Search

The Autism-Spectrum Quotient (AQ) is a widely used index to quantify autistic traits. Previous studies using clustering analysis have indicated that people showing high AQ and other autism indices may comprise several subtypes. However, those studies mainly focused on Big5 personality traits and analyzed a limited number of samples (participants). The inclusion of a larger set of personality traits and attributes would contribute not only to understanding autism more deeply, but also to characterizing AQ more precisely. In addition, an analysis of larger general populations would give new perspectives on people with high AQ scores. To address these issues, here we conducted a personality traits-based clustering of 1364 subjects who had an AQ score of 33 or higher (high AQ group) collected online. We identified three subclasses (subtypes): Group 1, characterized by emotional instability, low self-esteem, high hostility, anxiety, depressive tendency and schizotypal traits; Group 2, characterized by high BIS for an inhibitory attitude towards novelty, and high Interpersonal Reactivity Index (IRI) and Group 3, characterized by personality traits and attributes consistent of the average of the general population. Thus, this study provides empirical results showing subtypes of the high AQ population.

Objective Previous studies have shown that digital footprints (mainly Social Networking Services, or SNS) can predict personality traits centered on the Big Five. The present study investigates to what extent different types of SNS information predicts wider traits and attributes. Method We collected an intensive set of 24 (52 subscales) personality traits and attributes (N = 239) and examined whether machine learning models trained on four different types of SNS (i.e., Twitter) information (network, time, word statistics, and bag of words) predict the traits and attributes. Results We found that four types of SNS information can predict 23 subscales collectively. Furthermore, we validated our hypothesis that the network and word statistics information, respectively, exhibit unique strengths for the prediction of inter‐personal traits such as autism and mental health traits such as schizophrenia and anxiety. We also found that intelligence is predicted by all four types of SNS information. Conclusions These results reveal that the different types of SNS information can collectivity predict wider human traits and attributes than previously recognized, and also that each information type has unique predictive strengths for specific traits and attributes, suggesting that personality prediction from SNS is a powerful tool for both personality psychology and information technology.

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. Region of interest (ROI)-to-ROI analysis, graph theory analysis, seed-based analysis, and decoding analysis together provided compelling evidence that people who have a large social network size of active interactions, as measured by "reply," show higher fMRI connectivity of the left inferior frontal gyrus with the dorsomedial prefrontal cortex and temporoparietal junction, which represents the core of the theory of mind network. These results demonstrated that people who have a large social network size of active interactions maintain activity of the identified functional connectivity in daily life, possibly providing a mechanism for efficient information transmission between the brain networks related to language and theory-of-mind.

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. Conjunction functional magnetic resonance imaging (fMRI) analysis showed that emotionally and cognitively aversive cues activated the same portion of the dACC. When these cues were contiguously presented, the region demonstrated additive activity, further supporting the overlapping representation of the two different forms of aversiveness in the dACC. Additional effective connectivity analysis showed that the dACC was co-activated with different brain regions depending on the cue type, characterizing its behavioral control mechanism. Complementary multivariate analyses showed that the reaction time was negatively correlated with the activity of the dACC and that the activity of the dACC under the emotional cue was predicted by the individual state anxiety score but not under the cognitive cue. We also found that the superior part of the dACC was uniquely activated in response to cognitively aversive cues, partially supporting the functional segregation account. Collectively, our results provide evidence that the specific locus of the dACC is generally responsive to distinctive motivational information, whereas the other loci may have segregated functions. Discussion includes recent neurocomputational theories that seem to satisfactorily account for the present results.

Guilt aversion, which describes the tendency to reduce the discrepancy between a partner’s expectation and his/her actual outcome, is a key driving force for cooperation in both the East and West. A recent study based on functional magnetic resonance imaging and online behavioral experiments reported that men show stronger guilt aversion than women and also suggested that men’s predominance in guilt aversion arises from stronger sensitivity to social norms. However, since the participants of that study were all Japanese, it remains unaddressed how common the gender difference in guilt aversion is. Here, we conducted online behavioral studies on people from Korea and the UK (Korea; n = 294, UK; n = 347) using the same trust game. We confirmed that men exhibit stronger guilt aversion than women in both countries. Furthermore, consistent with the Japanese study, our Lasso regression analysis for UK participants revealed that Big Five Conscientiousness (rule-based decision) correlated with guilt aversion in men. In contrast, guilt aversion in Korean men correlated with Big Five Neuroticism. Thus, our results suggest that gender differences in guilt aversion are universal but the underlying cognitive processes may be influenced by cultural differences.

The most promising way to prevent the explosive spread of COVID-19 infection is to achieve herd immunity through vaccination. It is therefore important to motivate those who are less willing to be vaccinated. To address this issue, we conducted an online survey of 6232 Japanese people to investigate age- and gender- dependent differences in attitudes towards COVID-19 vaccination and the underlying psychological processes. We asked participants to read one of nine different messages about COVID-19 vaccination and rate their willingness to be vaccinated. We also collected their 17 social personality trait scores and demographic information. We found that males 10-20 years old showed the minimum willingness to be vaccinated. We also found that prosocial traits are the driving force for young people, but the motivation in older people also depends on risk aversion and self-interest. Furthermore, an analysis of 9 different messages demonstrated that for young people (particularly males), the message emphasizing the majority’s intention to vaccinate and scientific evidence for the safety of the vaccination had the strongest positive effect on the willingness to be vaccinated, suggesting that the herding effect arising from the “majority + scientific evidence” message nudges young people to show their prosocial nature in action.

For scientists, policy makers, and the general population, there is increasing interest in how humans form cooperative groups. However, how group-oriented behavior emerges during the dynamic process of group formation is still unknown. We hypothesize that humans will exhibit emergent prosocial behavior as their immediate group size increases. Using a network-embedded-dyad prisoner dilemma task, with periodic opportunities to retain or remove group members, we find subjects consistently follow a well-performing reciprocal base policy (tit-for-tat-like) across the experimental session. However, subjects’ strategies also became more forgiving and less exploitative as group size increased, with a default preference shift to cooperation. Thus, human cooperation may emerge from a desire to create and maintain larger and more cooperative groups, and multiscale strategy that considers both self-interest and group-interest.

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. We analyzed behaviors with a computational model that quantifies the tendencies of a participant’s (i) baseline propensity for bullying, (ii) reactive revenge, (iii) conformity to bullying, and (iv) capitulation to threat and estimated these effects on the choice of balls. We found only conformity had a positive effect on the throwing of strong balls. Furthermore, we identified a correlation between a participant’s conformity and social anxiety. Our mediation analysis of resting-state functional magnetic resonance imaging revealed that there were significant relationships of each participant’s functional connectivity between the amygdala and right temporoparietal junction (TPJ) and social anxiety to the participant’s conformity to bullying. We also found that amygdala–TPJ connectivity partially mediated the relationship between social anxiety and conformity. These results highlighted the anxiety-based conformity and amygdala network on peer-influenced bystander participation in bullying.

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 known to be more inequity averse than men, gender differences in guilt aversion remain unexplored. Here we conducted a functional magnetic resonance imaging (fMRI) study (n = 52) and a large-scale online behavioral study (n = 4723) of a trust game designed to investigate guilt and inequity aversions. The fMRI study demonstrated that men exhibited stronger guilt aversion and recruited right DLPFC-VMPFC connectivity more for guilt aversion than women, while VMPFC-DMPFC connectivity was commonly used in both genders. Furthermore, our regression analysis of the online behavioral data collected with Big Five and demographic factors replicated the gender differences and revealed that Big Five Conscientiousness (rule-based decision) correlated with guilt aversion only in men, but Agreeableness (empathetic consideration) correlated with guilt aversion in both genders. Thus, this study suggests that gender differences in prosocial behavior are heterogeneous depending on underlying motives in the brain and that the consideration of social norms plays a key role in the stronger guilt aversion in men. Significance Statement Although women are established to be more prosocial than men in terms of inequity aversion, gender differences in prosocial behavior based on motives prominent in guilt aversion are far less explored. Here we conducted a fMRI study and a large-scale online behavioral study to address gender differences in guilt aversion. We demonstrate that men are more sensitive to guilt aversion than women, and a prefrontal social-norm network is key to men’s predominance in guilt-based prosocial behavior. These findings revealed the heterogeneity of gender differences in prosocial behavior depending on underlying motives and underlying neural mechanisms.

Performance anxiety can profoundly affect motor performance, even in experts such as professional athletes and musicians. Previously, the neural mechanisms underlying anxiety-induced performance deterioration have predominantly been investigated for individual one-shot actions. Sports and music, however, are characterized by action sequences, where many individual actions are assembled to develop a performance. Here, utilizing a novel differential sequential motor learning paradigm, we first show that performance at the junctions between pre-learnt action sequences is particularly prone to anxiety. Next, utilizing functional magnetic resonance imaging (fMRI), we reveal that performance deterioration at the junctions is parametrically correlated with activity in the dorsal anterior cingulate cortex (dACC). Finally, we show that 1 Hz repetitive transcranial magnetic stimulation of the dACC attenuates the performance deterioration at the junctions. These results demonstrate causality between dACC activity and impairment of sequential motor performance due to anxiety, and suggest new intervention techniques against the deterioration., Performance anxiety can impair motor skill, and even affect expert athletes and musicians. Here, the authors show that anxiety affects performance at the ‘junction’ between two well-learned action sequences, and that this affect is associated with activity in dorsal anterior cingulate cortex (dACC).

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. Using behavioral analysis, modeling, and neuroimaging, we show three-stage processing of social value conversion from the offer to the effective value and then to the final decision value. First, a value of others' bonus on offer, called offered value, was encoded uniquely in the right temporoparietal junction (rTPJ) and also in the left dorsolateral prefrontal cortex (ldlPFC), which is commonly activated by offered self-bonus value. The effective value, an intermediate value representing the effective influence of the offer on the decision, was represented in the right anterior insula (rAI), and the final decision value was encoded in the medial prefrontal cortex (mPFC). Second, using psychophysiological interaction and dynamic causal modeling analyses, we demonstrated three-stage feedforward processing from the rTPJ and ldPFC to the rAI and then from rAI to the mPFC. Further, we showed that these characteristics of social conversion underlie distinct sociobehavioral phenotypes. We demonstrate that the variability in the conversion underlies the difference between prosocial and selfish subjects, as seen from the differential strength of the rAI and ldlPFC coupling to the mPFC responses, respectively. Together, these findings identified fundamental neural computation processes for social value conversion underlying complex social decision making behaviors. SIGNIFICANCE STATEMENT In daily life, we make decisions based on self-interest, but also in consideration for others' status. These social influences modulate valuation and decision signals in the brain, suggesting a fundamental process called value conversion that translates social information into self-referenced decisions. However, little is known about the conversion process and its underlying brain mechanisms. We investigated value conversion using human fMRI with computational modeling and found three essential stages in a progressive brain circuit from social to empathic and decision areas. Interestingly, the brain mechanism of conversion differed between prosocial and individualistic subjects. These findings reveal how the brain processes and merges social information into the elemental flow of self-interested decision making.

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. Our results suggest that the perception of risk is an overarching process across nonsocial and social decision-making domains, whereas the nonlinear weighting of others' acceptance probabilities appears to be more closely associated with social interactive decision-making situations in which others' valuation processes needs to be inferred. Despite the complexity of social interactive decision-making, human participants adjust their risk and probability weighting parameters while interacting with opponents with different social value orientations, and these parameters governing participants' decision-making strategies are influenced by the inferences participants make about their opponents (e.g., how prosocial they think their opponent is relative to themselves), as well as their own social value orientation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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., throwing strong balls). We also found that target-changing was correlated with a participant’s extraversion, consistent with a bistrategic view, in which both prosocial and coercive motivations drive interpersonal aggression. In contrast, conforming to others was correlated with social anxiety. In addition, questionnaires about participants’ past experiences of bullying suggested that target-changers and conformers were predominantly bullies and victims in the past. An analysis of resting-state functional magnetic resonance imaging (fMRI) revealed that functional connectivity between the dorsal anterior cingulate cortex (dACC) and insula were correlated with target-changing behavior, while functional connectivity between the amygdala and temporo-parietal junction (TPJ) was correlated with conformity. These results demonstrate that target-changing and conforming behaviors have dissociable behavioral and neural mechanisms and may contribute to real-world interpersonal aggressions differently.

Interacting with others to decide how finite resources should be allocated between parties which may have competing interests 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, by their nature, probabilistic and risky. Here, we highlight cognitive processes related to value computations in human social interactions, based on mathematical modelling of the proposer behavior in the Ultimatum Game. Our results suggest that the perception of risk is an overarching process across non-social and social decision-making, whereas nonlinear weighting of others’ acceptance probabilities is unique to social interactions in which others’ valuation processes needs to be inferred. Despite the complexity of social decision-making, human participants make near-optimal decisions by dynamically adjusting their decision parameters to the changing social value orientation of their opponents through influence by multidimensional inferences they make about those opponents (e.g. how prosocial they think their opponent is relative to themselves).

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. Such predictions were not possible using participant’s behavioural and socio-economic status measures. These findings suggest that sensitivity to economic inequity has a critical effect on human mood states, and the amygdala and hippocampus play a key role in individual differences in the effect. Haruno et al. combine functional magnetic resonance imaging, an economic game and depression self-reports to show that brain activity in the amygdala and hippocampus induced by inequity can predict present and future depression indices.

Efficient control of reciprocal activation and cocontraction of the muscles are critical to perform skillful actions with suitable force and impedance. However, it remains unclear how the brain controls force and impedance while recruiting the same set of muscles as actuators. Does control take place at the single muscle level leading to force and impedance, or are there higher-order centers dedicated to controlling force and impedance? We addressed this question using functional MRI during voluntary isometric wrist contractions with online electromyogram feedback. Comparison of the brain activity between the conditions requiring control of either wrist torque or cocontraction demonstrates that blood oxygen level-dependent activity in the caudo-dorsal premotor cortex (PMd) correlates well with torque, whereas the activity in the ventral premotor cortex (PMv) correlates well with the level of cocontraction. This suggests distinct roles of the PMd and PMv in the voluntary control of reciprocal activation and cocontraction of muscles, respectively.

Midbrain dopamine neurons signal reward value, their prediction error, and the salience of events. If they play a critical role in achieving specific distant goals, long-term future rewards should also be encoded as suggested in reinforcement learning theories. Here, we address this experimentally untested issue. We recorded 185 dopamine neurons in three monkeys that performed a multistep choice task in which they explored a reward target among alternatives and then exploited that knowledge to receive one or two additional rewards by choosing the same target in a set of subsequent trials. An analysis of anticipatory licking for reward water indicated that the monkeys did not anticipate an immediately expected reward in individual trials; rather, they anticipated the sum of immediate and multiple future rewards. In accordance with this behavioral observation, the dopamine responses to the start cues and reinforcer beeps reflected the expected values of the multiple future rewards and their errors, respectively. More specifically, when monkeys learned the multistep choice task over the course of several weeks, the responses of dopamine neurons encoded the sum of the immediate and expected multiple future rewards. The dopamine responses were quantitatively predicted by theoretical descriptions of the value function with time discounting in reinforcement learning. These findings demonstrate that dopamine neurons learn to encode the long-term value of multiple future rewards with distant rewards discounted.

Behavioral analysis of multi-joint arm reaching has allowed important advances in understanding the control of voluntary movements. Complementing this analysis with functional magnetic resonance imaging (fMRI) would give insight into the neural mechanisms behind this control. However, fMRI is very sensitive to artifacts created by head motion and magnetic field deformation caused by the moving limbs. It is thus necessary to attenuate these motion artifacts in order to obtain correct activation patterns. Most algorithms in literature were designed for slow changes of head position over several brain scans and are not very effective on data when the movement is of duration below the resolution of a brain scan. This paper introduces a simple model-based method to remove motion artifacts during short duration movements. The proposed algorithm can account for head movement and field deformations due to movements within and outside of the scanner's field of view. It uses information from the experimental design and subject kinematics to focus the artifact attenuation in time and space and minimize the loss of uncorrupted data. Applications of the algorithm on arm reaching experimental data obtained with blocked and event-related designs demonstrate attenuation of motion artifacts with minimal effect on the brain activations.