaining full network integration, whereas prototypes are not at all important. These results suggest the existence of collective behaviours in the cognitive systems of facial emotions and also describe why people can efficiently recognize facial emotions in terms of information transmission and propagation. For comparison, we construct three simulated networks—one based on the categorical model, one based on the dimensional model, and one random network. The results reveal that small-world connectivity in facial emotion networks is apparently different from those networks, suggesting that a small-world network is the most suitable model for capturing the cognitive basis of facial emotions.     

Temporal constraints on visual learning: a computational model

Given a constant stream of perceptual stimuli, how can the underlying invariances associated with a given input be learned? One approach consists of using generic truths about the spatiotemporal structure of the physical world as constraints on the types of quantities learned. The learning methodology employed here embodies one such truth: that perceptually salient properties (such as stereo disparity) tend to vary smoothly over time. Unfortunately, the units of an artificial neural network tend to encode superficial image properties, such as individual grey-level pixel values, which vary rapidly over time. However, if the states of units are constrained to vary slowly, then the network is forced to learn a smoothly varying function of the training data. We implemented this temporal-smoothness constraint in a backpropagation network which learned stereo disparity from random-dot stereograms. Temporal smoothness was formalized with the use of regularization theory by modifying the standard cost function minimised during training of a network. Temporal smoothness was found to be similar to other techniques for improving generalisation, such as early stopping and weight decay. However, in contrast to these, the theoretical underpinnings of temporal smoothing are intimately related to fundamental characteristics of the physical world. Results are discussed in terms of regularization theory and the physically realistic assumptions upon which temporal smoothing is based.