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 (2018) 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.
We represent the world in a variety of ways: through percepts, concepts, propositional attitudes, words, numerals, recordings, musical scores, photographs, diagrams, mimetic paintings, etc. Some of these representations are mental. It is customary for philosophers to distinguish two main kinds of mental representations: perceptual representation (e.g., vision, auditory, tactile) and conceptual representation. This essay presupposes a version of this dichotomy and explores the way in which a further kind of representation – procedural representation – represents. It is argued that, in some important respects, procedural representations represent differently from both purely conceptual representations and purely perceptual representations. Although procedural representations, just like conceptual and perceptual representations, involve modes of presentation, their modes of presentation are distinctively practical, in a sense which I will clarify. It is argued that an understanding of this sort of practical representation has important consequences for the debate on the nature of know-how.
In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their technological applications (e.g., brain-computer interfaces and early diagnosis of neuropathology), these techniques promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue that MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other “decoding” methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience.
I propose a possible development of the Global Neuronal Workspace (GNW) model of access consciousness. Its initial motivation is that the model does not offer a clear distinction between the neural signatures associated with information broadcasting (which is the main function of the GNW) and those of other processes also related to consciousness, such as information integration. I suggest that a theoretically interesting and neurally plausible signature of broadcasting can be provided by using the graph-theoretic approach to information dissemination in communication networks. The theoretical appeal of this framework lies in the fact that, in addition to distinguishing between broadcasting and other relevant communication processes, it can possibly also contribute to identifying the GNW mechanism. I suggest that the approach can provide precise predictions regarding the communication algorithms and wiring diagram that the GNW would implement if it were an efficient broadcasting network.
Two fundamental challenges of contemporary neuroscience are to make sense of the scalar relations in the nervous system and to understand the way behavior emerges from these relations while at the same time is affecting them. In this paper, we analyze the notion of enabling constraint and the way it can frame the two kinds of relations involved in the challenges: of different neural scales (e.g., molecular scale, genetic scale, single-neurons, neural networks, etc.) and between neural systems and behavior. We think the notion of enabling constraint provides a promising alternative to other classic, mechanistic understandings of these relations and the different issues contemporary neuroscience finds in them.