What is it to base a belief on reasons? Existing attempts to give an account of the basing relation encounter a dilemma: either one appeals to some kind of neutral process that does not adequately reflect the way basing is a content‐sensitive first‐personal activity, or one appeals to linking or bridge principles that over‐intellectualize and threaten regress. We explain why this dilemma arises, and diagnose the commitments that are key obstacles to providing a satisfactory account. We explain why they should (...) be rejected anyway, and then offer a new, knowledge‐first virtue epistemological theory of the basing relation that shows how we can substantially theorize about mental processes without these commitments. The resulting view plausibly captures the way in which basing is an inherently mental and content‐sensitive process. (shrink)

Over the last decade, AI models of language and word meaning have been dominated by what we might call a statistics-of-occurrence, strategy: these models are deep neural net structures that have been trained on a large amount of unlabeled text with the aim of producing a model that exploits statistical information about word and phrase co-occurrence in order to generate behavior that is similar to what a human might produce, or representations that can be probed to exhibit behavior similar to (...) what a human might produce (meaningsemblant behavior). Examples of what we can call Statistics-of-Occurrence Models (SOMs) include: Word2Vec (CBOW and Skip-Gram), BERT, GPT-3, and, most recently, ChatGPT. Increasingly, there have been suggestions that such systems have semantic understanding, or at least a proto-version of it. This paper argues against such claims. I argue that a necessary condition for a system to possess semantic understanding is that it function in ways that are causally explainable by appeal to its semantic properties. I then argue that SOMs do not plausibly satisfy this Functioning Criterion. Rather, the best explanation of their meaning-semblant behavior is what I call the Statistical Hypothesis: SOMs do not themselves function to represent or produce meaningful text; they just reflect the semantic information that exists in the aggregate given strong correlations between word placement and meaningful use. I consider and rebut three main responses to the claim that SOMs fail to meet the Functioning Criterion. The result, I hope, is increased clarity about why and how one should make claims about AI systems having semantic understanding. (shrink)

Recent accounts of human agency tend to overlook the role of conscious mental action in our daily lives, while contemporary accounts of the conscious mind often ignore the role of mental action and agency in shaping consciousness. This collection aims to establish the centrality of mental action for discussions of agency and mind.

Philosophy and Phenomenological Research, Volume 104, Issue 1, Page 243-249, January 2022.