Synthese; Synthese is available online at:http://www.springer.com/philosophy/epistemology+and+philosophy+of+science/jou rnal/11229Abstract. We sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms, in which some structural aspects of a mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated with multilevel mechanistic explanations.1 This paper was refereed in two rounds. John Symons graciously arranged for a round of blind refereeing; thanks are due to him and the anonymous referees. In addition, thanks to those who refereed the paper non-blindly: Ken Aizawa, Robert Cummins, and Dan Weiskopf. Many thanks to audiences at the 2010 APA Pacific Division, Saint Louis University, and University of Missouri -Columbia. This material is based upon work supported by the National Science Foundation under Grant No. SES-0924527 to Gualtiero Piccinini. 2 Integrating Psychology and Neuroscience via Multi-Level Mechanistic ExplanationWhen psychologists explain behavior, the explanations typically make reference to causes that precede the behavior and make a difference to whether and how it occurs.For instance, they explain that Anna ducked because she saw a looming ball. By contrast, when psychologists explain psychological capacities such as stereopsis or working memory, they typically do so by showing that these complex capacities are made up of more basic capacities organized together. In this paper, we focus exclusively on the latter sort of explanation, which is usually referred to as functional analysis. We argue that such decompositional, constitutive explanations gain their explanatory force by describing mechanisms (even approximately and with idealization) and, conversely, that they lack explanatory force to the extent that they fail to describe mechanisms.In arguing for this point, we sketch a framework for building a unified science of cognition. This unification is achieved by showing how functional analyses of cognitive capacities can be and in some cases have been integrated with the multilevel mechanistic explanations of neural systems. The core idea is that functional analyses are sketches of mechanisms, in which some structural aspects of a mechanistic explanation are omitted. Once the missing aspects are filled in, a functional analysis turns into a full-blown mechanistic explanation. By this process, functional analyses are seamlessly integrated with multilevel mechanistic explanations. 3The conclusion that functional analyses are mechanism sketches leads to a simple argument that psychological explanation is mechanistic. It is generally assumed that psychological explanation is functional-that it proceeds via t...

We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain cognition. To a large extent, practicing cognitive neuroscientists have already accepted this shift, but philosophical theory has not fully acknowledged and appreciated its significance. As a result, the explanatory framework underlying cognitive neuroscience has remained largely implicit. We explicate this framework and demonstrate its contrast with previous approaches.

This paper offers an account of what it is for a physical system to be a computing mechanism-a system that performs computations. A computing mechanism is a mechanism whose function is to generate output strings from input strings and (possibly) internal states, in accordance with a general rule that applies to all relevant strings and depends on the input strings and (possibly) internal states for its application. This account is motivated by reasons endogenous to the philosophy of computing, namely, doing justice to the practices of computer scientists and computability theorists. It is also an application of recent literature on mechanisms, because it assimilates computational explanation to mechanistic explanation. The account can be used to individuate computing mechanisms and the functions they compute and to taxonomize computing mechanisms based on their computing power.

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Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both - although others disagree vehemently. Yet different cognitive scientists use 'computation' and 'information processing' to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism, connectionism, and computational neuroscience on the other. We defend the relevance to cognitive science of both computation, at least in a generic sense, and information processing, in three important senses of the term. Our account advances several foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates' empirical aspects.