Compare and Contrast: How to Assess the Completeness of Mechanistic Explanation

Opponents of the new mechanistic account of scientific explanation argue that the new mechanists are committed to a ‘More Details Are Better’ claim: adding details about the mechanism always improves an explanation. Due to this commitment, the mechanistic account cannot be descriptively adequate as actual scientific explanations usually leave out details about the mechanism. In reply to this objection, defenders of the new mechanistic account have highlighted that only adding relevant mechanistic details improves an explanation and that relevance is to be determined relative to the phenomenon-to-be-explained. Craver and Kaplan (B J Philos Sci 71:287–319, 2020) provide a thorough reply along these lines specifying that the phenomena at issue are contrasts. In this paper, we will discuss Craver and Kaplan’s reply. We will argue that it needs to be modified in order to avoid three problems, i.e., what we will call the Odd Ontology Problem, the Multiplication of Mechanisms Problem, and the Ontic Completeness Problem. However, even this modification is confronted with two challenges: First, it remains unclear how explanatory relevance is to be determined for contrastive explananda within the mechanistic framework. Second, it remains to be shown as to how the new mechanistic account can avoid what we will call the ‘Vertical More Details are Better’ objection. We will provide answers to both challenges

Satisfaction conditions in anticipatory mechanisms

The purpose of this paper is to present a general mechanistic framework for analyzing causal representational claims, and offer a way to distinguish genuinely representational explanations from those that invoke representations for honorific purposes. It is usually agreed that rats are capable of navigation (even in complete darkness, and when immersed in a water maze) because they maintain a cognitive map of their environment. Exactly how and why their neural states give rise to mental representations is a matter of an ongoing debate. I will show that anticipatory mechanisms involved in rats’ evaluation of possible routes give rise to satisfaction conditions of contents, and this is why they are representationally relevant for explaining and predicting rats’ behavior. I argue that a naturalistic account of satisfaction conditions of contents answers the most important objections of antirepresentationalists

Spreading the Credit: Virtue Reliabilism and Weak Epistemic Anti-Individualism

Mainstream epistemologists have recently made a few isolated attempts to demonstrate the particular ways, in which specific types of knowledge are partly social. Two promising cases in point are Lackey’s (Learning from words: testimony as a source of knowledge. Oxford University Press, Oxford, 2008) dualism in the epistemology of testimony and Goldberg’s (Relying on others: an essay in epistemology. Oxford University Press, Oxford, 2010) process reliabilist treatment of testimonial and coverage-support justification. What seems to be missing from the literature, however, is a general approach to knowledge that could reveal the partly social nature of the latter anytime this may be the case. Indicatively, even though Lackey (Synthese 158(3):345–361, 2007) has recently launched an attack against the Credit Account of Knowledge (CAK) on the basis of testimony, she has not classified her view of testimonial knowledge into any of the alternative, general approaches to knowledge. Similarly, even if Goldberg’s attempt to provide a process reliabilist explanation of the social nature of testimonial knowledge is deemed satisfactory, his attempt to do the same in the case of coverage-support justification does not deliver the requisite result. This paper demonstrates that CAK can in fact provide, pace Lackey’s renunciation of the view, a promising account of the social nature of both testimonial and coverage-supported knowledge. Additionally, however, it can display further explanatory power by also revealing the social nature of knowledge produced on the basis of epistemic artifacts. Despite their disparities, all these types of knowledge count as partly social in nature, because in all these cases, according to CAK, the epistemic credit for the individual agent’s true belief must spread between the individual agent and certain parts of her epistemic community. Accordingly, CAK is a promising candidate for providing a unified approach to several and, perhaps all possible, instances of what we may call ‘weak epistemic anti-individualism’ within mainstream epistemology: i.e., the claim that the nature of knowledge can occasionally be both social and individual at the same time

Mind the Gap: Transitions Between Concepts of Information in Varied Domains

The concept of 'information' in five different realms – technological, physical, biological, social and philosophical – is briefly examined. The 'gaps' between these conceptions are dis‐ cussed, and unifying frameworks of diverse nature, including those of Shannon/Wiener, Landauer, Stonier, Bates and Floridi, are examined. The value of attempting to bridge the gaps, while avoiding shallow analogies, is explained. With information physics gaining general acceptance, and biology gaining the status of an information science, it seems rational to look for links, relationships, analogies and even helpful metaphors between them and the library/information sciences. Prospects for doing so, involving concepts of complexity and emergence, are suggested

From Computer Metaphor to Computational Modeling: The Evolution of Computationalism

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive (neuro)science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year