I argue for the role of reinforcement learning in the philosophy of mind. To start, I make several assumptions about the nature of reinforcement learning and its instantiation in minds like ours. I then review some of the contributions of reinforcement learning methods have made across the so-called 'decision sciences.' Finally, I show how principles from reinforcement learning can shape philosophical debates regarding the nature of perception and characterisations of desire.

Hohwy et al.’s (2008) model of binocular rivalry (BR) is taken as a classic illustration of predictive coding’s explanatory power. I revisit the account and show that it cannot explain the role of reward in BR. I then consider a more recent version of Bayesian model averaging, which recasts the role of reward in (BR) in terms of optimism bias. If we accept this account, however, then we must reconsider our conception of perception. On this latter view, I argue, organisms (...) engage in what amounts to policy-driven, motivated perception. (shrink)

In this paper, we consider one of the ways in which shared valuing is normatively significant. More specifically, we analyze the processes that can reliably provide normative grounding for the standing to rebuke others for their failures to treat something as valuable. Yet problems with grounding this normative standing quickly arise, as it is not immediately clear why shared valuing binds group members together in ways that can sustain the collective pursuit of shared ends. Responding to this difficulty is no (...) easy task, since doing so requires demonstrating that the standing to call on one's fellow participants because of shared forms of valuing is not merely a side effect of members authority to call on their fellow participants to do their fair share in a collective endeavor. This is, to the best of our knowledge, a problem that the sparse literature on shared valuing has yet to consider. We argue that the best way to address this difficulty is to consider the real‐world complexity of how forms of valuing come to be shared within well‐structured collectives and how members internalize the evaluative tendencies that sustain shared valuing. To accomplish those ends, we examine two different ways that shared valuing is cultivated within well‐structured groups and the corresponding ways that members internalize forms of valuing; specifically, we examine differences between forms of valuing that are passed downward from the top of a group, as they are in the U.S. Military, and forms of valuing that bubble‐up through local patterns of interaction, as they do among the Zapatistas of Chiapas. (shrink)

Recent behavioral experiments, along with imaging experiments and neuropsychological studies appear to support the hypothesis that emotions play a causal or constitutive role in moral judgment. Those who resist this hypothesis tend to suggest that affective mechanisms are better suited to play a modulatory role in moral cognition. But I argue that claims about the role of emotion in moral cognition frame the debate in ways that divert attention away from other plausible hypotheses. I suggest that the available data may (...) be more plausibly explained by appeal to predictive and evaluative mechanisms, which are neither wholly affective nor straightforwardly cognitive. By recognizing this fact, we can begin to see why questions about the role of emotion in moral psychology are likely to be empirically misguided. (shrink)

In this paper, I examine an evolutionary approach to the action selection problem and illustrate how it helps raise an objection to the predictive processing account. Clark examines the predictive processing account as a theory of brain function that aims to unify perception, action, and cognition, but - despite this aim - fails to consider action selection overtly. He off ers an account of action control with the implication that minimizing prediction error is an imperative of living organisms because, according (...) to the predictive processing account, action is employed to fulfill expectations and reduce prediction error. One way in which this can be achieved is by seeking out the least stimulating environment and staying there. Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently, the maximization of expectation. But, most living organisms do not find, and stay in, surprise free environments. This paper explores this objection, also called the “dark room problem”, and examines Clark’s response to the problem. Finally, I recommend that if supplemented with an account of action selection, Clark’s account will avoid the dark room problem. (shrink)

The so-called “dark room problem” makes vivd the challenges that purely predictive models face in accounting for motivation. I argue that the problem is a serious one. Proposals for solving the dark room problem via predictive coding architectures are either empirically inadequate or computationally intractable. The Free Energy principle might avoid the problem, but only at the cost of setting itself up as a highly idealized model, which is then literally false to the world. I draw at least one optimistic (...) conclusion, however. Real-world, real-time systems may embody motivational states in a variety of ways consistent with idealized principles like FEP, including ways that are intuitively embodied and extended. This may allow predictive coding theorists to reconcile their account with embodied principles, even if it ultimately undermines loftier ambitions. (shrink)