A neurocognitive mechanism for increased cooperation during group formation.

How do group size changes influence cooperation within groups? To examine this question, we performed a dynamic, network-based prisoner's dilemma experiment with fMRI. Across 83 human participants, we observed increased cooperation as group size increased. However, our computational modeling analysis of behavior and fMRI revealed that groups size itself did not increase cooperation.

Detailed analysis of drift diffusion model parameters estimated for the ultimatum game.

Bargaining is fundamental in human social interactions and often studied using the ultimatum game, where a proposer offers a division of resources, and the responder decides whether to accept or reject it. If accepted, the resources are divided as proposed, but neither party receives anything otherwise. While previous research has typically focused on either the choice or response time, a computational approach that integrates both can provide deeper insights into the cognitive and neural processes involved.

Model-free decision-making underlies motor errors in rapid sequential movements under threat.

Our movements, especially sequential ones, are usually goal-directed, i.e., coupled with task-level goals.

Unraveling how the third-party brain under stress responds to injustices.

How third-party individuals respond to injustices is important for resolving conflict in society. A study in PLOS Biology shows that individuals experiencing acute stress prefer to aid victims over punishing offenders, an opposite pattern to non-stress conditions.

Natural Language Content Mediates the Association Between Active Interactions on Social Network Services and Subjective Well-Being.

Previous studies indicated that active interactions on social networking services (SNS) are positively linked to subjective well-being (SWB). However, how semantic SNS content affects the association between the degree of SNS interaction and SWB has not been investigated. We addressed this issue by conducting a mediation analysis using natural language processing.

Distinctive types of aversiveness are represented as the same in a portion of the dorsal anterior cingulate cortex: An fMRI study with the cue paradigm.

Some studies have argued that the dorsal anterior cingulate cortex (dACC) is generally activated in response to aversive information, including pain, negative affect, and cognitive conflict. Other studies have claimed that the dACC has subdivisions, and each division has a specific function. By manipulating emotionally and cognitively aversive cues, the present study determined whether the dACC is generally responsive to aversiveness or it has subdivisions for specific forms of aversiveness.

The initial decrease in 7T-BOLD signals detected by hyperalignment contains information to decode facial expressions.

The initial decrease in the blood oxygenation level-dependent (BOLD) signal reflects primary neuronal activity more than the later hemodynamic positive peak responses. Moreover, ultra-high field BOLD has high sensitivity for the initial de-oxygenation signal. However, it is not fully understood how much information about task events and cognitive processes the initial decrease in the BOLD signal contains.

Resting functional connectivity of the left inferior frontal gyrus with the dorsomedial prefrontal cortex and temporoparietal junction reflects the social network size for active interactions.

The size of an individual active social network is a key parameter of human social behavior and is correlated with subjective well-being. However, it remains unknown how the social network size of active interactions is represented in the brain. Here, we examined whether resting-state functional magnetic resonance imaging (fMRI) connectivity is associated with the social network size of active interactions using behavioral data of a large sample (N = 222) on Twitter.

Functional Connectivity Basis and Underlying Cognitive Mechanisms for Gender Differences in Guilt Aversion.

Prosocial behavior is pivotal to our society. Guilt aversion, which describes the tendency to reduce the discrepancy between a partner's expectation and his/her actual outcome, drives human prosocial behavior as does well-known inequity aversion. Although women are reported to be more inequity averse than men, gender differences in guilt aversion remain unexplored.

Dissociable Behavioral and Neural Correlates for Target-Changing and Conforming Behaviors in Interpersonal Aggression.

Actors in interpersonal aggression such as bullies change their targets frequently, but the underlying behavioral and neural mechanisms are unknown. Here, using the catch-ball task we recently developed to examine human interpersonal aggression, we found target-changing and conforming to other participants' aggression are major driving forces of increased aggression (i.e.

Value computations underlying human proposer behavior in the ultimatum game.

Interacting with others to decide how finite resources should be allocated between potentially competing parties is an important part of social life. Considering that not all of our proposals to others are always accepted, the outcomes of such social interactions are probabilistic and risky. Here, we highlight cognitive processes related to value computations in human social interactions, based on mathematical modeling of the proposer behavior in the Ultimatum Game.

Computing Social Value Conversion in the Human Brain.

Social signals play powerful roles in shaping self-oriented reward valuation and decision making. These signals activate social and valuation/decision areas, but the core computation for their integration into the self-oriented decision machinery remains unclear. Here, we study how a fundamental social signal, social value (others' reward value), is converted into self-oriented decision making in the human brain.

Interactive effects of OXTR and GAD1 on envy-associated behaviors and neural responses.

Inequity aversion (negative feelings induced by outcome differences between the self and other) plays a key role in human social behaviors. The neurotransmitters oxytocin and GABA have been implicated in neural responses to inequity. However, it remains poorly understood not only how individual genetic factors related to oxytocin and GABA affect the neural mechanisms behind inequity aversion, but also how these genes interact.

Behavioral and functional connectivity basis for peer-influenced bystander participation in bullying.

Recent studies have shown that the reactions of bystanders who witness bullying significantly affect whether the bullying persists. However, the underlying behavioral and neural mechanisms that determine a peer-influenced bystander's participation in bullying remain largely unknown. Here, we designed a new 'catch-ball' task where four players choose to throw a sequence of normal or strong (aggressive) balls in turn and examined whether the players (n = 43) participated in other players' bullying.

Brain response patterns to economic inequity predict present and future depression indices.

Widening economic inequity has been suggested to associate with depression. However, little is known about the underlying neural mechanisms of this link. Here, we demonstrate that functional magnetic resonance imaging activity patterns in the amygdala and hippocampus induced by the inequity between the self and other rewards during an economic game can predict participants' present and future (measured one year later) depression indices.

Selective increase of intention-based economic decisions by noninvasive brain stimulation to the dorsolateral prefrontal cortex.

The intention behind another's action and the impact of the outcome are major determinants of human economic behavior. It is poorly understood, however, whether the two systems share a core neural computation. Here, we investigated whether the two systems are causally dissociable in the brain by integrating computational modeling, functional magnetic resonance imaging, and transcranial direct current stimulation experiments in a newly developed trust game task.

Effects of subconscious and conscious emotions on human cue-reward association learning.

Life demands that we adapt our behaviour continuously in situations in which much of our incoming information is emotional and unrelated to our immediate behavioural goals. Such information is often processed without our consciousness. This poses an intriguing question of whether subconscious exposure to irrelevant emotional information (e.

Activity in the nucleus accumbens and amygdala underlies individual differences in prosocial and individualistic economic choices.

Much decision-making requires balancing benefits to the self with benefits to the group. There are marked individual differences in this balance such that individualists tend to favor themselves whereas prosocials tend to favor the group. Understanding the mechanisms underlying this difference has important implications for society and its institutions.

Reward prediction error signal enhanced by striatum-amygdala interaction explains the acceleration of probabilistic reward learning by emotion.

Learning does not only depend on rationality, because real-life learning cannot be isolated from emotion or social factors. Therefore, it is intriguing to determine how emotion changes learning, and to identify which neural substrates underlie this interaction. Here, we show that the task-independent presentation of an emotional face before a reward-predicting cue increases the speed of cue-reward association learning in human subjects compared with trials in which a neutral face is presented.

Learning to simulate others' decisions.

A fundamental challenge in social cognition is how humans learn another person's values to predict their decision-making behavior. This form of learning is often assumed to require simulation of the other by direct recruitment of one's own valuation process to model the other's process. However, the cognitive and neural mechanism of simulation learning is not known.

MOSAIC for multiple-reward environments.

Reinforcement learning (RL) can provide a basic framework for autonomous robots to learn to control and maximize future cumulative rewards in complex environments. To achieve high performance, RL controllers must consider the complex external dynamics for movements and task (reward function) and optimize control commands. For example, a robot playing tennis and squash needs to cope with the different dynamics of a tennis or squash racket and such dynamic environmental factors as the wind.