Computing Mechanisms

Piccinini, Gualtiero

doi:10.1086/522851

Cited by 141 publications

(90 citation statements)

References 41 publications

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“…True, general purpose computers are different from most other systems precisely because they can do so many things depending on how they are programmed. But general purpose computers are still mechanisms, and the explanation of their behavior is still mechanistic (Piccinini 2007(Piccinini , 2008). Furthermore, the task analysis of a general purpose computer does place direct constraints on its mechanistic explanation and vice versa; in fact, even the task analysis of a general purpose computer is just an elliptical mechanistic explanation.…”

Section: Task Analysismentioning

confidence: 99%

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Integrating psychology and neuroscience: functional analyses as mechanism sketches

2011

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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...

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Section: Task Analysismentioning

confidence: 99%

“…But such an explanation is still mechanistic: it specifies the type of vehicle being processed (digital, analog, or what have you) as well as the structural components that do the processing, their organization, and the functions they compute. So computational explanations are mechanistic too (Piccinini 2007, Piccinini andScarantino 2010).…”

Section: Boxologymentioning

confidence: 99%

Integrating psychology and neuroscience: functional analyses as mechanism sketches

2011

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“…But this observation only reinforces the conclusion that random processes are not computations, for random processes, unlike computations, cannot go wrong. For more on the possibility of miscomputation as a feature of genuine computation, see Piccinini 2007b. course and wait until the n th value is generated. If this won't happen until a million years from now, that's too bad.…”

Section: Lack Of Confluencementioning

confidence: 99%

The Physical Church–Turing Thesis: Modest or Bold?

Piccinini

2011

The British Journal for the Philosophy of Science

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“…Separation Logic reasons about machines colloquially called 'computers'. However, the important part of Infer is that it predicts what a complex object we care about will do, not questions of whether physical objects compute (Piccinini 2007) or whether a computation is a miscomputation or dysfunctional (Floridi et al 2015). This distinction gives a sense of the extent to which tools like Infer are pragmatic, engineering projects.…”

Section: Introductionmentioning

confidence: 99%

Why Separation Logic Works

2018

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One might poetically muse that computers have the essence both of logic and machines. Through the case of the history of Separation Logic, we explore how this assertion is more than idle poetry. Separation Logic works because it merges the software engineer's conceptual model of a program's manipulation of computer memory with the logical model that interprets what sentences in the logic are true, and because it has a proof theory which aids in the crucial problem of scaling the reasoning task. Scalability is a central problem, and some would even say the central problem, in applications of logic in computer science. Separation Logic is an interesting case because of its widespread success in verification tools. For these two senses of model-the engineering/conceptual and the logical-to merge in a genuine sense, each must maintain their norms of use from their home disciplines. When this occurs, both the logic and engineering benefit greatly. Seeking this intersection of two different senses of model provides a strategy for how computer scientists and logicians may be successful. Furthermore, the history of Separation Logic for analysing programs provides a novel case for philosophers of science of how software engineers and computer scientists develop models and the components of such models. We provide three contributions: an exploration of the extent of models merging that is necessary for success in computer science; an introduction to the technical details of Separation Logic, which can be used for reasoning about other exhaustible resources; and an introduction to (a subset of) the problems, process, and results of computer scientists for those outside the field.

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Computing Mechanisms

Cited by 141 publications

References 41 publications

Integrating psychology and neuroscience: functional analyses as mechanism sketches

Integrating psychology and neuroscience: functional analyses as mechanism sketches

The Physical Church–Turing Thesis: Modest or Bold?

Why Separation Logic Works

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