Right dorsolateral prefrontal cortex regulates default prosociality preference.

The dorsolateral prefrontal cortex has been shown to be associated with prosocial behavior. However, the direction of this relationship remains controversial. To resolve inconsistencies in the existing literature, we introduced the concept of default prosociality preference and hypothesized that this preference moderates the relationship between gray matter volume in the dorsolateral prefrontal cortex and prosocial behavior. This study analyzed the data of 168 participants obtained from voxel-based morphometry, 4 types of economic games, and 3 different measures of social value orientation that represent default prosociality preference. Here we show that, in individuals who were consistently classified as proself on the 3 social value orientation measures, gray matter volume in the right dorsolateral prefrontal cortex was positively associated with prosocial behavior. However, in individuals who were consistently classified as prosocial, the direction of this association was vice versa. These results indicate that the right dorsolateral prefrontal cortex regulates default prosociality preference.

Association between arginine vasopressin receptor 1A (AVPR1A) polymorphism and inequity aversion.

Although numerous studies have focused on brain functions related to inequity aversion, few have examined its genetic basis. Here, we show the association between estimated inequity aversion and polymorphisms in three genes associated with human sociality. Non-student adult participants took part in five economic game experiments on different days. Disadvantageous inequity aversion (DIA) and advantageous inequity aversion (AIA) were calculated from behavioural responses using Bayesian estimation. We investigated the association between genetic polymorphisms in the oxytocin receptor (OXTR rs53576), arginine vasopressin receptor 1A (AVPR1A RS3) and opioid receptor mu 1 (OPRM1 rs1799971) and inequity aversion. Regarding AVPR1A RS3, participants with the SS genotype had higher AIA than those with the SL or LL genotypes, but no association was found for DIA. Moreover, we observed no aversion associations for OXTR rs53576 or OPRM1 rs1799971. The results suggest that AVPR1A plays an important role in aversion when one's own gain is greater than that of others. Our findings may provide a solid theoretical basis for future studies on the relationship between genetic polymorphisms and inequity aversion.

Response time in economic games reflects different types of decision conflict for prosocial and proself individuals.

Behavioral and neuroscientific studies explore two pathways through which internalized social norms promote prosocial behavior. One pathway involves internal control of impulsive selfishness, and the other involves emotion-based prosocial preferences that are translated into behavior when they evade cognitive control for pursuing self-interest. We measured 443 participants' overall prosocial behavior in four economic games. Participants' predispositions [social value orientation (SVO)] were more strongly reflected in their overall game behavior when they made decisions quickly than when they spent a longer time. Prosocially (or selfishly) predisposed participants behaved less prosocially (or less selfishly) when they spent more time in decision making, such that their SVO prosociality yielded limited effects in actual behavior in their slow decisions. The increase (or decrease) in slower decision makers was prominent among consistent prosocials (or proselfs) whose strong preference for prosocial (or proself) goals would make it less likely to experience conflict between prosocial and proself goals. The strong effect of RT on behavior in consistent prosocials (or proselfs) suggests that conflict between prosocial and selfish goals alone is not responsible for slow decisions. Specifically, we found that contemplation of the risk of being exploited by others (social risk aversion) was partly responsible for making consistent prosocials (but not consistent proselfs) spend longer time in decision making and behave less prosocially. Conflict between means rather than between goals (immediate versus strategic pursuit of self-interest) was suggested to be responsible for the time-related increase in consistent proselfs' prosocial behavior. The findings of this study are generally in favor of the intuitive cooperation model of prosocial behavior.

[Stimulus and reward information encoded by population neurons in the primate prefrontal cortex and striatum].

It has been reported that single-unit activity in the prefrontal cortex (PFC) and striatum represented visual stimulus and reward information. But how to encode these pieces of information is quite complex from the view of single-neuron activity. Different neurons represented stimulus or reward information in different task epochs with increasing or decreasing their activities relative to their baseline firing rates. The present paper was aimed to study whether population neurons in the two brain areas could stably encode task-relevant parameters in a whole trial period. We recorded single-unit activities in the lateral PFC (LPFC) and striatum while the monkey was performing a stimulus- reward prediction task, and analyzed the neuronal activities by the method of a multi-variable regression model and the linear support vector machine. The results showed that, although proportions of task-related neurons in the two areas varied largely in the whole trial period, LPFC population neurons encoded reward and stimulus information stably and reliably. Population neurons in the striatum encoded only reward information, not stimulus information. A group of neurons in the two areas represented combined information of stimulus and reward. Further analysis showed that LPFC neurons encoded reward information for a group of relevant stimuli, while striatal neurons encoded reward information for a specific stimulus. These results suggest that both LPFC and striatal population neurons are able to stably represent task-relevant information, but from different aspects of the task. The different strategies to encode information in the LPFC and striatum suggest their different contributions in reward-based decision making.

Psychosocial Twin Cohort Studies in Japan: The Keio Twin Research Center (KoTReC).

The Keio Twin Research Center (KoTReC) was established in 2009 at Keio University to combine two longitudinal cohort projects - the Keio Twin Study (KTS) for adolescence and adulthood and the Tokyo Twin Cohort Project (ToTCoP) for infancy and childhood. KoTReC also conducted a two-time panel study of self-control and psychopathology in twin adolescence in 2012 and 2013 and three independent anonymous cross-sectional twin surveys (ToTcross) before 2012 - the ToTCross, the Junior and Senior High School Survey and the High School Survey. This article introduces the recent research designs of KoTReC and its publications.

Rawlsian maximin rule operates as a common cognitive anchor in distributive justice and risky decisions.

Distributive justice concerns the moral principles by which we seek to allocate resources fairly among diverse members of a society. Although the concept of fair allocation is one of the fundamental building blocks for societies, there is no clear consensus on how to achieve "socially just" allocations. Here, we examine neurocognitive commonalities of distributive judgments and risky decisions. We explore the hypothesis that people's allocation decisions for others are closely related to economic decisions for oneself at behavioral, cognitive, and neural levels, via a concern about the minimum, worst-off position. In a series of experiments using attention-monitoring and brain-imaging techniques, we investigated this "maximin" concern (maximizing the minimum possible payoff) via responses in two seemingly disparate tasks: third-party distribution of rewards for others, and choosing gambles for self. The experiments revealed three robust results: (i) participants' distributive choices closely matched their risk preferences-"Rawlsians," who maximized the worst-off position in distributions for others, avoided riskier gambles for themselves, whereas "utilitarians," who favored the largest-total distributions, preferred riskier but more profitable gambles; (ii) across such individual choice preferences, however, participants generally showed the greatest spontaneous attention to information about the worst possible outcomes in both tasks; and (iii) this robust concern about the minimum outcomes was correlated with activation of the right temporoparietal junction (RTPJ), the region associated with perspective taking. The results provide convergent evidence that social distribution for others is psychologically linked to risky decision making for self, drawing on common cognitive-neural processes with spontaneous perspective taking of the worst-off position.

[Reward information encoded by power of local field potentials in the primate prefrontal cortex and striatum].

Prefrontal cortex and striatum are two major areas in the brain. Some research reports suggest that both areas are involved in many advanced cognitive processes, such as learning and memory, reward processing, and behavioral decision. Single-unit recording experiments have found that neurons in the prefrontal cortex and striatum can represent reward information, but it remains elusive whether and how local field potentials (LFPs) in the two areas encode reward information. To investigate these issues, we recorded LFPs simultaneously in the prefrontal cortex and striatum of two monkeys by performing a reward prediction task (a large amount reward vs a small amount reward). Recorded LFP signals were transformed from the time domain to the time and frequency domain using the method of short-time Fourier transform (STFT). We calculated the power in each frequency and time, and examined whether they were different in the two reward conditions. The results showed that power of LFPs in both the prefrontal cortex and striatum distinguished one reward condition from the other one. And the power in small reward trials was greater than that in large reward trials. Furthermore, it was found that the LFPs better encoded reward information in the beta band (14-30 Hz) rather than other frequency bands. Our results suggest that the LFPs in the prefrontal cortex and striatum effectively represent reward information, which would help to further understand functional roles of LFPs in reward processing.

Reward inference by primate prefrontal and striatal neurons.

The brain contains multiple yet distinct systems involved in reward prediction. To understand the nature of these processes, we recorded single-unit activity from the lateral prefrontal cortex (LPFC) and the striatum in monkeys performing a reward inference task using an asymmetric reward schedule. We found that neurons both in the LPFC and in the striatum predicted reward values for stimuli that had been previously well experienced with set reward quantities in the asymmetric reward task. Importantly, these LPFC neurons could predict the reward value of a stimulus using transitive inference even when the monkeys had not yet learned the stimulus-reward association directly; whereas these striatal neurons did not show such an ability. Nevertheless, because there were two set amounts of reward (large and small), the selected striatal neurons were able to exclusively infer the reward value (e.g., large) of one novel stimulus from a pair after directly experiencing the alternative stimulus with the other reward value (e.g., small). Our results suggest that although neurons that predict reward value for old stimuli in the LPFC could also do so for new stimuli via transitive inference, those in the striatum could only predict reward for new stimuli via exclusive inference. Moreover, the striatum showed more complex functions than was surmised previously for model-free learning.

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. This phenomenon was attributable to an increase in the learning rate, which regulates reward prediction errors. Parallel to these behavioral findings, functional magnetic resonance imaging demonstrated that presentation of an emotional face enhanced reward prediction error (RPE) signal in the ventral striatum. In addition, we also found a functional link between this enhanced RPE signal and increased activity in the amygdala following presentation of an emotional face. Thus, this study revealed an acceleration of cue-reward association learning by emotion, and underscored a role of striatum-amygdala interactions in the modulation of the reward prediction errors by emotion.

Performance Dip in motor response induced by task-irrelevant weaker coherent visual motion signals.

The Performance Dip is a newly characterized behavioral phenomenon, where, paradoxically, a weaker task-irrelevant visual stimulus causes larger disturbances on the accuracy of a main letter identification task than a stronger stimulus does. Understanding mechanisms of the Performance Dip may provide insight into unconsciousness behavior. Here, we investigated the generalization of the Performance Dip. Specifically, we tested whether the Performance Dip occurs in a motion-related Simon task, and if so, whether the Performance Dip involves the same brain region, that is, the dorsolateral prefrontal cortex (DLPFC), previously implicated in the Performance Dip, or the supplementary motor area (SMA) and pre-SMA, implicated in a motion-related Simon Task. Subjects made manual directional responses according to the color of stochastic moving dots while ignoring the global direction of moving dots, which could be either congruent or incongruent to the response appropriate to the main task. We found that weak incongruent task-irrelevant stimuli caused a Performance Dip, in which the SMA and pre-SMA, rather than DLPFC, played critical roles. Our results suggest a possible common brain mechanism across different neural circuits, in which weak, but not strong, task-irrelevant information is free from inhibition and intrudes into neural circuits relevant to the main task.

Only cortical prediction error signals are involved in visual learning, despite availability of subcortical prediction error signals.

Both the midbrain systems, encompassing the ventral striatum (VS), and the cortical systems, including the dorsal anterior cingulate cortex (dACC), play roles in reinforcing and enhancing learning. However, the specific contributions of signals from these regions in learning remains unclear. To investigate this, we examined how VS and dACC are involved in visual perceptual learning (VPL) through an orientation discrimination task. In the primary experiment, subjects fasted for 5 hours before each of 14 days of training sessions and 3 days of test sessions. Subjects were rewarded with water for accurate trial responses. During the test sessions, BOLD signals were recorded from regions including VS and dACC. Although BOLD signals in both areas were associated with positive and negative RPEs, only those in dACC associated with negative RPE showed a significant correlation with performance improvement. Additionally, no significant correlation was observed between BOLD signals associated with RPEs in VS and dACC. These results suggest that although signals associated with positive and negative RPEs from both midbrain and cortical systems are readily accessible, only RPE signals in the prefrontal system, generated without linking to RPE signals in VS, are utilized for the enhancement of VPL.

Proselfs depend more on model-based than model-free learning in a non-social probabilistic state-transition task.

Humans form complex societies in which we routinely engage in social decision-making regarding the allocation of resources among ourselves and others. One dimension that characterizes social decision-making in particular is whether to prioritize self-interest or respect for others-proself or prosocial. What causes this individual difference in social value orientation? Recent developments in the social dual-process theory argue that social decision-making is characterized by its underlying domain-general learning systems: the model-free and model-based systems. In line with this "learning" approach, we propose and experimentally test the hypothesis that differences in social preferences stem from which learning system is dominant in an individual. Here, we used a non-social state transition task that allowed us to assess the balance between model-free/model-based learning and investigate its relation to the social value orientations. The results showed that proselfs depended more on model-based learning, whereas prosocials depended more on model-free learning. Reward amount and reaction time analyses showed that proselfs learned the task structure earlier in the session than prosocials, reflecting their difference in model-based/model-free learning dependence. These findings support the learning hypothesis on what makes differences in social preferences and have implications for understanding the mechanisms of prosocial behavior.

Category representation and generalization in the prefrontal cortex.

Categorization is a function of the brain that serves to group together items and events in our environments. Here we review the following important issues related to category representation and generalization: namely, where categories are presented in the brain, and how the brain utilizes categorical membership to generate new information. Accumulated experimental evidence shows that the prefrontal cortex (PFC) plays a critical role in category formation and generalization. We propose that prefrontal neurons abstract the commonality beyond individual stimuli, and categorize these based on their common meaning by ignoring their physical properties and learning to represent the boundaries between behaviorally significant categories. We also claim that a subgroup of prefrontal neurons simultaneously receives the category-related information and specific property information (e.g. reward) associated with an exemplar, to form a category-based representation of that property, and propagates it among stimuli of the same category, possibly reflecting a neural basis for category generalization in the PFC. These results suggest that the PFC is involved in representing abstract rules, and generating new information on the basis of previously acquired knowledge.

Reward prediction based on stimulus categorization in primate lateral prefrontal cortex.

To adapt to changeable or unfamiliar environments, it is important that animals develop strategies for goal-directed behaviors that meet the new challenges. We used a sequential paired-association task with asymmetric reward schedule to investigate how prefrontal neurons integrate multiple already-acquired associations to predict reward. Two types of reward-related neurons were observed in the lateral prefrontal cortex: one type predicted reward independent of physical properties of visual stimuli and the other encoded the reward value specific to a category of stimuli defined by the task requirements. Neurons of the latter type were able to predict reward on the basis of stimuli that had not yet been associated with reward, provided that another stimulus from the same category was paired with reward. The results suggest that prefrontal neurons can represent reward information on the basis of category and propagate this information to category members that have not been linked directly with any experience of reward.

Dissecting the Prefrontal Network With Pathway-Selective Manipulation in the Macaque Brain-A Review.

Macaque monkeys are prime animal models for studying the neural mechanisms of decision-making because of their close kinship with humans. Manipulation of neural activity during decision-making tasks is essential for approaching the causal relationship between the brain and its functions. Conventional manipulation methods used in macaque studies are coarse-grained, and have worked indiscriminately on mutually intertwined neural pathways. To systematically dissect neural circuits responsible for a variety of functions, it is essential to analyze changes in behavior and neural activity through interventions in specific neural pathways. In recent years, an increasing number of studies have applied optogenetics and chemogenetics to achieve fine-grained pathway-selective manipulation in the macaque brain. Here, we review the developments in macaque studies involving pathway-selective operations, with a particular focus on applications to the prefrontal network. Pathway selectivity can be achieved using single viral vector transduction combined with local light stimulation or ligand administration directly into the brain or double-viral vector transduction combined with systemic drug administration. We discuss the advantages and disadvantages of these methods. We also highlight recent technological developments in viral vectors that can effectively infect the macaque brain, as well as the development of methods to deliver photostimulation or ligand drugs to a wide area to effectively manipulate behavior. The development and dissemination of such pathway-selective manipulations of macaque prefrontal networks will enable us to efficiently dissect the neural mechanisms of decision-making and innovate novel treatments for decision-related psychiatric disorders.

Social impact and governance of AI and neurotechnologies.

Advances in artificial intelligence (AI) and brain science are going to have a huge impact on society. While technologies based on those advances can provide enormous social benefits, adoption of new technologies poses various risks. This article first reviews the co-evolution of AI and brain science and the benefits of brain-inspired AI in sustainability, healthcare, and scientific discoveries. We then consider possible risks from those technologies, including intentional abuse, autonomous weapons, cognitive enhancement by brain-computer interfaces, insidious effects of social media, inequity, and enfeeblement. We also discuss practical ways to bring ethical principles into practice. One proposal is to stop giving explicit goals to AI agents and to enable them to keep learning human preferences. Another is to learn from democratic mechanisms that evolved in human society to avoid over-consolidation of power. Finally, we emphasize the importance of open discussions not only by experts, but also including a diverse array of lay opinions.

Association Between Polymorphism (5-HTTLPR) of the Serotonin Transporter Gene and Behavioral Response to Unfair Distribution.

Behavioral responses to unfair distribution have been measured mainly using the Ultimatum Game (UG). Recent studies examining the biological basis of behavioral responses to unfair distribution have focused attention on the role of the serotonin transporter gene. However, studies, to date, have been conducted on non-Asians, and it has not been confirmed whether similar results can be seen in other ethnic groups. It has also been shown that behavioral responses to unfair distribution are not only seen in the case of victims themselves but also in the case of third parties not directly affected. This study aimed to determine whether the results of the previous study would be replicated in an Asian population and whether the serotonin transporter gene would also be associated with behavior toward unfair distribution by third parties. We examined the association between polymorphisms (5-HTTLPR) of the serotonin transporter gene and participants' behavior in the UG and the third-party punishment game (TPPG). The results did not show an association between punishment for unfair proposals in the TPPG and genetic polymorphisms, while participants with the SL/LL genotype were more likely to reject unfair offers in the UG than those with the SS genotype. These results indicate that 5-HTTLPR is associated with behavior when unfair intentions are directed at oneself.

A whole brain probabilistic generative model: Toward realizing cognitive architectures for developmental robots.

Building a human-like integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model (PGM)-based cognitive architecture to develop a cognitive system for developmental robots by integrating PGMs. The proposed development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information. In this paper, we describe the rationale for WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, WB-PGM provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics.

The neuroanatomy of social trust predicts depression vulnerability.

Trust attitude is a social personality trait linked with the estimation of others' trustworthiness. Trusting others, however, can have substantial negative effects on mental health, such as the development of depression. Despite significant progress in understanding the neurobiology of trust, whether the neuroanatomy of trust is linked with depression vulnerability remains unknown. To investigate a link between the neuroanatomy of trust and depression vulnerability, we assessed trust and depressive symptoms and employed neuroimaging to acquire brain structure data of healthy participants. A high depressive symptom score was used as an indicator of depression vulnerability. The neuroanatomical results observed with the healthy sample were validated in a sample of clinically diagnosed depressive patients. We found significantly higher depressive symptoms among low trusters than among high trusters. Neuroanatomically, low trusters and depressive patients showed similar volume reduction in brain regions implicated in social cognition, including the dorsolateral prefrontal cortex (DLPFC), dorsomedial PFC, posterior cingulate, precuneus, and angular gyrus. Furthermore, the reduced volume of the DLPFC and precuneus mediated the relationship between trust and depressive symptoms. These findings contribute to understanding social- and neural-markers of depression vulnerability and may inform the development of social interventions to prevent pathological depression.

Temporally extended dopamine responses to perceptually demanding reward-predictive stimuli.

Midbrain dopamine neurons respond to reward-predictive stimuli. In the natural environment reward-predictive stimuli are often perceptually complicated. Thus, to discriminate one stimulus from another, elaborate sensory processing is necessary. Given that previous studies have used simpler types of reward-predictive stimuli, it has yet to be clear whether and, if so, how dopamine neurons obtain reward information from perceptually complicated stimuli. To investigate this, we recorded the activities of monkey dopamine neurons while they were performing discrimination between two coherent motion directions in random-dot motion stimuli. These coherent directions were paired with different magnitudes of reward. We found that dopamine neurons showed reward-predictive responses to random-dot motion stimuli. Moreover, dopamine neurons showed temporally extended activity correlated with changes in reward prediction (i.e., reward prediction error) from coarse to fine scales between initial motion detection and subsequent motion discrimination phases. Noticeably, dopamine reward-predictive responses became differential in a later phase than previously reported. This response pattern was consistent with the time course of processing required for the estimation of expected reward value that parallels the motion direction discrimination processing. The results demonstrate that dopamine neurons are able to reflect the reward value of perceptually complicated stimuli, and suggest that dopamine neurons use the moment-to-moment reward prediction associated with environmental stimuli to compute a reward prediction error.