My dissertation is a defense of the immediacy and underminability of perceptual justification (i.e. perceptual Dogmatism), with a particular emphasis on developing an attractive combination of Dogmatism and Bayesianism.  You can read my dissertation abstract here.

Here are a couple of my papers:

How to be a Bayesian Dogmatist* (Australasian Journal of Philosophy 94.4 (2016): 766-780)

*AJP Best Paper Award for the best paper published in AJP in 2016

Abstract: Rational agents have (more or less) consistent beliefs.  Bayesianism is a theory of consistency for partial belief states.  Rational agents also respond appropriately to experience.  Dogmatism is a theory of how to respond appropriately to experience.  Hence Dogmatism and Bayesianism are theories of two very different aspects of rationality.  It’s surprising, then, that in recent years it has become common to claim that Dogmatism and Bayesianism are actually inconsistent: how can two independently consistent theories with distinct subject matter be jointly inconsistent?  In this essay I argue that Bayesianism and Dogmatism are inconsistent only with the addition of a specific hypothesis about how the appropriate responses to perceptual experience are to be incorporated into the formal models of the Bayesian.  That hypothesis isn’t essential either to Bayesianism or to Dogmatism, and so Bayesianism and Dogmatism are consistent.  That leaves the matter of how experiences and consistent partial belief states are related, and so in the remainder of the essay I offer an alternative account of how perceptual justification as the Dogmatist understands it can be incorporated in the Bayesian formalism.

Updating, Undermining, and Perceptual Learning (Philosophical Studies 174.9 (2017): 2187–2209)

Abstract:  As I head home from work, I’m not sure whether my daughter’s new bike is green, and I’m also not sure whether I’m on drugs that distort my color perception. One thing that I am sure about is that my attitudes towards those possibilities are evidentially independent of one another, in the sense that changing my confidence in one shouldn’t affect my confidence in the other. Later I see the bike and it looks green, so I increase my confidence that it is green. But something else has changed: now an increase in my confidence that I’m on color-drugs would undermine my confidence that the bike is green. Jonathan Weisberg and Jim Pryor argue that the preceding story is problematic for standard Bayesian accounts of perceptual learning. Due to the ‘rigidity’ of Jeffrey conditionalization, a negative probabilistic correlation between two propositions cannot be introduced by updating on one of them. Hence if my beliefs about my own color-sobriety start out independent of my beliefs about the color of the bike then they must remain independent after I have my perceptual experience and update accordingly. Weisberg takes this to be a reason to reject Jeffrey conditionalization. I argue that this conclusion is too pessimistic: conditionalization is only part of the Bayesian story of perceptual learning, and the other part needn’t preserve independence. Hence Bayesian accounts of perceptual learning are perfectly consistent with potential undercutters for perceptual beliefs.