Multiple realisation prompts the question: how is it that multiple systems all exhibit the same phenomena despite their different underlying properties? In this paper I develop a framework for addressing that question and argue that multiple realisation can be reductively explained. I illustrate this position by applying the framework to a simple example – the multiple realisation of electrical conductivity. I defend my account by addressing potential objections:contra Polger and Shapiro, Batterman, and Sober, I claim that multiple realisation is commonplace, (...) that it can be reductively explained, but that it requires asui generisreductive explanatory strategy. (shrink)

It is commonly maintained that neuroplastic mechanisms in the brain provide empirical support for the hypothesis of multiple realizability. We show in various case studies that neuroplasticity stems from preexisting mechanisms and processes inherent in the neural structure of the brain. We argue that not only does neuroplasticity fail to provide empirical evidence of multiple realization, its inability to do so strengthens the mind-body identity theory. Finally, we argue that a recently proposed identity theory called Flat Physicalism can be enlisted (...) to explain the current state of the mind-body problem more adequately. (shrink)

In this paper, I defend an updated account of functional kinds, initially presented by Daniel Weiskopf, from the criticism that functional kinds will not qualify as scientific kinds. An important part of Weiskopf’s account is that functional kinds are multiply realisable. The criticisms I consider avoid discussion of multiple realisability. Instead, it is argued that functional kinds carry inferior counterfactual profiles when compared to other accounts of kinds. I respond to this charge by arguing that this criticism fails to take (...) into consideration the role that multiple realisability can play in providing important explanatory counterfactuals. I do so by highlighting some points made by Lauren Ross that highlight where multiple realisability is explanatorily pertinent. I then argue that the criticisms of Weiskopf’s account fail to establish the explanatory inferiority of functional kinds. (shrink)

Deep neural networks (DNNs) have found many useful applications in recent years. Of particular interest have been those instances where their successes imitate human cognition and many consider artificial intelligences to offer a lens for understanding human intelligence. Here, we criticize the underlying conflation between the predictive and explanatory power of DNNs by examining the goals of modeling.