Abstract
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.
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See Daugman (1990) for more detailed discussion of the role of technology and metaphor in the study of the human mind and body.
A computer is universal just in case it can compute any computable function until it runs out of memory and time. A computer is program-controlled just in case it computes different functions depending on which program it executes. Contemporary digital computers are both universal and program-controlled. Different kinds of analogies may be drawn between digital computers and brains, some of which are stronger than others (cf. Piccinini 2008, Sect. 5 for a more detailed discussion). At the same time, it was widely recognized that there are significant architectural and performance differences between artificial digital computers and natural cognitive systems.
Some argue that at least some explanations in cognitive neuroscience are not mechanistic but are instead “dynamical” (e.g., Chemero and Silberstein 2008). We lack the space to discuss this putative alternative to mechanistic explanation, except to point out that mechanistic explanations are often dynamical in the relevant sense (cf. Bechtel and Abrahamsen 2013) and thus are consistent with describing the dynamics of a system, whereas dynamical descriptions may or may not be explanatory in the relevant sense (cf. Kaplan and Craver 2011).
A recent example: “My key claim is that the use of the term ‘normalization’ in neuroscience retains much of its original mathematical-engineering sense. It indicates a mathematical operation—a computation—not a biological mechanism” (Chirimuuta 2014, p. 124). Chirimuuta also cites some neuroscientists who draw a similar contrast between computations and mechanisms.
Not all mathematical models in cognitive neuroscience ascribe computations to the nervous system; only those that explain phenomena through computations performed by the target systems do so.
In fairness to the critics, some mechanists may give the impression of advocating such a view: “the more accurate and detailed the model is for a target system or phenomenon the better it explains that phenomenon, all other things being equal” (Kaplan 2011, p. 347). Kaplan points out that some details may be omitted from a model either for reasons of computational tractability or because they are unknown. Similarly, Craver writes: “Between sketches and complete descriptions lies a continuum of mechanism schemata whose working is only partially understood” (Craver 2007, p. 114). To drive this point home, Craver aligns the sketch-schema-mechanism axis with the epistemic axis of “how possibly-plausibly-actually”: “Progress in building mechanistic explanations involves movement along both the possibly-plausibly-actually axis and along the sketch-schema-mechanism axis” (Craver 2007, p. 114). Contrary to what Craver appears to imply, progress may consist in abstracting away from irrelevant details to construct an appropriate schema, and in some epistemic contexts even a mechanism sketch may provide all the explanatory information that is needed (more on this in this section). And in fairness to Craver and Kaplan, we should note that there are also passages where they accept that abstraction and idealization play legitimate roles in explanation.
Issues related to tractability and solubility of mathematical models quickly get into deeper philosophical water than can be adequately treated here. Such issues spread across most domains of scientific inquiry. For instance, foundational work in continuum mechanics—i.e. the Navier–Stokes equations—developed around failures to model the flows of fluids through containers as trajectories of point particles; rather, the Navier–Stokes equations describe velocity fields at given points in space and time (see Batterman 2013 for an extended discussion). The extent to which the successes of these “top-down” modeling strategies can be treated merely as idealizations and approximations rather than reflecting more fundamental differences in the phenomena under investigation and our understanding of those phenomena at different levels of analysis is currently a topic of rich philosophical debate.
This is not to say that all analyses of neural computation or information-processing are mechanistic. Some focus only on the information content and efficiency of a neural code without saying anything about the processing mechanisms (Dayan and Abbott 2001, xiii; Chirimuuta 2014, p. 143ff). These models are not especially relevant here because they do not provide the kind of constitutive explanations that are the present topic, and that functional analysis and mechanistic explanation are competing accounts of.
Bechtel and Shagrir (forthcoming) is a good entry into the extensive literature on Marr’s levels, including how they might fit within a mechanistic framework. We cannot do justice to that debate here.
This point is reminiscent of Lycan’s underappreciated critique of “two-levelism” (Lycan 1990). But Lycan lacked the accounts of mechanistic explanation and computational explanation that have been developed in detail in the past decade, and that provide the foundation that we are building upon.
Here we depart from Craver (2007, pp. 212ff.), who distinguishes between levels of mechanistic organization and levels of realization. Craver adopts the view that realization is a relation between two properties of one and the same whole system, not to be confused with the relation that holds between levels of mechanistic organization. (According to Craver, as according to us, levels of mechanistic organization are systems of components, their capacities, and their organizational relations, and they are related compositionally to other levels of mechanistic organization.) We reject the account of realization adopted by Craver; we hold that each level of mechanistic organization realizes the mechanistic level above it and is realized by the mechanistic level below it (Piccinini and Maley 2014). Realization, in its most useful sense, is precisely the relation that obtains between two adjacent mechanistic levels in a multi-level mechanism and is thus a compositional relation.
The purely biophysical level is reached when our explanation of the processes no longer appeals solely to differences between different portions of the vehicles along relevant dimensions of variation—which in the case of neural vehicles are mostly spike frequency and timing—in favor of the specific biophysical properties of neurons, such as the flow of specific ions through their cell membranes.
We are not committed to the adequacy of this particular explanation of visual processing, just to its exemplifying the explanatory strategy of iterated computational mechanisms that we are explicating here.
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The authors are listed alphabetically—the paper was thoroughly collaborative. Otávio Bueno graciously arranged for the double blind refereeing of this paper; thanks to him and the anonymous referees for helpful comments. Thanks to our audiences at Georgia State University, Washington University in St. Louis, the 2014 Society for Philosophy and Psychology meeting, 2014 Central APA meeting and to our APA commentator, Robert Rupert. Thanks to Sergio Barberis, Mazviita Chirimuuta, and Corey Maley for helpful comments. Thanks to Elliott Risch for editorial assistance. This material is based on work supported in part by a University of Missouri research award to Gualtiero Piccinini.
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Boone, W., Piccinini, G. The cognitive neuroscience revolution. Synthese 193, 1509–1534 (2016). https://doi.org/10.1007/s11229-015-0783-4
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DOI: https://doi.org/10.1007/s11229-015-0783-4