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.

Over the last 100 years, numerous studies have examined the effective visual stimulus properties for inducing illusory self-motion (known as vection). This vection is often experienced more strongly in daily life than under controlled experimental conditions. One well-known example of vection in real life is the so-called 'train illusion'. In the present study, we showed that this train illusion can also be generated in the laboratory using virtual computer graphics-based motion stimuli. We also demonstrated that this vection can be modified by altering the meaning of the visual stimuli (i.e., top down effects). Importantly, we show that the semantic meaning of a stimulus can inhibit or facilitate vection, even when there is no physical change to the stimulus.

Sometimes we regard just an artifact as a lifelike one and other times not; it is considered to depend on how we deal and interact with the artifact. We experimentally examined whether differences in the manner of interacting with a moving robot (operating it or only observing its movements) influenced one's perception of the robot's animacy and, if so, whether the strength of this influence depended on the apparent goal-directedness of the robot's movements. We found that people only observing the robot perceived it most animated when its movements seemed most goal-directed but that people controlling the robot perceived it more animated when 1/f noise made its movements seem less goal-directed. Our perception of a moving object's animacy thus depends on whether we interact with the object or just observe it while someone else interacts with it. This result suggests that robotics researchers should design how a robot interacts with its users, in order to elicit higher degree of animacy perception for the robot.

Interpersonal touch is said to have significant effects on social interaction. We used the ultimatum game to examine whether touch from a robot could inhibit a negative feeling to the robot. We set two experimental conditions: the one was "touch condition" in which unfair proposals were offered to a participant when a robot touched his/her arm and the other was "no touch condition" in which unfair proposals were offered when the same robot did not. We compared Medial Frontal Negativity (MFN) measured by EEG, whose amplitude is correlated with feeling of unfairness, between the two conditions. Result shows that MFN amplitude was larger in the no touch condition than in the touch condition. This indicates that touch from a robot may inhibit a sense of unfairness for the robot. Our finding suggests that touch from a robot could enhance positive feeling to the robot through human-robot interaction.