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Full-Text Articles in Cognitive Science
Knowledge And The Objection To Religious Belief From Cognitive Science, Kelly James Clark, Dani Rabinowitz
Knowledge And The Objection To Religious Belief From Cognitive Science, Kelly James Clark, Dani Rabinowitz
University Faculty Publications and Creative Works
A large chorus of voices has grown around the claim that theistic belief is epistemically suspect since, as some cognitive scientists have hypothesized, such beliefs are a byproduct of cognitive mechanisms which evolved for rather different adaptive purposes. Th is paper begins with an overview of the pertinent cognitive science followed by a short discussion of some relevant epistemic concepts. Working from within a largely Williamsonian framework, we then present two different ways in which this research can be formulated into an argument against theistic belief. We argue that neither version works.
Reformed Epistemology And The Cognitive Science Of Religion, Kelly James Clark
Reformed Epistemology And The Cognitive Science Of Religion, Kelly James Clark
University Faculty Publications and Creative Works
No abstract provided.
Reformed Epistemology And The Cognitive Science Of Religion, Kelly James Clark
Reformed Epistemology And The Cognitive Science Of Religion, Kelly James Clark
University Faculty Publications and Creative Works
No abstract provided.
Default Probability, Daniel N. Osherson, Joshua Stern, Ormond Wilkie, Michael Stob
Default Probability, Daniel N. Osherson, Joshua Stern, Ormond Wilkie, Michael Stob
University Faculty Publications and Creative Works
A probability may be called "default" if it is neither derived from preestablished probabilities nor based on considerations of frequency or symmetry. Default probabilities presumably arise through reasoning based on causality and similarity. This article advances a model of default probability based on a featural approach to similarity. The accuracy of the model is assessed by comparing its predictions to the probabilities provided by undergraduates asked to reason about mammals.
Ideal Learning Machines, Daniel N. Osherson, Michael Stob, Scott Weinstein
Ideal Learning Machines, Daniel N. Osherson, Michael Stob, Scott Weinstein
University Faculty Publications and Creative Works
We examine the prospects for finding "best possible" or "ideal" computing machines for various learning tasks. For this purpose, several precise senses of "ideal machine" are considered within the context of formal learning theory. Generally negative results are provided concerning the existence of ideal learning-machines in the senses considered.