In this opinionated review, I draw attention to some of the contributions reinforcement learning can make to questions in the philosophy of mind. In particular, I highlight reinforcement learning's foundational emphasis on the role of reward in agent learning, and canvass two ways in which the framework may advance our understanding of perception and motivation.

I draw on neurobiological evidence to defend the rationalist thesis that moral judgments are essentially dependent on reasoning, not emotions (conceived as distinct from inference). The neuroscience reveals that moral cognition arises from domain-general capacities in the brain for inferring, in particular, the consequences of an agent’s action, the agent’s intent, and the rules or norms relevant to the context. Although these capacities entangle inference and affect, blurring the reason/emotion dichotomy doesn’t preferentially support sentimentalism. The argument requires careful consideration of (...) the empirical evidence (from neuroimaging to psychopathology) and philosophical analysis of the commitments of rationalism versus sentimentalism in ethics. (shrink)

Research on public value is inevitable interdisciplinary in its nature due to its aim and purpose. Both philosophical and empirical approaches are necessary to conduct such research in a successful manner. In the present paper, I intend to discuss the importance of empirical approaches in research on public values, particularly psychological and neuroscientific approaches with concrete examples. I proposed that such empirical approaches are essential in better understanding the processes and mechanisms associated with how people address issues engaging in public (...) value in the modern society. Also, I overviewed how the psychological and neuroscientific approaches contribute to practical aspects related to public value, such as policy making and education. Based on the aforementioned points, I briefly discussed directions for future research in research on public value as an interdisciplinary field. (shrink)