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Deeper understanding of computation requires us to grapple with these questions. CTM has attracted numerous objections. In many cases, the objections apply only to specific versions of CTM such as classical computationalism or connectionist computationalism. Here are a few prominent objections. See also the entry the Chinese room argument for a widely discussed objection to classical computationalism advanced by John Searle A recurring worry is that CTM is trivial , because we can describe almost any physical system as executing computations.
Searle claims that a wall implements any computer program, since we can discern some pattern of molecular movements in the wall that is isomorphic to the formal structure of the program. Putnam — defends a less extreme but still very strong triviality thesis along the same lines. Triviality arguments play a large role in the philosophical literature. Anti-computationalists deploy triviality arguments against computationalism, while computationalists seek to avoid triviality. Computationalists usually rebut triviality arguments by insisting that the arguments overlook constraints upon computational implementation, constraints that bar trivializing implementations.
For example, David Chalmers , a and B. Other philosophers say that a physical system must have representational properties to implement a computational model Fodor 11—12; Ladyman ; Sprevak or at least to implement a content-involving computational model Rescorla , b. The details here vary considerably, and computationalists debate amongst themselves exactly which types of computation can avoid which triviality arguments. But most computationalists agree that we can avoid any devastating triviality worries through a sufficiently robust theory of the implementation relation between computational models and physical systems. Pancomputationalism holds that every physical system implements a computational model.
This thesis is plausible, since any physical system arguably implements a sufficiently trivial computational model e. As Chalmers notes, pancomputationalism does not seem worrisome for computationalism. What would be worrisome is the much stronger triviality thesis that almost every physical system implements almost every computational model. For further discussion of triviality arguments and computational implementation, see Sprevak and the entry computation in physical systems. Lucas develops this position into a famous critique of CCTM. Various philosophers and logicians have answered the critique, arguing that existing formulations suffer from fallacies, question-begging assumptions, and even outright mathematical errors Bowie ; Chalmers b; Feferman ; Lewis , ; Putnam —, ; Shapiro There is a wide consensus that this criticism of CCTM lacks any force.
Could a computer compose the Eroica symphony? Or discover general relativity? Intuitive, creative, or skillful human activity may seem to resist formalization by a computer program Dreyfus , More generally, one might worry that crucial aspects of human cognition elude computational modeling, especially classical computational modeling. Ironically, Fodor promulgates a forceful version of this critique.
The pessimism becomes more pronounced in his later writings , , which focus especially on abductive reasoning as a mental phenomenon that potentially eludes computational modeling. His core argument may be summarized as follows:. Some concede step 3 but dispute step 4 , insisting that we have promising non-Turing-style models of the relevant mental processes Pinker Partly spurred by such criticisms, Fodor elaborates his argument in considerable detail.
To defend 4 , he critiques various theories that handle abduction through non-Turing-style models 46—53; , such as connectionist networks. The scope and limits of computational modeling remain controversial. We may expect this topic to remain an active focus of inquiry, pursued jointly with AI. Mental activity unfolds in time. Moreover, the mind accomplishes sophisticated tasks e. Many critics worry that computationalism, especially classical computationalism, does not adequately accommodate temporal aspects of cognition. A Turing-style model makes no explicit mention of the time scale over which computation occurs.
One could physically implement the same abstract Turing machine with a silicon-based device, or a slower vacuum-tube device, or an even slower pulley-and-lever device. Critics recommend that we reject CCTM in favor of some alternative framework that more directly incorporates temporal considerations. Eliasmith , 12—13 uses it to support his Neural Engineering Framework. Computationalists respond that we can supplement an abstract computational model with temporal considerations Piccinini ; Weiskopf But we can supplement our model by describing how long each stage lasts, thereby converting our non-temporal Turing machine model into a theory that yields detailed temporal predictions. Many advocates of CTM employ supplementation along these lines to study temporal properties of cognition Newell Similar supplementation figures prominently in computer science, whose practitioners are quite concerned to build machines with appropriate temporal properties.
Computationalists conclude that a suitably supplemented version of CTM can adequately capture how cognition unfolds in time. A second temporal objection highlights the contrast between discrete and continuous temporal evolution van Gelder and Port Computation by a Turing machine unfolds in discrete stages, while mental activity unfolds in a continuous time. Thus, there is a fundamental mismatch between the temporal properties of Turing-style computation and those of actual mental activity. We need a psychological theory that describes continuous temporal evolution. Computationalists respond that this objection assumes what is to be shown: that cognitive activity does not fall into explanatory significant discrete stages Weiskopf Assuming that physical time is continuous, it follows that mental activity unfolds in continuous time.
It does not follow that cognitive models must have continuous temporal structure. A personal computer operates in continuous time, and its physical state evolves continuously. A complete physical theory will reflect all those physical changes. But our computational model does not reflect every physical change to the computer. Our computational model has discrete temporal structure. Why assume that a good cognitive-level model of the mind must reflect every physical change to the brain? Even if there is a continuum of evolving physical states, why assume a continuum of evolving cognitive states? The mere fact of continuous temporal evolution does not militate against computational models with discrete temporal structure.
Embodied cognition is a research program that draws inspiration from the continental philosopher Maurice Merleau-Ponty, the perceptual psychologist J. Gibson, and other assorted influences. It is a fairly heterogeneous movement, but the basic strategy is to emphasize links between cognition, bodily action, and the surrounding environment. See Varela, Thompson, and Rosch for an influential early statement. In many cases, proponents deploy tools of dynamical systems theory. Proponents typically present their approach as a radical alternative to computationalism Chemero ; Kelso ; Thelen and Smith CTM, they complain, treats mental activity as static symbol manipulation detached from the embedding environment.
It neglects myriad complex ways that the environment causally or constitutively shapes mental activity. We should replace CTM with a new picture that emphasizes continuous links between mind, body, and environment. Agent-environment dynamics, not internal mental computation, holds the key to understanding cognition. Often, a broadly eliminativist attitude towards intentionality propels this critique. Computational models can take into account how mind, body, and environment continuously interact. After all, computational models can incorporate sensory inputs and motor outputs. There is no obvious reason why an emphasis upon agent-environment dynamics precludes a dual emphasis upon internal mental computation Clark —; Rupert Computationalists maintain that CTM can incorporate any legitimate insights offered by the embodied cognition movement.
They also insist that CTM remains our best overall framework for explaining numerous core psychological phenomena. Turing machines 2. Artificial intelligence 3. The classical computational theory of mind 3. Neural networks 4. Computation and representation 5. Alternative conceptions of computation 6. Arguments against computationalism 7. Turing machines The intuitive notions of computation and algorithm are central to mathematics. More literally, the memory locations might be physically realized in various media e. There is a central processor, which can access one memory location at a time.
The central processor can enter into finitely many machine states. A machine table dictates which elementary operation the central processor performs, given its current machine state and the symbol it is currently accessing. Thus, the machine table enshrines a finite set of routine mechanical instructions governing computation. Artificial intelligence Rapid progress in computer science prompted many, including Turing, to contemplate whether we could build a computer capable of thought. The classical computational theory of mind Warren McCulloch and Walter Pitts first suggested that something resembling the Turing machine might provide a good model for the mind. The formalism seems too restrictive in several ways: Turing machines execute pure symbolic computation.
The inputs and outputs are symbols inscribed in memory locations. In contrast, the mind receives sensory input e. A complete theory must describe how mental computation interfaces with sensory inputs and motor outputs. A Turing machine has infinite discrete memory capacity. Ordinary biological systems have finite memory capacity. A plausible psychological model must replace the infinite memory store with a large but finite memory store Modern computers have random access memory : addressable memory locations that the central processor can directly access.
Turing machine memory is not addressable. The central processor can access a location only by sequentially accessing intermediate locations. Computation without addressable memory is hopelessly inefficient. For that reason, C. Gallistel and Adam King argue that addressable memory gives a better model of the mind than non-addressable memory. A Turing machine has a central processor that operates serially , executing one instruction at a time.
Other computational formalisms relax this assumption, allowing multiple processing units that operate in parallel. Classical computationalists can allow parallel computations Fodor and Pylyshyn ; Gallistel and King See Gandy and Sieg for general mathematical treatments that encompass both serial and parallel computation. Turing computation is deterministic : total computational state determines subsequent computational state. One might instead allow stochastic computations. In a stochastic model, current state does not dictate a unique next state.
Rather, there is a certain probability that the machine will transition from one state to another. Functionalism offers notable advantages over logical behaviorism and type-identity theory: Behaviorists want to associate each mental state with a characteristic pattern of behavior—a hopeless task, because individual mental states do not usually have characteristic behavioral effects. Behavior almost always results from distinct mental states operating together e. Functionalism avoids this difficulty by individuating mental states through characteristic relations not only to sensory input and behavior but also to one another.
Type-identity theorists want to associate each mental state with a characteristic physical or neurophysiological state. Putnam casts this project into doubt by arguing that mental states are multiply realizable : the same mental state can be realized by diverse physical systems, including not only terrestrial creatures but also hypothetical creatures e. Functionalism is tailor-made to accommodate multiple realizability. According to functionalism, what matters for mentality is a pattern of organization, which could be physically realized in many different ways. See the entry multiple realizability for further discussion of this argument. A prime virtue of RTM is how readily it accommodates productivity and systematicity: Productivity : RTM postulates a finite set of primitive Mentalese expressions, combinable into a potential infinity of complex Mentalese expressions.
A complete model will: describe the Mentalese symbols manipulated by the process; isolate elementary operations that manipulate the symbols e. Neural networks In the s, connectionism emerged as a prominent rival to classical computationalism. Yet classical computation and neural network computation are not mutually exclusive: One can implement a neural network in a classical model. Indeed, every neural network ever physically constructed has been implemented on a digital computer.
One can implement a classical model in a neural network. Modern digital computers implement Turing-style computation in networks of logic gates. Alternatively, one can implement Turing-style computation using an analog recurrent neural network whose nodes take continuous activation values Graves, Wayne, and Danihelka , Other Internet Resources; Siegelmann and Sontag ; Siegelmann and Sontag A few examples: Real neurons are much more heterogeneous than the interchangeable nodes that figure in typical connectionist networks.
Real neurons emit discrete spikes action potentials as outputs. But the nodes that figure in many prominent neural networks, including the best known deep neural networks, instead have continuous outputs. The backpropagation algorithm requires that weights between nodes can vary between excitatory and inhibitory, yet actual synapses cannot so vary Crick and Asanuma Moreover, the algorithm assumes target outputs supplied exogenously by modelers who know the desired answer.
In that sense, learning is supervised. Very little learning in actual biological systems involves anything resembling supervised training. Although Gallistel and King do not carefully distinguish between eliminativist and implementationist connectionism, we may summarize their argument as follows: Eliminativist connectionism cannot explain how organisms combine stored memories e. There are a virtual infinity of possible combinations that might be useful, with no predicting in advance which pieces of information must be combined in future computations.
However, the mechanisms that connectionists usually propose for implementing memory are not plausible. Existing proposals are mainly variants upon a single idea: a recurrent neural network that allows reverberating activity to travel around a loop Elman There are many reasons why the reverberatory loop model is hopeless as a theory of long-term memory. For example, noise in the nervous system ensures that signals would rapidly degrade in a few minutes. There are several differences between connectionism and computational neuroscience: Neural networks employed by computational neuroscientists are much more biologically realistic than those employed by connectionists.
The computational neuroscience literature is filled with talk about firing rates, action potentials, tuning curves, etc. These notions play at best a limited role in connectionist research, such as most of the research canvassed in Rogers and McClelland Computational neuroscience is driven in large measure by knowledge about the brain, and it assigns huge importance to neurophysiological data e. Connectionists place much less emphasis upon such data. Their research is primarily driven by behavioral data although more recent connectionist writings cite neurophysiological data with somewhat greater frequency.
Computational neuroscientists usually regard individual nodes in neural networks as idealized descriptions of actual neurons. Connectionists usually instead regard nodes as neuron-like processing units Rogers and McClelland while remaining neutral about how exactly these units map onto actual neurophysiological entities. To illustrate: Beliefs are the sorts of things that can be true or false.
Perceptual states are the sorts of things that can be accurate or inaccurate. My perceptual experience as of a red sphere is accurate only if a red sphere is before me. Desires are the sorts of things that can fulfilled or thwarted. My desire to eat chocolate is fulfilled if I eat chocolate, thwarted if I do not eat chocolate. Content externalism raises serious questions about the explanatory utility of representational content for scientific psychology: Argument from Causation Fodor , : How can mental content exert any causal influence except as manifested within internal neurophysiology? Externalists also question internalist arguments that scientific psychology requires narrow content: Argument from Causation : Externalists insist that wide content can be causally relevant.
Alternative conceptions of computation The literature offers several alternative conceptions, usually advanced as foundations for CTM. We may instructively compare structuralist computationalism with some other theories discussed above: Machine functionalism. Arguments against computationalism CTM has attracted numerous objections. Bibliography Aitchison, L.
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