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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.

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. Using behavioral and fMRI data collected during the performance of the ultimatum game, we show that individual differences in social preferences for resource allocation, so-called “social value orientation,” is linked with activity in the nucleus accumbens and amygdala elicited by inequity, rather than activity in insula, ACC, and dorsolateral pFC. Importantly, the presence of cognitive load made prosocials behave more prosocially and individualists more individualistically, suggesting that social value orientation is driven more by intuition than reflection. In parallel, activity in the nucleus accumbens and amygdala, in response to inequity, tracked this behavioral pattern of prosocials and individualists. In addition, we conducted an impunity game experiment with different participants where they could not punish unfair behavior and found that the inequity-correlated activity seen in prosocials during the ultimatum game disappeared. This result suggests that the accumbens and amygdala activity of prosocials encodes “outcome-oriented emotion” designed to change situations (i.e., achieve equity or punish). Together, our results suggest a pivotal contribution of the nucleus accumbens and amygdala to individual differences in sociality.

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).

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.

In humans, it is generally not possible to use invasive techniques in order to identify brain activity corresponding to activity of individual muscles. Further, it is believed that the spatial resolution of non-invasive brain imaging modalities is not sufficient to isolate neural activity related to individual muscles. However, this study shows that it is possible to reconstruct muscle activity from functional magnetic resonance imaging (fMRI). We simultaneously recorded surface electromyography (EMG) from two antagonist muscles and motor cortices activity using fMRI, during an isometric task requiring both reciprocal activation and co-activation of the wrist muscles. Bayesian sparse regression was used to identify the parameters of a linear mapping from the fMRI activity in areas 4 (M1) and 6 (pre-motor, SMA) to EMG, and to reconstruct muscle activity in an independent test data set. The mapping obtained by the sparse regression algorithm showed significantly better generalization than those obtained from algorithms commonly used in decoding, i.e., support vector machine and least square regression. The two voxel sets corresponding to the activity of the antagonist muscles were intermingled but disjoint. They were distributed over a wide area of pre-motor cortex and M1 and not limited to regions generally associated with wrist control. These results show that brain activity measured by fMRI in humans can be used to predict individual muscle activity through Bayesian linear models, and that our algorithm provides a novel and non-invasive tool to investigate the brain mechanisms involved in motor control and learning in humans.

To select appropriate behaviors leading to rewards, the brain needs to learn associations among sensory stimuli, selected behaviors, and rewards. Recent imaging and neural-recording studies have revealed that the dorsal striatum plays an important role in learning such stimulus-action-reward associations. However, the putamen and caudate nucleus are embedded in distinct cortico-striatal loop circuits, predominantly connected to motor-related cerebral cortical areas and frontal association areas, respectively. This difference in their cortical connections suggests that the putamen and caudate nucleus are engaged in different functional aspects of stimulus-action-reward association learning. To determine whether this is the case, we conducted an event-related and computational model–based functional MRI (fMRI) study with a stochastic decision-making task in which a stimulus-action-reward association must be learned. A simple reinforcement learning model not only reproduced the subject's action selections reasonably well but also allowed us to quantitatively estimate each subject's temporal profiles of stimulus-action-reward association and reward-prediction error during learning trials. These two internal representations were used in the fMRI correlation analysis. The results revealed that neural correlates of the stimulus-action-reward association reside in the putamen, whereas a correlation with reward-prediction error was found largely in the caudate nucleus and ventral striatum. These nonuniform spatiotemporal distributions of neural correlates within the dorsal striatum were maintained consistently at various levels of task difficulty, suggesting a functional difference in the dorsal striatum between the putamen and caudate nucleus during stimulus-action-reward association learning.

Humans can acquire appropriate behaviors that maximize rewards on a trial-and-error basis. Recent electrophysiological and imaging studies have demonstrated that neural activity in the midbrain and ventral striatum encodes the error of reward prediction. However, it is yet to be examined whether the striatum is the main locus of reward-based behavioral learning. To address this, we conducted functional magnetic resonance imaging (fMRI) of a stochastic decision task involving monetary rewards, in which subjects had to learn behaviors involving different task difficulties that were controlled by probability. We performed a correlation analysis of fMRI data by using the explanatory variables derived from subject behaviors. We found that activity in the caudate nucleus was correlated with short-term reward and, furthermore, paralleled the magnitude of a subject's behavioral change during learning. In addition, we confirmed that this parallelism between learning and activity in the caudate nucleus is robustly maintained even when we vary task difficulty by controlling the probability. These findings suggest that the caudate nucleus is one of the main loci for reward-based behavioral learning.

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. We show not only that right dorsolateral prefrontal cortex (DLPFC) activity is correlated with intention-based economic decisions and that ventral striatum and amygdala activity are correlated with outcome-based decisions, but also that stimulation to the DLPFC selectively enhances intention-based decisions. These findings suggest that the right DLPFC is involved in the implementation of intention-based decisions in the processing of cooperative decisions. This causal dissociation of cortical and subcortical backgrounds may indicate evolutionary and developmental differences in the two decision systems.

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.g. the surrounding social atmosphere) affects the way we learn. Here, we addressed this issue by examining whether the learning of cue-reward associations changes when an emotional facial expression is shown subconsciously or consciously prior to the presentation of a reward-predicting cue. We found that both subconscious (0.027 s and 0.033 s) and conscious (0.047 s) emotional signals increased the rate of learning and this increase was smallest at the border of conscious duration (0.040 s). These data suggest not only that the subconscious and conscious processing of emotional signals enhances value-updating in cue–reward association learning, but also that the computational processes underlying the subconscious enhancement is at least partially dissociable from its conscious counterpart.

This paper describes a novel and practical Japanese parser that uses decision trees. First, we construct a single decision tree to estimate modification probabilities; how one phrase tends to modify another. Next, we introduce a boosting algorithm in which several decision trees are constructed and then combined for probability estimation. The constructed parsers are evaluated using the EDR Japanese annotated corpus. The single-tree method significantly outperforms the conventional Japanese stochastic methods. Moreover, the boosted version of the parser is shown to have great advantagess (1) a better parsing accuracy than its single-tree counterpart for any amount of training data and (2) no over-fitting to data for various iterations. The presented parser, the first non-English stochastic parser with practical performance, should tighten the coupling between natural language processing and machine learning.

This paper describes an accurate and robust text alignment system for structurally different languages. Among structurally different languages such as Japanese and English, there is a limitation on the amount of word correspondences that can be statistically acquired. The main reason for this is the systems of functional (closed) words are quite different in the two languages. The proposed method makes use of two kinds of word correspondences in aligning bilingual texts. One is a bilingual dictionary of general use. The other is the word correspondences that are statistically acquired in the alignment process. Our method gradually determines sentence pairs (anchors) that correspond to each other by relaxing parameters. The method, by combining two kinds of word correspondences, achieves adequate word correspondences for complete alignment. As a result, texts of various length and of various genres in structurally different languages can be aligned with high precision. Experimental results show our system outperforms conventional methods for various kinds of Japanese–English texts.

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.

Many real life tasks that require impedance control to minimize motion error are characterized by multiple solutions where the task can be performed either by co-contracting muscle groups, which requires a large effort, or, conversely, by relaxing muscles. However, human motor optimization studies have focused on tasks that are always satisfied by increasing impedance and that are characterized by a single error-effort optimum. To investigate motor optimization in the presence of multiple solutions and hence optima, we introduce a novel paradigm that enables us to let subjects repetitively (but inconspicuously) use different solutions and observe how exploration of multiple solutions affect their motor behavior. The results show that the behavior is largely influenced by motor memory with subjects tending to involuntarily repeat a recent suboptimal task-satisfying solution even after sufficient experience of the optimal solution. This suggests that the CNS does not optimize co-activation tasks globally but determines the motor behavior in a tradeoff of motor memory, error, and effort minimization.

During behavioral adaptation through interaction with human and nonhuman agents, marked individual differences are seen in both real-life situations and games. However, the underlying neural mechanism is not well understood. We conducted a neuroimaging experiment in which subjects maximized monetary rewards by learning in a prisoner's dilemma game with two computer agents: agent A, a tit-for-tat player who repeats the subject's previous action, and agent B, a simple stochastic cooperator oblivious to the subject's action. Approximately 1/3 of the subjects (group I) learned optimally in relation to both A and B, while another 1/3 (group II) did so only for B. Postexperiment interviews indicated that group I exploited the agent strategies more often than group II. Significant differences in learning-related brain activity between the two groups were only found in the superior temporal sulcus (STS) for both A and B. Furthermore, the learning performance of each group I subject was predictable based on this STS activity, but not in the group II subjects. This differential activity could not be attributed to a behavioral difference since it persisted in relation to agent B for which the two groups behaved similarly. In sharp contrast, the brain structures for reward processing were recruited similarly by both groups. These results suggest that STS provides knowledge of the other agent's strategies for association between action and reward and highlights learning competence during interactive reinforcement learning.

An important question in motor neuroscience is how the nervous system controls the spatiotemporal activation patterns of redundant muscles in generating accurate movements. The redundant muscles may not only underlie the flexibility of our movements but also pose the challenging problem of how to select a specific sequence of muscle activation from the huge number of possible activations. Here, we propose that noise in the motor command that has an influence on task achievement should be considered in determining the optimal motor commands over redundant muscles. We propose an optimal control model for step-tracking wrist movements with redundant muscles that minimizes the end-point variance under signal-dependent noise. Step-tracking wrist movements of human and nonhuman primates provide a detailed data set to investigate the control mechanisms in movements with redundant muscles. The experimental EMG data can be summarized by two eminent features: 1) amplitude-graded EMG pattern, where the timing of the activity of the agonist and antagonist bursts show slight variations with changes in movement directions, and only the amplitude of activity is modulated; and 2) cosine tuning for movement directions exhibited by the agonist and antagonist bursts, and the discrepancy found between a muscle's agonist preferred direction and its pulling direction. In addition, it is also an important observation that subjects often overshoot the target. We demonstrate that the proposed model captures not only the spatiotemporal activation patterns of wrist muscles but also trajectory overshooting. This suggests that when recruiting redundant muscles, the nervous system may optimize the motor commands across the muscles to reduce the negative effects of motor noise.

In natural language tasks like text categorization, we usually have an enormous amount of unlabeled data in addition to a small amount of labeled data. We present here a transductive boosting method for text categorization in order to make use of the large amount of unlabeled data efficiently. Our experiments show that the transductive method outperforms conventional boosting techniques that employ only labeled data.

Udgivelsesdato: 2010-Feb 'Social value orientation' characterizes individual differences in anchoring attitudes toward the division of resources. Here, by contrasting people with prosocial and individualistic orientations using functional magnetic resonance imaging, we demonstrate that degree of inequity aversion in prosocials is predictable from amygdala activity and unaffected by cognitive load. This result suggests that automatic emotional processing in the amygdala lies at the core of prosocial value orientation.

This paper describes novel and practical Japanese parsers that uses decision trees. First, we construct a single decision tree to estimate modification probabilities; how one phrase tends to modify another. Next, we introduce a boosting algorithm in which several decision trees are constructed and then combined for probability estimation. The two constructed parsers are evaluated by using the EDR Japanese annotated corpus. The single-tree method outperforms the conventional Japanese stochastic methods by 4%. Moreover, the boosting version is shown to have significant advantages; 1) better parsing accuracy than its single-tree counterpart for any amount of training data and 2) no over-fitting to data for various iterations.

This paper proposes a mistake-driven mixture method for learning a tag model. The method iteratively performs two procedures: 1. constructing a tag model based on the current data distribution and 2. updating the distribution by focusing on data that are not well predicted by the constructed model. The final tag model is constructed by mixing all the models according to their performance. To well reflect the data distribution, we represent each tag model as a hierarchical tag (i.e., NTT

This paper proposes a new method for learning bilingual collocations from sentence-aligned parallel corpora. Our method comprises two steps: (1) extracting useful word chunks (n-grams) by word-level sorting and (2) constructing bilingual collocations by combining the word-chunks acquired in stage (1). We apply the method to a very challenging text pair: a stock market bulletin in Japanese and its abstract in English. Domain specific collocations are well captured even if they were not contained in the dictionaries of economic terms.

SummaryA 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. Using behavior, modeling, and fMRI, we show that simulation involves two learning signals in a hierarchical arrangement. A simulated-other's reward prediction error processed in ventromedial prefrontal cortex mediated simulation by direct recruitment, being identical for valuation of the self and simulated-other. However, direct recruitment was insufficient for learning, and also required observation of the other's choices to generate a simulated-other's action prediction error encoded in dorsomedial/dorsolateral prefrontal cortex. These findings show that simulation uses a core prefrontal circuit for modeling the other's valuation to generate prediction and an adjunct circuit for tracking behavioral variation to refine prediction.