1 The Rise and Fall of Computational Functionalism 1. Introduction Hilary Putnam is the father of computational functionalism, a doctrine he developed in a series of papers beginning with “Minds and machines” (1960) and culminating in “The nature of mental states” (1967b). Enormously influential ever since, it became the received view of the nature of mental states. In recent years, however, there has been growing dissatisfaction with computational functionalism. Putnam himself, having advanced powerful arguments against the very doctrine he had previously championed, is largely responsible for its demise. Today, Putnam has little patience for either computational functionalism or its underlying philosophical agenda. Echoing despair of naturalism, Putnam dismisses computational functionalism as a utopian enterprise. My aim in this article is to present both Putnam’s arguments for computational functionalism, and his later critique of the position.1 In section 2, I examine the rise of computational functionalism. In section 3, I offer an account of its demise, arguing that it can be attributed to recognition of the gap between the computational-functional aspects of mentality, and its intentional character. This recognition can be traced to two of Putnam’s results: the familiar Twin-Earth argument, and the less familiar theorem that every ordinary physical system implements every finite automaton. I close with implications for cognitive science. 2. The rise of computational functionalism Computational functionalism is the view that mental states and events – pains, beliefs, desires, thoughts and so forth – are computational states of the brain, and so are defined in terms of “computational parameters plus relations to biologically characterized inputs and outputs” (1988: 7). The nature of the mind is independent of the physical making of 2 the brain: “we could be made of Swiss cheese and it wouldn’t matter” (1975b: 291).2 What matters is our functional organization: the way in which mental states are causally related to each other, to sensory inputs, and to motor outputs. Stones, trees, carburetors and kidneys do not have minds, not because they are not made out of the right material, but because they do not have the right kind of functional organization. Their functional organization does not appear to be sufficiently complex to render them minds. Yet there could be other thinking creatures, perhaps even made of Swiss cheese, with the appropriate functional organization. The theory of computational functionalism was an immediate success, though several key elements of it were not worked out until much later. For one thing, computational functionalism presented an attractive alternative to the two dominant theories of the time: classical materialism and behaviorism. Classical materialism – the hypothesis that mental states are brain states – was revived in the 1950s by Place (1956), Smart (1959) and Feigl (1958). Behaviorism – the hypothesis that mental states are behavior-dispositions – was advanced, in different forms, by Carnap (1932/33), Hempel (1949) and Ryle (1949), and was inspired by the dominance of the behaviorist approach in psychology at the time. Both doctrines, however, were plagued by difficulties that did not, or so it seemed, beset computational functionalism. Indeed, Putnam’s main argument for functionalism is that it is a more reasonable hypothesis than classical materialism and behaviorism. The rise of computational functionalism can be also explained by the “cognitive revolution” of the mid-1950s. Noam Chomsky’s devastating review of Skinner’s Verbal Behavior, and the development of experimental instruments in psychological research, led to the replacement of the behaviorist approach in psychology by the cognitivist. In addition, Chomsky’s novel mentalistic theory of language (Chomsky 1957), which revolutionized the field of linguistics, and the emerging research in the area of artificial intelligence, together produced a new science of the mind, now known as cognitive science. The working hypothesis in this science has been that the mechanisms underlying our cognitive capacities are species of information processing, namely, computations that operate on mental representations. Computational functionalism was inspired by these 3 dramatic developments. Putnam, and even more so Jerry Fodor (1968, 1975) thought of mental states in terms of the computational theories of cognitive science. Many even see computational functionalism as furnishing the requisite conceptual foundations for cognitive science. Given its close relationship with the new science of the mental, it is not surprising computational functionalism was so eagerly embraced. Putnam develops computational functionalism in two phases. In the earlier papers, Putnam (1960, 1964) does not put forward a theory about the nature of mental states. Rather, he uses an analogy between minds and machines to show that “the various issues and puzzles that make up the traditional mind-body problem are wholly linguistic and logical in character… all the issues arise in connection with any computing system capable of answering questions about its own structure” (1960: 362). Only in 1967 does Putnam make the additional move of identifying mental states with functional states, suggesting that “to know for certain that a human being has a particular belief, or preference, or whatever, involves knowing something about the functional organization of the human being” (1967a: 424). In “The nature of mental states”, Putnam explicitly proposes “the hypothesis that pain, or the state of being in pain, is a functional state of a whole organism” (1967b: 433). 2.1 The analogy between minds and machines Putnam advances the analogy between minds and machines because he thinks that the case of machines and robots “will carry with it clarity with respect to the ‘central area’ of talk about feelings, thoughts, consciousness, life, etc.” (1964: 387). According to Putnam, this does not mean that the issues associated with the mind-body problem arise for machines. At this stage Putnam does not propose a theory of the mind. His claim is just that it is possible to clarify issues pertaining to the mind in terms of a machine analogue, “and that all of the question of ‘mind-body identity’ can be mirrored in terms of the analogue” (1960: 362). The type of machine used for the analogy is the Turing machine, still the paradigm example of a computing machine. 4 A Turing machine is an abstract device consisting of a finite program, a read- write head, and a memory tape (figure 1). The memory tape is finite, though indefinitely extendable, and divided into cells, each of which contains exactly one (token) symbol from a finite alphabet (an empty cell is represented by the symbol B). The tape’s initial configuration is described as the ‘input’; the final configuration as the ‘output’. The read- write mechanism is always located above one of the cells. It can scan the symbol printed in the cell, erase it, or replace it with another. The program consists of a finite number of states, e.g., A, B, C, D, in figure 1. It can be presented as a machine table, quadruples, or, as in our case, a flow chart. The computation, which mediates an input and an output, proceeds stepwise. At each step, the read-write mechanism scans the symbol from the cell above which it is located, and the machine then performs one or more of the following simple operations: (1) erasing the scanned symbol, replacing it with another symbol, or moving the read- write mechanism to the cell immediately to the right or left of the cell just scanned; (2) changing the state of the machine program; (3) halting. The operations the machine performs at each step are uniquely determined by the scanned symbols and the program’s instructions. If, in our example, the scanned symbol is ‘1’ and the machine is in state A, then it will follow the instruction specified for state A, e.g., 1:R, meaning that it will move the read-write mechanism to the cell immediately to the right, and will stay in state A. Overall, any Turing machine is completely described by a flow chart. The machine described by the flow chart in figure 1 is intended to compute the function of addition, e.g., ‘111+11’, where the numbers are represented in unary notation. The machine starts in state A, with the read-write mechanism above the leftmost ‘1’ of the output. The machine scans the first ‘1’ and then proceeds to arrive at the sum by replacing the ‘+’ symbol by ‘1’, and erasing the rightmost ‘1’ of the input. Thus if the input is ‘111+11’, the printed output is ‘11111’. The notion of a Turing machine immediately calls into question some of the classic arguments for the superiority of minds over machines. Take for example Descartes’ claim that no machine, even one whose parts are identical to those of human 5 body, cannot produce the variety of human behavior: “even though such machines might do some things as well as we do them, or perhaps even better, they would inevitably fail in others” (1637/1985: 140). It is true that our Turing machine is only capable of computing addition. But as Turing proved in 1936, there is also a universal Turing machine capable of computing any function that can be computed by a Turing machine. In fact, almost all the computing machines used today are such universal machines. Assuming that human behavior is governed by some finite rule, it is hard to see why a machine cannot manifest the same behavior.3 As Putnam shows, however, minds and Turing machines are not just analogous in the behavior they are capable of generating, but also in their internal composition. Take our Turing machine. One characterization of it is given in terms of the program it runs, i.e., the flow chart, which determines the order in which the states succeed each other, and what symbols are printed when.