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Philosophy of artificial intelligence :: Computationalism

• Computationalism in cognitive science [49]
• Computation and physical systems [31]

Computationalism in cognitive science

See also: Cognitive models of consciousness, The language of thought, Externalism and computation, Machine functionalism.

• Philip Agre (2002). The practical logic of computer work.   (More)
  Additional links for this entry:
  http://lib.org.by/_djvu/Cs_Computer science/CsAi_AI, knowledge/Scheutz M. (ed.) Computationalism (MIT, 2002)(223s).pdf#page=143
  
• Mark Bickhard (1996). Troubles with computationalism.   (More)
• Ned Block (1995). The mind as the software of the brain.   (Abstract & more)
  Abstract: In this section, we will start with an influential attempt to define `intelligence', and then we will move to a consideration of how human intelligence is to be investigated on the machine model. The last part of the section will discuss the relation between the mental and the biological
  Additional links for this entry:
  http://luna.cas.usf.edu/~weiskopf/philmind/Block-The Mind as the Software of the Brain.pdf
  
• Selmer Bringsjord (1994). Computation, among other things, is beneath us.   (Abstract & more)
  Abstract:   What''s computation? The received answer is that computation is a computer at work, and a computer at work is that which can be modelled as a Turing machine at work. Unfortunately, as John Searle has recently argued, and as others have agreed, the received answer appears to imply that AI and Cog Sci are a royal waste of time. The argument here is alarmingly simple: AI and Cog Sci (of the Strong sort, anyway) are committed to the view that cognition is computation (or brains are computers); butall processes are computations (orall physical things are computers); so AI and Cog Sci are positively silly.I refute this argument herein, in part by defining the locutions x is a computer and c is a computation in a way that blocks Searle''s argument but exploits the hard-to-deny link between What''s Computation? and the theory of computation. However, I also provide, at the end of this essay, an argument which, it seems to me, implies not that AI and Cog Sci are silly, but that they''re based on a form of computation that is well beneath human persons
  Additional links for this entry:
  http://www.springerlink.com/content/content/l487452322177105/fulltext.pdf
  http://www.springerlink.com/content/l487452322177105/fulltext.pdf
  http://www.springerlink.com/index/L487452322177105.pdf
  
• Selmer Bringsjord & Michael Zenzen (1997). Cognition is not computation: The argument from irreversibility.   (Abstract & more)
  Abstract:   The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine
  Additional links for this entry:
  http://www.rpi.edu/~brings/SELPAP/irr.ps
  http://www.rpi.edu/~brings/SELPAP/irr.pdf
  http://citeseer.ist.psu.edu/bringsjord96cognition.html
  http://www.phil.canterbury.ac.nz/tom_bestor/e-texts/Bringsjord, selmer - cognition is not computation.pdf
  http://www.ingentaconnect.com/content/klu/synt/1997/00000113/00000002/00152330
  http://www.springerlink.com/content/n887505850j434ur/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=152330&PDF=1
  http://www.springerlink.com/index/N887505850J434UR.pdf
  
• Selmer Bringsjord (2000). Clarifying the logic of anti-computationalism: Reply to Hauser.   (More)
  Additional links for this entry:
  http://www.springerlink.com/content/content/w67u62604h262341/fulltext.pdf
  http://www.springerlink.com/content/w67u62604h262341/fulltext.pdf
  http://www.springerlink.com/index/W67U62604H262341.pdf
  
• Selmer Bringsjord (2001). In computation, parallel is nothing, physical everything.   (Abstract & more)
  Abstract:   Andrew Boucher (1997) argues that ``parallel computation is fundamentally different from sequential computation'' (p. 543), and that this fact provides reason to be skeptical about whether AI can produce a genuinely intelligent machine. But parallelism, as I prove herein, is irrelevant. What Boucher has inadvertently glimpsed is one small part of a mathematical tapestry portraying the simple but undeniable fact that physical computation can be fundamentally different from ordinary, ``textbook'' computation (whether parallel or sequential). This tapestry does indeed immediately imply that human cognition may be uncomputable
  Additional links for this entry:
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000001/00272655
  http://www.springerlink.com/content/content/q23582463313005k/fulltext.pdf
  http://www.springerlink.com/content/q23582463313005k/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=272655&PDF=1
  http://www.springerlink.com/index/Q23582463313005K.pdf
  
• Selmer Bringsjord (2004). The modal argument for hypercomputing minds.   (More)
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=1011184.1011194
  http://www.cs.queensu.ca/home/akl/cisc879/papers/PAPERS_FROM_THEORETICAL_COMPUTER_SCIENCE/05051011121215925.pdf
  http://linkinghub.elsevier.com/retrieve/pii/S0304397503006376
  
• David Buller (1993). Confirmation and the computational paradigm, or, why do you think they call it artificial intelligence?   (Abstract & more)
  Abstract:   The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that none of these kinds of evidence can be relevant to a choice among competing computational models unless there are in fact type-type correlations between the states postulated by computational models and brain states. Thus, I conclude, research into the computational procedures employed in human cognition must be conducted hand-in-hand with research into the brain processes which realize those procedures
  Additional links for this entry:
  http://cogprints.org/327/0/confirm.htm
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:327
  http://www.springerlink.com/content/content/j0385146724gm815/fulltext.pdf
  http://www.springerlink.com/content/j0385146724gm815/fulltext.pdf
  http://www.springerlink.com/index/J0385146724GM815.pdf
  
• David Chalmers (1994). A computational foundation for the study of cognition.   (Abstract & more)
  Abstract: Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of computation requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that (a) computation can specify general patterns of causal organization, and (b) mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science
  Additional links for this entry:
  http://consc.net/papers/computation.html
  http://cogprints.org/319/00/computation.html
  http://citeseer.ist.psu.edu/chalmers94computational.html
  http://www.cogsci.indiana.edu/pub/chalmers.computation.ps
  http://www.u.arizona.edu/~chalmers/papers/computation.html
  http://cogprints.ecs.soton.ac.uk/archive/00000319/00/computation.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:319
  
• Christopher Cherniak (1988). Undebuggability and cognitive science.   (More)
  Additional links for this entry:
  http://www.glue.umd.edu/~cherniak/cherniak-CommofACM-1988.pdf
  
• Austen Clark (1984). Seeing and summing: Implications of computational theories of vision.   (Abstract & more)
  Abstract: Marr's computational theory of stereopsis is shown to imply that human vision employs a system of representation which has all the properties of a number system. Claims for an internal number system and for neural computation should be taken literally. I show how these ideas withstand various skeptical attacks, and analyze the requirements for describing neural operations as computations. Neural encoding of numerals is shown to be distinct from our ability to measure visual physiology. The constructs in Marr's theory are neither propositional nor pictorial, and provide a counter example to many commonly held dichotomies concerning mental representation
  Additional links for this entry:
  http://www.ucc.uconn.edu/~wwwphil/marr.htm
  
• Jack Copeland (1994). Turing, Wittgenstein, and the science of the mind.   (More)
• Eric Dietrich (2000). Cognitive science and the mechanistic forces of darkness.   (More)
• Eric Dietrich (2001). It does so: Review of The Mind Doesn't Work That Way: The Scope and Limits of Computational Psychology.   (Abstract & more)
  Abstract: Objections to AI and computational cognitive science are myriad. Accordingly, there are many different reasons for these attacks. But all of them come down to one simple observation: humans seem a lot smarter that computers -- not just smarter as in Einstein was smarter than I, or I am smarter than a chimpanzee, but more like I am smarter than a pencil sharpener. To many, computation seems like the wrong paradigm for studying the mind. (Actually, I think there are deeper and darker reasons why AI, especially, is so often the brunt of polemics, see Dietrich, 2000.) But the truth is this: AI is making exciting progress, and will one day make a robot as intelligent as a person; indeed the robot will be conscious. And all this is because of another truth: the computational paradigm is the best thing to come down the pike since the wheel
  Additional links for this entry:
  http://bingweb.binghamton.edu/~dietrich/Papers/ComputationlismRep/revuFodors-MDWTW.pdf
  
• Chris Eliasmith (2000). Is the brain analog or digital?: The solution and its consequences for cognitive science.   (More)
• Marco Ernandes (2005). Artificial intelligence & games: Should computational psychology be revalued?   (Abstract & more)
  Abstract: The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to promote the belief that games represent an excellent tool for the project of computational psychology (CP). To underline how, despite this, GP has mainly adopted an engineering-inspired methodology and in doing so has distorted the framework of cognitive functionalism. Many successes (i.e. chess, checkers) have been achieved refusing human-like reasoning. The AI has appeared to work well despite ignoring an intrinsic motivation, that of creating an explanatory link between machines and mind. To assert that substantial improvements in GP may be obtained in the future only by renewed interest in human-inspired models of reasoning and in other cognitive studies. In fact, if we increase the complexity of games (from NP-Completeness to AI-Completeness) in order to reproduce real-life problems, computer science techniques enter an impasse. Many of AI’s recent GP experiences can be shown to validate this. The lack of consistent philosophical foundations for cognitive AI and the minimal philosophical commitment of AI investigation are two of the major reasons that play an important role in explaining why CP has been overlooked
  Additional links for this entry:
  http://www.springerlink.com/content/m37411736226vh26/fulltext.pdf
  http://www.springerlink.com/index/M37411736226VH26.pdf
  
• Jordi Fernandez (2003). Explanation by computer simulation in cognitive science.   (Abstract & more)
  Abstract:   My purpose in this essay is to clarify the notion of explanation by computer simulation in artificial intelligence and cognitive science. My contention is that computer simulation may be understood as providing two different kinds of explanation, which makes the notion of explanation by computer simulation ambiguous. In order to show this, I shall draw a distinction between two possible ways of understanding the notion of simulation, depending on how one views the relation in which a computing system that performs a cognitive task stands to the program that the system runs while performing that task. Next, I shall suggest that the kind of explanation that results from simulation is radically different in each case. In order to illustrate the difference, I will point out some prima facie methodological difficulties that need to be addressed in order to ensure that simulation plays a legitimate explanatory role in cognitive science, and I shall emphasize how those difficulties are very different depending on the notion of explanation involved
  Additional links for this entry:
  http://www.consc.net/cgi/go.fcgi?id=FEREBC&free=0&u=http://www.springerlink.com/index/U7U434131M595425.pdf
  http://portal.acm.org/citation.cfm?id=641429.641445
  http://www.ingentaconnect.com/content/klu/mind/2003/00000013/00000002/05115873
  http://www.springerlink.com/content/content/u7u434131m595425/fulltext.pdf
  http://www.springerlink.com/content/u7u434131m595425/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=5115873&PDF=1
  http://www.springerlink.com/index/U7U434131M595425.pdf
  
• James Fetzer (1997). Thinking and computing: Computers as special kinds of signs.   (Abstract & more)
  Abstract:   Cognitive science has been dominated by the computational conception that cognition is computation across representations. To the extent to which cognition as computation across representations is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from their interpretation by human users. Strictly speaking, computers as such — apart from human users — are not only incapable of cognition, but even incapable of computation, properly construed. If we want to understand the nature of thought, then we have to study thinking, not computing, because they are not the same thing
  Additional links for this entry:
  http://www.digitalpeirce.fee.unicamp.br/ComputersAsSigns.pdf
  http://www.ingentaconnect.com/content/klu/mind/1997/00000007/00000003/00140000
  http://www.springerlink.com/content/content/t1775k25pq713537/fulltext.pdf
  http://www.springerlink.com/content/t1775k25pq713537/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=140000&PDF=1
  http://www.springerlink.com/index/T1775K25PQ713537.pdf
  
• Christopher Green (2000). Is AI the right method for cognitive science?   (More)
  Additional links for this entry:
  http://www.cogsci.soton.ac.uk/cgi/psyc/newpsy?11.061
  
• Rick Grush & Patricia Churchland (1998). Computation and the brain.   (Abstract & more)
  Abstract: Two very different insights motivate characterizing the brain as a computer. One depends on mathematical theory that defines computability in a highly abstract sense. Here the foundational idea is that of a Turing machine. Not an actual machine, the Turing machine is really a conceptual way of making the point that any well-defined function could be executed, step by step, according to simple 'if-you-are-in-state-P-and-have-input-Q-then-do-R' rules, given enough time (maybe infinite time) [see COMPUTATION]. Insofar as the brain is a device whose input and output can be characterized in terms of some mathematical function -- however complicated -- then in that very abstract sense, it can be mimicked by a Turning machine. Given what is known so far brains do seem to depend on cause-effect operations, and hence brains appear to be, in some formal sense, equivalent to a Turing machine [see CHURCH-TURING THESIS]. On its own, however, this reveals nothing at all of how the mind-brain actually works. The second insight depends on looking at the brain as a biological device that processes information from the environment to build complex representations that enable the brain to make predictions and select advantageous behaviors. Where necessary to avoid ambiguity, we will refer to the first notion of computation as..
  Additional links for this entry:
  http://mind.ucsd.edu/papers/reviews/compbrain/compbrain.pdf
  http://mind.ucsd.edu/papers/reviews/compbrain/compbrain-text.html
  
• Stevan Harnad (1994). Computation is just interpretable symbol manipulation; cognition isn't.   (Abstract & more)
  Abstract:   Computation is interpretable symbol manipulation. Symbols are objects that are manipulated on the basis of rules operating only on theirshapes, which are arbitrary in relation to what they can be interpreted as meaning. Even if one accepts the Church/Turing Thesis that computation is unique, universal and very near omnipotent, not everything is a computer, because not everything can be given a systematic interpretation; and certainly everything can''t be givenevery systematic interpretation. But even after computers and computation have been successfully distinguished from other kinds of things, mental states will not just be the implementations of the right symbol systems, because of the symbol grounding problem: The interpretation of a symbol system is not intrinsic to the system; it is projected onto it by the interpreter. This is not true of our thoughts. We must accordingly be more than just computers. My guess is that the meanings of our symbols are grounded in the substrate of our robotic capacity to interact with that real world of objects, events and states of affairs that our symbols are systematically interpretable as being about
  Additional links for this entry:
  http://cogprints.soton.ac.uk/documents/disk0/00/00/15/92/
  http://cogprints.org/1592/0/harnad94.computation.cognition.html
  http://cogprints.org/1592/00/harnad94.computation.cognition.html
  http://citebase.eprints.org/cgi-bin/citations?id=oai:eprints.ecs.soton.ac.uk:3361
  http://cogsci.soton.ac.uk/~harnad/Papers/Harnad/harnad94.computation.cognition.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Harnad/harnad94.computation.cognition.html
  http://eprints.ecs.soton.ac.uk/archive/00003361/02/harnad94.computation.cognition.html
  http://citebase.eprints.org/cgi-bin/citations?archiveID=oai:cogprints.soton.ac.uk:1592
  http://cogprints.ecs.soton.ac.uk/archive/00001592/00/harnad94.computation.cognition.html
  http://wexler.free.fr/library/files/harnad (1994) computation is just interpretable symbol manipulation; cognition isn't.pdf
  http://www.springerlink.com/content/j583016425716646/fulltext.pdf
  http://www.springerlink.com/index/J583016425716646.pdf
  
• Jeffrey Hershfield (2005). Is there life after the death of the computational theory of mind?   (More)
  Additional links for this entry:
  http://www.ingentaconnect.com/content/klu/mind/2005/00000015/00000002/00002003
  http://www.springerlink.com/content/kn77885235l47011/fulltext.pdf
  http://www.springerlink.com/index/KN77885235L47011.pdf
  
• Terence Horgan (2002). Themes in my philosophical work.   (Abstract & more)
  Abstract: ����������� I invoked the notion of supervenience in my doctoral disseration, Microreduction and the Mind-Body Problem, completed at the University of Michigan in 1974 under the direction of Jaegwon Kim. I had been struck by the appeal to supervenience in Hare (1952), a classic work in twentieth century metaethics that I studied at Michigan in a course on metaethics taught by William Frankena; and I also had been struck by the brief appeal to supervenience in Davidson (1970). Kim was already, in effect, construing the relation between physical and mental properties as a supervenience relation�although he was not yet using the word �supervenience�. I assumed that a materialistic metaphysics was correct, and that integral to materialism is the idea that higher-level sciences (including psychology) are reducible to lower-level ones�ultimately to microphysics. One idea I pressed in the dissertation was that biconditional �bridge laws� would not suffice for genuine intertheoretic reduction if these inter-level laws were additional fundamental laws of nature alongside those of the reducing science; they would be what Herbert Feigl and J.J. C. Smart, in their writings on the psychophysical identity theory, called �nomological danglers.� I argued that the higher-level property in a bridge law should bear a relation of strict supervenience to its correlated lower-level property, rather than merely being nomically correlated with it. The basic idea was that there are no two physically possible worlds w1 and w2�where a physically possible world is, roughly, a world in which the laws of microphysics obtain and in which there are no nonphysical substances like entelechies or Cartesian souls�such that the actual-world bridge laws obtain in world w1 but not in world w2. (Thus, the bridge laws themselves are fixed relative to the fundamental physical facts and fundamental laws, rather than being fundamental laws themselves alongside those of microphysics.) Already when
  Additional links for this entry:
  http://www.ingentaconnect.com//content/rodopi/gps/2002/00000063/00000001/art00002
  http://www.ingentaconnect.com/content/rodopi/gps/2002/00000063/00000001/art00002
  http://www.ingentaconnect.com/content/rodopi/gps/2002/00000063/00000001/art00002
  
• Steven Horst (1999). Symbols and computation: A critique of the computational theory of mind.   (Abstract & more)
  Abstract:   Over the past several decades, the philosophical community has witnessed the emergence of an important new paradigm for understanding the mind.1 The paradigm is that of machine computation, and its influence has been felt not only in philosophy, but also in all of the empirical disciplines devoted to the study of cognition. Of the several strategies for applying the resources provided by computer and cognitive science to the philosophy of mind, the one that has gained the most attention from philosophers has been the Computational Theory of Mind (CTM). CTM was first articulated by Hilary Putnam (1960, 1961), but finds perhaps its most consistent and enduring advocate in Jerry Fodor (1975, 1980, 1981, 1987, 1990, 1994). It is this theory, and not any broader interpretations of what it would be for the mind to be a computer, that I wish to address in this paper. What I shall argue here is that the notion of symbolic representation employed by CTM is fundamentally unsuited to providing an explanation of the intentionality of mental states (a major goal of CTM), and that this result undercuts a second major goal of CTM, sometimes refered to as the vindication of intentional psychology. This line of argument is related to the discussions of derived intentionality by Searle (1980, 1983, 1984) and Sayre (1986, 1987). But whereas those discussions seem to be concerned with the causal dependence of familiar sorts of symbolic representation upon meaning-bestowing acts, my claim is rather that there is not one but several notions of meaning to be had, and that the notions that are applicable to symbols are conceptually dependent upon the notion that is applicable to mental states in the fashion that Aristotle refered to as paronymy. That is, an analysis of the notions of meaning applicable to symbols reveals that they contain presuppositions about meaningful mental states, much as Aristotle's analysis of the sense of healthy that is applied to foods reveals that it means conducive to having a healthy body, and hence any attempt to explain mental semantics in terms of the semantics of symbols is doomed to circularity and regress. I shall argue, however, that this does not have the consequence that computationalism is bankrupt as a paradigm for cognitive science, as it is possible to reconstruct CTM in a fashion that avoids these difficulties and makes it a viable research framework for psychology, albeit at the cost of losing its claims to explain intentionality and to vindicate intentional psychology. I have argued elsewhere (Horst, 1996) that local special sciences such as psychology do not require vindication in the form of demonstrating their reducibility to more fundamental theories, and hence failure to make good on these philosophical promises need not compromise the broad range of work in empirical cognitive science motivated by the computer paradigm in ways that do not depend on these problematic treatments of symbols
  Additional links for this entry:
  http://www.wesleyan.edu/~shorst/papers/ctmarticle.htm
  http://www.ingentaconnect.com/content/klu/mind/1999/00000009/00000003/00206289
  http://www.springerlink.com/content/h1l35k7x30385118/fulltext.pdf
  http://www.kluweronline.com/article.asp?PIPS=206289&PDF=1
  http://www.springerlink.com/index/H1L35K7X30385118.pdf
  
• Steven Horst (online). The computational theory of mind.   (More)
• Drew McDermott (2001). The digital computer as red Herring.   (Abstract & more)
  Abstract: Stevan Harnad correctly perceives a deep problem in computationalism, the hypothesis that cognition is computation, namely, that the symbols manipulated by a computational entity do not automatically mean anything. Perhaps, he proposes, transducers and neural nets will not have this problem. His analysis goes wrong from the start, because computationalism is not as rigid a set of theories as he thinks. Transducers and neural nets are just two kinds of computational system, among many, and any solution to the semantic problem that works for them will work for most other computational systems
• Gualtiero Piccinini (2003). Computations and Computers in the Sciences of Mind and Brain. Dissertation.   (More)
  Additional links for this entry:
  http://etd.library.pitt.edu/ETD/available/etd-08132003-155121/
  
• Gualtiero Piccinini (online). Computational explanation and mechanistic explanation of mind.   (Abstract & more)
  Abstract: According to the computational theory of mind (CTM), mental capacities are explained by inner computations, which in biological organisms are realized in the brain. Computational explanation is so popular and entrenched that it’s common for scientists and philosophers to assume CTM without argument
• Gualtiero Piccinini (2006). Computational explanation in neuroscience.   (Abstract & more)
  Abstract: According to some philosophers, computational explanation is proprietary to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds
  Additional links for this entry:
  http://www.umsl.edu/~piccininig/Computational Explanation in Neuroscience.pdf
  http://www.ingentaconnect.com/content/klu/synt/2006/00000153/00000003/00009096
  http://www.springerlink.com/content/l5g8m78413883088/fulltext.pdf
  http://www.springerlink.com/index/L5G8M78413883088.pdf
  
• Gualtiero Piccinini (2004). Functionalism, computationalism, and mental contents.   (Abstract & more)
  Abstract: Arguably Ramsified functionalism is the best hope for an adequate physicalist theory of the mind. As I will use the term, a posteriori functionalism is the doctrine that the base psychological theory from which Ramsifying functionalists derive their definitions of the mental properties and relations is the result of a posteriori scientific investigation. I will argue that the a posteriori functionalist faces the following dilemma: either she must adopt the results of cognitive science wholesale (in which case her Ramsified definitions will be chauvinistic) or she must admit that the psychological principles relevant to giving functional definitions
  Additional links for this entry:
  http://www.umsl.edu/~piccininig/Functionalism_Computationalism_and_Mental_States.pdf
  http://www.umsl.edu/~piccininig/Functionalism_Computationalism_and_Mental_Contents.pdf
  http://www.umsl.edu/~piccininig/Functionalism, Computationalism, and Mental States.pdf
  
• Gualtiero Piccinini (2004). Functionalism, computationalism, and mental states.   (Abstract & more)
  Abstract: Arguably Ramsified functionalism is the best hope for an adequate physicalist theory of the mind. As I will use the term, a posteriori functionalism is the doctrine that the base psychological theory from which Ramsifying functionalists derive their definitions of the mental properties and relations is the result of a posteriori scientific investigation. I will argue that the a posteriori functionalist faces the following dilemma: either she must adopt the results of cognitive science wholesale (in which case her Ramsified definitions will be chauvinistic) or she must admit that the psychological principles relevant to giving functional definitions
  Additional links for this entry:
  http://www.umsl.edu/~piccininig/Functionalism_Computationalism_and_Mental_States.pdf
  http://www.umsl.edu/~piccininig/Functionalism_Computationalism_and_Mental_Contents.pdf
  http://www.umsl.edu/~piccininig/Functionalism, Computationalism, and Mental States.pdf
  http://linkinghub.elsevier.com/retrieve/pii/S0039368104000883
  
• Gualtiero Piccinini (online). Symbols, strings, and spikes.   (Abstract & more)
  Abstract: An argument that in the sense relevant to computational theories of mind, minds (or brains) are not computing mechanisms
• Gualtiero Piccinini (online). The mind as neural software.   (Abstract & more)
  Abstract: In this paper, I give a novel formulation of computational functionalism, based on the view that a piece of software is a particular component of a computer. (Specifically, a piece of software is a component whose function is to control the computer’s behavior.) I argue that my formulation of computational functionalism is philosophically more satisfactory than the standard formulation, because (i) it is faithful to our ordinary notions of hardware and software, thereby vindicating the original motivation of computational functionalism, and (ii) it does not conflate functionalism and computationalism, thereby leaving computationalism open to refutation independently of functionalism
• Paul Pietroski (1996). Experiencing the facts: Critical notice of Mind and World, by John McDowell.   (Abstract & more)
  Abstract: Paul Pietroski, McGill University The general topic of_ Mind and World_, the written version of John McDowell's 1991 John Locke Lectures, is how `concepts mediate the relation between minds and the world'. And one of the main aims is `to suggest that Kant should still have a central place in our discussion of the way thought bears on reality' (1).1 In particular, McDowell urges us to adopt a thesis that he finds in Kant, or perhaps in Strawson's Kant: the content of experience is conceptualized; _what_ we experience is always the kind of thing that we could also believe. When an agent has a veridical experience, she `takes in, for instance sees, _that things are thus and so_' (9). McDowell's argument for this thesis is indirect, but potentially powerful. He discusses a tension concerning the roles of experience and conceptual capacities in thought, and he claims that the only adequate resolution involves granting that experiences have conceptualized content. The tension, elaborated below, can be expressed roughly as follows: judgments must be somehow constrained by features of the external environment, else judgments would be utterly divorced from the world they purport to be about; yet our judgments must be somehow free of external control, else we could give no sense to the idea that we are responsible for our judgments
  Additional links for this entry:
  http://www.wam.umd.edu/~pietro/research/papers/mcdowell.pdf
  
• Zenon Pylyshyn (1989). Computing and cognitive science.   (Abstract & more)
  Abstract: influence. One of the principal characteristics that distinguishes Cognitive Science from more traditional studies of cognition within Psychology, is the extent to which it has been influenced by both the ideas and the techniques of computing. It may come as a surprise to the outsider, then, to discover that there is no unanimity within the discipline on either (a) the nature (and in some cases the desireabilty) of the influence and (b) what computing is –- or at least on its
  Additional links for this entry:
  http://portal.acm.org/citation.cfm?id=102955
  http://ruccs.rutgers.edu/ftp/pub/papers/posner.pdf
  http://portal.acm.org/citation.cfm?id=166791.166796
  http://citeseer.ist.psu.edu/pylyshyn89computing.html
  
• William Rapaport (1998). How minds can be computational systems.   (Abstract & more)
  Abstract: The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test± passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably be persons
  Additional links for this entry:
  http://citeseer.ist.psu.edu/rapaport98how.html
  http://www.cse.buffalo.edu/~rapaport/Papers/jetai.pdf
  http://www.cse.buffalo.edu/~rapaport/Papers/jetai-sspp98.pdf
  http://calculemus.org/lect/L-I-MNS/12/computComplexity/sspp.ps
  http://www.calculemus.org/lect/L-I-MNS/12/computComplexity/sspp.ps
  http://historical.ncstrl.org/litesite-data/suny_buffalo_cs/96-10.ps
  http://historical.ncstrl.org/litesite-data/suny_buffalo_cs/96-10.ps.Z
  http://www.phil.canterbury.ac.nz/tom_bestor/e-texts/Rapaport, William - How Minds Can Be Computational Systems (1998 jetai).pdf
  http://www.ingentaconnect.com/content/tandf/teta/1998/00000010/00000004/art00003
  http://taylorandfrancis.metapress.com/index/RDFHTNHN8FEGV24Y.pdf
  http://www.informaworld.com/index/RDFHTNHN8FEGV24Y.pdf
  
• Georges Rey (2003). Why Wittgenstein ought to have been a computationalist (and what a computationalist can gain from Wittgenstein).   (More)
• Matthias Scheutz (2002). Computationalism: The next generation.   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=Y59zyNWnNfYC&oi=fnd&pg=PA1&ots=BHZ5CpdWNg&sig=4RJJYbHUGKum3UWJDXja8FHNo4g
  http://lib.org.by/_djvu/Cs_Computer science/CsAi_AI, knowledge/Scheutz M. (ed.) Computationalism (MIT, 2002)(223s).pdf#page=15
  
• Matthias Scheutz (2001). Computational vs. causal complexity.   (Abstract & more)
  Abstract:   The main claim of this paper is that notions of implementation based on an isomorphic correspondence between physical and computational states are not tenable. Rather, ``implementation'' has to be based on the notion of ``bisimulation'' in order to be able to block unwanted implementation results and incorporate intuitions from computational practice. A formal definition of implementation is suggested, which satisfies theoretical and practical requirements and may also be used to make the functionalist notion of ``physical realization'' precise. The upshot of this new definition of implementation is that implementation cannot distinguish isomorphic bisimilar from non-isomporphic bisimilar systems anymore, thus driving a wedge between the notions of causal and computational complexity. While computationalism does not seem to be affected by this result, the consequences for functionalism are not clear and need further investigations
  Additional links for this entry:
  http://www.phil.canterbury.ac.nz/tom_bestor/e-texts/Scheutz, Matthias - Computational versus Causal Complxity (2001).pdf
  http://www.ingentaconnect.com/content/klu/mind/2001/00000011/00000004/00352666
  http://www.springerlink.com/content/t6532p66078w74v4/fulltext.pdf
  http://www.springerlink.com/index/T6532P66078W74V4.pdf
  
• Matthias Scheutz (2002). New computationalism.   (More)
  Additional links for this entry:
  http://books.google.com/books?hl=en&lr=&id=Y59zyNWnNfYC&oi=fnd&pg=PR7&dq=+New+computationalism+Scheutz&ots=BG52Hq9RLa&sig=TggvUxOEuNqpXf6OF1osw81b-6M
  http://books.google.com/books?hl=en&lr=&id=Y59zyNWnNfYC&oi=fnd&pg=PR7&dq=+New+computationalism+Scheutz&ots=BG62Ct8VNa&sig=Qpp-Q4dZY1vNQQlDJF0BM-khRbo
  
• John Searle (1990). Is the brain a digital computer?   (Abstract & more)
  Abstract: There are different ways to present a Presidential Address to the APA; the one I have chosen is simply to report on work that I am doing right now, on work in progress. I am going to present some of my further explorations into the computational model of the mind.\**
  Additional links for this entry:
  http://citeseer.ist.psu.edu/searle04is.html
  http://cogsci.soton.ac.uk/~harnad/Papers/Py104/searle.comp.html
  http://www.ecs.soton.ac.uk/~harnad/Papers/Py104/searle.comp.html
  http://philosophy.wisc.edu/shapiro/Phil554/PAPERS/Is the Brain a Digital Computer.htm
  http://www.engadin.nl/bibliotheek/bibliotheek/download_files/Searle BrainDigitalcomputer.pdf
  http://links.jstor.org/sici?sici=0065-972X(199011)64:3<21:ITBADC>2.0.CO;2-8
  
• Oron Shagrir (2006). Why we view the brain as a computer.   (Abstract & more)
  Abstract: The view that the brain is a sort of computer has functioned as a theoretical guideline both in cognitive science and, more recently, in neuroscience. But since we can view every physical system as a computer, it has been less than clear what this view amounts to. By considering in some detail a seminal study in computational neuroscience, I first suggest that neuroscientists invoke the computational outlook to explain regularities that are formulated in terms of the information content of electrical signals. I then indicate why computational theories have explanatory force with respect to these regularities:in a nutshell, they underscore correspondence relations between formal/mathematical properties of the electrical signals and formal/mathematical properties of the represented objects. I finally link my proposal to the philosophical thesis that content plays an essential role in computational taxonomy
  Additional links for this entry:
  http://www.ingentaconnect.com/content/klu/synt/2006/00000153/00000003/00009099
  http://www.springerlink.com/content/5tp71886371w672j/fulltext.pdf
  http://www.springerlink.com/index/5TP71886371W672J.pdf
  
• Stuart Shapiro (1995). Computationalism.   (Abstract & more)
  Abstract:   Computationalism, the notion that cognition is computation, is a working hypothesis of many AI researchers and Cognitive Scientists. Although it has not been proved, neither has it been disproved. In this paper, I give some refutations to some well-known alleged refutations of computationalism. My arguments have two themes: people are more limited than is often recognized in these debates; computer systems are more complicated than is often recognized in these debates. To underline the latter point, I sketch the design and abilities of a possible embodied computer system
  Additional links for this entry:
  http://citeseer.ist.psu.edu/shapiro95computationalism.html
  http://www.springerlink.com/content/x489704852314qt0/fulltext.pdf
  http://www.springerlink.com/index/X489704852314QT0.pdf
  
• Aaron Sloman (2002). Architecture-based conceptions of mind.   (More)
  Additional links for this entry:
  http://www.cs.bham.ac.uk/research/cogaff/sloman-lmpsfinal.pdf
  
• Aaron Sloman (1996). What sort of architecture is required for a human-like agent?   (More)
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