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Causal approaches to explanation often assume that a model explains by describing features that make a difference regarding the phenomenon. Chirimuuta claims that this idea can be also used to understand non-causal explanation in computational neuroscience. She argues that mathematical principles that figure in efficient coding explanations are non-causal differencemakers. Although these principles cannot be causally altered, efficient coding models can be used to show how would the phenomenon change if the principles were modified in counterpossible situations. The problem is that efficient coding models also involve difference-makers that, prima facie, cannot be characterized as non-causal in this sense. Mathematical principles always involve variables which have counterfactual (instead of counterpossible) relations between them. However, we cannot simply assume that these difference-makers are causal. They can also be found in paradigmatic non-causal explanations and therefore they must be characteriz ed as non-causal in some sense. I argue that, despite appearances, Chirimuuta's view can be applied to these cases. The mentioned counterfactual relations presuppose the counterpossible conditionals that describe the modification of a relevant mathematical principle. If these conditionals are the hallmark of non-causal relations, then Chirimuuta's criterion has the desired implication that variables in mathematical principles are non-causal difference-makers. [ABSTRACT FROM AUTHOR]

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An argument recently proposed by Chirimuuta (2014) seems to motivate the rejection of the claims that every neurocognitive phenomenon can have a mechanistic explanation and that every neurocognitive explanation is mechanistic. In this paper, I focus on efficient coding models involving the so-called "canonical neural computations" and argue that although they imply some form of pluralism, they are compatible with two mechanistic generalizations: all neurocognitive explanations are (at least in part) mechanistic; and all neurocognitive phenomena that have an explanation have (at least) a purely mechanistic explanation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to determine the relevance of an informational theory of knowledge to address recently discussed epistemic problems. I argue that Dretske’s classical approach is equivalent to the safety condition proposed by Pritchard to avoid epistemic luck. However, I claim that Pritchard’s condition has to be modified to avoid the so-called “generality problem” for reliabilism. I propose an alternative version of the requirement to deal with these problems and provide an equivalent informational condition. I conclude that this informational version is preferable since it reveals conceptual relationships between the constituents of knowledge. [ABSTRACT FROM AUTHOR]

Copyright of Tópicos is the property of Asociacion Revista de Filosofia de Santa Fe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)