Simulating Fiction: Individual Differences in Literature Comprehension Revealed with fMRI

When we read literary fiction, we are transported to fictional places, and we feel and think along with the characters. Despite the importance of narrative in adult life and during development, the neurocognitive mechanisms underlying fiction comprehension are unclear. We used functional magnetic resonance imaging (fMRI) to investigate how individuals differently employ neural networks important for understanding others’ beliefs and intentions (mentalizing), and for sensori-motor simulation while listening to excerpts from literary novels. Localizer tasks were used to localize both the cortical motor network and the mentalizing network in participants after they listened to excerpts from literary novels. Results show that participants who had high activation in anterior medial prefrontal cortex (aMPFC; part of the mentalizing network) when listening to mentalizing content of literary fiction, had lower motor cortex activity when they listened to action-related content of the story, and vice versa. This qualifies how people differ in their engagement with fiction: some people are mostly drawn into a story by mentalizing about the thoughts and beliefs of others, whereas others engage in literature by simulating more concrete events such as actions. This study provides on-line neural evidence for the existence of qualitatively different styles of moving into literary worlds, and adds to a growing body of literature showing the potential to study narrative comprehension with neuroimaging methods.     

The Quality of Spouses’ Social Networks Contributes to Each Other’s Cardiovascular Risk

Objectives

Although the quality of one’s own social relationships has been related to cardiovascular morbidity and mortality, whether a partner’s social network quality can similarly influence one’s cardiovascular risk is unknown. In this study we tested whether the quality of a partner’s social networks influenced one’s own ambulatory blood pressure (ABP).

Methods

The quality of 94 couples’ social networks was determined using a comprehensive model of relationships that separates out social ties that are sources of positivity(supportive), negativity (aversive), and both positivity and negativity (ambivalent). We then utilized statistical models (actor-partner analyses) that allowed us to separate out the links between one’s own social network quality on ABP (actor influences), a partner’s social network quality on ABP (partner influences), and a couple’s network quality combined on ABP (actor X partner interactions).

Results

Independent of one’s own relationship quality, results showed that an individual’s ABP was lower if their spouse had more supportive ties, and higher if a spouse had more aversive and ambivalent ties. In addition, couples’ networks in combination were associated with higher ABP but only if both had a low number of supportive ties, or a high number of aversive or ambivalent ties.

Conclusions

These data suggest that the social ties of those we have close relationships with may influence our cardiovascular risk and opens new opportunities to capitalize on untapped social resources or to mitigate hidden sources of social strain.     

Preschool Children’s Behavioral Tendency toward Social Indirect Reciprocity

Social indirect reciprocity seems to be crucial in enabling large-scale cooperative networks among genetically unrelated individuals in humans. However, there are relatively few studies on social indirect reciprocity in children compared to adults. Investigating whether young children have a behavioral tendency toward social indirect reciprocity will help us understand how and when the fundamental ability to form cooperative relationships among adults is acquired. Using naturalistic observation at a nursery school, this study examined whether 5- to 6-year-olds show a behavioral tendency to engage in social indirect reciprocity in response to their peers’ prosocial behavior toward a third party. The results revealed that bystander children tended to display prosocial behavior toward their peers more frequently after observing these peers’ prosocial behavior toward third-party peers, compared with control situations; this suggests that 5- to 6-year-olds may have an essential behavioral tendency to establish social indirect reciprocity when interacting with peers in their daily lives. In addition, bystanders tended to display affiliative behavior after observing focal children’s prosocial behavior. In other words, observing peers’ prosocial behavior toward third-party peers evoked bystanders’ positive emotions toward the helpers. Considering both the present results and previous findings, we speculate that in preschoolers, such positive emotions might mediate the increase in the bystander’s prosocial behavior toward the helper. In addition, an intuitional emotional process plays an important role in the preschooler’s behavioral tendency toward social indirect reciprocity in natural interactions with peers.     

Brain functional network modeling and analysis based on fMRI: a systematic review

In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer’s disease, Parkinson’s disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.     

Do Social Networks Influence Small-Scale Fishermen’s Enforcement of Sea Tenure?

Resource systems with enforced rules and strong monitoring systems typically have more predictable resource abundance, which can confer economic and social benefits to local communities. Co-management regimes demonstrate better social and ecological outcomes, but require an active role by community members in management activities, such as monitoring and enforcement. Previous work has emphasized understanding what makes fishermen comply with rules. This research takes a different approach to understand what influences an individual to enforce rules, particularly sea tenure. We conducted interviews and used multiple regression and Akaike’s Information Criteria model selection to evaluate the effect of social networks, food security, recent catch success, fisherman’s age and personal gear investment on individual’s enforcement of sea tenure. We found that fishermen’s enforcement of sea tenure declined between the two time periods measured and that social networks, age, food security, and changes in gear investment explained enforcement behavior across three different communities on Nicaragua’s Atlantic Coast, an area undergoing rapid globalization.     

Social Networks,the ‘Work’ and Work Force of Chronic Illness Self-Management: A Survey Analysis of Personal Communities

Self-management support forms a central aspect of chronic Illness management nationally and globally. Evidence for the success of self-management support has mainly focussed on individually-centred outcomes of behavioural change. While it is recognised that social network members play an important role there is currently a gap in knowledge regarding who provides what type of support and under what circumstances. This is relevant for understanding the division of labour and the meeting of needs for those living with a long-term condition. We therefore took a network approach to explore self-management support conceptualising it as types of illness ‘work’ undertaken within peoples’ social networks. 300 people from deprived areas and with chronic illnesses took part in a survey conducted in 2010 in the North West of England. A concentric circles diagram was used as a research tool with which participants identified 2,544 network members who contributed to illness management. The results provide an articulation of how social network members are substantially involved in illness management. Whilst partners and close family make the highest contributions there is evidence of inputs from a wide range of relationships. Network member characteristics (type of relationship, proximity, frequency of contact) impact on the amount of illness work undertaken in peoples’ networks. In networks with ‘no partner’ other people tend to contribute more in the way of illness related work than in networks with a partner. This indicates a degree of substitutability between differently constituted networks, and that the level and type of input by different members of a network might change according to circumstances. A network perspective offers an opportunity to redress the balance of an exclusively individual focus on self-management because it addresses the broader set of contributions and resources available to people in need of chronic illness management and support.     

Sex Differences in How Social Networks and Relationship Quality Influence Experimental Pain Sensitivity

This is the first study to examine how both structural and functional components of individuals’ social networks may moderate the association between biological sex and experimental pain sensitivity. One hundred and fifty-two healthy adults (mean age = 22yrs., 53% males) were measured for cold pressor task (CPT) pain sensitivity (i.e., intensity ratings) and core aspects of social networks (e.g., proportion of friends vs. family, affection, affirmation, and aid). Results showed consistent sex differences in how social network structures and intimate relationship functioning modulated pain sensitivity. Females showed higher pain sensitivity when their social networks consisted of a higher proportion of intimate types of relationship partners (e.g., kin vs. non kin), when they had known their network partners for a longer period of time, and when they reported higher levels of logistical support from their significant other (e.g., romantic partner). Conversely, males showed distinct patterns in the opposite direction, including an association between higher levels of logistical support from one’s significant other and lower CPT pain intensity. These findings show for the first time that the direction of sex differences in exogenous pain sensitivity is likely dependent on fundamental components of the individual’s social environment. The utility of a social-signaling perspective of pain behaviors for examining, comparing, and interpreting individual and group differences in experimental and clinical pain reports is discussed.     

The actions of others act as a pseudo-reward to drive imitation in the context of social reinforcement learning

While there is no doubt that social signals affect human reinforcement learning, there is still no consensus about how this process is computationally implemented. To address this issue, we compared three psychologically plausible hypotheses about the algorithmic implementation of imitation in reinforcement learning. The first hypothesis, decision biasing (DB), postulates that imitation consists in transiently biasing the learner’s action selection without affecting their value function. According to the second hypothesis, model-based imitation (MB), the learner infers the demonstrator’s value function through inverse reinforcement learning and uses it to bias action selection. Finally, according to the third hypothesis, value shaping (VS), the demonstrator’s actions directly affect the learner’s value function. We tested these three hypotheses in 2 experiments (N = 24 and N = 44) featuring a new variant of a social reinforcement learning task. We show through model comparison and model simulation that VS provides the best explanation of learner’s behavior. Results replicated in a third independent experiment featuring a larger cohort and a different design (N = 302). In our experiments, we also manipulated the quality of the demonstrators’ choices and found that learners were able to adapt their imitation rate, so that only skilled demonstrators were imitated. We proposed and tested an efficient meta-learning process to account for this effect, where imitation is regulated by the agreement between the learner and the demonstrator. In sum, our findings provide new insights and perspectives on the computational mechanisms underlying adaptive imitation in human reinforcement learning.

This study investigates imitation from a computational perspective; three experiments show that, in the context of reinforcement learning, imitation operates via a durable modification of the learner''s values, shedding new light on how imitation is computationally implemented and shapes learning and decision-making.     

Contagion of Cooperation in Static and Fluid Social Networks

Cooperation is essential for successful human societies. Thus, understanding how cooperative and selfish behaviors spread from person to person is a topic of theoretical and practical importance. Previous laboratory experiments provide clear evidence of social contagion in the domain of cooperation, both in fixed networks and in randomly shuffled networks, but leave open the possibility of asymmetries in the spread of cooperative and selfish behaviors. Additionally, many real human interaction structures are dynamic: we often have control over whom we interact with. Dynamic networks may differ importantly in the goals and strategic considerations they promote, and thus the question of how cooperative and selfish behaviors spread in dynamic networks remains open. Here, we address these questions with data from a social dilemma laboratory experiment. We measure the contagion of both cooperative and selfish behavior over time across three different network structures that vary in the extent to which they afford individuals control over their network ties. We find that in relatively fixed networks, both cooperative and selfish behaviors are contagious. In contrast, in more dynamic networks, selfish behavior is contagious, but cooperative behavior is not: subjects are fairly likely to switch to cooperation regardless of the behavior of their neighbors. We hypothesize that this insensitivity to the behavior of neighbors in dynamic networks is the result of subjects’ desire to attract new cooperative partners: even if many of one’s current neighbors are defectors, it may still make sense to switch to cooperation. We further hypothesize that selfishness remains contagious in dynamic networks because of the well-documented willingness of cooperators to retaliate against selfishness, even when doing so is costly. These results shed light on the contagion of cooperative behavior in fixed and fluid networks, and have implications for influence-based interventions aiming at increasing cooperative behavior.     

Money Affects Theory of Mind Differently by Gender

Theory of Mind (ToM) ─ the ability to understand other’s thoughts, intentions, and emotions ─ is important for navigating interpersonal relationships, avoiding conflict, and empathizing. Prior research has identified many factors that affect one’s ToM ability, but little work has examined how different kinds of monetary incentives affect ToM ability. We ask: Does money affect ToM ability? If so, how does the effect depend on the structure of monetary incentives? How do the differences depend on gender? We hypothesize that money will affect ToM ability differently by gender: monetary rewards increase males’ motivation to express ToM ability while simultaneously crowding out females’ motivation. This prediction is confirmed in an experiment that varies the structure of monetary rewards for correct answers in the Reading the Mind in the Eyes Test (RMET). RMET scores decrease for females and increase for males with individual payments, and this effect is stronger with competitively-structured payments. RMET scores do not significantly change when monetary earnings go to a charity. Whether money improves or hinders ToM ability, and, hence, success in social interactions, thus depends on the interaction of gender and monetary incentive structure.     

Processing Demands Impact 3-Year-Olds’ Performance in a Spontaneous-Response Task: New Evidence for the Processing-Load Account of Early False-Belief Understanding

Prior to age four, children succeed in non-elicited-response false-belief tasks but fail elicited-response false-belief tasks. To explain this discrepancy, the processing-load account argues that the capacity to represent beliefs emerges in infancy, as indicated by early success on non-elicited-response tasks, but that children’s ability to demonstrate this capacity depends on the processing demands of the task and children’s processing skills. When processing demands exceed young children’s processing abilities, such as in standard elicited-response tasks, children fail despite their capacity to represent beliefs. Support for this account comes from recent evidence that reducing processing demands improves young children’s performance: when demands are sufficiently reduced, 2.5-year-olds succeed in elicited-response tasks. Here we sought complementary evidence for the processing-load account by examining whether increasing processing demands impeded children’s performance in a non-elicited-response task. 3-year-olds were tested in a preferential-looking task in which they heard a change-of-location false-belief story accompanied by a picture book; across children, we manipulated the amount of linguistic ambiguity in the story. The final page of the book showed two images: one that was consistent with the main character’s false belief and one that was consistent with reality. When the story was relatively unambiguous, children looked reliably longer at the false-belief-consistent image, successfully demonstrating their false-belief understanding. When the story was ambiguous, however, this undermined children’s performance: looking times to the belief-consistent image were correlated with verbal ability, and only children with verbal skills in the upper quartile of the sample demonstrated a significant preference for the belief-consistent image. These results support the processing-load account by demonstrating that regardless of whether a task involves an elicited response, children’s performance depends on the processing demands of the task and their processing skills. These findings also have implications for alternative, deflationary accounts of early false-belief understanding.     

Regimes and mechanisms of transient amplification in abstract and biological neural networks

Neuronal networks encode information through patterns of activity that define the networks’ function. The neurons’ activity relies on specific connectivity structures, yet the link between structure and function is not fully understood. Here, we tackle this structure-function problem with a new conceptual approach. Instead of manipulating the connectivity directly, we focus on upper triangular matrices, which represent the network dynamics in a given orthonormal basis obtained by the Schur decomposition. This abstraction allows us to independently manipulate the eigenspectrum and feedforward structures of a connectivity matrix. Using this method, we describe a diverse repertoire of non-normal transient amplification, and to complement the analysis of the dynamical regimes, we quantify the geometry of output trajectories through the effective rank of both the eigenvector and the dynamics matrices. Counter-intuitively, we find that shrinking the eigenspectrum’s imaginary distribution leads to highly amplifying regimes in linear and long-lasting dynamics in nonlinear networks. We also find a trade-off between amplification and dimensionality of neuronal dynamics, i.e., trajectories in neuronal state-space. Networks that can amplify a large number of orthogonal initial conditions produce neuronal trajectories that lie in the same subspace of the neuronal state-space. Finally, we examine networks of excitatory and inhibitory neurons. We find that the strength of global inhibition is directly linked with the amplitude of amplification, such that weakening inhibitory weights also decreases amplification, and that the eigenspectrum’s imaginary distribution grows with an increase in the ratio between excitatory-to-inhibitory and excitatory-to-excitatory connectivity strengths. Consequently, the strength of global inhibition reveals itself as a strong signature for amplification and a potential control mechanism to switch dynamical regimes. Our results shed a light on how biological networks, i.e., networks constrained by Dale’s law, may be optimised for specific dynamical regimes.     

You Seem Certain but You Were Wrong Before: Developmental Change in Preschoolers’ Relative Trust in Accurate versus Confident Speakers

The present study tested how preschoolers weigh two important cues to a person’s credibility, namely prior accuracy and confidence, when deciding what to learn and believe. Four- and 5-year-olds (N = 96) preferred to believe information provided by a confident rather than hesitant individual; however, when confidence conflicted with accuracy, preschoolers increasingly favored information from the previously accurate but hesitant individual as they aged. These findings reveal an important developmental progression in how children use others’ confidence and prior accuracy to shape what they learn and provide a window into children’s developing social cognition, scepticism, and critical thinking.     

Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks

The diverse, specialized genes present in today’s lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins’ binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes’ evolutionary properties. Slowly evolving (“cold”), old genes tend to interact with each other, as do rapidly evolving (“hot”), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN’s community structures and its genes’ evolutionary properties provide new perspectives for understanding evolutionary genetics.     

Benevolent Ideology and Women’s Economic Decision-Making: When Sexism Is Hurting Men’s Wallet

Can ideology, as a widespread “expectation creator,” impact economic decisions? In two studies we investigated the influence of the Benevolent Sexism (BS) ideology (which dictates that men should provide for passive and nurtured women) on women’s economic decision-making. In Study 1, using a Dictator Game in which women decided how to share amounts of money with men, results of a Generalized Linear Mixed Model analysis show that higher endorsement of BS and contextual expectations of benevolence were associated with more very unequal offers. Similarly, in an Ultimatum Game in which women received monetary offers from men, Study 2’s Generalized Linear Mixed Model’s results revealed that BS led women to reject more very unequal offers. If women’s endorsement of BS ideology and expectations of benevolence prove contrary to reality, they may strike back at men. These findings show that BS ideology creates expectations that shape male-female relationships in a way that could be prejudicial to men.     

Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks

Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network’s stability by adjusting the network’s properties and give some proposal on the designs of these types of networks from the points of game theory and stability.     

Inequalities in Global Trade: A Cross-Country Comparison of Trade Network Position,Economic Wealth,Pollution and Mortality

In this paper we investigate how structural patterns of international trade give rise to emissions inequalities across countries, and how such inequality in turn impact countries’ mortality rates. We employ Multi-regional Input-Output analysis to distinguish between sulfur-dioxide (SO2) emissions produced within a country’s boarders (production-based emissions) and emissions triggered by consumption in other countries (consumption-based emissions). We use social network analysis to capture countries’ level of integration within the global trade network. We then apply the Prais-Winsten panel estimation technique to a panel data set across 172 countries over 20 years (1990–2010) to estimate the relationships between countries’ level of integration and SO2 emissions, and the impact of trade integration and SO2 emission on mortality rates. Our findings suggest a positive, (log-) linear relationship between a country’s level of integration and both kinds of emissions. In addition, although more integrated countries are mainly responsible for both forms of emissions, our findings indicate that they also tend to experience lower mortality rates. Our approach offers a unique combination of social network analysis with multiregional input-output analysis, which better operationalizes intuitive concepts about global trade and trade structure.     

Building Social Networks for Maternal and Newborn Health in Poor Urban Settlements: A Cross-Sectional Study in Bangladesh

Background

The beneficial influence of social networks on health and wellbeing is well-established. In poor urban settlements in Bangladesh, BRAC’s Manoshi programme trains community health workers (CHWs) to support women through pregnancy, delivery and postpartum periods. This paper test the hypothesis that the introduction of CHWs as weak ties into the social networks of Manoshi members mediates improvements in maternal and neonatal health (MNH) best practices by providing support, facilitating ideational change, connecting mother to resources, and strengthening or countering the influence of strong ties.

Methods

1000 women who had given birth in the last three months were identified and interviewed as part of ongoing monitoring of 5 poor urban settlements in Dhaka, Bangladesh. A social networks questionnaire was administered which elicited women’s perceived networks around pregnancy, delivery and post-partum periods. Mediation analysis was performed to test the hypothesis that penetration of Manoshi CHWs into women’s perceived networks has a beneficial effect on MNH best practises.

Results

The presence and influence of Manoshi CHWs in women’s networks significantly mediated the effect of Manoshi membership on MNH best practices. Respondents who were Manoshi members and who listed Manoshi CHWs as part of their support networks were significantly more likely to deliver with a trained birth attendant (OR 3.61; 95%CI 2.36–5.51), to use postnatal care (OR 3.09; 95%CI 1.83–5.22), and to give colostrum to their newborn (OR 7.51; 95%CI 3.51–16.05).

Conclusion

Manoshi has succeeded in penetrating the perceived pregnancy, delivery and post-partum networks of poor urban women through the introduction of trained CHWs. Study findings demonstrate the benefits of moving beyond urban health care delivery models that concentrate on the provision of clinical services by medical providers, to an approach that nurtures the power of social networks as a means to support the poorest and most marginalized in changing behaviour and effectively accessing appropriate maternal services.     

Followers Are Not Enough: A Multifaceted Approach to Community Detection in Online Social Networks

In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a ‘community’ as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of ‘community.’ In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure.     

Directed Network Motifs in Alzheimer’s Disease and Mild Cognitive Impairment

Directed network motifs are the building blocks of complex networks, such as human brain networks, and capture deep connectivity information that is not contained in standard network measures. In this paper we present the first application of directed network motifs in vivo to human brain networks, utilizing recently developed directed progression networks which are built upon rates of cortical thickness changes between brain regions. This is in contrast to previous studies which have relied on simulations and in vitro analysis of non-human brains. We show that frequencies of specific directed network motifs can be used to distinguish between patients with Alzheimer’s disease (AD) and normal control (NC) subjects. Especially interesting from a clinical standpoint, these motif frequencies can also distinguish between subjects with mild cognitive impairment who remained stable over three years (MCI) and those who converted to AD (CONV). Furthermore, we find that the entropy of the distribution of directed network motifs increased from MCI to CONV to AD, implying that the distribution of pathology is more structured in MCI but becomes less so as it progresses to CONV and further to AD. Thus, directed network motifs frequencies and distributional properties provide new insights into the progression of Alzheimer’s disease as well as new imaging markers for distinguishing between normal controls, stable mild cognitive impairment, MCI converters and Alzheimer’s disease.