This paper addresses a fundamental line of research in neuroscience: the identification of a putative neural processing core of the cerebral cortex, often claimed to be “canonical”. This “canonical” core would be shared by the entire cortex, and would explain why it is so powerful and diversified in tasks and functions, yet so uniform in architecture. The purpose of this paper is to analyze the search for canonical explanations over the past 40 years, discussing the theoretical frameworks informing this research. (...) It will highlight a bias that, in my opinion, has limited the success of this research project, that of overlooking the dimension of cortical development. The earliest explanation of the cerebral cortex as canonical was attempted by David Marr, deriving putative cortical circuits from general mathematical laws, loosely following a deductive-nomological account. Although Marr’s theory turned out to be incorrect, one of its merits was to have put the issue of cortical circuit development at the top of his agenda. This aspect has been largely neglected in much of the research on canonical models that has followed. Models proposed in the 1980s were conceived as mechanistic. They identified a small number of components that interacted as a basic circuit, with each component defined as a function. More recent models have been presented as idealized canonical computations, distinct from mechanistic explanations, due to the lack of identifiable cortical components. Currently, the entire enterprise of coming up with a single canonical explanation has been criticized as being misguided, and the premise of the uniformity of the cortex has been strongly challenged. This debate is analyzed here. The legacy of the canonical circuit concept is reflected in both positive and negative ways in recent large-scale brain projects, such as the Human Brain Project. One positive aspect is that these projects might achieve the aim of producing detailed simulations of cortical electrical activity, a negative one regards whether they will be able to find ways of simulating how circuits actually develop. (shrink)

Dynamical systems play a central role in explanations in cognitive neuroscience. The grounds for these explanations are hotly debated and generally fall under two approaches: non-mechanistic and mechanistic. In this paper, I first outline a neurodynamical explanatory schema that highlights the role of dynamical systems in cognitive phenomena. I next explore the mechanistic status of such neurodynamical explanations. I argue that these explanations satisfy only some of the constraints on mechanistic explanation and should be considered pseudomechanistic explanations. I defend this (...) argument against three alternative interpretations of the neurodynamical explanatory schema. The independent interpretation holds that neurodynamical explanations and mechanisms are independent. The constitutive interpretation holds that neurodynamical explanations are constitutive but otherwise non-mechanistic. Both the independent and constitutive interpretations fail to account for all the features of neurodynamical explanations. The partial interpretation assumes that the targets of dynamical systems models are mechanisms and so holds that neurodynamical explanations are incomplete because they lack mechanistic details. I contend instead that the targets of those models are dynamical systems distinct from mechanisms and defend this claim against several objections. I conclude with a defense of the pseudomechanistic interpretation and a discussion of the source of their explanatory power in relation to a causal-mechanical description of the world. (shrink)

ABSTRACTWhile over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating “artificial minded intelligence” that can help to unify existing approaches and provide new avenues for research. I first outline (...) three desiderata that a framework for AMI research should satisfy. In Section 1, I further motivate the desiderata as well as the need for a new framework. Section 2 develops a general methodological approach, the “generative methodology,” and Section 3 develops a version of this methodology, the “Competence Framework for AI research”. (shrink)

At present, the science of consciousness is structured around the search for the neural correlates of consciousness. One of the alleged advantages of the NCCs framework is its metaphysical neutrality—the fact that it begs no contested questions with respect to debates about the fundamental nature of consciousness. Here, we argue that even if the NCC framework is metaphysically neutral, it is structurally committed, for it presupposes a certain model—what we call the Lite-Brite model—of consciousness. This, we argue, represents a serious (...) liability for the NCC framework for the plausibility of the Lite-Brite model is very much an open question, and the science of consciousness would be better served by a framework that does not presuppose it. Drawing on interventionist ideas in the philosophy of science, we suggest that the Difference-Maker framework can provide just such an alternative. Instead of searching for the neural correlates of consciousness, we ought to be searching for the difference makers of consciousness. We detail how a shift to searching DMCs will change both the practice of consciousness science and the interpretation of existing results. (shrink)

Distinguishing mechanistic components from mere causally relevant background conditions remains a difficulty for mechanistic accounts of explanation. By distinguishing resources from mechanical parts, I argue that we can more effectively draw this boundary. Further, the distinction makes obvious that there are distinctive resource explanations which are not captured by a traditional part-based mechanistic account. While this suggests a straightforward extension of the mechanistic model, I argue that incorporating resources and resource explanations requires moving beyond the purely local account of levels (...) that some mechanists advocate. (shrink)