An fMRI Study of Emotional engagement in decicion-making

It has been suggested that decision making depends on sensitive feelings associated with cognitive processing rather than cognitive processing alone. From human lesions, we know the medial anterior inferior-ventral prefrontal cortex processes the sensitivity associated with cognitive processing, it being essentially responsible for decision making. In this fMRI (functional Magnetic Resonance Image) study 15 subjects were analyzed using moral dilemmas as probes to investigate the neural basis for painful-emotional sensitivity associated with decision making. We found that a network comprising the posterior and anterior cingulate and the medial anterior prefrontal cortex was significantly and specifically activated by painful moral dilemmas, but not by non-painful dilemmas. These findings provide new evidence that the cingulate and medial anterior prefrontal are involved in processing painful emotional sensibility, in particular, when decision making takes place. We speculate that decision making has a cognitive component processed by cognitive brain areas and a sensitivity component processed by emotional brain areas. The structures activated suggest that decision making depends on painful emotional feeling processing rather than cognitive processing when painful feeling processing happens

Embedding Emotional Context in Recommender Systems

Emotions are crucial for user's decision making in recommendation processes. We first introduce ambient recommender systems, which arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We then explain some results of these new trends in real-world applications through the smart prediction assistant (SPA) platform in an intelligent learning guide with more than three million users. While most approaches to recommending have focused on algorithm performance. SPA makes recommendations to users on the basis of emotional information acquired in an incremental way. This article provides a cross-disciplinary perspective to achieve this goal in such recommender systems through a SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world rccommender systems