Understanding the link between neurobiology and cognition requires that neuroscience moves beyond mere structure-function correlations. An explicit systems perspective is needed in which putative mechanisms of how brain function is
constrained by brain structure are mathematically formalized and made accessible for experimental investigation. Such a systems approach critically rests on a better understanding of brain connectivity in its various forms. Since 2002, frontier topics of connectivity and neural system analysis have been discussed in a multidisciplinary annual meeting, the Brain Connectivity Workshop (BCW), bringing together experimentalists and theorists from various fields. This article summarizes some of the main discussions at the two most recent workshops, 2006 at Sendai, Japan, and 2007 at Barcelona, Spain: (i) investigation of cortical micro- & macrocircuits, (ii)
models of neural dynamics at multiple scales, (iii) analysis of "resting state" networks, and (iv) linking anatomical to functional connectivity. Finally, we outline some central challenges and research trajectories in computational systems neuroscience for the next years.
Models of effective connectivity characterize the influence that neuronal populations exert over each other. Additionally, some approaches, for example Dynamic Causal Modelling (DCM) and variants of Structural Equation Modelling, describe how effective connectivity is modulated by experimental manipulations. Mathematically, both are based on bilinear equations, where the bilinear term models
the effect of experimental manipulations on neuronal interactions. The bilinear framework, however, precludes an important aspect of neuronal interactions that has been established with invasive electrophysiological recording studies; i.e., how the connection between two neuronal units is enabled or gated by activity in other units. These gating processes are critical for controlling the gain of
neuronal populations and are mediated through interactions between synaptic inputs (e.g. by means of voltage-sensitive ion channels). They represent a key mechanism for various neurobiological processes, including top-down (e.g. attentional) modulation, learning and neuromodulation.
This paper presents a nonlinear extension of DCM that models such processes (to second order) at the
neuronal population level. In this way, the modulation of network interactions can be assigned to an explicit neuronal population. We present simulations and empirical results that demonstrate the validity and usefulness of this model. Analyses of synthetic data showed that nonlinear and bilinear mechanisms can be distinguished by our extended DCM. When applying the model to empirical fMRI
data from a blocked attention to motion paradigm, we found that attention-induced increases in V5 responses could be best explained as a gating of the V1→V5 connection by activity in posterior parietal cortex. Furthermore, we analysed fMRI data from an event-related binocular rivalry paradigm and found that interactions amongst percept-selective visual areas were modulated by activity in the
middle frontal gyrus. In both practical examples, Bayesian model selection favoured the nonlinear models over corresponding bilinear ones.
The ability to share the other’s feelings, known as empathy, has recently become the focus of social neuroscience studies. We review converging evidence that empathy with, for example, the pain of another person, activates part of the neural pain network of the empathizer, without first hand pain stimulation to the empathizer’s body. The amplitude of empathic brain responses is modulated by the intensity of the displayed emotion, the appraisal of the situation, characteristics of the suffering person such as perceived fairness, and features of the empathizer such as gender or previous experience with pain-inflicting situations. Future studies in the field should address inter-individual differences in empathy, development and plasticity of the empathic brain over the life span and the link between empathy, compassionate motivation and prosocial behavior.
Economic growth theory and theoretical ecology represent independent traditions of modeling aggregate consumer-resource systems. Both focus on different but equally important forces underlying the dynamics of human societies. Though the two traditions have unknowingly converged in some ways, they each have curious conventions from the perspective of the other. These conventions are reviewed, and two separate modeling frameworks that integrate the two traditions in a simple and straightforward fashion are developed and analyzed. The resulting models represent a consumer species (e.g. humans) that both produces and consumes its resources and then
reproduces biologically according to the consumption of its resources. Depending on the balance between production, consumption, and reproduction, the models can exhibit
stagnant behavior, like some predator-prey models, or growth, like many mutualism and economic growth models. When growth occurs, in the long term it takes one of two
forms. Either resources per capita grow and the human population size converges to a constant, which may be zero, or resources per capita converge to a constant and the
human population grows. The difference depends on initial conditions and the particular mix of biological conditions and human technology.
Conformity is a type of social learning that has received considerable attention among social psychologists and human evolutionary ecologists, but existing empirical research does not identify conformity cleanly. Conformity is more than just a tendency to follow the majority; it involves an exaggerated tendency to follow the majority. The “exaggerated” part of this definition ensures that conformists do not show just any bias toward the majority, but a bias sufficiently strong to increase the size of the majority through time. This definition of conformity is compelling because it is the only form of frequency-dependent social influence that produces behaviorally
homogeneous social groups. We conducted an experiment to see if players were conformists by separating individual and social learners. Players chose between two technologies repeatedly. Payoffs were random, but one technology had a higher expected payoff. Individual learners knew their realized payoffs after each choice, while social learners only knew the distribution of choices among individual learners. A subset of social learners behaved according to a classic model of conformity. The remaining social learners did not respond to frequency information. They were
neither conformists nor non-conformists, but mavericks. Given this heterogeneity in learning strategies, a tendency to conform increased earnings dramatically.
We present a new method for quantitatively documenting concerns for economic fairness. In particular we focus on the method’s potential for identifying variation in
prosociality within and across societies. Specifically, we conducted multiple dictator games per player in two small-scale societies. Each game presented the decision maker with a choice between an equitable and an inequitable payoff distribution. The games varied in terms of the type of inequality the decision maker faced and in terms of
the cost to the decision maker of eliminating inequality. This latter variation in cost is what makes the method suitable for identifying the price one is willing to pay for
equality. To analyze the data, we developed a novel set of statistical models that directly link experimental results and player heterogeneity with the formal theory of
inequality aversion. The paper concludes with a discussion of how the experimental method can be generalized to allow maximum flexibility in data analysis.
Human cooperation is unparalleled in the animal world and rests on an altruistic concern for the welfare of genetically unrelated strangers. The evolutionary roots of human altruism, however, remain poorly understood. Recent evidence suggests a discontinuity between humans and other primates because individual chimpanzees do not spontaneously
provide food to other group members, indicating a lack of concern for their welfare. Here, we demonstrate that common marmoset monkeys (Callithrix jacchus) do spontaneously
provide food to non-reciprocating and genetically unrelated individuals, indicating that other-regarding preferences are not unique to humans and that their evolution did not
require advanced cognitive abilities such as theory of mind. Because humans and marmosets are cooperative breeders and the only two primate taxa in which such unsolicited prosociality has been found, we conclude that these prosocial predispositions may emanate from cooperative breeding.
All known human societies establish social order by punishing violators of social norms. However, little is known about how the human brain processes the punishment threat associated with norm violations. We use fMRI to study the neural circuitry behind forced norm compliance by comparing a treatment in which norm violations can be punished with a control treatment in which punishment is impossible. Individuals’ increase in norm compliance when punishment is possible exhibits a strong positive correlation with activations in the lateral orbitofrontal cortex and right dorsolateral prefrontal cortex. These
activations are also modulated by the social nature of the task. Moreover, activation in lateral orbitofrontal cortex shows a strong positive correlation with Machiavellian personality characteristics. These findings indicate a neural network involved in forced norm compliance that may constitute an important basis for human sociality. Different activations of this network reveal individual differences in the behavioral response to the punishment threat and may thus provide a deeper understanding of the neurobiological sources of pathologies such as antisocial personality disorder.
Human social interaction is strongly shaped by other-regarding preferences. These preferences are key for a unique aspect of human sociality – large scale cooperation with genetic strangers – but little is known about their developmental roots. We show here that young children’s other-regarding preferences assume a particular form – inequality aversion – that develops strongly between the ages of 3 and 8. At age 3-4, the overwhelming majority of children behave selfishly, while the vast majority at age 7-8 prefers resource allocations that remove advantageous or disadvantageous inequality. Moreover, inequality aversion is strongly shaped by parochialism, a preference for favouring the members of one’s own social group. These results indicate that human egalitarianism and parochialism have deep developmental roots, and the simultaneous
emergence of altruistic sharing and parochialism during childhood is intriguing in view of recent evolutionary theories which predict that the same evolutionary process jointly drives both human altruism and parochialism.
Low frequency “off-line” repetitive transcranial magnetic stimulation (rTMS) over the course of several minutes has attained considerable attention as a research tool in cognitive neuroscience due to its ability to induce functional disruptions of brain areas. This disruptive rTMS effect is highly valuable for revealing a causal relationship between brain and behavior. However, its influence on remote interconnected areas and, more importantly, the duration of the induced neurophysiological effects, remain unknown. These aspects are critical for a study design in the context of cognitive neuroscience. In order to investigate these issues, 12 healthy male subjects underwent 8 H215O positron emission tomography (PET) scans after application of long-train low frequency rTMS to the right dorsolateral prefrontal cortex (DLPFC). Immediately after the stimulation train, regional cerebral blood flow (rCBF) increases were present under the stimulation site as well as in other prefrontal cortical areas, including the ventrolateral prefrontal cortex (VLPFC) ipsilateral to the stimulation site. The mean increases in rCBF returned to baseline within nine minutes. The duration of this unilateral prefrontal rTMS effect on rCBF is of particular interest to those who aim to influence behavior in cognitive paradigms that use an “off-line” approach.
