-->
The Computational Theory of Mind (Stanford Encyclopedia of Philosophy)
Stanford Encyclopedia of Philosophy
Menu
Browse
Table of Contents
What's New
Random Entry
Chronological
Archives
About
Editorial Information
About the SEP
Editorial Board
How to Cite the SEP
Special Characters
Advanced Tools
Contact
Support SEP
Support the SEP
PDFs for SEP Friends
Make a Donation
SEPIA for Libraries
Entry Navigation
Entry Contents
Bibliography
Academic Tools
Friends PDF Preview
Author and Citation Info
Back to Top
The Computational Theory of Mind<br>First published Fri Oct 16, 2015; substantive revision Wed Dec 18, 2024
Could a machine think? Could the mind itself be a thinking machine?<br>The computer revolution transformed discussion of these questions,<br>offering our best prospects yet for machines that emulate reasoning,<br>decision-making, problem solving, perception, linguistic<br>comprehension, and other mental processes. Advances in computing raise<br>the prospect that the mind itself is a computational system—a<br>position known as the computational theory of mind (CTM).<br>Computationalists are researchers who endorse CTM, at least<br>as applied to certain important mental processes. CTM played a central<br>role within cognitive science during the 1960s and 1970s. For many<br>years, it enjoyed orthodox status. More recently, it has come under<br>pressure from various rival paradigms. A key task facing<br>computationalists is to explain what one means when one says that the<br>mind “computes”. A second task is to argue that the mind<br>“computes” in the relevant sense. A third task is to<br>elucidate how computational description relates to other common types<br>of description, especially neurophysiological description<br>(which cites neurophysiological properties of the organism’s<br>brain or body) and intentional description (which cites<br>representational properties of mental states).
1. Turing machines
2. Artificial intelligence
3. The classical computational theory of mind
3.1 Machine functionalism
3.2 The representational theory of mind
4. Neural networks
4.1 Relation between neural networks and classical computation
4.2 Arguments for connectionism
4.3 Systematicity and productivity
4.4 Computational neuroscience
5. Computation and representation
5.1 Computation as formal
5.2 Externalism about mental content
5.3 Content-involving computation
6. Alternative conceptions of computation
6.1 Information-processing
6.2 Function evaluation
6.3 Structuralism
6.4 Mechanistic theories
6.5 Pluralism
7. Arguments against computationalism
7.1 Triviality arguments
7.2 Gödel’s incompleteness theorem
7.3 Limits of computational modeling
7.4 Temporal arguments
7.5 Embodied cognition
Bibliography
Academic Tools
Other Internet Resources
Related Entries
1. Turing machines
The intuitive notions of computation and algorithm<br>are central to mathematics. Roughly speaking, an algorithm is an<br>explicit, step-by-step procedure for answering some question or<br>solving some problem. An algorithm provides routine mechanical<br>instructions dictating how to proceed at each step. Obeying the<br>instructions requires no special ingenuity or creativity. For example,<br>the familiar grade-school algorithms describe how to compute addition,<br>multiplication, and division. Until the early twentieth century,<br>mathematicians relied upon informal notions of computation and<br>algorithm without attempting anything like a formal analysis.<br>Developments in the foundations of mathematics eventually impelled<br>logicians to pursue a more systematic treatment. Alan Turing’s<br>landmark paper “On Computable Numbers, With an Application to<br>the Entscheidungsproblem” (Turing 1936) offered the analysis<br>that has proved most influential.
A Turing machine is an abstract model of an idealized<br>computing device with unlimited time and storage space at its<br>disposal. The device manipulates symbols, much as a human<br>computing agent manipulates pencil marks on paper during arithmetical<br>computation. Turing says very little about the nature of symbols. He<br>assumes that primitive symbols are drawn from a finite alphabet. He<br>also assumes that symbols can be inscribed or erased at “memory<br>locations”. Turing’s model works as follows:
There are infinitely many memory locations, arrayed in a linear<br>structure. Metaphorically, these memory locations are<br>“cells” on an infinitely long “paper tape”.<br>More literally, the memory locations might be physically realized in<br>various media (e.g., silicon chips).
There is a central processor, which can access one memory location<br>at a time. Metaphorically, the central processor is a<br>“scanner” that moves along the paper tape one<br>“cell” at a time.
The central processor can enter into finitely many machine<br>states.
The central processor can perform four elementary operations:<br>write a symbol at a memory location; erase a symbol from...