In the brain, there are only electrochemical processes and these seem not to have anything to do with Herodotus. Philosophy of perception is concerned with the nature of perceptual experience and the status of perceptual objects, in particular how perceptual experience relates to appearances and beliefs about the world.
The main contemporary views within philosophy of perception include naive realism , enactivism and representational views. Humans are corporeal beings and, as such, they are subject to examination and description by the natural sciences. Since mental processes are intimately related to bodily processes, the descriptions that the natural sciences furnish of human beings play an important role in the philosophy of mind. The list of such sciences includes: biology , computer science , cognitive science , cybernetics , linguistics , medicine , pharmacology , and psychology.
The theoretical background of biology, as is the case with modern natural sciences in general, is fundamentally materialistic.
Graham Oppy, N. N. Trakakis
The objects of study are, in the first place, physical processes, which are considered to be the foundations of mental activity and behavior. Within the field of neurobiology, there are many subdisciplines that are concerned with the relations between mental and physical states and processes:  Sensory neurophysiology investigates the relation between the processes of perception and stimulation. The methodological breakthroughs of the neurosciences, in particular the introduction of high-tech neuroimaging procedures, has propelled scientists toward the elaboration of increasingly ambitious research programs: one of the main goals is to describe and comprehend the neural processes which correspond to mental functions see: neural correlate.
Computer science concerns itself with the automatic processing of information or at least with physical systems of symbols to which information is assigned by means of such things as computers. A simple example is multiplication. It is not clear whether computers could be said to have a mind.
Could they, someday, come to have what we call a mind? This question has been propelled into the forefront of much philosophical debate because of investigations in the field of artificial intelligence AI. Within AI, it is common to distinguish between a modest research program and a more ambitious one: this distinction was coined by John Searle in terms of a weak AI and strong AI. The exclusive objective of "weak AI", according to Searle, is the successful simulation of mental states, with no attempt to make computers become conscious or aware, etc.
The objective of strong AI, on the contrary, is a computer with consciousness similar to that of human beings. As an answer to the question "Can computers think? Essentially, Turing's view of machine intelligence followed the behaviourist model of the mind—intelligence is as intelligence does.
The Turing test has received many criticisms, among which the most famous is probably the Chinese room thought experiment formulated by Searle. The question about the possible sensitivity qualia of computers or robots still remains open. Some computer scientists believe that the specialty of AI can still make new contributions to the resolution of the "mind—body problem". They suggest that based on the reciprocal influences between software and hardware that takes place in all computers, it is possible that someday theories can be discovered that help us to understand the reciprocal influences between the human mind and the brain wetware.
Psychology is the science that investigates mental states directly. It uses generally empirical methods to investigate concrete mental states like joy , fear or obsessions. Psychology investigates the laws that bind these mental states to each other or with inputs and outputs to the human organism.
An example of this is the psychology of perception. Scientists working in this field have discovered general principles of the perception of forms. A law of the psychology of forms says that objects that move in the same direction are perceived as related to each other. However, it does not suggest anything about the nature of perceptual states.
The laws discovered by psychology are compatible with all the answers to the mind—body problem already described. Cognitive science is the interdisciplinary scientific study of the mind and its processes. It examines what cognition is, what it does, and how it works. It includes research on intelligence and behavior, especially focusing on how information is represented, processed, and transformed in faculties such as perception, language, memory, reasoning, and emotion within nervous systems human or other animal and machines e.
Cognitive science consists of multiple research disciplines, including psychology , artificial intelligence , philosophy , neuroscience , linguistics , anthropology , sociology , and education. Rowlands argues that cognition is enactive, embodied, embedded, affective and potentially extended. The position is taken that the "classical sandwich" of cognition sandwiched between perception and action is artificial; cognition has to be seen as a product of a strongly coupled interaction that cannot be divided this way.
Most of the discussion in this article has focused on one style or tradition of philosophy in modern Western culture, usually called analytic philosophy sometimes described as Anglo-American philosophy. With reference specifically to the discussion of the mind, this tends to translate into attempts to grasp the concepts of thought and perceptual experience in some sense that does not merely involve the analysis of linguistic forms.
Immanuel Kant's Critique of Pure Reason , first published in and presented again with major revisions in , represents a significant intervention into what will later become known as the philosophy of mind. Kant's first critique is generally recognized as among the most significant works of modern philosophy in the West. Kant's work develops an in-depth study of transcendental consciousness, or the life of the mind as conceived through universal categories of consciousness.
See also Hegel's The Phenomenology of Spirit. Nonetheless, Hegel's work differs radically from the style of Anglo-American philosophy of mind. In , Henri Bergson made in Matter and Memory "Essay on the relation of body and spirit" a forceful case for the ontological difference of body and mind by reducing the problem to the more definite one of memory, thus allowing for a solution built on the empirical test case of aphasia.
In modern times, the two main schools that have developed in response or opposition to this Hegelian tradition are phenomenology and existentialism. Phenomenology, founded by Edmund Husserl , focuses on the contents of the human mind see noema and how processes shape our experiences. Existential-phenomenology represents a major branch of continental philosophy they are not contradictory , rooted in the work of Husserl but expressed in its fullest forms in the work of Martin Heidegger , Jean-Paul Sartre , Simone de Beauvoir and Maurice Merleau-Ponty.
There are countless subjects that are affected by the ideas developed in the philosophy of mind. Clear examples of this are the nature of death and its definitive character, the nature of emotion , of perception and of memory. Questions about what a person is and what his or her identity consists of also have much to do with the philosophy of mind.
There are two subjects that, in connection with the philosophy of the mind, have aroused special attention: free will and the self. In the context of philosophy of mind, the problem of free will takes on renewed intensity. This is certainly the case, at least, for materialistic determinists. Mental states, and therefore the will as well, would be material states, which means human behavior and decisions would be completely determined by natural laws.
Some take this reasoning a step further: people cannot determine by themselves what they want and what they do. Consequently, they are not free. This argumentation is rejected, on the one hand, by the compatibilists. Those who adopt this position suggest that the question "Are we free? The opposite of "free" is not "caused" but "compelled" or "coerced". It is not appropriate to identify freedom with indetermination. A free act is one where the agent could have done otherwise if it had chosen otherwise. In this sense a person can be free even though determinism is true.
On the other hand, there are also many incompatibilists who reject the argument because they believe that the will is free in a stronger sense called libertarianism. Critics of the second proposition b accuse the incompatibilists of using an incoherent concept of freedom. They argue as follows: if our will is not determined by anything, then we desire what we desire by pure chance. And if what we desire is purely accidental, we are not free. So if our will is not determined by anything, we are not free. The philosophy of mind also has important consequences for the concept of "self". If by "self" or "I" one refers to an essential, immutable nucleus of the person , some modern philosophers of mind, such as Daniel Dennett believe that no such thing exists.
According to Dennett and other contemporaries, the self is considered an illusion. Such an idea is unacceptable to modern philosophers with physicalist orientations and their general skepticism of the concept of "self" as postulated by David Hume , who could never catch himself not doing, thinking or feeling anything. From Wikipedia, the free encyclopedia. Branch of philosophy concerned with the nature of the mind. Main article: Mind—body problem. See also: Mind in eastern philosophy. Main article: Behaviorism. Main article: Type physicalism. Main article: Functionalism philosophy of mind.
Main article: Physicalism. Main article: Emergentism. Main article: Eliminative materialism. Main article: New mysterianism. Main article: Qualia. Main article: Intentionality. Main article: Philosophy of perception. Main article: Neuroscience. Main article: Computer science. Main article: Psychology. Main article: Free will. Main article: Philosophy of self. Animal consciousness Artificial consciousness Collective intentionality Outline of human intelligence Outline of thought Theory of mind in animals.
Honderich, Ted ed. Problems in the Philosophy of Mind. Oxford Companion to Philosophy. Oxford: Oxford University Press. New York: Oxford University Press. Discourse on Method and Meditations on First Philosophy. Hacket Publishing Company. Journal of Philosophical Research. Gibb; E. Lowe; R. Ingthorsson 21 March Mental Causation and Ontology. OUP Oxford. European Journal of Analytic Philosophy. The Internet Encyclopedia of Philosophy. Archived from the original on Retrieved See also Dempsey, L. See also Baltimore, J.
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Cambridge: MIT Press. It: Come Funziona la Mente. Milan:Mondadori, Neuroscience: Exploring The Brain. Baltimore, Maryland, Williams and Wilkins. J Prentice Hall. Introduction to the Theory of Computation. Boston, Mass. The Behavioral and Brain Sciences. Artificial Intelligence:A Modern Approach. New Jersey: Prentice Hall, Inc. The new science of the mind: From extended mind to embodied phenomenology. Cognition as enacted, embodied, embedded, affective and extended".
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Mazdakism Zoroastrianism Zurvanism. Nevertheless, most connectionists endorse a generalized formality thesis : computation is insensitive to semantic properties. The generalized formality thesis raises many of the same philosophical issues raised by FSC. We focus here on FSC, which has received the most philosophical discussion. Strikingly, mental activity tracks semantic properties in a coherent way. For example, deductive inference carries premises to conclusions that are true if the premises are true. How can we explain this crucial aspect of mental activity?
Formalization shows that syntactic manipulations can track semantic properties, and computer science shows how to build physical machines that execute desired syntactic manipulations. If we treat the mind as a syntax-driven machine, then we can explain why mental activity tracks semantic properties in a coherent way. Moreover, our explanation does not posit causal mechanisms radically different from those posited within the physical sciences. We thereby answer the pivotal question: How is rationality mechanically possible? They recommend that cognitive science model the mind in formal syntactic terms, eschewing intentionality altogether.
They grant that mental states have representational properties, but they ask what explanatory value scientific psychology gains by invoking those properties. Why supplement formal syntactic description with intentional description? Cognitive science should proceed along the lines suggested by Stich and Field, delineating purely formal syntactic computational models.
Interpretive practice is governed by holistic and heuristic constraints, which stymie attempts at converting intentional discourse into rigorous science. For Putnam, as for Field and Stich, the scientific action occurs at the formal syntactic level rather than the intentional level.
One criticism targets the causal relevance of representational content Block ; Figdor ; Kazez Intuitively speaking, the contents of mental states are causally relevant to mental activity and behavior. For example, my desire to drink water rather than orange juice causes me to walk to the sink rather than the refrigerator. The content of my desire that I drink water seems to play an important causal role in shaping my behavior. Formal syntactic activity implements intentional mental activity, thereby ensuring that intentional mental states causally interact in accord with their contents.
However, it is not so clear that this analysis secures the causal relevance of content. Here is an analogy to illustrate the worry. Nevertheless, shadow position at one time does not influence shadow position at a later time. If the mind is a syntax-driven machine, then causal efficacy seems to reside at the syntactic rather the semantic level.
The conclusion may not trouble eliminativists, but intentional realists usually want to avoid it. A second criticism dismisses the formal-syntactic picture as speculation ungrounded in scientific practice. Tyler Burge a,b, — contends that formal syntactic description of mental activity plays no significant role within large areas of cognitive science, including the study of theoretical reasoning, practical reasoning, and perception.
In each case, Burge argues, the science employs intentional description rather than formal syntactic description. For example, perceptual psychology individuates perceptual states not through formal syntactic properties but through representational relations to distal shapes, sizes, colors, and so on.
To understand this criticism, we must distinguish formal syntactic description and neurophysiological description. Everyone agrees that a complete scientific psychology will assign prime importance to neurophysiological description. However, neurophysiological description is distinct from formal syntactic description, because formal syntactic description is supposed to be multiply realizable in the neurophysiological. The issue here is whether scientific psychology should supplement intentional descriptions and neurophysiological descriptions with multiply realizable, non-intentional formal syntactic descriptions.
Burge extends this conclusion from linguistic reference to mental content. He argues that Twin Earthlings instantiate mental states with different contents. For example, if Oscar on Earth thinks that water is thirst-quenching , then his duplicate on Twin Earth thinks a thought with a different content, which we might gloss as that twater is thirst-quenching.
Burge concludes that mental content does not supervene upon internal neurophysiology. This position is externalism about mental content. Formal syntactic properties of mental states are widely taken to supervene upon internal neurophysiology. For example, Oscar and Twin Oscar instantiate the same formal syntactic manipulations. Assuming content externalism, it follows that there is a huge gulf between ordinary intentional description and formal syntactic description. 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? Differences in the physical environment impact behavior only by inducing differences in local brain states. So the only causally relevant factors are those that supervene upon internal neurophysiology. Externally individuated content is causally irrelevant. Folk psychology may taxonomize mental states through relations to the external environment, but scientific psychology should taxonomize mental states entirely through factors that supervene upon internal neurophysiology.
It should treat Oscar and Twin Oscar as psychological duplicates. Some authors pursue the two arguments in conjunction with one another. Both arguments reach the same conclusion: externally individuated mental content finds no legitimate place within causal explanations provided by scientific psychology. Stich argues along these lines to motivate his formal-syntactic eliminativism. Many philosophers respond to such worries by promoting content internalism.
Whereas content externalists favor wide content content that does not supervene upon internal neurophysiology , content internalists favor narrow content content that does so supervene. Narrow content is what remains of mental content when one factors out all external elements. While conceding that wide content should not figure in scientific psychology, he maintained that narrow content should play a central explanatory role. Radical internalists insist that all content is narrow.
A typical analysis holds that Oscar is thinking not about water but about some more general category of substance that subsumes XYZ, so that Oscar and Twin Oscar entertain mental states with the same contents. Tim Crane and Gabriel Segal endorse such an analysis. They hold that folk psychology always individuates propositional attitudes narrowly.
A less radical internalism recommends that we recognize narrow content in addition to wide content. Folk psychology may sometimes individuate propositional attitudes widely, but we can also delineate a viable notion of narrow content that advances important philosophical or scientific goals. Internalists have proposed various candidate notions of narrow content Block ; Chalmers ; Cummins ; Fodor ; Lewis ; Loar ; Mendola See the entry narrow mental content for an overview of prominent candidates. Externalists complain that existing theories of narrow content are sketchy, implausible, useless for psychological explanation, or otherwise objectionable Burge ; Sawyer ; Stalnaker Externalists also question internalist arguments that scientific psychology requires narrow content:.
Argument from Causation : Externalists insist that wide content can be causally relevant. The details vary among externalists, and discussion often becomes intertwined with complex issues surrounding causation, counterfactuals, and the metaphysics of mind. See the entry mental causation for an introductory overview, and see Burge , Rescorla a , and Yablo , for representative externalist discussion. Argument from Explanation : Externalists claim that psychological explanation can legitimately taxonomize mental states through factors that outstrip internal neurophysiology Peacocke Burge observes that non-psychological sciences often individuate explanatory kinds relationally , i.
For example, whether an entity counts as a heart depends roughly upon whether its biological function in its normal environment is to pump blood. So physiology individuates organ kinds relationally. For a notable exchange on these issues, see Burge , , and Fodor , Externalists doubt that we have any good reason to replace or supplement wide content with narrow content. They dismiss the search for narrow content as a wild goose chase. Burge , a defends externalism by analyzing current cognitive science. He argues that many branches of scientific psychology especially perceptual psychology individuate mental content through causal relations to the external environment.
He concludes that scientific practice embodies an externalist perspective. By contrast, he maintains, narrow content is a philosophical fantasy ungrounded in current science. Suppose we abandon the search for narrow content. The most promising option emphasizes levels of explanation. We can say that intentional psychology occupies one level of explanation, while formal-syntactic computational psychology occupies a different level.
Fodor advocates this approach in his later work , He comes to reject narrow content as otiose. He suggests that formal syntactic mechanisms implement externalist psychological laws. Mental computation manipulates Mentalese expressions in accord with their formal syntactic properties, and these formal syntactic manipulations ensure that mental activity instantiates appropriate law-like patterns defined over wide contents. Internalists can respond that suitable formal syntactic manipulations determine and maybe even constitute narrow contents, so that internalist intentional description is already implicit in suitable formal syntactic description cf.
Field Perhaps this response vindicates intentional realism, perhaps not. Crucially, though, no such response is available to content externalists. Externalist intentional description is not implicit in formal syntactic description, because one can hold formal syntax fixed while varying wide content.
Once we accept that mental computation is sensitive to syntax but not semantics, it is far from clear that any useful explanatory work remains for wide content. Fodor addresses this challenge at various points, offering his most systematic treatment in The Elm and the Expert See also Dretske , which pursues an alternative strategy for vindicating the explanatory relevance of wide content. The perceived gulf between computational description and intentional description animates many writings on CTM.
A few philosophers try to bridge the gulf using computational descriptions that individuate computational states in representational terms. On the content-involving approach, there is no rigid demarcation between computational and intentional description. In particular, certain scientifically valuable descriptions of mental activity are both computational and intentional. Call this position content-involving computationalism. Content-involving computationalists need not say that all computational description is intentional.
To illustrate, suppose we describe a simple Turing machine that manipulates symbols individuated by their geometric shapes. Then the resulting computational description is not plausibly content-involving. Accordingly, content-involving computationalists do not usually advance content-involving computation as a general theory of computation. They claim only that some important computational descriptions are content-involving. One can develop content-involving computationalism in an internalist or externalist direction.
Internalist content-involving computationalists hold that some computational descriptions identify mental states partly through their narrow contents. Murat Aydede recommends a position along these lines. Externalist content-involving computationalism holds that certain computational descriptions identify mental states partly through their wide contents.
Oron Shagrir advocates a content-involving computationalism that is neutral between internalism and externalism.
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Externalist content-involving computationalists typically cite cognitive science practice as a motivating factor. Quite plausibly, representational relations to specific distal sizes and depths do not supervene on internal neurophysiology. Quite plausibly, then, perceptual psychology type-identifies perceptual computations through wide contents.
So externalist content-involving computationalism seems to harmonize well with current cognitive science. A major challenge facing content-involving computationalism concerns the interface with standard computationalism formalisms, such as the Turing machine. How exactly do content-involving descriptions relate to the computational models found in logic and computer science?
Philosophers usually assume that these models offer non-intentional descriptions. If so, that would be a major and perhaps decisive blow to content-involving computationalism. Arguably, though, many familiar computational formalisms allow a content-involving rather than formal syntactic construal. To illustrate, consider the Turing machine. Arguably, the formalism allows us to individuate symbols partly through their contents.
Of course, the machine table for a Turing machine does not explicitly cite semantic properties of symbols e. Nevertheless, the machine table can encode mechanical rules that describe how to manipulate symbols, where those symbols are type-identified in content-involving terms. In this way, the machine table dictates transitions among content-involving states without explicitly mentioning semantic properties.
Aydede suggests an internalist version of this view, with symbols type-identified through their narrow contents. He argues that some Turing-style models describe computational operations over externalistically individuated Mentalese symbols. In principle, one might embrace both externalist content-involving computational description and formal syntactic description.
One might say that these two kinds of description occupy distinct levels of explanation. Peacocke suggests such a view. Other content-involving computationalists regard formal syntactic descriptions of the mind more skeptically. For example, Burge questions what explanatory value formal syntactic description contributes to certain areas of scientific psychology such as perceptual psychology. We should not assume that formal syntactic descriptions are explanatorily valuable and then ask what value intentional descriptions contribute.
We should instead embrace the externalist intentional descriptions offered by current cognitive science and then ask what value formal syntactic description contributes. Proponents of formal syntactic description respond by citing implementation mechanisms. Externalist description of mental activity presupposes that suitable causal-historical relations between the mind and the external physical environment are in place.
Fodor , argues in this way to motivate the formal syntactic picture. For possible externalist responses to the argument from implementation mechanisms, see Burge b , Shea , and Sprevak Debate over this argument, and more generally over the relation between computation and representation, seems likely to continue into the indefinite future.
The literature offers several alternative conceptions, usually advanced as foundations for CTM. In many cases, these conceptions overlap with one another or with the conceptions considered above. Lacking clarification, the description is little more than an empty slogan. The intuitive idea is that information measures reduction in uncertainty , where reduced uncertainty manifests as an altered probability distribution over possible states.
Shannon codified this idea within a rigorous mathematical framework, laying the foundation for information theory Cover and Thomas Shannon information is fundamental to modern engineering. It finds fruitful application within cognitive science, especially cognitive neuroscience. Consider an old-fashioned tape machine that records messages received over a wireless radio.
Still, the machine does not seem to implement a non-trivial computational model. Arguably, then, a system can process Shannon information without executing computations in any interesting sense. Alternatively, one might insist that the tape machine executes non-trivial computations. Piccinini and Scarantino advance a highly general notion of computation—which they dub generic computation —with that consequence. Natural meaning involves reliable, counterfactual-supporting correlations.
For example, tree rings correlate with the age of the tree, and pox correlate with chickenpox. We colloquially describe tree rings as carrying information about tree age, pox as carrying information about chickenpox, and so on. Such descriptions suggest a conception that ties information to reliable, counterfactual-supporting correlations. Fred Dretske develops this conception into a systematic theory, as do various subsequent philosophers.
Consider an old-fashioned bimetallic strip thermostat. Two metals are joined together into a strip. Differential expansion of the metals causes the strip to bend, thereby activating or deactivating a heating unit. Yet the thermostat does not seem to implement any non-trivial computational model. One would not ordinarily regard the thermostat as computing. Arguably, then, a system can process Dretske-style information without executing computations in any interesting sense.
Of course, one might try to handle such examples through maneuvers parallel to those from the previous paragraph. A third prominent notion of information is semantic information , i. In that sense, information-processing is necessary for computation. However, this position is debatable. Chalmers and Piccinini a contend that a Turing machine might execute computations even though symbols manipulated by the machine have no semantic interpretation.
On this view, representational content is not necessary for a physical system to count as computational. Nevertheless, the slogan seems unlikely to disappear from the literature anytime soon. For further discussion of possible connections between computation and information, see Gallistel and King 1—26 , Lizier, Flecker, and Williams , Milkowski , and Piccinini and Scarantino Frances Egan elaborates the functional conception over a series of articles , , , , , Like Marr, she treats computational description as description of input-output relations.
She also claims that computational models characterize a purely mathematical function: that is, a mapping from mathematical inputs to mathematical outputs. Visua and Twin Visua instantiate perceptual states with different representational properties. Nevertheless, Egan says, vision science treats Visua and Twin Visua as computational duplicates.
Visua and Twin Visua compute the same mathematical function, even though the computations have different representational import in the two cases. Intentional attribution is just a heuristic gloss upon underlying computational description. Chalmers argues that the functional conception neglects important features of computation. As he notes, computational models usually describe more than just input-output relations.
They describe intermediate steps through which inputs are transformed into outputs. Critics complain that Egan mistakenly elevates mathematical functions, at the expense of intentional explanations routinely offered by cognitive science Burge ; Rescorla ; Silverberg ; Sprevak We cite the number 5 to identify the depth-estimate. But our choice of this number depends upon our arbitrary choice of measurement units. Critics contend that the content of the depth-estimate, not the arbitrarily chosen number through which we theorists specify that content, is what matters for psychological explanation.
According to Egan, computational explanation should describe the visual system as computing a particular mathematical function that carries particular mathematical inputs into particular mathematical outputs. Those particular mathematical inputs and outputs depend upon our arbitrary choice of measurement units, so they arguably lack the explanatory significance that Egan assigns to them.
We should distinguish the functional approach, as pursued by Marr and Egan, from the functional programming paradigm in computer science. The functional programming paradigm models evaluation of a complex function as successive evaluation of simpler functions. It plays an important role in AI and theoretical computer science.
Some authors suggest that it offers special insight into mental computation Klein ; Piantadosi, Tenenbaum, and Goodman However, many computational formalisms do not conform to the functional paradigm: Turing machines; imperative programming languages, such as C; logic programming languages, such as Prolog; and so on.
Even though the functional paradigm describes numerous important computations possibly including mental computations , it does not plausibly capture computation in general. Many philosophical discussions embody a structuralist conception of computation : a computational model describes an abstract causal structure, without taking into account particular physical states that instantiate the structure.
Chalmers , a, , develops it in detail. He introduces the combinatorial-state automaton CSA formalism, which subsumes most familiar models of computation including Turing machines and neural networks. Computational description specifies a causal topology. Chalmers deploys structuralism to delineate a very general version of CTM. He assumes the functionalist view that psychological states are individuated by their roles in a pattern of causal organization. Psychological description specifies causal roles, abstracted away from physical states that realize those roles.
So psychological properties are organizationally invariant , in that they supervene upon causal topology. Since computational description characterizes a causal topology, satisfying a suitable computational description suffices for instantiating appropriate mental properties. It also follows that psychological description is a species of computational description, so that computational description should play a central role within psychological explanation. Thus, structuralist computation provides a solid foundation for cognitive science.
Mentality is grounded in causal patterns, which are precisely what computational models articulate. Structuralism comes packaged with an attractive account of the implementation relation between abstract computational models and physical systems. Under what conditions does a physical system implement a computational model?
A computational model describes a physical system by articulating a formal structure that mirrors some relevant causal topology. Chalmers elaborates this intuitive idea, providing detailed necessary and sufficient conditions for physical realization of CSAs. Few if any alternative conceptions of computation can provide so substantive an account of the implementation relation. We may instructively compare structuralist computationalism with some other theories discussed above:.
Machine functionalism. Structuralist computationalism embraces the core idea behind machine functionalism: mental states are functional states describable through a suitable computational formalism. Putnam advances CTM as an empirical hypothesis, and he defends functionalism on that basis.
In contrast, Chalmers follows David Lewis by grounding functionalism in the conceptual analysis of mentalistic discourse. Whereas Putnam defends functionalism by defending computationalism, Chalmers defends computationalism by assuming functionalism. Classical computationalism, connectionism, and computational neuroscience. Structuralist computationalism emphasizes organizationally invariant descriptions, which are multiply realizable.
In that respect, it diverges from computational neuroscience. Structuralism is compatible with both classical and connectionist computationalism, but it differs in spirit from those views. Classicists and connectionists present their rival positions as bold, substantive hypotheses. Chalmers advances structuralist computationalism as a relatively minimalist position unlikely to be disconfirmed. Intentional realism and eliminativism. Structuralist computationalism is compatible with both positions. CSA description does not explicitly mention semantic properties such as reference, truth-conditions, representational content, and so on.
Structuralist computationalists need not assign representational content any important role within scientific psychology. On the other hand, structuralist computationalism does not preclude an important role for representational content. The formal-syntactic conception of computation. Wide content depends on causal-historical relations to the external environment, relations that outstrip causal topology.
Thus, CSA description leaves wide content underdetermined. Narrow content presumably supervenes upon causal topology, but CSA description does not explicitly mention narrow contents. Overall, then, structuralist computationalism prioritizes a level of formal, non-semantic computational description. In that respect, it resembles FSC. For example, Rescorla denies that causal topology plays the central explanatory role within cognitive science that structuralist computationalism dictates.
He suggests that externalist intentional description rather than organizationally invariant description enjoys explanatory primacy. Coming from a different direction, computational neuroscientists will recommend that we forego organizationally invariant descriptions and instead employ more neurally specific computational models. In response to such objections, Chalmers argues that organizationally invariant computational description yields explanatory benefits that neither intentional description nor neurophysiological description replicate: it reveals the underlying mechanisms of cognition unlike intentional description ; and it abstracts away from neural implementation details that are irrelevant for many explanatory purposes.
The mechanistic nature of computation is a recurring theme in logic, philosophy, and cognitive science. Gualtiero Piccinini , , and Marcin Milkowski develop this theme into a mechanistic theory of computing systems. A functional mechanism is a system of interconnected components, where each component performs some function within the overall system. Mechanistic explanation proceeds by decomposing the system into parts, describing how the parts are organized into the larger system, and isolating the function performed by each part. A computing system is a functional mechanism of a particular kind.
Computational explanation decomposes the system into parts and describes how each part helps the system process the relevant vehicles. If the system processes discretely structured vehicles, then the computation is digital. If the system processes continuous vehicles, then the computation is analog. Milkowski and Piccinini deploy their respective mechanistic theories to defend computationalism.
Mechanistic computationalists typically individuate computational states non-semantically. They therefore encounter worries about the explanatory role of representational content, similar to worries encountered by FSC and structuralism. In this spirit, Shagrir complains that mechanistic computationalism does not accommodate cognitive science explanations that are simultaneously computational and representational.
We have surveyed various contrasting and sometimes overlapping conceptions of computation: classical computation, connectionist computation, neural computation, formal-syntactic computation, content-involving computation, information-processing computation, functional computation, structuralist computation, and mechanistic computation.
Each conception yields a different form of computationalism. Each conception has its own strengths and weaknesses. One might adopt a pluralistic stance that recognizes distinct legitimate conceptions. Rather than elevate one conception above the others, pluralists happily employ whichever conception seems useful in a given explanatory context. Edelman takes a pluralistic line, as does Chalmers in his most recent discussion. The pluralistic line raises some natural questions.
Can we provide a general analysis that encompasses all or most types of computation?
Do all computations share certain characteristic marks with one another? Are they perhaps instead united by something like family resemblance? Deeper understanding of computation requires us to grapple with these questions. CTM has attracted numerous objections.
Realistic Rationalism by Jerrold J. Katz
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 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 Siegelmann and Sontag 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. My belief that Barack Obama is president is true if Barack Obama is president, false if he is not.
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 Arjo, D. Aydede, M. Irzik and G. Bechtel, W. Abrahamsen, , Connectionism and the Mind , Malden: Blackwell. Block, N. Boolos ed. Smith and B. Boden, M. Bontly, T. Bowie, G. Brogan, W. Englewood Cliffs: Prentice Hall. Burge, T. Woodfield ed. Reprinted in Burge MacDonald and G.
MacDonald eds , Oxford: Blackwell. Camp, E. Lurz ed , Cambridge: Cambridge University Press. Carruthers, P. Chalmers, D. Chalmers ed. Chemero, A. Cheney, D. Chomsky, N. Church, A. Churchland, P. Koch, and T. Schwartz ed. Clark, A. Clayton, N. Emery, and A. Nudds and S. Hurley eds , Oxford: Oxford University Press. Copeland, J. Cover, T. Crane, T. Crick, F. Cummins, R. Davidson, D. Dayan, P. Dennett, D. Dietrich, E. Donahoe, J. Gallistel and A.
Dreyfus, H. Dretske, F. Heil and A. Mele eds , Oxford: Clarendon Press. Edelman, S. Egan, F. Antony and N. Hornstein eds , Malden: Blackwell. Eliasmith, C. Elman, J. Feferman, S. Feldman, J. Field, H.
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Figdor, C. Fodor, J. Reprinted in Fodor Frege, G. Bauer-Mengelberg trans. Gallistel, C. Gandy, R.