Daniel Weissglass

A key question about the value of truth in epistemology is whether the truthfulness of some proposition is a factor in our evaluation of beliefs. The traditional view—evidenced in introductory texts and academic journals :349–369, 2002, p. 350)—is that the truth of a belief should not impact our evaluations of it. Recent work has raised empirical objections to this default position of truth-insensitivity by suggesting that our ordinary belief evaluations assign considerable weight to the truth value of the believed proposition. If correct, this work grants truth a larger role in our epistemology—suggesting that it serves both as an element of our ordinary epistemic evaluations and as an intuitive norm for belief evaluation :4009–4030, 2015. https://doi.org/10.1007/s11229-015-0727-z, Philos Phenomenol Res 93:348–367, 2016. https://doi.org/10.1111/phpr.12218). However, this position is not convincing. In this paper, I argue that this empirical work, while revelatory of some interesting features of belief evaluation, falls far short of demonstrating that belief evaluation is truth-sensitive, or that truth-sensitivity is intuitive.

Causal theories of content, a popular family of approaches to defining the content of mental states, commonly run afoul of two related and serious problems that prevent them from providing an adequate theory of mental content—the misrepresentation problem and the disjunction problem. In this paper, I present a causal theory of content, built on information theoretic tools, that solves these problems and provides a viable model of mental content. This is the greatest surprise reduction theory of content, which identifies the content of a signal as the event the surprisal of which is most reduced by that signal. Conceptually, this amounts to the claim that the content of a signal is the event the probability of which has increased by the largest proportion, or the event that the signal makes the most less surprising to us. I develop the greatest surprise reduction theory of content in four stages. First, I introduce the general project of causal theories of content, and the challenges presented to this project by the misrepresentation and disjunction problems. Next, I review two recent and prominent causal theories of content and demonstrate the serious challenges faced by these approaches, both clarifying the need for a solution to the misrepresentation and disjunction problems and providing a conceptual background for the greatest surprise reduction theory. Then, I develop the greatest surprise reduction theory of content, demonstrate its ability to resolve the misrepresentation and disjunction problems, and explore some additional applications it may have. Finally, I conclude with a discussion of a particularly difficult challenge that remains to be addressed—the partition problem—and sketch a path to a potential solution.