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)

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, 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)

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.

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.

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)