Algorithm and Parameters

Solving the Generality Problem for Reliabilism

Bibliographic Data

ID10692927
AuthorsJack C Lyons (0000-0003-3012-5827, University of Arkansas, corresponding author)
Year2019
Volume128
Issue4
Pages463-509
Publication date2019-10-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueThe Philosophical Review (JOURNAL)
Journal identifiersISSN: 0031-8108 • E-ISSN: 1558-1470
PublisherDuke University Press (PUBLISHER • US)
DOI10.1215/00318108-7697876
OpenAlexW2980161873
LanguageEN
Citations received16
References cited106

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

Algorithm · Cognition · Cognitive psychology · Cognitive science · Epistemology · Folk psychology · Generality · Perception · Process (computing) · Security token · Computer Science · Embodied and Extended Cognition · Epistemology, Ethics, and Metaphysics · Philosophy · Philosophy and Theoretical Science · Psychology

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