Abstract
According to the computational theory of cognition (CTC), cognitive capacities are explained by inner computations, which in biological organisms are realized in the brain. Computational explanation is so popular and entrenched that it’s common for scientists and philosophers to assume CTC without argument. But if we presuppose that neural processes are computations before investigating, we turn CTC into dogma. If, instead, our theory is to be genuinely empirical and explanatory, it needs to be empirically testable. To bring empirical evidence to bear on CTC, we need an appropriate notion of computation. In order to ground an empirical theory of cognition, as CTC was designed to be, a satisfactory notion of computation should satisfy at least two requirements: it should employ a robust notion of computation, such that there is a fact of the matter as to which computations are performed by which systems, and it should not be empirically vacuous, as it would be if CTC could be established a priori. In order to satisfy these requirements, the computational theory of cognition should be grounded in a mechanistic account of computation. Once that is done, I evaluate the computational theory of cognition on empirical grounds in light of our best neuroscience. I reach two main conclusions: cognitive capacities are explained by the processing of spike trains by neuronal populations, and the processing of spike trains is a kind of computation that is interestingly different from both digital computation and analog computation.
This paper is a substantially revised and updated descendant of Piccinini 2007, which it supersedes. Accounts of computation in the same spirit are also defended in Fresco 2014 and Milkoswki 2013. Thanks to an anonymous referee 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.
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Notes
- 1.
Some philosophers have argued that the realizers of cognitive states and processes include not only the nervous system but also some things outside it (e.g., Wilson 2004). I will ignore this possible complication because this simplifies the exposition without affecting my conclusions.
- 2.
I am using “concept” in a pre-theoretical sense. Of course, there may be ways of individuating concepts independently of their content, ways that may be accessible to those who possess a scientific theory of concepts but not to ordinary speakers.
- 3.
The distinction between essential and accidental representation is closely related to the distinction between original and derived intentionality. Derived intentionality is intentionality conferred on something by something that already has it; original intentionality is intentionality that is not derived (Haugeland 1997). If something has original intentionality, presumably it is an essential representation (it has its content essentially); if something has derived intentionality, presumably it is an accidental representation. These distinctions should not be confused with the distinction between intrinsic and extrinsic intentionality. Intrinsic intentionality is the intentionality of entities that are intentional regardless of their relations with anything else (Searle 1983). Something may be an essential representation without having intrinsic intentionality, because its intentionality may be due to the relations it bears to other things.
- 4.
To be a bit more precise, for each digital computing system, there is a finite alphabet out of which strings of digits can be formed and a fixed rule that specifies, for any input string on that alphabet (and for any internal state, if relevant), whether there is an output string defined for that input (internal state), and which output string that is. If the rule defines no output for some inputs (internal states), the mechanism should produce no output for those inputs (internal states). For more details, see Piccinini 2015.
- 5.
Medium-independence entails multiple realizability but not vice versa. Any medium-independent vehicle or process is realizable by different media, thus it is multiply realizable. But the converse does not hold. Functionally defined kinds, such as mousetrap and corkscrew, are typically multiply realizable—that is, they can be realized by different kinds of mechanisms (Piccinini and Maley 2014). But most functionally defined kinds, including mousetrap and corkscrew, are not medium-independent—they are defined in terms of specific physical effects, such as catching mice or lifting corks out of bottles.
- 6.
The exact level of sophistication of this feedback control is irrelevant here. Cf. Grush (2003) for some options.
- 7.
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Piccinini, G. (2016). The Computational Theory of Cognition. In: Müller, V.C. (eds) Fundamental Issues of Artificial Intelligence. Synthese Library, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-26485-1_13
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