The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical (...) signals. I then indicate why computational theories have explanatory force with respect to these regularities:in a nutshell, they underscore correspondence relations between formal/mathematical properties of the electrical signals and formal/mathematical properties of the represented objects. I finally link my proposal to the philosophical thesis that content plays an essential role in computational taxonomy. (shrink)

Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions. # 2004 Elsevier Ltd. All rights reserved.

Are there representations in the brain? It depends on what you mean by representations, and it depends on what you want them to do for you—both in terms of the causal role they play in the system, and in terms of their explanatory value. But ideally, we would like an account of representation that allows us to assign a representational role and content to the appropriate mechanistic precursors of behavior that in fact play that role and conversely, search for the (...) mechanistic realizers of representational roles that are posited by our models of behavior and cognition. Such an account would be methodologically valuable in neuroscience. Lately, people have started asking similar questions about deep neural networks. What representations do they learn and use, and what is the relation between those representations and the sometimes impressive capacities these networks exhibit? More philosophically puzzling, perhaps, is the question of why the internal activities or dispositions labeled as such in neuroscience and AI research should count as representations at all. I think we can give a unified answer to both sets of questions, in the form of a kind of representational pragmatism. What makes something a representation just is that we can identify and re-identify it as such, and moreover, that we can manipulate it effectively to do whatever it is that we think representations ought to do for us in that particular context. For the first condition, the idea is that so long as we have some probe that allows us to pick out the relevant causally effective candidate, we should consider that to be a candidate representation-relative-to-that-probe. The second condition can be understood as a kind of anti-gerrymandering constraint: one ought to be able to intervene on the candidate, and see effects of that intervention consistent with the functional role the representation is supposed to play. Naturally the details need to be spelled out for different contexts—and I will articulate them for a few illustrative cases—which effectively allows for a kind of pluralism about representation depending on the setting and the kinds of questions being asked. With the extra components filled in, representational pragmatism can make sense of existing practices in neuroscience and AI, as well as their relationship to naturalistic theories of representation in philosophy. (shrink)

According to Marr, a computational-level theory consists of two elements, the what and the why. This article highlights the distinct role of the Why element in the computational analysis of vision. Three theses are advanced: that the Why element plays an explanatory role in computational-level theories, that its goal is to explain why the computed function is appropriate for a given visual task, and that the explanation consists in showing that the functional relations between the representing cells are similar to (...) the “external” mathematical relations between the entities that these cells represent. (shrink)

Over the past three decades, philosophy of science has grown increasingly “local.” Concerns have switched from general features of scientific practice to concepts, issues, and puzzles specific to particular disciplines. Philosophy of neuroscience is a natural result. This emerging area was also spurred by remarkable recent growth in the neurosciences. Cognitive and computational neuroscience continues to encroach upon issues traditionally addressed within the humanities, including the nature of consciousness, action, knowledge, and normativity. Empirical discoveries about brain structure and function suggest (...) ways that “naturalistic” programs might develop in detail, beyond the abstract philosophical considerations in their favor. -/- The literature distinguishes “philosophy of neuroscience” and “neurophilosophy.” The former concerns foundational issues within the neurosciences. The latter concerns application of neuroscientific concepts to traditional philosophical questions. Exploring various concepts of representation employed in neuroscientific theories is an example of the former. Examining implications of neurological syndromes for the concept of a unified self is an example of the latter. In this entry, we will assume this distinction and discuss examples of both. (shrink)

According to some philosophers, computational explanation is proprietary
to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that (...) are being developed by a number of authors. (shrink)

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. (shrink)

Volume 33, Issue 8, November 2020, Page 1096-1120.

In 1958, Francis Crick distinguished the flow of information from the flow of matter and the flow of energy in the mechanism of protein synthesis. Crick’s claims about information flow and coding in molecular biology are viewed from the perspective of a new characterization of mechanisms and from the perspective of information as holding a key to distinguishing work in molecular biology from that of biochemistry in the 1950s–1970s . Flow of matter from beginning to end does not occur in (...) the protein synthesis mechanism; flow of information does. The flow of information and coding in molecular biological mechanisms are distinguished, on the one hand, from formal information theory, and, on the other, from information as used in cognitive neuroscience, where information and representation are often coupled. (shrink)