An underlying assumption in computational approaches in cognitive and brain sciences is that the nervous system is an input–output model of the world: Its input–output functions mirror certain relations in the target domains. I argue that the input–output modelling assumption plays distinct methodological and explanatory roles. Methodologically, input–output modelling serves to discover the computed function from environmental cues. Explanatorily, input–output modelling serves to account for the appropriateness of the computed function to the explanandum information-processing task. I compare very briefly the modelling explanation to mechanistic and optimality explanations, noting that in both cases the explanations can be seen as complementary rather than contrastive or competing.
Lotem Elber-Dorozko and Oron Shagrir. 2018. “Levels in Computational Explanations.” In Routledge Handbook of the Computational Mind, edited by Matteo Colombo and Mark Sprevak, Pp. 205-222. Routledege.
Jack Copeland, Oron Shagrir, and Mark Sprevak. 2018. “Zuse's Thesis, Gandy's Thesis, and Penrose's Thesis.” In Computational Perspectives on Physics, Physical Perspectives on Computation, edited by Michael Cuffaro and Sam Fletcher, Pp. 39-59. Cambridge University Press.
A key component of scientific inquiry, especially inquiry devoted to developing mechanistic explanations, is delineating the phenomenon to be explained. The task of delineating phenomena, however, has not been sufficiently analyzed, even by the new mechanistic philosophers of science. We contend that Marr’s characterization of what he called the computational level (CL) provides a valuable resource for understanding what is involved in delineating phenomena. Unfortunately, the distinctive feature of Marr’s computational level, his dual emphasis on both what is computed and why it is computed, has not been appreciated in philosophical discussions of Marr. Accordingly we offer a distinctive account of CL. This then allows us to develop two important points about delineating phenomena. First, the accounts of phenomena that figure in explanatory practice are typically not qualitative but precise, formal or mathematical, representations. Second, delineating phenomena requires consideration of the demands the environment places on the mechanism—identifying, as Marr put it, the basis of the computed function in the world. As valuable as Marr’s account of CL is in characterizing phenomena, we contend that ultimately he did not go far enough. Determining the relevant demands of the environment on the mechanism often requires detailed empirical investigation. Moreover, often phenomena are reconstituted in the course of inquiry on the mechanism itself.
Jack Copeland, Mark Sprevak, and Oron Shagrir. 2017. “Is the Universe Computational?.” In The Turing Guide, edited by Jonathan Bowen, Jack Copeland, Mark Sprevak, and Robin Wilson, Pp. 445-462. Oxford University Press.
Are all three of Marr's levels needed? Should they be kept distinct? We argue for the distinct contributions and methodologies of each level of analysis. It is important to maintain them because they provide three different perspectives required to understand mechanisms, especially information-processing mechanisms. The computational perspective provides an understanding of how a mechanism functions in broader environments that determines the computations it needs to perform (and may fail to perform). The representation and algorithmic perspective offers an understanding of how information about the environment is encoded within the mechanism and what are the patterns of organization that enable the parts of the mechanism to produce the phenomenon. The implementation perspective yields an understanding of the neural details of the mechanism and how they constrain function and algorithms. Once we adequately characterize the distinct role of each level of analysis, it is fairly straightforward to see how they relate.
Most computational neuroscientists assume that nervous systems computeand process information. We discuss foundational issues such as what we mean by ‘computation’ and ‘information processing’ in nervous systems; whether computation and information processing are matters of objective fact or of conventional, observer-dependent description; and how computational descriptions and explanations are related to other levels of analysis and organization.
Over the last three decades a vast literature has been dedicated to supervenience. Much of it has focused on the analysis of different concepts of supervenience and their philosophical consequences. This paper has two objectives. One is to provide a short, up-do-date, guide to the formal relations between the different concepts of supervenience. The other is to reassess the extent to which these concepts can establish metaphysical theses, especially about dependence. The conclusion is that strong global supervenience is the most advantageous notion of supervenience that we have.
In "A Computational Foundation for the Study of Cognition" David Chalmers articulates, justifies and defends the computational sufficiency thesis (CST). Chalmers advances a revised theory of computational implementation, and argues that implementing the right sort of computational structure is sufficient for the possession of a mind, and for the possession of a wide variety of mental properties. I argue that Chalmers`s theory of implementation is consistent with the nomological possibility of physical systems that possess different entire minds. I further argue that this brain-possessing-two-minds result challenges CST in three ways. It implicates CST with a host of epistemological problems; it undermines the underlying assumption that the mental supervenes on the physical; and it calls into question the claim that CST provides conceptual foundations for the computational science of the mind.
Putnam (Representations and reality. MIT Press, Cambridge, 1988) and Searle (The rediscovery of the mind. MIT Press, Cambridge, 1992) famously argue that almost every physical system implements every finite computation. This universal implementation claim, if correct, puts at the risk of triviality certain functional and computational views of the mind. Several authors have offered theories of implementation that allegedly avoid the pitfalls of universal implementation. My aim in this paper is to suggest that these theories are still consistent with a weaker result, which is the nomological possibility of systems that simultaneously implement different complex automata. Elsewhere I (Shagrir in J Cogn Sci, 2012) argue that this simultaneous implementation result challenges a computational sufficiencythesis (articulated by Chalmers in J Cogn Sci, 2012). My focus here is on theories of implementation. After presenting the basic simultaneous implementation construction, I argue that these theories do not avoid the simultaneous implementation result. The conclusion is that the idea that the implementation of the right kind of automaton suffices for a possession of a mind is dubious.