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Warren McCulloch and Walter Pitts (1943) first suggested thatsomething resembling the Turing machine might provide a good model forthe mind. In the 1960s, Turing computation became central to theemerging interdisciplinary initiative cognitive science,which studies the mind by drawing upon psychology, computer science(especially AI), linguistics, philosophy, economics (especially gametheory and behavioral economics), anthropology, and neuroscience. Thelabel classical computational theory of mind (which we willabbreviate as CCTM) is now fairly standard. According to CCTM, themind is a computational system similar in important respects to aTuring machine, and core mental processes (e.g., reasoning,decision-making, and problem solving) are computations similar inimportant respects to computations executed by a Turing machine. Theseformulations are imprecise. CCTM is best seen as a family of views,rather than a single well-definedview.[1]
Second, CCTM is not intended metaphorically. CCTM does not simplyhold that the mind is like a computing system. CCTM holdsthat the mind literally is a computing system. Of course, themost familiar artificial computing systems are made from silicon chipsor similar materials, whereas the human body is made from flesh andblood. But CCTM holds that this difference disguises a morefundamental similarity, which we can capture through a Turing-stylecomputational model. In offering such a model, we prescind fromphysical details. We attain an abstract computational description thatcould be physically implemented in diverse ways (e.g., through siliconchips, or neurons, or pulleys and levers). CCTM holds that a suitableabstract computational model offers a literally true description ofcore mental processes.
In the 1980s, connectionism emerged as a prominent rival toclassical computationalism. Connectionists draw inspiration fromneurophysiology rather than logic and computer science. They employcomputational models, neural networks, that differsignificantly from Turing-style models. A neural network is acollection of interconnected nodes. Nodes fall into threecategories: input nodes, output nodes,and hidden nodes (which mediate between input and outputnodes). Nodes have activation values, given by real numbers. One nodecan bear a weighted connection to another node, also given bya real number. Activations of input nodes are determined exogenously:these are the inputs to computation. Total input activationof a hidden or output node is a weighted sum of the activations ofnodes feeding into it. Activation of a hidden or output node is afunction of its total input activation; the particular function varieswith the network. During neural network computation, waves ofactivation propagate from input nodes to output nodes, as determinedby weighted connections between nodes.
Eliminative connectionists advance connectionism as a rival toclassical computationalism. They argue that the Turing formalism isirrelevant to psychological explanation. Often, though not always,they seek to revive the associationist tradition inpsychology, a tradition that CCTM had forcefully challenged. Often,though not always, they attack the mentalist, nativist linguisticspioneered by Noam Chomsky (1965). Often, though not always, theymanifest overt hostility to the very notion of mentalrepresentation. But the defining feature of eliminative connectionismis that it uses neural networks as replacements forTuring-style models. Eliminative connectionists view the mind as acomputing system of a radically different kind than the Turingmachine. A few authors explicitly espouse eliminative connectionism(Churchland 1989; Rumelhart and McClelland1986; Horgan and Tienson1996), and many others incline towards it.
FSC stems from innovations in mathematical logic during the late19th and early 20th centuries, especiallyseminal contributions by George Boole and Gottlob Frege. Inhis Begriffsschrift (1879/1967), Frege effected athoroughgoing formalization of deductive reasoning. Toformalize, we specify a formal language whose componentlinguistic expressions are individuated non-semantically (e.g., bytheir geometric shapes). We may have some intended interpretation inmind, but elements of the formal language are purely syntacticentities that we can discuss without invoking semantic properties suchas reference or truth-conditions. In particular, we canspecify inference rules in formal syntactic terms. If wechoose our inference rules wisely, then they will cohere with ourintended interpretation: they will carry true premises to trueconclusions. Through formalization, Frege invested logic withunprecedented rigor. He thereby laid the groundwork for numeroussubsequent mathematical and philosophical developments.
Argument from Causation: Externalists insist that widecontent can be causally relevant. The details vary among externalists,and discussion often becomes intertwined with complex issuessurrounding causation, counterfactuals, and the metaphysics of mind.See the entry mental causation foran introductory overview, and see Burge (2007), Rescorla (2014a), andYablo (1997, 2003) for representative externalist discussion.
In principle, one might embrace both externalist content-involvingcomputational description and formal syntactic description.One might say that these two kinds of description occupy distinctlevels of explanation. Peacocke suggests such a view. Othercontent-involving computationalists regard formal syntacticdescriptions of the mind more skeptically. For example, Burgequestions what explanatory value formal syntactic descriptioncontributes to certain areas of scientific psychology (such asperceptual psychology). From this viewpoint, the eliminativistchallenge posed in §5.1 has mattersbackwards. We should not assume that formal syntactic descriptions areexplanatorily valuable and then ask what value intentionaldescriptions contribute. We should instead embrace the externalistintentional descriptions offered by current cognitive science and thenask what value formal syntactic description contributes.
Computationalists respond that this objection assumes what is to beshown: that cognitive activity does not fall into explanatorysignificant discrete stages (Weiskopf 2004). Assuming that physicaltime is continuous, it follows that mental activity unfolds incontinuous time. It does not follow that cognitive modelsmust have continuous temporal structure. A personal computer operatesin continuous time, and its physical state evolves continuously. Acomplete physical theory will reflect all those physical changes. Butour computational model does not reflect every physicalchange to the computer. Our computational model has discrete temporalstructure. Why assume that a good cognitive-level model of the mindmust reflect every physical change to the brain? Even if there is acontinuum of evolving physical states, why assume a continuumof evolving cognitive states? The mere fact of continuoustemporal evolution does not militate against computational models withdiscrete temporal structure.
Embodied cognition is a research program that draws inspirationfrom the continental philosopher Maurice Merleau-Ponty, the perceptualpsychologist J.J. Gibson, and other assorted influences. It is afairly heterogeneous movement, but the basic strategy is to emphasizelinks between cognition, bodily action, and the surroundingenvironment. See Varela, Thompson, and Rosch (1991) for an influentialearly statement. In many cases, proponents deploy tools of dynamicalsystems theory. Proponents typically present their approach as aradical alternative to computationalism (Chemero 2009; Kelso 1995;Thelen and Smith 1994). CTM, they complain, treats mental activity asstatic symbol manipulation detached from the embedding environment. Itneglects myriad complex ways that the environment causally orconstitutively shapes mental activity. We should replace CTM with anew picture that emphasizes continuous links between mind, body, andenvironment. Agent-environment dynamics, not internal mentalcomputation, holds the key to understanding cognition. Often, abroadly eliminativist attitude towards intentionality propels thiscritique.
analogy and analogical reasoning | anomalous monism | causation: the metaphysics of | Chinese room argument | Church-Turing Thesis | cognitive science | computability and complexity | computation: in physical systems | computer science, philosophy of | computing: modern history of | connectionism | culture: and cognitive science | externalism about the mind | folk psychology: as mental simulation | frame problem | functionalism | Gödel, Kurt | Gödel, Kurt: incompleteness theorems | Hilbert, David: program in the foundations of mathematics | language of thought hypothesis | mental causation | mental content: causal theories of | mental content: narrow | mental content: teleological theories of | mental imagery | mental representation | mental representation: in medieval philosophy | mind/brain identity theory | models in science | multiple realizability | other minds | reasoning: automated | reasoning: defeasible | reduction, scientific | simulations in science | Turing, Alan | Turing machines | Turing test | zombies
Thus, whether taken as a specific or general statement, the arguments made by the Solicitor General and adopted by the Supreme Court, do not support the functional equivalence of the operations of digital computers in relationship to human minds.
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