AbstractHow do people update their impressions of close others? Although people may be motivated to maintain their positive impressions, they may also update their impressions when their expectations are violated (i.e. prediction error). Combining neuroimaging and computational modeling, we test the hypothesis that brain regions associated with theory of mind, especially right temporoparietal junction (rTPJ), underpin both motivated impression maintenance and impression updating evoked by prediction error. Participants had money either given to or taken away from them by a friend or a stranger and were then asked to rate each partner on trustworthiness and closeness across trials. Overall, participants engaged in less impression updating for friends vs strangers. Decreased rTPJ activity in response to a friend’s negative behavior (taking money) was associated with reduced negative updating and increased positive ratings of the friend. However, to the extent that participants did update their impressions (more negative ratings) of friends, this behavioral pattern was explained by greater prediction error and greater rTPJ activity. These findings suggest that rTPJ recruitment represents the integration of prediction error signals and the capacity to overcome people’s motivation to maintain positive impressions of friends in the face of conflicting evidence.
In the Fourth Industrial Revolution environment, plenty of automatic and smart software in diverse fields will come out, such as virtual reality/augmented reality (VR/AR), autonomous robots and vehicles, smart factoring, and so on. In Korea, especially, one important issue is the cyber–physical system (CPS), used for monitoring and controlling the smart and automatic system in a smart city. This kind of system, therefore, needs to have good performance; otherwise, it may not respond in time. To solve this, we propose a code visualization approach to reduce code complexity based on a static analysis, which identifies bad codes against performance. To ensure better performance, we make our queries identify performance degradation factors, store statically analyzed data into database (DB) tables, and visualize bad operation patterns. For performance improvement, we can refactor with them. As a result, we reduce the code complexity of CPS-based software to obtain good performance. With this approach, we expect to have better performance and a reduction in the complexity of CPS software without even power consumption.
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