Parsing the Behavioral and Brain Mechanisms of Third-Party Punishment

The evolved capacity for third-party punishment is considered crucial to the emergence and maintenance of elaborate human social organization and is central to the modern provision of fairness and justice within society. Although it is well established that the mental state of the offender and the severity of the harm he caused are the two primary predictors of punishment decisions, the precise cognitive and brain mechanisms by which these distinct components are evaluated and integrated into a punishment decision are poorly understood.Using a brain-scanning technique known as functional magnetic resonance imaging (fMRI), we implemented a novel experimental design to functionally dissociate the mechanisms underlying evaluation, integration, and decision. This work revealed that multiple parts of the brain – some analytic, some subconscious or emotional – work in a systematic pattern to decide blameworthiness, assess harms, integrate those two decisions, and then ultimately select how a person should be punished. Specifically, harm and mental state evaluations are conducted in two different brain networks and then combined in the medial prefrontal and posterior cingulate areas of the brain, while the amygdala acts as a pivotal hub of the interaction between harm and mental state. This integrated information is then used by the right dorsolateral prefrontal cortex when the brain is making a decision on punishment amount. These findings provide a blueprint of the brain mechanisms by which neutral third parties make punishment decisions

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods