A compositional theory of perceptual representations would explain how the accuracy conditions of a given type of perceptual state depend on the contents of constituent perceptual representations and the way those constituents are structurally related. Such a theory would offer a basic framework for understanding the nature, grounds, and epistemic significance of perception. But an adequate semantics of perceptual representations must accommodate the holistic nature of perception. In particular, perception is replete with context effects, in which the way one perceptually (...) represents one aspect of a scene (including the position, size, orientation, shape, color, motion, or even unity of an object) normally depends on how one represents many other aspects of the scene. The ability of existing accounts of the semantics of perception to analyze context effects is at best unclear. Context effects have even been thought to call into question the very feasibility of a systematic semantics of perception. After outlining a compositional semantics for a rudimentary set of percepts, I draw on empirical models from perceptual psychology to show how such a theory must be modified to analyze context effects. Context effects arise from substantive constraints on how perceptual representations can combine and from the different semantic roles that perceptual representations can have. I suggest that context effects are closely tied to the objectivity of perception. They arise from a perceptual grammar that functions to facilitate the composition of reliably accurate representations in an uncertain but structured world. (shrink)

Can perceptual experiences be states of uncertainty? We might expect them to be, if the perceptual processes from which they're generated, as well as the behaviors they help produce, take account of probabilistic information. Yet it has long been presumed that perceptual experiences purport to tell us about our environment, without hedging or qualifying. Against this long-standing view, I argue that perceptual experiences may well occasionally be states of uncertainty, but that they are never probabilistically structured. I criticize a powerful (...) line of reasoning that we should expect perceptual experience to be probabilistic, given their interfaces with unconscious probabilistic information, with behavior responsive to it, and with credences. (shrink)

According to the recent Perceptual Confidence view, perceptual experiences possess not only a representational content, but also a degree of confidence in that content. The motivations for this view are partly phenomenological and partly epistemic. We discuss both the phenomenological and epistemic motivations for the view, and the resulting account of the interface between perceptual experiences and degrees of belief. We conclude that, in their present state of development, orthodox accounts of perceptual experience are still to be favoured over the (...) perceptual confidence view. (shrink)

Conscious experiences are characterized by mental qualities, such as those involved in seeing red, feeling pain, or smelling cinnamon. The standard framework for modeling mental qualities represents them via points in geometrical spaces, where distances between points inversely correspond to degrees of phenomenal similarity. This paper argues that the standard framework is structurally inadequate and develops a new framework that is more powerful and flexible. The core problem for the standard framework is that it cannot capture precision structure: for example, (...) consider the phenomenal contrast between seeing an object as crimson in foveal vision versus merely as red in peripheral vision. The solution I favor is to model mental qualities using regions, rather than points. I explain how this seemingly simple formal innovation not only provides a natural way of modeling precision, but also yields a variety of further theoretical fruits: it enables us to formulate novel hypotheses about the space and structures of mental qualities, formally differentiate two dimensions of phenomenal similarity, generate a quantitative model of the phenomenal sorites, and define a measure of discriminatory grain. A noteworthy consequence is that the structure of the mental qualities of conscious experiences is fundamentally different from the structure of the perceptible qualities of external objects. (shrink)

Perceptual experience supports the assignment of confidences in belief – doxastic confidences. To explain this fact, many philosophers appeal to Perceptual Indeterminacy, which holds that perceptual content can be more or less determinate. Others instead appeal to Perceptual Confidence, which says that perceptual experience supports doxastic confidences because it assigns confidences too. Morrison argues that a primary reason to favour Perceptual Confidence is that it is uniquely capable of accounting for bell-shaped doxastic confidence distributions; we call this the bell curve (...) objection to Perceptual Indeterminacy. Here we show that two recent defences of Perceptual Indeterminacy, due to Nanay and Raleigh and Vindrola, fail to adequately address the bell curve objection. But we also argue that all is not lost for proponents of Perceptual Indeterminacy. They can counter the bell curve objection by embracing a third view, which we call Perceptual Noise. (shrink)

Perception can’t have disjunctive content. Whereas you can think that a box is blue or red, you can’t see a box as being blue or red. Based on this fact, I develop a new problem for the ambitious predictive processing theory, on which the brain is a machine for minimizing prediction error, which approximately implements Bayesian inference. I describe a simple case of updating a disjunctive belief given perceptual experience of one of the disjuncts, in which Bayesian inference and predictive (...) coding pull in opposite directions, with the former implying that one’s confidence in the belief should increase, and the latter implying that it should decrease. Thus, predictive coding fails to approximately implement Bayesian inference across the interface between belief and perception. (shrink)

Perceptual Confidence is the thesis that perceptual experiences can be probabilistic. This thesis has been defended and criticised based on a variety of phenomenological, epistemological, and explanatory arguments. One gap in these arguments is that they neglect the question of whether perceptual experiences satisfy the formal conditions that define the notion of probability to which Perceptual Confidence is committed. Here, we focus on this underexplored question and argue that perceptual experiences do not satisfy such conditions. But if they do not, (...) then ascriptions of perceptual confidence are undefined; and so, Perceptual Confidence cannot be true. (shrink)

I argue that some perceptual experiences are vague. To do so, I identify a characteristic feature of vagueness and show that some perceptual experiences have this feature. These include blurry experiences, experiences of color under low lighting, and experiences of number, as in the case of the speckled hen. The conclusion that these experiences are vague has two noteworthy consequences. First, it presses us to see whether and how existing theories of vagueness can be extended to perceptual experience. Second, it (...) sheds light on several puzzles in the philosophy of perception. (shrink)