I argue against mnemic reliabilism, an influential view that successful remembering must be produced by a reliable memory process. Drawing on empirical evidence from psychology and neuroscience, I refute mnemic reliabilism by demonstrating that: (1) patients with memory impairments (e.g., Alzheimer’s disease) can also successfully remember the past despite the unreliability of their corresponding memory processes; (2) some reliability-affecting factors (e.g., stress, divided attention, and insufficient encoding time) can render the memory processes of healthy individuals unreliable without preventing them from (...) occasionally resulting in successful remembering. Potential responses available for mnemic reliabilism are also evaluated. I show that those responses are susceptible to a challenge akin to the generality problem for process reliabilism. (shrink)

Process reliabilist accounts claim that a belief is justified when it is the result of a reliable belief-forming process. Yet over what range of possible token processes is this reliability calculated? I argue against the idea that _all_ possible token processes (in the actual world, or some other subset of possible worlds) are to be considered using the case of a user acquiring beliefs based on the output of an AI system, which is typically reliable for a substantial local range (...) but unreliable when all possible inputs are considered. I show that existing solutions to the generality problem imply that these cases cannot be solved by a more fine-grained typing of the belief-forming process. Instead, I suggest that reliability is evaluated over a range restricted by the content of the actual belief and by the similarity of the input to the actual input. (shrink)

The generality problem is one of the most pressing challenges for process reliabilism about justification. Thus far, one of the more promising responses is James Beebe’s tri-level statistical solution. Despite the initial plausibility of Beebe’s approach, the tri-level statistical solution has been shown to generate implausible justification verdicts on a variety of cases. Recently, Samuel Kampa has offered a new statistical solution to the generality problem. Kampa argues that the new statistical solution overcomes the challenges that undermined Beebe’s original statistical (...) solution. However, there’s good reason to believe that Kampa is mistaken. In this paper, I show that Kampa’s new statistical solution faces problems that are no less serious than the original objections to Beebe’s solution. Depending on how we interpret Kampa’s proposal, the new statistical solution either types belief-forming processes far too narrowly, or the new statistical solution fails to clarify the epistemic implications of reliabilism altogether. Either way, the new statistical solution fails to make substantive progress towards solving the generality problem. (shrink)

The generality problem is one of the most pressing challenges for reliabilism. The problem begins with this question: of all the process types exemplified by a given process token, which types are the relevant ones for determining whether the resultant belief counts as knowledge? As philosophers like Earl Conee and Richard Feldman have argued, extant responses to the generality problem have failed, and it looks as if no solution is forthcoming. In this paper, I present a new response to the (...) generality problem that illuminates the nature of knowledge-enabling reliability. My response builds upon the insights of Juan Comesaña’s well-founded solution to the generality problem, according to which relevant types are content–evidence pairs, i.e., descriptions of both the target belief’s content and the evidence on which the belief was based. While most responses to the generality problem, including Comesaña’s, only posit one relevant type for any given process token, I argue that knowledge-enabling reliability requires a process token to be reliable with respect to multiple content–evidence pairs, each with varying degrees of descriptive specificity. I call this solution multi-type evidential reliabilism. After offering a clear formulation of MTE, I conclude by arguing that MTE is sufficiently informative to rebut Conee and Feldman’s generality problem objection to a reliability condition on knowledge. (shrink)

The generality problem is commonly seen as one of the most pressing issues for process reliabilism. The generality problem starts with the following question: of all the process types exemplified by a given process token, which type is the relevant one for measuring reliability? Defenders of the generality problem claim that process reliabilists have a burden to produce an informative account of process type relevance. As they argue, without such a successful account, the reasonability of process reliabilism is significantly undermined. (...) One way for the reliabilist to respond is to attempt to construct such a theory of type relevance. But another way of responding is to argue that, if finding an account of type relevance is a burden for the reliabilist, then it is also a burden for everyone else. Thus, the generality problem doesn’t present some unique reason to reject process reliabilism. I call this latter strategy a parrying response. In this essay, I examine the contemporary parrying responses of Michael Bishop and Juan Comesaña, which have both faced recent criticism. I respond to these critics, and argue that parrying responses are far stronger than defenders of the generality problem have appreciated. (shrink)

The paper offers a solution to the generality problem for a reliabilist epistemology, by developing an “algorithm and parameters” scheme for type-individuating cognitive processes. Algorithms are detailed procedures for mapping inputs to outputs. Parameters are psychological variables that systematically affect processing. The relevant process type for a given token is given by the complete algorithmic characterization of the token, along with the values of all the causally relevant parameters. The typing that results is far removed from the typings of folk (...) psychology, and from much of the epistemology literature. But it is principled and empirically grounded, and shows good prospects for yielding the desired epistemological verdicts. The paper articulates and elaborates the theory, drawing out some of its consequences. Toward the end, the fleshed-out theory is applied to two important case studies: hallucination and cognitive penetration of perception. (shrink)

The Generality Problem is widely recognized to be a serious problem for reliabilist theories of justification. James R. Beebe's Statistical Solution is one of only a handful of attempted solutions that has garnered serious attention in the literature. In their recent response to Beebe, Julien Dutant and Erik J. Olsson successfully refute Beebe's Statistical Solution. This paper presents a New Statistical Solution that countenances Dutant and Olsson's objections, dodges the serious problems that trouble rival solutions, and retains the theoretical virtues (...) that made Beebe's solution so attractive in the first place. There indeed exists a principled, rigorous, conceptually sparse, and plausible solution to the Generality Problem: it is the New Statistical Solution. (shrink)

One of the main goals of epistemologists is to provide a substantive and explanatory account of the conditions under which a belief has some desirable epistemic status (typically, justification or knowledge). According to the reliabilist approach to epistemology, any adequate account will need to mention the reliability of the process responsible for the belief, or truth-conducive considerations more generally. Historically, one major motivation for reliabilism—and one source of its enduring interest—is its naturalistic potential. According to reliabilists, epistemic properties can be (...) explained in terms of reliability, which in turn can be understood without reference to any unreduced epistemic notions, such as evidence or knowledge. This article begins by surveying some of the main forms of reliabilism, concentrating on process reliabilism as a theory of justification. It proceeds to review some of the main objections to reliabilism, and some of the responses that have been offered on the reliabilist’s behalf. After canvassing some recent developments in reliabilist epistemology, the article concludes by considering various cousins and spin-offs of reliabilist epistemology, including virtue reliabilism and various evidentialist-reliabilist hybrids. (shrink)