China leads the world in the development and rollout of 5G network, yet less research has been done on the drivers of Chinese people’s adoption of 5G network, especially the specific role of national sentiments, such as the influence of patriotism on their attitude toward 5G network. The study obtained 804 effective online questionnaires from the respondents of various ages, genders, areas, and educational levels. The results based on the structural equation modeling analysis showed that patriotism was an antecedent to (...) Chinese users’ cognition of the conditions provided by the government and telecom operators and the extent that users were influenced by media and surrounding people, which in turn increased users’ expectations of the performance of 5G network and their confidence in adapting to 5G network, and resulted in an increased willingness to use 5G network. This research helps to understand the role of patriotism, which is an emotional factor, in stimulating Chinese users’ attitudes toward 5G network at the time when 5G network is putting into large-scale commercial use in China. (shrink)

The ongoing generation of expectations is fundamental to listeners’ experience of music, but research into types of statistical information that listeners extract from musical melodies has tended to emphasize transition probabilities and n‐grams, with limited consideration given to other types of statistical learning that may be relevant. Temporal associations between scale degrees represent a different type of information present in musical melodies that can be learned from musical corpora using expectation networks, a computationally simple method based on activation (...) and decay. Expectation networks infer the expectation of encountering one scale degree followed in the near (but not necessarily immediate) future by another given scale degree, with previous work suggesting that scale degree associations learned by expectation networks better predict listener ratings of pitch similarity than transition probabilities. The current work outlines how these learned scale degree associations can be combined to predict melodic continuations and tests the resulting predictions on a dataset of listener responses to a musical cloze task previously used to compare two other models of melodic expectation, a variable‐order Markov model (IDyOM) and Temperley's music‐theoretically motivated model. Under multinomial logistic regression, all three models explain significant unique variance in human melodic expectations, with coefficient estimates highest for expectation networks. These results suggest that generalized scale degree associations informed by both adjacent and nonadjacent relationships between melodic notes influence listeners’ melodic predictions above and beyond n‐gram context, highlighting the need to consider a broader range of statistical learning processes that may underlie listeners’ expectations for upcoming musical events. (shrink)

Prediction‐based processes appear to play an important role in language. Few studies, however, have sought to test the relationship within individuals between prediction learning and natural language processing. This paper builds upon existing statistical learning work using a novel paradigm for studying the on‐line learning of predictive dependencies. Within this paradigm, a new “prediction task” is introduced that provides a sensitive index of individual differences for developing probabilistic sequential expectations. Across three interrelated experiments, the prediction task and results thereof are (...) used to bridge knowledge of the empirical relation between statistical learning and language within the context of nonadjacency processing. We first chart the trajectory for learning nonadjacencies, documenting individual differences in prediction learning. Subsequent simple recurrent network simulations then closely capture human performance patterns in the new paradigm. Finally, individual differences in prediction performances are shown to strongly correlate with participants’ sentence processing of complex, long‐distance dependencies in natural language. (shrink)

Data on the number of adults that an individual contacts at least once a month in a set of British populations yield estimates of network sizes that correspond closely to those of the typical “sympathy group” size in humans. Men and women do not differ in their total network size, but women have more females and more kin in their networks than men do. Kin account for a significantly higher proportion of network members than would be expected by chance. (...) The number of kin in the network increases in proportion to the size of the family; as a result, people from large families have proportionately fewer non-kin in their networks, suggesting that there is either a time constraint or a cognitive constraint on network size. A small inner clique of the network functions as a support group from whom an individual is particularly likely to seek advice or assistance in time of need. Kin do not account for a significantly higher proportion of the support clique than they do for the wider network of regular social contacts for either men or women, but each sex exhibits a strong preference for members of their own sex. (shrink)

Human decision making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a neural (...) network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. In order to substantiate our viewpoint, we discern and explain four basic neural network principles: (1) Association, (2) Compatibility (3) Retainment, and (4) Focus. These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor functions. They form the basis for our inclinations to associate and combine (unrelated) information, to prioritize information that is compatible with our present state (such as knowledge, opinions and expectations), to retain given information that sometimes could better be ignored, and to focus on dominant information while ignoring relevant information that is not directly activated. The supposed mechanisms are complementary and not mutually exclusive. For different cognitive biases they may all contribute in varying degrees to distortion of information. The present viewpoint not only complements the earlier three viewpoints, but also provides a unifying and binding framework for many cognitive bias phenomena. (shrink)

Purpose: This study explores social networkers' interest in and attitudes toward personal genome testing (PGT), focusing on expectations related to the clinical integration of PGT results. Methods: An online survey of 1,087 social networking users was conducted to assess 1) use and interest in PGT; 2) attitudes toward PGT companies and test results; and 3) expectations for the clinical integration of PGT. Descriptive statistics were calculated to summarize respondents' characteristics and responses. Results: Six percent of respondents have used PGT, 64% (...) would consider using PGT, and 30% would not use PGT. Of those who would consider using PGT, 74% report they would use it to gain knowledge about disease in their family. 34% of all respondents consider the information obtained from PGT to be a medical diagnosis. 78% of those who would consider PGT would ask their physician for help interpreting test results, and 61% of all respondents believe physicians have a professional obligation to help individuals interpret PGT results. Conclusion: Respondents express interest in using PGT services, primarily for purposes related to their medical care and expect physicians to help interpret PGT results. Physicians should therefore be prepared for patient demands for information and counsel on the basis of PGT results. (shrink)

The information overload in E-Discovery proceedings makes reviewing expensive and it increases the risk of failure to produce results on time and consistently. New interactive techniques have been introduced to increase reviewer productivity. In contrast, the techniques presented in this article propose an alternative method that tries to reduce information during culling so that less information needs to be reviewed. The proposed method first focuses on mapping the email collection universe using straightforward statistical methods based on keyword filtering combined with (...) date time and custodian identities. Subsequently, a social network is constructed from the email collection that is analyzed by filtering on date time and keywords. By using the network context we expect to provide a better understanding of the keyword hits and the ability to discard certain parts of the collection. (shrink)

This study explored attachment networks in long-term couples who differed in parenting choice and relationship status. Attachment networks were defined in terms of attachment functions, attachment strength, the presence of a primary figure, and full-blown attachments. Participants were 198 couples, married or cohabiting, either expecting their first child or childless-by-choice. Results indicated that partners were the most significant figures on all indicators. However, expectant women reported more proximity seeking and stronger attachments to mothers, while expectant men relied more (...) on fathers for safe haven. Married participants indicated less proximity seeking to partners than cohabiting couples, and married women reported less reliance on partners for safe haven than married men and cohabiting women. This study supports previous findings underlining the particular importance of partners for members of long-term couples. Further, it extends past research by showing the robustness of this finding across parenting choice, and by revealing gender differences in the attachment networks of committed couples. (shrink)

Competitive state anxiety is an integral feature of sports performance but despite its pervasiveness, there is still much debate concerning the measurement of the construct. Adopting a network approach that conceptualizes symptoms of a construct as paired associations, we proposed re-examining competitive state anxiety as a system of interacting components in a dataset of 485 competitive athletes from the United Kingdom. Following a process of data reduction, we estimated a network structure for 15 items from the modified Three Factor Anxiety (...) Inventory using the graphical LASSO algorithm. We then examined network connectivity using node predictability. Exploratory graph analysis was used to detect communities in the network and bridge expected influence calculated to estimate the influence of items from one community to items in other communities. The resultant network produced a range of node predictability values. Community detection analysis derived three communities that corresponded with previous research and several nodes were identified that bridged these communities. We conclude that network analysis is a useful tool to explore the competitive state anxiety response and we discuss how the results of our analysis might inform the assessment of the construct and how this assessment might inform interventions. (shrink)

Why and under what conditions are individuals altruistic to family and friends in their social networks? Evolutionary psychology suggests that such behaviour is primarily the product of adaptations for kin- and reciprocal altruism, dependent on the degree of genetic relatedness and exchange of benefits, respectively. For this reason, individuals are expected to be more altruistic to family members than to friends: whereas family members can be the recipients of kin and reciprocal altruism, friends can be the recipients of reciprocal (...) altruism only. However, there is a question about how the effect of kinship is implemented at the proximate psychological level. One possibility is that kinship contributes to some general measure of relationship quality (such as ‘emotional closeness’), which in turn explains altruism. Another possibility is that the effect of kinship is independent of relationship quality. The present study tests between these two possibilities. Participants (N= 111) completed a self-report questionnaire about their willingness to be altruistic, and their emotional closeness, to 12 family members and friends at different positions in their extended social networks. As expected, altruism was greater for family than friends, and greater for more central layers of the network. Crucially, the results showed that kinship made a significant unique contribution to altruism, even when controlling for the effects of emotional closeness. Thus, participants were more altruistic towards kin than would be expected if altruism was dependent on emotional closeness alone – a phenomenon we label a ‘kinship premium’. These results have implications for the ongoing debate about the extent to which kin relations and friendships are distinct kinds of social relationships, and how to measure the ‘strength of ties’ in social networks. (shrink)

Philosophers have long tried to understand scientific change in terms of a dynamics of revision within ‘theoretical frameworks,’ ‘disciplinary matrices,’ ‘scientific paradigms’ or ‘conceptual schemes.’ No-one, however, has made clear precisely how one might model such a conceptual scheme, nor what form change dynamics within such a structure could be expected to take. In this paper we take some first steps in applying network theory to the issue, modeling conceptual schemes as simple networks and the dynamics of change as (...) cascades on those networks. The results allow a new understanding of two traditional approaches—Popper and Kuhn—as well as introducing the intriguing prospect of viewing scientific change using the metaphor of selforganizing criticality. (shrink)

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of (...) semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category-exemplar, mediated and backward prime-target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model. (shrink)

Globalization has increased the economic power of the multinational corporation (MNC), engendering calls for greater corporate social responsibility (CSR) from these companies. However, the current mechanisms of global governance are inadequate to codify and enforce recognized CSR standards. One method by which companies can impact positively on global governance is through the mechanism of Global Public Policy Networks (GPPN). These networks build on the individual strength of MNCs, domestic governments, and non-governmental organizations to create expected standards of behaviour (...) in such areas as labour rights, environmental standards, and working conditions. This article models GPPN in the issue area of CSR. The potential benefits of GPPN include better overall coordination among industry and government in establishing what social expectations the modern MNC will be expected to fill. (shrink)

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the (...) other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization. (shrink)

This paper presents a modified random network model to illustrate how groups can form in the absence of evolutionary forces, assuming groups are collections of entities at any level of organization. This model is inspired by the Zero Force Evolutionary Law, which states that there is always a tendency for diversity and complexity to increase in any evolutionary system containing variation and heredity. That is, in the absence of evolutionary forces, the expectation is a continual increase in diversity and (...) complexity at any level of biological hierarchy. I show that, when modeled, this expectation of increasing variation results not only in the formation of groups, but also in a higher probability of group formation than is found in a model that is purely random. (shrink)

Fibromyalgia is a chronic pain syndrome characterized by dysfunctional processing of nociceptive stimulation. Neuroimaging studies have pointed out that pain-related network functioning seems to be altered in these patients. It is thought that this clinical symptomatology may be maintained or even strengthened because of an enhanced expectancy for painful stimuli or its forthcoming appearance. However, neural electrophysiological correlates associated with such attentional mechanisms have been scarcely explored. In the current study, expectancy processes of upcoming laser stimulation (painful and non-painful) and (...) its further processing were explored by event-related potentials (ERPs). Nineteen fibromyalgia patients and twenty healthy control volunteers took part in the experiment. Behavioral measures (reaction times and subjective pain perception) were also collected. We manipulated the pain/no pain expectancy through an S1–S2 paradigm (cue-target). S1 (image: triangle or square) predicted the S2 appearance (laser stimulation: warmth or pinprick sensation). Laser stimuli were delivered using a CO2 laser device. Temporal and spatial principal component analyses were employed to define and quantify the ERP component reliability. Statistical analyses revealed the existence of an abnormal pattern of pain expectancy in patients with fibromyalgia. Specifically, our results showed attenuated amplitudes at posterior lCNV component in anticipation of painful stimulation that was not found in healthy participants. In contrast, although larger P2 amplitudes to painful compared to innocuous events were shown, patients did not show any amplitude change in this laser-evoked response as a function of pain predictive cues (as occurred in the healthy control group). Additionally, analyses of the subjective perception of pain and reaction time indicated that laser stimuli preceded by pain cues were rated as more painful than those signaling non-pain expectancy and were associated with faster responses. Differences between groups were not found. The present findings suggest the presence of dysfunction in pain expectation mechanisms in fibromyalgia that eventually may make it difficult for patients to correctly interpret signs that prevent pain symptoms. Furthermore, the abnormal pattern in pain expectancy displayed by fibromyalgia patients could result in ineffective pain coping strategies. Understanding the neural correlates of pain processing and its modulatory factors is crucial to identify treatments for chronic pain syndromes. (shrink)

This article presents a network perspective on whistleblowing. It considers how whistleblowing affects, and is affected by, the preexisting distribution of power inside and outside an organization, where power is conceptualized as deriving from the network positions of the key actors. The article also highlights four characteristic features of whistleblowing: third‐party detriment, local subversion, appeal to central or external power, and reasonable expectation of concern. The feature of local subversion succinctly explains why whistleblowing is difficult. The feature of appeal (...) to central or external power highlights that contrary to the perception of a democratizing phenomenon, whistleblowing tends to redistribute discretion away from local power toward more central power. This suggests a need for caution about institutional measures to promote whistleblowing in contexts where governance is already highly centralized. (shrink)

This article proposes to model and empirically study social networks as emerging, stabilizing, and changing in the process of communication. Rather than starting from actors, communicative events are conceptualized as the basic units. In the sequence of communication, these events are attributed to actors, together with underlying dispositions. Relational expectations about the behavior of actors towards others result, effectively structuring communication and making for the regularities of communication we observe as relationships and networks. Not only individuals, but also (...) collective and corporate actors can feature as nodes in social networks – as long as action is attributed to them, and relational expectations about their behavior to other actors develop. The approach combines recent developments in the theory of social networks by Harrison White and others with Niklas Luhmann’s theory of communication. (shrink)

In recent years, neural networks have attracted much attention in the machine learning and the deep learning technologies. Bio-inspired functions and intelligence are also expected to process efficiently and improve existing technologies. In the visual pathway, the prominent features consist of nonlinear characteristics of squaring and rectification functions observed in the retinal and visual cortex networks, respectively. Further, adaptation is an important feature to activate the biological systems, efficiently. Recently, to overcome short-comings of the deep learning techniques, orthogonality (...) for the weights in the networks has been developed for the signal propagation and the efficient optimization of the learning. In this paper, bio-inspired asymmetric networks with nonlinear characteristics are proposed, which are derived from the retinal networks in the biological visual pathway. The asymmetric network proposed here was verified to detect the movement of the object, efficiently in our previous studies. This paper shows a new characteristic of the adaptive orthogonality in the asymmetric networks. First, it is shown that the asymmetric network with nonlinear characteristics is effective for generating orthogonality. Second, the proposed asymmetric network with Gabor filters is compared with the conventional energy model from the point of the orthogonality characteristics. Finally, the asymmetric networks with nonlinear characteristics can generate the extended orthogonal bases in independent subspaces, which are useful for classification and efficient learning. (shrink)

For a better detection in Network information security monitoring system, the author proposes a method based on adaptive depth detection. A deep belief network was designed and implemented, and the intrusion detection system model was combined with a support vector machine. The data set adopts the NSL-KDD network communication data set, and this data set is authoritative in the security field. Redundant cleaning, data type conversion, normalization, and other processing operations are performed on the data set. Using the data conversion (...) method based on the probability mass function probability mass function coding, a standard data set with low redundancy and low dimensionality can be obtained. Research indicates that when the batch size reaches 64, the accuracy of the test set reaches its maximum value. As the batch size increases, the accuracy first increases and then decreases. When the batch size continues to increase, the model will inevitably fall into the local optimal state, resulting in the degradation of the detection performance of the system. In terms of the false alarm rate, the DBN-SVM model is also the highest; however, it is only 10.73%. Under the premise of increasing the detection rate, the false alarm rate is improved; for the overall detection performance of the model, it is within an acceptable range. In terms of accuracy, the DBN-SVM model also scored the highest. The accuracy rate is the ratio of normal and correct classification for intrusion detection. It can explain the detection ability of the model. In summary, the overall detection ability of the DBN-SVM model is the best. The good classification ability to use SVM is proved, and the classification of low-dimensional features is expected to increase the detection rate of the system. (shrink)

Introduction. The development of online marketing in social networks creates unique opportunities for personal selling. Especially these opportunities are manifested in online education when they buy a brand of an expert with experience in a particular field. That is why a competitive space is being formed in the Instagram social network, where a personal brand acts as a product or service. -/- Materials and methods. Studying the effectiveness of promoting a personal brand in social networks based on the (...) Instagram platform was chosen to have great visual opportunities for self-presentation. As part of the collection of empirical material, two methods were used: a survey (N-200) and content analysis of three blogger accounts with high rates of activity and popularity. -/- Results and discussion. Content analysis of bloggers showed that an algorithmic feed on a social network allows bloggers to control the content. To help them, Instagram provides statistical data on user reach, thereby capturing trends in the movement of the blogger’s audience. The main task of a blogger is to combine real and “virtual” images so as not to lose consumer confidence. A survey of social network users confirmed the importance of a personal brand for them. The survey also made it possible to identify the most popular audience requests that they expect from bloggers and their accounts: valuable reviews and recommendations, case studies and author’s solutions, storytelling, blogger’s reflections and motivating messages, live broadcasts, and stories are important. -/- Conclusions. The results obtained underline followers’ high level of interest in the bloggers’ personal brand on Instagram. The study results show that the most significant number of those involved (those who give feedback and are constantly involved in interaction with the account) is where the formation of a personal brand is built to a greater extent on the emotional level of perception of the individual. (shrink)

Social network analysis has become an increasingly important tool among political scientists for understanding legislative cooperation in modern, democratic nation-states. Recent research has demonstrated the influence that group affinity (homophily) and mutual exchanges (reciprocity) have in structuring political relationships. However, this literature has typically focused on political cooperation where costs are low, relationships are not exclusive, and/or partisan competition is high. Patterns of legislative behavior in alternative contexts are less clear and remain largely unexamined. Here, we compare theoretical expectations of (...) cooperation in these contexts from the political and biosocial sciences and implement the first assessment of political alliance formation in a novel legislative environment where costs to cooperation are high and party salience low. We implement a stochastic actor-oriented model (SAOM) to examine bill floor sponsorship, a process in which a “floor sponsor” becomes the exclusive advocate for a colleague’s piece of legislation, in the Utah state legislature from 2005 to 2008—a context in which gender (male) and political party (Republican) supermajorities exist. We find that (1) party and gender homophily predict who legislators recruit as floor sponsors, whereas seniority does not, and (2) legislators frequently engage in reciprocal exchanges of floor sponsorship. In addition, whereas gender homophily increases the likelihood of reciprocity, party homophily decreases it. Our findings suggest that when the cost of cooperation is high, political actors use in-group characteristics for initiating alliances, but once a cooperative relationship is established with an out-group political member, it is reinforced through repeated exchanges. These findings may be useful for understanding the rise of political polarization and gridlock in democracies internationally. (shrink)

The last two decades have produced unprecedented successes in the fields of artificial intelligence and machine learning (ML), due almost entirely to advances in deep neural networks (DNNs). Deep hierarchical memory networks are not a novel concept in cognitive science and can be traced back more than a half century to Simon's early work on discrimination nets for simulating human expertise. The major difference between DNNs and the deep memory nets meant for explaining human cognition is that the (...) latter are symbolic networks meant to model the dynamics of human memory and learning. Cognition-inspired symbolic deep networks (SDNs) address several known issues with DNNs, including (1) learning efficiency, where a much larger number of training examples are required for DNNs than would be expected for a human; (2) catastrophic interference, where what is learned by a DNN gets unlearned when a new problem is presented; and (3) explainability, where there is no way to explain what is learned by a DNN. This paper explores whether SDNs can achieve similar classification accuracy performance to DNNs across several popular ML datasets and discusses the strengths and weaknesses of each approach. Simulations reveal that (1) SDNs provide similar accuracy to DNNs in most cases, (2) SDNs are far more efficient than DNNs, (3) SDNs are as robust as DNNs to irrelevant/noisy attributes in the data, and (4) SDNs are far more robust to catastrophic interference than DNNs. We conclude that SDNs offer a promising path toward human-level accuracy and efficiency in category learning. More generally, ML frameworks could stand to benefit from cognitively inspired approaches, borrowing more features and functionality from models meant to simulate and explain human learning. (shrink)

The decline in live social contact during the Covid-19 pandemic is driving an increase in the demand for social media communication. This study is an in-depth continuation of an earlier study on the topic of self-esteem and value orientations of youth. A survey was conducted and helped to determine the values ​​of life that are broadcast on social media profiles and are actively welcomed by students in Facebook, YouTube, Twitter, Pinterest, Instagram, Tik Tok: independence, high aspirations, health and beauty, material (...) values. The high level of activity of the respondents in social networks was investigated and the medium level of Internet addiction was determined. The respondents revealed the level of conformity and individual-typological personality traits. The students are characterized by a high level of anxiety and tension. The conformity level helps to identify the individuals who are most vulnerable to the effects of social media. The terminal and instrumental values ​​of students are investigated: health and beauty, material values, entertainment, family life, self-confidence, love, freedom, rationalism, Irreconcilability to shortcomings, independence, self-control and high claims. Correlation links were found in the indicators of conformism / nonconformism / Internet addiction and value orientations of the individual. Work in groups was carried out with the aim of abandoning a set of social masks, selected taking into account the expectations of others. The work is based on the metaphor of diminution from Slavic myths and fairy tales. (shrink)

Public health care interventions—regarding vaccination, obesity, and HIV, for example—standardly take the form of information dissemination across a community. But information networks can vary importantly between different ethnic communities, as can levels of trust in information from different sources. We use data from the Greater Pittsburgh Random Household Health Survey to construct models of information networks for White and Black communities--models which reflect the degree of information contact between individuals, with degrees of trust in information from various sources (...) correlated with positions in that social network. With simple assumptions regarding belief change and social reinforcement, we use those modeled networks to build dynamic agent-based models of how information can be expected to flow and how beliefs can be expected to change across each community. With contrasting information from governmental and religious sources, the results show importantly different dynamic patterns of belief polarization within the two communities. (shrink)

Diversity of social identities can improve the performance of groups through varied cognitive and communicative pathways. Recently, research efforts have focused on identifying when we should expect to see these potential benefits in real-world settings. While most research to date has studied this topic at individual and interpersonal levels, in this paper, we develop an agent-based model to explore how various aspects of homophily, the tendency of individuals to associate with similar others, affects performance at a larger scale. Study 1 (...) examines how two types of homophily---identity-driven and opinion-driven---impact collective performance on a sequential decision-making task via modulating network formation and trust relations. Study 2 considers how the presence of identity-based conformity pressure can affect the findings from the first study. Overall, we find that the effect of homophily on performance is complex, depending on the operative dimensions of similarity, mediating pathways, and the specific outcome of interest. Finally, we discuss the implications of our results for policy interventions aiming to improve group performance. (shrink)

The processes of post-socialist transformation, especially large-scale migration from Eastern Europe to the Western hemisphere, are creating an ‘expansion of space’ from the local to the supra-local. This process involves the expansion of personal-, familial- and friendship-based networking practices which acquire significance as transnational mobile livelihoods and as significant dimensions of urban dynamics in global cities like Chicago. What are the networks, attachments and social bonds of Eastern European migrants in Chicago? Ethnographic fieldwork conducted in Chicago in 2013 among (...) recent Lithuanian immigrants brought out the importance of a cultural identity of East European-ness involving contested loyalties and limited integration. While living locally, Lithuanian immigrants are expected both to be bound to the ethnic community and to be immersed in the multicultural life-style of the mega city. However the research has shown that livelihoods and social relations among ‘one’s own people’ are involved in trans-ethnic networks and that the bonds of intimacy and the alliances among ‘one’s own people’ run through homeland roots and patrimonial linkages rather than through the citizenship loyalties of the state (the United States and/or Lithuania). The circle of ‘one’s own people’ implies extensive reciprocity and social networking among ‘friends’ and co-workers based on ‘one’s own resourcefulness’ a kind of social capital. Thus, ‘sharing important acquaintances’ ought to involve ‘doing favours’ and livelihood experiences transplanted from oversees are practised in Chicago as ‘local’ life-styles and are used for transnational networking, securing in the process the social status of those involved. (shrink)

It is not only difficult to be a sports expert but also difficult to grow from a sports expert to a sports elite. Professional athletes are often concerned about the differences between an expert and an elite and how to eventually become an elite athlete. To explore the differences in brain neural mechanism between experts and elites in the process of motor behavior and reveal the internal connection between motor performance and brain activity, we collected and analyzed the electroencephalography findings (...) of 14 national archers and 14 provincial archers during aiming and resting states and constructed the EEG brain network of the two archer groups based on weighted phase lag index; the graph theory was used to analyze and compare the network characteristics via local network and global network topologies. The results showed that compared with the expert archers, the elite archers had stronger functional coupling in beta1 and beta2 bands, and the difference was evident in the frontal and central regions; in terms of global characteristics of brain network topology, the average clustering coefficient and global efficiency of elite archers were significantly higher than that of expert archers, and the eigenvector centrality of expert archers was higher; for local characteristics, elite archers had higher local efficient; and the brain network characteristics of expert archers showed a strong correlation with archery performance. This suggests that compared with expert archers, elite archers showed stronger functional coupling, higher integration efficiency of global and local information, and more independent performance in the archery process. These findings reveal the differences in brain electrical network topologies between elite and expert archers in the archery preparation stage, which is expected to provide theoretical reference for further training and promotion of professional athletes. (shrink)

This study investigates how sadness and minor/moderate depression influences the three functions of attention: alerting, orienting, and executive control using the Attention Network Test. The aim of the study is to investigate whether minor-to-moderate depression is more similar to sadness or clinical depression with regard to attentional processing. It was predicted that both induced sadness and minor-to-moderate depression will influence executive control by narrowing spatial attention and in turn this will lead to less interference from the flanker items due to (...) a focused attentional state. No differences were predicted for alerting or orienting functions. The results from the two experiments, the first inducing sadness and the second measuring subclinical depression, show that, as expected, participants who are sad or minor to moderately depressed showed less flanker interference compared to participants who were neither sad nor depressed. This study provides strong evidence, that irrespective of its aetiology, sadness and minor/moderate depression have similar effects on spatial attention. (shrink)

This article reports on a researcher's experience of being invited to improve upon an organisational situation in a hospital in Denmark. Being engaged with different networks of participants in the organisational situation, the researcher found himself wrapped up in various agendas, with different sections of the staff trying to persuade him to support their own respective interests. The article theorises these persuasions as "seductions." Consequently, the task of the researcher involves selecting, prioritising, and working upon his connections with various (...) networks, while each continues to represent a different set of values, expectations, interests, and experiences. Based on this conceptualisation, the article interrogates the notion of interventionist research. Intervention is not limited only to a simple one-way causation where the interventionist does something useful in a studied field; it also involves engagement with multiple networks present in the field, each of which tries to seduce the researcher in order to befriend this potentially powerful collaborator. Using the term "interference," rather than intervention, to represent the researcher’s action, the article suggests that the researcher is often not able to control the effect of his or her action unilaterally. Neither is the researcher able to establish an overarching perspective which can be used to evaluate the final outcome. The article calls for fresh thinking on how a researcher may be engaged usefully in an organisational situation, working within the boundaries defined by the institutional logic, confronting the seductions from multiple sources, and still seeking to maintain a ground that justifies one's identity as a researcher. (shrink)

Axelrod (The evolution of cooperation, 1984) and others explain how cooperation can emerge in repeated 2-person prisoner’s dilemmas. But in public good games with anonymous contributions, we expect a breakdown of cooperation because direct reciprocity fails. However, if agents are situated in a social network determining which agents interact, and if they can influence the network, then cooperation can be a viable strategy. Social networks are modelled as graphs. Agents play public good games with their neighbours. After each game, (...) they can terminate connections to others, and new connections are created. Cooperative agents do well because they manage to cluster with cooperators and avoid defectors. Computer simulations demonstrate that group formation and exclusion are powerful mechanisms to promote cooperation in dilemma situations. This explains why social dilemmas can often be solved if agents can choose with whom they interact. (shrink)

The field of social entrepreneurship, a domain focused on implementing solutions to social, cultural and environmental issues, remains highly male-dominated. Research continues to emphasise that women social entrepreneurs are often expected to behave in masculine ways in order to become successful. The study presented in this article explored the perceptions and experiences of thirty-three women living in the United Kingdom who were developing their skills in social entrepreneurship. Documenting their experiences, we sought to understand how women work in a male-dominated (...) field. Our analysis primarily builds on a Bourdieusian-revisionist approach of emotional capital to advance understandings of how women’s networks with other women allow them to navigate social entrepreneurship. Drawing on emotional capital, as a theory, we examine the ways in which these women find these spaces generative and how it contributes to their problematising of masculine orthodoxies. (shrink)

A new method of observation is currently emerging in psychiatry, based on data collection and behavioral profiling of smartphone users. Numerical phenotyping is a paradigmatic example. This behavioral investigation method uses computerized measurement tools in order to collect characteristics of different psychiatric disorders. First, it is necessary to contextualize the emergence of these new methods and to question their promises and expectations. The international mental health research framework invites us to reflect on methodological issues and to draw conclusions from certain (...) impasses related to the clinical complexity of this field. From this contextualization, the investigation method relating to digital phenotyping can be questioned in order to identify some of its potentials. These new methods are also an opportunity to test psychoanalysis. It is then necessary to identify the elements of fruitful analysis that clinical experience and research in psychoanalysis have been able to deploy regarding the challenges of digital technology. An analysis of this theme's litterature shows that psychoanalysis facilitates a reflection on the psychological effects related to digital methods. It also shows how it can profit from the research potential offered by new technical tools, considering the progress that has been made over the past fifty years. This cross-fertilization of the potential and limitations of digital methods in mental health intervention in the context of theoretical issues at the international level invites us to take a resolutely non-reductionist position. In the field of research, psychoanalysis offers a specific perspective that can well be articulated to an epistemology of networks. Rather than aiming at a numerical phenotyping of patients according to the geneticists' model, the case formulation method appears to be a serious prerequisite to give a limited and specific place to the integration of smartphones in clinical investigation. (shrink)

In this paper, faculty librarians at an academic institution explore the ethical dimensions of conducting research with user-generated social networking service (SNS) data. In an effort to guide librarian-researchers, this paper first offers a background discussion of privacy ethics across disciplines and then proposes a library-specific ethical framework for conducting SNS research.,By surveying the literature in other disciplines, three key considerations are identified that can inform ethical practice in the field of library science: context, expectation, and value analysis. For (...) each of these considerations, the framework is tailored to consider ethical issues, as they relate to libraries and our practice as librarian-researchers.,The unique role of the librarian-researcher demands an ethical framework specific to that practice. The findings of this paper propose such a framework.,Librarian-researchers are at a unique point in our history. In exploring SNSs as a source of data to conduct research and improve services, we become challenged by conflicting and equally cherished values of patron privacy and information access. By evaluating research according to context, expectations, and value, this framework provides an ethical path forward for research using SNS data.,As of this paper’s publication, there is no existing ethical framework for conducting SNS research in libraries. The proposed framework is informed both by library values and by broader research values, and therefore provides unique guidelines for the librarian-researcher. (shrink)

Infertility is a global problem affecting 48 to 186 million couples of reproductive age. In Poland, it concerns approx. 1.5 million couples, which amounts to 20% of the population capable of reproducing. One of the factors influencing the incidence of fertility disorders may be lifestyle, understood as a multi-disciplinary accumulation of everyday behaviours and habits. In the study, a group of 201 young adults, students of medical and related faculties, were surveyed in order to check the actual level of knowledge (...) about the impact of lifestyle on reproductive health. The Kohonen network, which is an example of a self-learning neural network, was used to find non-obvious connections between the data. The trained Kohonen neural network formed 4 clusters with different characteristics. Based on analyses of the structure of each cluster, it was found that 2nd year students of Medicine are internally divided into 3 fractions. The first fraction declared a high level of knowledge, but did not have real knowledge. The second fraction was aware of their ignorance, as confirmed by the knowledge test. The last fraction was characterized by a high level of self-confidence regarding their knowledge about reproductive health and obtained a high result in the knowledge test. It was confirmed that people studying at the Medical faculty know more than students of the same year at faculties other than Medicine. Interesting results were obtained for a group of 3rd year students of first-cycle studies in Dietetics. They did not obtain a significantly better result in the knowledge test concerning the influence of diet and lifestyle on reproductive health. It would seem that one could expect at least a few highly knowledgeable students in a group of 3rd year students, but this was not confirmed by the study. In view of the obtained results, it was concluded that the Kohonen neural network is applicable to the analysis of data on the actual state of knowledge about the impact of lifestyle on reproductive health. (shrink)

Higher education institutes both foster the advancement of knowledge and address society's socioeconomic and environmental challenges. To fulfil these multiple missions requires significant changes to how the role of a researcher is perceived e.g. a researcher identity that is congruent with the objective of contributing to fundamental knowledge while also engaging with non-academic actors, broadly, and entrepreneurship, in particular. We argue that the early stages of an academic career—namely the PhD training trajectory—and the knowledge networks formed during this period (...) have a major influence on the scientist’s future capacity to develop an appropriate researcher role identity. We draw on knowledge network and identity theories to investigate how the knowledge networks (i.e. business, scientific and career knowledge networks) of PhD students promote changes to, reinforce or conflict with the perception of a researcher role identity. Our longitudinal qualitative network study includes PhD students and their supervisors funded by the H2020 FINESSE project. At the network level, we show that scientific knowledge is distributed equally throughout young academics’ networks but that entrepreneurial (business) and career knowledge tend to be concentrated around certain individuals in these networks. On the PhD student level, we observe different pronunciations of the researcher role identity linked to students’ interactions with their knowledge networks. We distinguish identity conflicts due to misalignment between ego and alters which leads to withdrawal from the network. Our findings have practical implications and suggest that universities and PhD student supervisors should support PhD students to develop a researcher identity which is in line with the individual PhD student’s expectations. (shrink)

This essay proposes to view networks as sociocultural structures. Following authors from Leopold von Wiese and Norbert Elias to Gary Alan Fine and Harrison White, networks are configurations of social relationships interwoven with meaning. Social relationships as the basic building blocks of networks are conceived of as dynamic structures of reciprocal (but not necessarily symmetric) expectations between alter and ego. Through their transactions, alter and ego construct an idiosyncratic "relationship culture" comprising symbols, narratives, and relational identities. The (...) coupling of social relationships to networks, too, is heavily laden with meaning. The symbolic construction of persons is one instance of this coupling. Another instance is the application of social categories (like race or gender), which both map and structure social networks. The conclusion offers an agenda for research on this "meaning structure of social networks.". (shrink)

The mobile game “Immortal Conquest,” created by NetEase Games, caused a dramatic user dissatisfaction event after an introduction of a sudden and uninvited “pay-to-win” update. As a result, many players filed grievances against NetEase in a court. The official game website issued three apologies, with mix results, to mitigate the crisis. The goal of the present study is to understand user feedback content from the perspective of Situational Crisis Communication Theory through semantic network analysis and sentiment analysis to explore how (...) an enterprise’s crisis communication strategy affects users’ attitudes. First, our results demonstrate that the diminishing crisis communication strategies do not change players’ negative attitudes. It was not a failure because it successfully alleviated the players’ legal complaints and refocused their attention on the game itself. Second, the rebuild strategy was effective because it significantly increased the percentage of positive emotions and regenerated expectations for the game. The litigation crisis was identified within gamer communications with respect to Chinese gaming companies for the first time. Nevertheless, this does not indicate an increase in overall legal awareness among the larger Chinese population. It may only reflect greater legal awareness among Chinese online gamers. Fourth, gamers emphasized that they and enterprises should be equally involved when communicating with each other. Finally, in-game paid items should be reasonably priced, otherwise, they will drive users to competitors. (shrink)

Nowadays, around half of the global population lives in urban areas. This rate is expected to increase up to two-thirds by the year 2050. Most studies analyze urban dynamics in wide geographic ranges, focusing mainly on cities. According to them, the global population is spatially distributed in two extremes: large urban agglomerations and rural deserts. However, this remark is excessively general and imprecise. For this reason, it remains essential to analyze these dynamics at other spatial scales. A close-up look in (...) thinly populated regions shows how urban dynamics are also noticeable. In this paper, we analyze spatiotemporal patterns of population distribution in a predominantly rural area by applying a local-scale approach. These patterns are represented by using spatial networks with nodes representing the human settlements and links showing hierarchies between nodes. This case study is conducted in a small municipality located in northwestern Spain. It is a predominantly rural area with a very particular spatial pattern of population distribution. (shrink)

Current challenges to sustaining public support for health data research have directed attention to the governance of data-intensive health research networks. Accountability is hailed as an important element of trustworthy governance frameworks for data-intensive health research networks. Yet the extent to which adequate accountability regimes in data-intensive health research networks are currently realized is questionable. Current governance of data-intensive health research networks is dominated by the limitations of a drawing board approach. As a way forward, we (...) propose a stronger focus on accountability as learning to achieve accountable governance. As an important step in that direction, we provide two pathways: (1) developing an integrated structure for decision-making and (2) establishing a dialogue in ongoing deliberative processes. Suitable places for learning accountability to thrive are dedicated governing bodies as well as specialized committees, panels or boards which bear and guide the development of governance in data-intensive health research networks. A continuous accountability process which comprises learning and interaction accommodates the diversity of expectations, responsibilities and tasks in data-intensive health research networks to achieve responsible and effective governance. (shrink)

In the current paper, we propose a latent interdependence approach to modeling psychometric data in social networks. The idea of latent interdependence is adopted from social relations models, which formulate a mutual-rating process by both dyad members’ characteristics. Under the framework of the latent interdependence approach, we introduce two psychometric models: The first model includes the main effects of both rating-sender and rating-receiver, and the second model includes a latent distance effect to assess the influence from the dissimilarity between (...) the latent characteristics of both sides. The latent distance effect is quantified by the Euclidean distance between both sides’ trait scores. Both models use Bayesian estimation via Markov chain Monte Carlo. How accurately model parameters were estimated was evaluated in a simulation study. Parameter recovery results showed that all parameters were accurately recovered under most of the conditions investigated. As expected, the accuracy of model estimation was significantly improved as network size grew. Also, through analyzing empirical data, we showed how to use the estimates of model parameters to predict the latent weight of connections among group members and rebuild either a univariate or multivariate network at a latent trait level. Finally, we discuss issues regarding model comparison and offer suggestions for future studies. (shrink)

BackgroundSocial cognitive impairments adversely affect social functioning in patients with schizophrenia. Although pharmacological interventions have been suggested to provide some benefits on social cognition, little information is available on the comparative efficacy of pharmacotherapy. Thus, the aim of this planned systematic review and network meta-analysis is to perform a quantitative comparison of the effects of various psychotropic drugs, including supplements, on social cognition disturbances of schizophrenia.MethodsThe literature search will be carried out using the PubMed, Embase, Cochrane Central Register of Controlled (...) Trials, PsycINFO, ClinicalTrials.gov, and International Clinical Trials Registry Platform databases from inception onward. Randomized controlled trials that examined the efficacy of drugs in social cognitive disturbances will be included, based on the most recent studies and the broader literature than previously searched. This protocol defines a priori the methods that will be used for study selection, data collection, quality assessment, and statistical syntheses.DiscussionThe findings this work are expected to help promote the development of better therapeutics of social cognitive impairments in schizophrenia and related psychiatric conditions.Systematic Review Registration[www.crd.york.ac.uk/prospero], identifier [CRD42021293224]. (shrink)

By assessing interrelationships among variables within a specified theoretical framework, network analysis provides nuanced insights into how associations between psychological constructs are related to outcome measures. Noting this, the authors used NA to examine connections between Paranormal Belief, cognitive-perceptual factors, and well-being. Data derived from a sample of 3,090 participants who completed standardised self-report measures capturing the study constructs online. Transliminality, Unusual Experiences, and Depressive Experience demonstrated high expected influence centrality. This indicated that these factors were the most strongly connected (...) and influential in the network. Moreover, Transliminality was a connecting variable between Paranormal Belief, positive schizotypy, and psychopathology. Depressive Experience bridged the relationship between Transliminality and well-being. The conceptual implications of these outcomes are discussed with regards to better understanding relationships between Paranormal Belief, cognitive-perceptual factors, and well-being. (shrink)

Given the current popularization of computer programming and the trends of informatization and digitization, colleges have actively responded by making programming lessons compulsory for students of all disciplines. However, students from different ethnic groups often have different learning responses to such lessons due to their respective cultural backgrounds, the environment in which they grew up, and their consideration for future employment. In this study, an AI-assisted programming module was developed and used to compare the differences between multi-ethnic college students in (...) terms of their theoretical and actual learning expectancy, motivation, and effectiveness. The module conducted analysis through the deep learning network and examined the relevant processes that the students underwent during programming lessons, as well as the types of errors they had committed. Their learning motivation for and actual learning performance in programming were then examined based on the cognitive learning theory. The results of the experiment, which involved 96 multi-ethnic college students, indicated that the two groups had dissimilar theoretical performance in terms of their expectancy and motivation for learning programming. The indigenous students’ main concern was whether programming would affect their families or tribes, and this concern affected and was reflected in their learning outcomes. In contrast, the learning motivation and goals of Han Chinese students were driven by the cognition of the value of programming to themselves. The research findings can contribute toward the cognition and understanding of multi-ethnic students when learning computer programming and development of the appropriate teaching methods, and serve as a reference for subsequent research on integrating multiculturalism into computer programming lessons. (shrink)

In this study, the focus was on the development of green energy and future prediction for the consumption of current energy sources and green energy development using an improved deep learning algorithm. In addition to the analysis of the current energy consumption used for the natural gas and oil as fuel, deep neural network algorithm is used to train the system as well as to process the data obtained previously, ranging from literature from the year 2003 until the year 2019, (...) for consumption of fuel. Also, using the proposed algorithm to predict the development of green energy consumption till 2030 is presented in terms of solar and wind generators. The resulting study also focuses on depletion of energy currently used or pollution caused because of it. The green energy controlling issue can take effect by using multiple layers of handling different features extracted from different sources and then learning the system to control it.This study aims to take advantage of carbon emissions to reduce their impact and dependence in the future on environmentally friendly renewable energies. Predicting the correct and precise amount of energy consumption and increasing the amount of environmentally friendly energy lead to a healthy ecosystem. The expected green energy consumption in the future is almost 78.25 EJ in 2030 and will be, in total energy average, 56% in 2045. The aim is to reduce dependency on costly and environmentally harmful fuels. (shrink)

Functional near-infrared spectroscopy has attracted increasing attention in the field of brain–computer interfaces owing to their advantages such as non-invasiveness, user safety, affordability, and portability. However, fNIRS signals are highly subject-specific and have low test-retest reliability. Therefore, individual calibration sessions need to be employed before each use of fNIRS-based BCI to achieve a sufficiently high performance for practical BCI applications. In this study, we propose a novel deep convolutional neural network -based approach for implementing a subject-independent fNIRS-based BCI. A total (...) of 18 participants performed the fNIRS-based BCI experiments, where the main goal of the experiments was to distinguish a mental arithmetic task from an idle state task. Leave-one-subject-out cross-validation was employed to evaluate the average classification accuracy of the proposed subject-independent fNIRS-based BCI. As a result, the average classification accuracy of the proposed method was reported to be 71.20 ± 8.74%, which was higher than the threshold accuracy for effective BCI communication as well as that obtained using conventional shrinkage linear discriminant analysis. To achieve a classification accuracy comparable to that of the proposed subject-independent fNIRS-based BCI, 24 training trials were necessary for the traditional subject-dependent fNIRS-based BCI. It is expected that our CNN-based approach would reduce the necessity of long-term individual calibration sessions, thereby enhancing the practicality of fNIRS-based BCIs significantly. (shrink)

Despite a plethora of studies examining hosting experiences of Airbnb guests, the wellbeing of hosts has received limited attention. Drawing on both top-down and bottom-up theories of wellbeing, we explore the different ways in which Airbnb enhances or diminishes host wellbeing using a multidimensional lens. Data is collected from in-depth interviews with twenty-two Airbnb hosts. We also identify tensions and conflicts in the host-guest relationship using the three interactional hospitality domains of commercial, social and private. Through a deductive process, we (...) find that participating in Airbnb both enhances and diminishes the material, relational and subjective aspects of wellbeing for hosts. Inductively, we find that a lack of territorial boundaries in shared accommodation contribute to conflicts that reduce wellbeing. This exemplifies a tension that exists in the private-commercial domain of network hospitality provision. We provide implications for peer-to-peer accommodation providers on developing the managerial skills of hosts, and helping hosts set realistic expectations around hosting to reduce the conflicts and tensions that arise from the intersectionality of the various Airbnb hospitality domains in such a way that the wellbeing of hosts is enhanced. (shrink)