There is an apparent connection between reinforcement learning and agency. Artificial entities controlled by reinforcement learning algorithms are standardly referred to as agents, and the mainstream view in the psychology and neuroscience of agency is that humans and other animals are reinforcement learners. This article examines this connection, focusing on artificial reinforcement learning systems and assuming that there are various forms of agency. Artificial reinforcement learning systems satisfy plausible conditions for minimal agency, and those which use models of the environment (...) to perform forward search are capable of a form of agency which may reasonably be called action for reasons. (shrink)

Moods, orthodoxy holds, exist outside the space of reasons. A depressed subject may change their thoughts and behaviors as a result of their depression. But, according to this view, their mood gives them no genuine reason to do so. Instead, moods are mere causal influences on cognition. The issue is that moods, with their diffuse phenomenology, appear to lack intentionality (Directionlessness). But intentionality appears to be a necessary condition on rationality (The Content Constraint). Together, these principles conflict with the idea (...) that moods are rational states of mind (Mood Rationality). The inconsistency of these three principles is the Puzzle of Mood Rationality. Now to many, this puzzle is hardly vexing: we should reject Mood Rationality. But, I argue, Mood Rationality is true despite its unpopularity. Thus, we need another way of resolving the puzzle. To do so, I distinguish intentionality as a first‐personal, phenomenological notion from representation as a third‐personal, cognitive scientific notion. I then argue that moods satisfy a revised version of the Content Constraint and sketch an account of moods as representational but non‐intentional mental states, drawing on the Valuationist paradigm in affective neuroscience. I end by showing how this account enables us to explain moods’ rationality. (shrink)