Kishida, Kenneth T.2019-06-032019-06-032012-09-27http://hdl.handle.net/10919/89707Human choice is not free—we are bounded by a multitude of biological constraints. Yet, within the various landscapes we face, we do express choice, preference, and varying degrees of so-called willful behavior. Moreover, it appears that the capacity for choice in humans is variable. Empirical studies aimed at investigating the experience of “free will” will benefit from theoretical disciplines that constrain the language used to frame the relevant issues. The combination of game theory and computational reinforcement learning theory with empirical methods is already beginning to provide valuable insight into the biological variables underlying capacity for choice in humans and how things may go awry in individuals with brain disorders. These disciplines operate within abstract quantitative landscapes, but have successfully been applied to investigate strategic and adaptive human choice guided by formal notions of optimal behavior. Psychiatric illness is an extreme, but interesting arena for studying human capacity for choice. The experiences and behaviors of patients suggest these individuals fundamentally suffer from a diminished capacity of willful choice. Herein, I will briefly discuss recent applications of computationally guided approaches to human choice behavior and the underlying neurobiology. These approaches can be integrated into empirical investigation at multiple temporal scales of analysis including the growing body of experiments in human functional magnetic resonance imaging (fMRI), and newly emerging sub-second electrochemical and electrophysiological measurements in the human brain. These cross-disciplinary approaches hold promise for revealing the underlying neurobiological mechanisms for the variety of choice capacity in humans.6 pagesapplication/pdfenCreative Commons Attribution 4.0 Internationalfree willhuman decision-makingdopamineneuroeconomicscomputational psychiatryfMRIelectrochemistrycomputational reinforcement learning theoryArticle - Refereedhttps://doi.org/10.3389/fnint.2012.000856