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.
References
Adrian, E. D. (1928). The basis of sensation: The action of the sense organs. New York: Norton.
Barberis, S. D. (2013). Functional analyses, mechanistic explanations, and explanatory tradeoffs. Journal of Cognitive Science, 14(3), 229–251.
Bechtel, W. (2008). Mental mechanisms: Philosophical perspectives on cognitive neuroscience. London: Routledge.
Bechtel, W., & Richardson, R. C. (1993). Discovering complexity: Decomposition and localization as scientific research strategies. Princeton: Princeton University Press.
Block, N. (1978). Troubles with functionalism. In C. W. Savage (Ed.), Perception and cognition: Issues in the foundations of psychology (6th ed., pp. 261–325). Minneapolis: University of Minnesota Press.
Boone, T., & Piccinini, G. (2015). “The cognitive neuroscience revolution”. Synthese. doi:10.1007/s11229-015-0783-4
Bringsjord, S. (1995). Computation, among other things, is beneath us. Minds and Machines, 4, 469–488.
Burge, T. (1986). Individualism and psychology. Philosophical Review, 95, 3–45.
Chalmers, D. (2011). A computational foundation for the study of cognition. Journal of Cognitive Science, 12(4), 323–357.
Chirimuuta, M. (2014). Mazviita Chirimuuta, minimal models and canonical neural computations: The distinctness of computational explanation in neuroscience. Synthese.
Churchland, P. S., & Sejnowski, T. J. (1992). The computational brain. Cambridge, MA: MIT Press.
Copeland, B. J. (2000). Narrow versus wide mechanism: Including a re-examination of Turing’s views on the mind-machine issue. The Journal of Philosophy, XCVI(1), 5–32.
Craver, C. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford: Oxford University Press.
Cummins, R. (1983). The nature of psychological explanation. Cambridge, MA: MIT Press.
Cummins, R. (2000). “How does it work?” vs. “What are the laws?” Two conceptions of psychological explanation. In F. C. Keil & R. A. Wilson (Eds.), Explanation and cognition. Cambridge, MA: MIT Press.
Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge, MA: MIT Press.
Dennett, D. C. (1978). Brainstorms. Cambridge, MA: MIT Press.
Dretske, F. I. (1988). Explaining behavior: Reasons in a world of causes. Cambridge, MA: MIT Press.
Dreyfus, H. L. (1998). Response to my critics. In T. W. Bynum & J. H. Moor (Eds.), The digital phoenix: How computers are changing philosophy (pp. 193–212). Malden: Oxford, Blackwell.
Edelman, G. M. (1992). Bright air, brilliant fire: On the matter of the mind. New York: Basic Books.
Egan, F. (1995). Computation and content. Philosophical Review, 104, 181–203.
Eliasmith, C. (2003). Moving beyond metaphors: Understanding the mind for what it is. Journal of Philosophy, C(10), 493–520.
Ermentrout, G. B., & Terman, D. H. (2010). Mathematical foundations of neuroscience. New York: Springer.
Erneling, C. E., & Johnson, D. M. (2005). The mind as a scientific object: Between brain and culture. Oxford: Oxford University Press.
Fetzer, J. H. (2001). Computers and cognition: Why minds are not machines. Dordrecht: Kluwer.
Fodor, J. A. (1968). Psychological explanation. New York: Random House.
Fodor, J. A. (1997). Special sciences: Still autonomous after all these years. Philosophical Perspectives, 11, 149–163.
Fodor, J. A. (1998). Concepts. Oxford: Clarendon Press.
Fresco, N. (2014). Physical computation and cognitive science. New York: Springer.
Gallistel, C. R., & King, A. P. (2009). Memory and the computational brain: Why cognitive science will transform neuroscience. Malden: Wiley-Blackwell.
Garson, J. (2003). The introduction of information into neurobiology. Philosophy of Science, 70, 926–936.
Gerard, R. W. (1951). Some of the problems concerning digital notions in the central nervous system. In H. v. Foerster, M. Mead, & H. L. Teuber (Eds.), Cybernetics: Circular causal and feedback mechanisms in biological and social systems. Transactions of the seventh conference (pp. 11–57). New York: Macy Foundation.
Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
Glennan, S. S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 64, 605.
Globus, G. G. (1992). Towards a noncomputational cognitive neuroscience. Journal of Cognitive Neuroscience, 4(4), 299–310.
Grush, R. (2003). In defense of some ‘Cartesian’ assumptions concerning the brain and its operation. Biology and Philosophy, 18, 53–93.
Harnad, S. (1996). Computation is just interpretable symbol manipulation; cognition isn’t. Minds and Machines, 4, 379–390.
Haugeland, J. (1997). What is mind design? In J. Haugeland (Ed.), Mind design II (pp. 1–28). Cambridge, MA: MIT Press.
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79, 2554–2558.
Horst, S. W. (1996). Symbols, computation, and intentionality: A critique of the computational theory of mind. Berkeley: University of California Press.
Johnson, D. M., & Erneling, C. E. (Eds.). (1997). The future of the cognitive revolution. New York: Oxford University Press.
Koch, C. (1999). Biophysics of computation: Information processing in single neurons. New York: Oxford University Press.
Lucas, J. R. (1996). Minds, machines, and Gödel: A retrospect. In P. J. R. Millikan & A. Clark (Eds.), Machines and thought: The legacy of Alan Turing. Oxford: Clarendon.
Levy, A., & Bechtel, W. (2013). Abstraction and the organization of mechanisms. Philosophy of Science, 80(2), 241–261.
Machamer, P. K., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.
Maley, C., & Piccinini, G. (forthcoming). The ontology of functional mechanisms. In D. Kaplan (Ed.), Integrating psychology and neuroscience: Prospects and problems. Oxford: Oxford University Press.
Marr, D. (1982). Vision. New York: Freeman.
Maudlin, T. (1989). Computation and consciousness. Journal of Philosophy, 86(8), 407–432.
McCulloch, W. S., & Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 7, 115–133.
Mellor, D. H. (1989). How much of the mind is a computer? In P. Slezak & W. R. Albury (Eds.), Computers, brains and minds (pp. 47–69). Dordrecht: Kluwer.
Milkowski, M. (2013). Explaining the computational, mind. Cambridge, MA: MIT Press.
Minsky, M., & Papert, S. (1969). Perceptrons. Cambridge, MA: MIT Press.
Morgan, A. (2014). Representations gone mental. Synthese, 191(2), 213–244.
Newell, A., & Simon, H. A. (1976). Computer science as an empirical enquiry: Symbols and search. Communications of the ACM, 19, 113–126.
Ó Nualláin, S., & Mc Kevitt, P. (Eds.). (1997). Two sciences of mind: Readings in cognitive science and consciousness. Philadelphia: John Benjamins.
O’Reilly, R. C., & Munakata, Y. (2000). Computational explorations in cognitive neuroscience: Understanding the mind by simulating the brain. Cambridge, MA: MIT Press.
Penrose, R. (1994). Shadows of the mind. Oxford: Oxford University Press.
Pereboom, D., & Kornblith, H. (1991). The metaphysics of irreducibility. Philosophical Studies, 63.
Perkel, D. H. (1990). Computational neuroscience: Scope and structure. In E. L. Schwartz (Ed.), Computational neuroscience (pp. 38–45). Cambridge, MA: MIT Press.
Piccinini, G. (2004a). The first computational theory of mind and brain: A close look at McCulloch and Pitts’s ‘logical calculus of ideas immanent in nervous activity’. Synthese, 141(2), 175–215.
Piccinini, G. (2004b). Functionalism, computationalism, and mental contents. Canadian Journal of Philosophy, 34(3), 375–410.
Piccinini, G. (2004b). Functionalism, computationalism, and mental states. Studies in the History and Philosophy of Science, 35(4), 811–833.
Piccinini, G. (2007a). Computational modeling vs. computational explanation: Is everything a Turing machine, and does it matter to the philosophy of mind? Australasian Journal of Philosophy, 85(1), 93–115.
Piccinini, G. (2007b). Computing mechanisms. Philosophy of Science, 74(4), 501–526.
Piccinini, G. (2007). Computational explanation and mechanistic explanation of mind. In M. De Caro, F. Ferretti, & M. Marraffa (Eds.), Cartographies of the mind: Philosophy and psychology in intersection (pp. 23–36). Dordrecht: Springer.
Piccinini, G. (2015). Physical computation: A mechanistic account. Oxford: Oxford University Press.
Piccinini, G., & Bahar, S. (2013). Neural computation and the computational theory of cognition. Cognitive Science, 34, 453–488.
Piccinini, G., & Craver, C. (2011). Integrating psychology and neuroscience: Functional analyses as mechanism sketches. Synthese, 183(3), 283–311.
Piccinini, G., & Maley, C. (2014). The metaphysics of mind and the multiple sources of multiple realizability. In M. Sprevak & J. Kallestrup (Eds.), New waves in the philosophy of mind (125–152). Palgrave Macmillan.
Piccinini, G., & Scarantino, A. (2011). Information processing, computation, and cognition. Journal of Biological Physics, 37(1), 1–38.
Port, R. F., & van Gelder, T. (Eds.). (1995). Mind and motion: Explorations in the dynamics of cognition. Cambridge, MA: MIT Press.
Putnam, H. (1967). Psychological predicates (Art, philosophy, and religion). Pittsburgh: University of Pittsburgh Press.
Putnam, H. (1988). Representation and reality. Cambridge, MA: MIT Press.
Pylyshyn, Z. W. (1984). Computation and cognition. Cambridge, MA: MIT Press.
Ramsey, W. (2007). Representation reconsidered. Cambridge: Cambridge University Press.
Rubel, L. A. (1985). The brain as an analog computer. Journal of Theoretical Neurobiology, 4, 73–81.
Rumelhart, D. E., & McClelland, J. M. (1986). Parallel distributed processing. Cambridge, MA: MIT Press.
Searle, J. R. (1983). Intentionality: An essay in the philosophy of mind. Cambridge: Cambridge University Press.
Searle, J. R. (1992). The rediscovery of the mind. Cambridge, MA: MIT Press.
Segal, G. (1991). Defence of a reasonable individualism. Mind, 100, 485–493.
Shadlen, M. N., & Newsome, W. T. (1998). The variable discharge of cortical neurons: Implications for connectivity, computation, and information coding. Journal of Neuroscience, 18(10), 3870–3896.
Shagrir, O. (2001). Content, computation and externalism. Mind, 110(438), 369–400.
Shagrir, O. (2006). Why we view the brain as a computer. Synthese, 153(3), 393–416.
Siegelmann, H. T. (1999). Neural networks and analog computation: Beyond the Turing limit. Boston: Birkhäuser.
Sullivan, J. (2009). The multiplicity of experimental protocols: A challenge to reductionist and non-reductionist models of the unity of neuroscience. Synthese, 167, 511–539.
Taube, M. (1961). Computers and common sense: The myth of thinking machines. New York: Columbia University Press.
Thelen, E., & Smith, L. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press.
Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. In M. Davis (Ed.), The undecidable. Hewlett: Raven.
van Gelder, T. (1995). What might cognition be, if not computation? The Journal of Philosophy, XCII(7), 345–381.
Varela, F. J., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. Cambridge, MA: MIT Press.
von Neumann, J. (1958). The computer and the brain. New Haven: Yale University Press.
Weiskopf, D. (2011). Models and mechanisms in psychological explanation. Synthese, 183, 313–338.
Wiener, N. (1948). Cybernetics or control and communication in the animal and the machine. Cambridge, MA: MIT Press.
Wilson, R. A. (2004). Boundaries of the mind: The individual in the fragile sciences. Cambridge: Cambridge University Press.
Wright, C. (1995). Intuitionists are not (Turing) machines. Philosophia Mathematica, 3(3), 86–102.
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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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