Perceptual Content, Phenomenal Contrasts, and Externalism

in Journal of Philosophy (2022), 119(11), 602-627

According to Sparse views of perceptual content, the phenomenal character of perceptual experience is exhausted by the experiential presentation of ‘low-level’ properties such as (in the case of vision ... [more ▼]

According to Sparse views of perceptual content, the phenomenal character of perceptual experience is exhausted by the experiential presentation of ‘low-level’ properties such as (in the case of vision) shapes, colors, and textures Whereas, according to Rich views of perceptual content, the phenomenal character of perceptual experience can also sometimes involve experiencing ‘high-level’ properties such as natural kinds, artefactual kinds, causal relations, linguistic meanings, and moral properties. An important dialectical tool in the debate between Rich and Sparse theorists is the so-called ‘method of phenomenal contrast’. I explore how this method of phenomenal contrast interacts with the sort of content-externalism made familiar by Putnam. I show that the possibility of Twin Earth style cases places important restrictions on the range of properties that the method of phenomenal contrast could plausibly apply to. Moreover, these restrictions would apply to some paradigmatically low-level properties as well as to some of the frequently advanced high-level properties. I also draw some general lessons about the different ways one might conceive of the relation between phenomenal character and representational content. [less ▲]

Philosophy of Perception & Liberal Naturalism

in Macarthur, David; De Caro, Mario (Eds.) The Routledge Handbook of Liberal Naturalism (2022)

XAI and philosophical work on explanation: A roadmap

in Proceedings of 1st Workshop on Bias, Ethical AI, Explainability and the Role of Logic and Logic Programming (2022), 3319

What Deep Neural Networks (DNNs) can do is impressive, yet they are notoriously opaque. Responding to the worries associated with this opacity, the field of XAI has produced a plethora of methods ... [more ▼]

What Deep Neural Networks (DNNs) can do is impressive, yet they are notoriously opaque. Responding to the worries associated with this opacity, the field of XAI has produced a plethora of methods purporting to explain the workings of DNNs. Unsurprisingly, a whole host of questions revolves around the notion of explanation central to this field. This note provides a roadmap of the recent work that tackles these questions from the perspective of philosophical ideas on explanations and models in science. [less ▲]

Visual Acquaintance, Action and the Explanatory Gap

in Synthese (2021), 198

Perceptual Experience and Degrees of Belief

in Philosophical Quarterly (2021), 71(2), 378-406

Plenty of room left for the Dogmatist

in Analysis (2020)