Philosophical inquiries into the nature of consciousness have long been intrinsically tied to questions regarding the nature of the self. Although philosophers of mind seldom make reference to the role of cultural context in shaping consciousness, since antiquity culture has played a notable role in philosophical conceptions of the self. Western philosophers, from Plato to Locke, have emphasized an individualistic view of the self that is autonomous and consistent across situations, while Eastern philosophers, such as Lao Tzu and Confucius, have (...) argued for a collectivistic view of the self, one that is interconnected to others and embedded within specific social contexts and situations. Here we argue that a comprehensive theory of consciousness needs to account for the role of cultural context and its bidirectional interaction with neural and genetic mechanisms in shaping a variety of conscious phenomena, from visual perception to self- awareness. We review recent evidence of cultural variation in neurobiological mechanisms underlying these phenomena and discuss the implications of these cultural neuroscience findings for the study of consciousness. (shrink)

This study examined the effects of mood and risk-taking on divergent and convergent thinking using a Q-learning computation model. The results revealed that while mood was not significantly related to divergent or convergent thinking (as creative thinking types), risk-taking exerted positive effects on divergent thinking in the face of negative rewards. The results were consistent with the representational change theory in insight problem solving. Although this theory accounts directly for insight, the underlying idea of going beyond current contexts and implicit (...) constrains could be applied to creative thinking as well. The results indeed accounted for the relevance of this theory to divergent thinking. The current study is one of the first empirical studies simultaneously examining the role of mood and risk-taking in creativity. In particular, no related studies exist that took a computational approach to estimate the relevant parameters in the framework of dynamic optimization. Our Q learning model enables to distinguish and identify the different roles of mood and risk-taking in updatingQvalues and making decisions. (shrink)