David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Behavioral and Brain Sciences 3 (1):111-32 (1980)
The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach (the "proprietary vocabulary hypothesis") is that there is a natural domain of human functioning (roughly what we intuitively associate with perceiving, reasoning, and acting) that can be addressed exclusively in terms of a formal symbolic or algorithmic vocabulary or level of analysis. Much of the paper elaborates various conditions that need to be met if a literal view of mental activity as computation is to serve as the basis for explanatory theories. The coherence of such a view depends on there being a principled distinction between functions whose explanation requires that we posit internal representations and those that we can appropriately describe as merely instantiating causal physical or biological laws. In this paper the distinction is empirically grounded in a methodological criterion called the "cognitive impenetrability condition." Functions are said to be cognitively impenetrable if they cannot be influenced by such purely cognitive factors as goals, beliefs, inferences, tacit knowledge, and so on. Such a criterion makes it possible to empirically separate the fixed capacities of mind (called its "functional architecture") from the particular representations and algorithms used on specific occasions. In order for computational theories to avoid being ad hoc, they must deal effectively with the "degrees of freedom" problem by constraining the extent to which they can be arbitrarily adjusted post hoc to fit some particular set of observations. This in turn requires that the fixed architectural function and the algorithms be independently validated. It is argued that the architectural assumptions implicit in many contemporary models run afoul of the cognitive impenetrability condition, since the required fixed functions are demonstrably sensitive to tacit knowledge and goals. The paper concludes with some tactical suggestions for the development of computational cognitive theories
|Keywords||cognitive science artificial intelligence computational models computer simulation cognition mental representation mental process imagery philosophical foundations functionalism philosophy of mind|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Franz Brentano (1960). Genuine and Fictitious Objects. In Roderick Chisholm (ed.), Realism and the Background of Phenomenology. Ridgeview
Noam Chomsky (1965). Aspects of the Theory of Syntax. The MIT Press.
Patricia S. Churchland (1978). Fodor on Language Learning. Synthese 38 (May):149-59.
Paul M. Churchland (1979). Scientific Realism and the Plasticity of Mind. Cambridge University Press.
L. Jonathan Cohen, Jean Nicod, John Bell & Michael Woods (1971). Geometry and Induction. Philosophical Quarterly 21 (85):376.
Citations of this work BETA
Dustin Stokes (2013). Cognitive Penetrability of Perception. Philosophy Compass 8 (7):646-663.
Jerry A. Fodor & Zenon W. Pylyshyn (1981). How Direct is Visual Perception? Some Reflections on Gibson's 'Ecological Approach'. Cognition 9 (2):139-96.
Eric Dietrich & A. Markman (2003). Discrete Thoughts: Why Cognition Must Use Discrete Representations. Mind and Language 18 (1):95-119.
S. Ullman (1980). Against Direct Perception. Behavioral and Brain Sciences 3 (3):333-81.
Axel Cleeremans (2014). Connecting Conscious and Unconscious Processing. Cognitive Science 38 (6):1286-1315.
Similar books and articles
C. Glymour (1994). On the Methods of Cognitive Neuropsychology. British Journal for the Philosophy of Science 45 (3):815-35.
Gualtiero Piccinini & Andrea Scarantino (2010). Computation Vs. Information Processing: Why Their Difference Matters to Cognitive Science. Studies in History and Philosophy of Science Part A 41 (3):237-246.
James H. Fetzer (1997). Thinking and Computing: Computers as Special Kinds of Signs. [REVIEW] Minds and Machines 7 (3):345-364.
Paul R. Thagard (2002). How Molecules Matter to Mental Computation. Philosophy of Science 69 (3):497-518.
Gualtiero Piccinini & Andrea Scarantino (2011). Information Processing, Computation, and Cognition. Journal of Biological Physics 37 (1):1-38.
Gordana Dodig Crnkovic & Susan Stuart (eds.) (2007). Computation, Information, Cognition: The Nexus and the Liminal. Cambridge Scholars Press.
Selmer Bringsjord (1998). Cognition is Not Computation: The Argument From Irreversibility. Synthese 113 (2):285-320.
David J. Chalmers (2011). A Computational Foundation for the Study of Cognition. Journal of Cognitive Science 12 (4):323-357.
John Haugeland (1987). Book Review:Computation and Cognition: Toward a Foundation for Cognitive Science Zenon W. Pylyshyn. [REVIEW] Philosophy of Science 54 (2):309-.
Added to index2009-01-28
Total downloads14 ( #170,159 of 1,699,835 )
Recent downloads (6 months)6 ( #105,649 of 1,699,835 )
How can I increase my downloads?