Newell (1980; 1990) proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization. There would be greater theoretical progress if we evaluated theories (...) by a broad set of criteria such as these and attended to the weaknesses such evaluations revealed. To illustrate how theories can be evaluated we apply these criteria to both classical connectionism (McClelland & Rumelhart 1986; Rumelhart & McClelland 1986b) and the ACT-R theory (Anderson & Lebiere 1998). The strengths of classical connectionism on this test derive from its intense effort in addressing empirical phenomena in such domains as language and cognitive development. Its weaknesses derive from its failure to acknowledge a symbolic level to thought. In contrast, ACT-R includes both symbolic and subsymbolic components. The strengths of the ACT-R theory derive from its tight integration of the symbolic component with the subsymbolic component. Its weaknesses largely derive from its failure, as yet, to adequately engage in intensive analyses of issues related to certain criteria on Newell's list. Key Words: cognitive architecture; connectionism; hybrid systems; language; learning; symbolic systems. (shrink)

We present an account of processing capacity in the ACT-R theory. At the symbolic level, the number of chunks in the current goal provides a measure of relational complexity. At the subsymbolic level, limits on spreading activation, measured by the attentional parameter W, provide a theory of processing capacity, which has been applied to performance, learning, and individual differences data.

because whenever an observer observes, he creates a contingent distinction between what is observed and what is by necessity left unobserved. ...

O'Brien & Opie's connectionist interpretation of “vehicle,” “process,” and “explicit representation” depends heavily on the notions of “information” and “information processing” that underlie the classic account. When the “cognitivist” assumptions, shared by both accounts, are removed, the connectionist versus classic contrast appears to be between behavioral and linguistic accounts.

Van Gelder's characterization of the differences between the dynamical and computational hypotheses, in terms of the contrast between change versus state and geometry versus structure, suggests that the dynamical approach is also at odds with classical mechanism. Dynamical and mechanistic approaches are in fact allies: mechanism can identify components whose properties define the variables that are related in dynamical analyses.

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction with their (...) styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms. (shrink)

van Gelder argues that computational and dynamical systems are mathematically distinct kinds of systems. Although there are real experimental and theoretical differences between adopting a computational or dynamical perspective on cognition, and the dynamical approach has much to recommend it, the debate cannot be framed this rigorously. Instead, what is needed is careful study of concrete models to improve our intuitions.

Modeling cognition by structural analysis of representation leads to systematic difficulties which are not resolvable. We analyse the merits and limits of a representation-based methodology to modeling cognition by treating Jackendoff's Consciousness and the Computational Mind as a good case study. We note the effects this choice of methodology has on the view of consciousness he proposes, as well as a more detailed consideration of the computational mind. The fundamental difficulty we identify is the conflict between the desire for modular (...) processors which map directly onto representations and the need for dynamically interacting control. Our analysis of this approach to modeling cognition is primarily directed at separating merits from problems and inconsistencies by a critique internal to this approach; we also step outside the framework to note the issues it ignores. (shrink)

John Ziman-- the much-missed-- reminds us that 'no man is an island', and takes us to task for working from an individualistic theoretical base. That 'us' includes nearly all social scientists, and most Anglo-American philosophers too. For sure, it includes cognitive scientists, who theorize people in terms of concepts drawn from cybernetics and/or artificial intelligence. (I'll use the term 'computational concepts' broadly, to cover both types.) Indeed, it's a common complaint that cognitive science is overly individualistic.

Humans grasp discrete infinities within several cognitive domains, such as in language, thought, social cognition and tool-making. It is sometimes suggested that any such generative ability is based on a computational system processing hierarchical and recursive mental representations. One view concerning such generativity has been that each of the mind’s modules defining a cognitive domain implements its own recursive computational system. In this paper recent evidence to the contrary is reviewed and it is proposed that there is only one supramodal (...) computational system with recursion in the human mind. A recursion thesis is defined, according to which the hominin cognitive evolution is constituted by a recent punctuated genetic mutation that installed the general, supramodal capacity for recursion into the human nervous system on top of the existing, evolutionarily older cognitive structures, and it is argued on the basis of empirical evidence and theoretical considerations that the recursion thesis constitutes a plausible research program for cognitive science. (shrink)

In this paper I place Jim Fetzer's esemplastic burial of the computational conceptionof mind within the context of both my own burial and the theory of mind I would put in place of this dead doctrine. My view..

I know that those of you who know my mind know that I think I know that we can't know Gödel's mind through computation: ``The Impact : Failing to Know " If computationalism is false, observant philosophers willing to get their hands dirty should be able to find tell-tale signs today: automated theorem proving tomorrow (Eastern APA): robots as zombanimals But let's start with little 'ol me, and literary, not mathematical, creativity: Selmer (samples) vs. Brutus1 (samples again).

Language isn't the only way to cross modules, nor is it the only module with access to both input and output. Minds don't generally work across modules because this leads to combinatorial explosion in search and planning. Language is special in being a good vector for mimetics, so it becomes associated with useful cross-module concepts we acquire culturally. Further, language is indexical, so it facilitates computationally expensive operations.

Modules are widely held to play a central role in explaining mental development and in accounts of the mind generally. But there is much disagreement about what modules are, which shows that we do not adequately understand modularity. This paper outlines a Fodoresque approach to understanding one type of modularity. It suggests that we can distinguish modular from nonmodular cognition by reference to the kinds of process involved, and that modular cognition differs from nonmodular forms of cognition in being a (...) special kind of computational process. The paper concludes by considering implications for the role of modules in explaining mental development. (shrink)

Landauer's principle, often regarded as the basic principle of the thermodynamics of information processing, holds that any logically irreversible manipulation of information, such as the erasure of a bit or the merging of two computation paths, must be accompanied by a corresponding entropy increase in non-information-bearing degrees of freedom of the information-processing apparatus or its environment. Conversely, it is generally accepted that any logically reversible transformation of information can in principle be accomplished by an appropriate physical mechanism operating in a (...) thermodynamically reversible fashion. These notions have sometimes been criticized either as being false, or as being trivial and obvious, and therefore unhelpful for purposes such as explaining why Maxwell's Demon cannot violate the second law of thermodynamics. Here I attempt to refute some of the arguments against Landauer's principle, while arguing that although in a sense it is indeed a straightforward consequence or restatement of the Second Law, it still has considerable pedagogic and explanatory power, especially in the context of other influential ideas in nineteenth and twentieth century physics. Similar arguments have been given by Jeffrey Bub (2002). (shrink)

We respond to Morris and Richardson's (1995) claim that Pickering and Chater's (1995) arguments about the lack of a relation between cognitive science and folk psychology are flawed. We note that possible controversies about the appropriate uses for the two terms do not affect our arguments. We then address their claim that computational explanation of knowledge-rich processes has proved possible in the domains of problem solving, scientific discovery, and reasoning. We argue that, in all cases, computational explanation is only (...) possible for aspects of those processes that do not make reference to general knowledge. We conclude that consideration of the issues raised by Morris and Richardson reinforces our original claim that there are two fundamentally distinct projects for understanding the mind, one based on justification, and the other on computational explanation, and that these apply to non-overlapping aspects of mental life. (shrink)

Summary. The “New Computationalism” that is the subject of this special issue requires an appropriate notion of representation. The purpose of this essay is to recommend such a notion. In cognitive science generally, there have been two primary candidates for spelling out what it is to be a representation: teleological accounts and accounts based on “decoupling.” I argue that the latter sort of account has two serious problems. First, it is multiply ambiguous; second, it is revisionist and alienating to many (...) of the potential allies of the “New Computationalism”. I also suggest that teleological accounts do not suffer from these problems, making them more appropriate as the foundation of any new computationalism. (shrink)

Summary. A distinction is made between two senses of the claim “cognition is computation”. One sense, the opaque reading, takes computation to be whatever is described by our current computational theory and claims that cognition is best understood in terms of that theory. The transparent reading, which has its primary allegiance to the phenomenon of computation, rather than to any particular theory of it, is the claim that the best account of cognition will be given by whatever theory turns out (...) to be the best account of the phenomenon of computation. The distinction is clarified and defended against charges of circularity and changing the subject. Several well-known objections to computationalism are then reviewed, and for each the question of whether the transparent reading of the computationalist claim can provide a response is considered. (shrink)

In this chapter we consider unsupervised learning from two perspectives. First, we briefly look at its advantages and disadvantages as an engineering technique applied to large corpora in natural language processing. While supervised learning generally achieves greater accuracy with less data, unsupervised learning offers significant savings in the intensive labour required for annotating text. Second, we discuss the possible relevance of unsupervised learning to debates on the cognitive basis of human language acquisition. In this context we explore the implications of (...) recent work on grammar induction for poverty of stimulus arguments that purport to motivate a strong bias model of language learning, commonly formulated as a theory of Universal Grammar (UG). We examine the second issue both as a problem in computational learning theory, and with reference to empirical work on unsupervised Machine Learning (ML) of syntactic structure. We compare two models of learning theory and the place of unsupervised learning within each of them. Looking at recent work on part of speech tagging and the recognition of syntactic structure, we see how far unsupervised ML methods have come in acquiring different kinds of grammatical knowledge from raw text. (shrink)

Selinger and Engstrom, A moratorium on cyborgs: Computation, cognition and commerce, 2008 (this issue) urge upon us a moratorium on ‘cyborg discourse’. But the argument underestimates the richness and complexity of our ongoing communal explorations. It leans on a somewhat outdated version of the machine metaphor (exemplified perhaps by a frozen 1970’s Cyborg). The modern cyborg, informed by an evolving computational model of mind, can play a positive role in the critical discussions that Selinger and Engstrom seek.

The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprised of networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons and the neural networks (...) incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena. (shrink)

We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.

Computer science is an engineering science whose objective is to determine how to best control interactions among computational objects. We argue that it is a fundamental computer science value to design computational objects so that the dependencies required by their interactions do not result in couplings, since coupling inhibits change. The nature of knowledge in any science is revealed by how concepts in that science change through paradigm shifts, so we analyze classic paradigm shifts in both natural and computer science (...) in terms of decoupling. We show that decoupling pervades computer science both at its core and in the wider context of computing at large, and lies at the very heart of computer science’s value system. (shrink)

It is widely mooted that a plausible computational cognitive model should involve both symbolic and connectionist components. However, sound principles for combining these components within a hybrid system are currently lacking; the design of such systems is oftenad hoc. In an attempt to ameliorate this we provide a framework of types of hybrid systems and constraints therein, within which to explore the issues. In particular, we suggest the use of system independent constraints, whose source lies in general considerations about cognitive (...) systems, rather than in particular technological or task-based considerations. We illustrate this through a detailed examination of an interruptibility constraint: handling interruptions is a fundamental facet of cognition in a dynamic world. Aspects of interruptions are delineated, as are their precise expression in symbolic and connectionist systems. We illustrate the interaction of the various constraints from interruptibility in the different types of hybrid systems. The picture that emerges of the relationship between the connectionist and the symbolic within a hybrid system provides for sufficient flexibility and complexity to suggest interesting general implications for cognition, thus vindicating the utility of the framework. (shrink)

in M. Marraffa, M. De Caro and F. Ferretti (eds.), Cartographies of the Mind: Philosophy and Psychology in Intersection, Springer, Berlin-Heidelberg, 2007, pp. 37-49. PDF.

Perruchet & Vinter do not fully resolve issues about the role of consciousness and the unconscious in cognition and learning, and it is doubtful that consciousness has been computationally implemented. The cascade-correlation (CC) connectionist model develops high-order feature detectors as it learns a problem. We describe an extension, knowledge-based cascade-correlation (KBCC), that uses knowledge to learn in a hierarchical fashion.

I have been asked to discuss how computers have affected my work in philosophy. This paper discusses the use of artificial intelligence (AI) models to investigate both the representation of scientific knowledge and reasoning strategies for scientific change. The focus is on the reasoning strategies used to revise a theory, given an anomaly, which is a failed prediction of the theory.

We again press the case for computationalism by considering the latest in illconceived attacks on this foundational idea. We briefly but clearly define and delimit computationalism and then consider three authors from a new anticomputationalist collection.

It has been argued, partly from the lack of any widely accepted solution to the measurement problem, and partly from recent results from quantum information theory, that measurement in quantum theory is best treated as a black box. However, there is a crucial difference between ‘having no account of measurement' and ‘having no solution to the measurement problem'. We know a lot about measurements. Taking into account this knowledge sheds light on quantum theory as a theory of information and computation. (...) In particular, the scheme of ‘one-way quantnum computation' takes on a new character in light of the role that reference frames play in actually carrying out any one-way quantum comptuation. ‡Thanks to audiences at the PSA and the Centre for Time, University of Sydney, for helpful comments and questions. †To contact the author, please write to: Department of Philosophy, University of South Carolina, Columbia, SC 29208; e-mail: dickson@sc.edu. (shrink)

We again press the case for computationalism by considering the latest in ill- conceived attacks on this foundational idea. We briefly but clearly define and delimit computationalism and then consider three authors from a new anti- computationalist collection.

We again press the case for computationalism by considering the latest in illconceived attacks on this foundational idea. We briefly but clearly define and delimit computationalism and then consider three authors from a new anticomputationalist collection.

The new kid on the block in cognitive science these days is dynamic systems. This way of thinking about the mind is, as usual, radically opposed to computationalism - - the hypothesis that thinking is computing. The use of dynamic systems is just the latest in a series of attempts, from Searle's Chinese Room Argument, through the weirdnesses of postmodernism, to overthrown computationalism, which as we all know is a perfectly nice hypothesis about the mind that never hurt anyone.

Good sciences have good metaphors. Indeed, good sciences are good because they have good metaphors. AI could use more good metaphors. In this editorial, I would like to propose a new metaphor to help us understand intelligence. Of course, whether the metaphor is any good or not depends on whether it actually does help us. (What I am going to propose is not something opposed to computationalism -- the hypothesis that cognition is computation. Noncomputational metaphors are in vogue these days, (...) and to date they have all been equally plausible and equally successful. And, just to be explicit, I do not mean “IQ” by “intelligence.” I am using “intelligence” in the way AI uses it: as a semi-techical term referring to a general property of all intelligent systems, animal (including humans), or machine, alike.). (shrink)

For many years now, Harnad has argued that transduction is special among cognitive capacities -- special enough to block Searle's Chinese Room Argument. His arguments (as well as Searle's) have been important and useful, but not correct, it seems to me. Their arguments have provided the modern impetus for getting clear about computationalism and the nature of computing. This task has proven to be quite difficult. Which is simply to say that dealing with Harnad's arguments (as well as Searle's) has (...) been difficult. Turing, it turns out, only got us started. But Harnad's (and Searle's) arguments ultimately fail. Turing, it turns out, was on the right track. (shrink)

We argue that the dynamical and computational hypotheses are compatible and in fact need each other: they are about different aspects of cognition. However, only computationalism is about the information-processing aspect. We then argue that any form of information processing relying on matching and comparing, as cognition does, must use discrete representations and computations defined over them.

It is usual when writing on research methodology in dissertations and thesis work within Software Engineering to refer to Empirical Methods, Grounded Theory and Action Research. Analysis of Constructive Research Methods which are fundamental for all knowledge production and especially for concept formation, modeling and the use of artifacts is seldom given, so the relevant first-hand knowledge is missing. This article argues for introducing of the analysis of Constructive Research Methods, as crucial for understanding of research process and knowledge production. (...) The paper provides characterization of the Constructive Research Method and its relations to Action Research and Grounded Theory. Illustrative example of Blue Brain Project is presented. Finally, foundations of Constructive Research are analyzed within the framework of Info-Computationalism which provides models of knowledge construction by information processing in a cognizing agent. (shrink)

Both Cybersemiotics and Info-computationalist research programmes represent attempts to unify understanding of information, knowledge and communication. The first one takes into account phenomenological aspects of signification which are insisting on the human experience "from within". The second adopts solely the view "from the outside" based on scientific practice, with an observing agent generating inter-subjective knowledge in a research community. The process of knowledge production, embodied into networks of cognizing agents interacting with the environment and developing through evolution is studied on (...) different levels of abstraction in both frames of reference. In order to develop scientifically tractable models of evolution of intelligence in informational structures from pre-biotic/chemical to living networked intelligent organisms, including the implementation of those models in artificial agents, a basic level language of Info-Computationalism has shown to be suitable. There are however contexts in which we deal with complex informational structures essentially dependent on human first person knowledge where high level language such as Cybersemiotics is the appropriate tool for conceptualization and communication. Two research projects are presented in order to exemplify the interplay of info-computational and higher-order approaches: The Blue Brain Project where the brain is modeled as info-computational system, a simulation in silico of a biological brain function, and Biosemiotics research on genes, information, and semiosis in which the process of semiosis is understood in info-computational terms. The article analyzes differences and convergences of Cybersemiotics and Info-computationalist approaches which by placing focus on distinct levels of organization, help elucidate processes of knowledge production in intelligent agents. (shrink)

This commentary is an elaboration on Schyns, Goldstone & Thibaut's proposal for flexible features in categorization in the light of three areas not explicitly discussed by the authors: connectionist models of categorization, computational learning theory, and constructivist theories of the mind. In general, the authors' proposal is strongly supported, paving the way for model extensions and for interesting novel cognitive research. Nor is the authors' proposal incompatible with theories positing some fixed set of features.

A working hypothesis of computationalism is that Mind arises, not from the intrinsic nature of the causal properties of particular forms of matter, but from the organization of matter. If this hypothesis is correct, then a wide range of physical systems (e.g. optical, chemical, various hybrids, etc.) should support Mind, especially computers, since they have the capability to create/manipulate organizations of bits of arbitrarily complexity and dynamics. In any particular computer, these bit patterns are quite physical, but their particular physicality (...) is considered irrelevant (since they could be replaced by other physical substrata). (shrink)

In quantum computation non classical features such as superposition states and entanglement are used to solve problems in new ways, impossible on classical digital computers.We illustrate by Deutsch algorithm how a quantum computer can use superposition states to outperform any classical computer. We comment on the view of a quantum computer as a massive parallel computer and recall Amdahls law for a classical parallel computer. We argue that the view on quantum computation as a massive parallel computation disregards the presence (...) of entanglement in a general quantum computation and the non classical way in which parallel results are combined to obtain the final output. (shrink)

The essays included in the special issue dedicated to the philosophy of computer science examine new philosophical questions that arise from reflection upon conceptual issues in computer science and the insights such an enquiry provides into ongoing philosophical debates.

We examine the philosophical disputes among computer scientists concerning methodological, ontological, and epistemological questions: Is computer science a branch of mathematics, an engineering discipline, or a natural science? Should knowledge about the behaviour of programs proceed deductively or empirically? Are computer programs on a par with mathematical objects, with mere data, or with mental processes? We conclude that distinct positions taken in regard to these questions emanate from distinct sets of received beliefs or paradigms within the discipline: – The rationalist (...) paradigm, which was common among theoretical computer scientists, defines computer science as a branch of mathematics, treats programs on a par with mathematical objects, and seeks certain, a priori knowledge about their ‘correctness’ by means of deductive reasoning. – The technocratic paradigm, promulgated mainly by software engineers and has come to dominate much of the discipline, defines computer science as an engineering discipline, treats programs as mere data, and seeks probable, a posteriori knowledge about their reliability empirically using testing suites. – The scientific paradigm, prevalent in the branches of artificial intelligence, defines computer science as a natural (empirical) science, takes programs to be entities on a par with mental processes, and seeks a priori and a posteriori knowledge about them by combining formal deduction and scientific experimentation. We demonstrate evidence corroborating the tenets of the scientific paradigm, in particular the claim that program-processes are on a par with mental processes. We conclude with a discussion in the influence that the technocratic paradigm has been having over computer science. (shrink)

We present a computational simulation which captures aspects of negotiation as the interaction of agents searching for an agreement over their own mental model. Specifically this simulation relates the beliefs of each agent about the action of cause and effect to the resulting negotiation dialogue. The model highlights the difference between negotiating to find any solution and negotiating to obtain the best solution from the point of view of each agent. The later case corresponds most closely to what is commonly (...) called "haggling". This approach also highlights the importance of what each agent thinks is possible in terms of actions causing changes and in what the other agents are able to do in any situation to the course and outcome of a negotiation. This simulation greatly extends other simulations of bargaining which usually only focus on the case of haggling over a limited number of numerical indexes. Three detailed examples are considered. The simulation framework is relatively well suited for participatory methods of elicitation since the "nodes and arrows" representation of beliefs is commonly used and thus accessible to stakeholders and domain experts. (shrink)

The properties of Turing’s famous ‘universal machine’ has long sustained functionalist intuitions about the nature of cognition. Here, I show that there is a logical problem with standard functionalist arguments for multiple realizability. These arguments rely essentially on Turing’s powerful insights regarding computation. In addressing a possible reply to this criticism, I further argue that functionalism is not a useful approach for understanding what it is to have a mind. In particular, I show that the difficulties involved in distinguishing implementation (...) from function make multiple realizability claims untestable and uninformative. As a result, I conclude that the role of Turing machines in philosophy of mind needs to be reconsidered. (shrink)

In addressing the shortcomings of computationalism, we should not throw the baby out with the bathwater. That consciousness is not merely an epiphenomenon with optional access to unconscious computations does not imply that unconscious computations, in the limited domain where they do occur (e.g., occipital transformations of visual data), cannot be reformulated in a way consistent with a self-organizational view.

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement (...) among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations. (shrink)

Dietmar Heinke and Eirini Mavritsaki (eds): Computational Modelling in Behavioural Neuroscience Content Type Journal Article Category Book Review Pages 57-60 DOI 10.1007/s11023-011-9265-8 Authors Juan Felipe Martinez Florez, Institute of Psychology, Universidad del Valle, Campus Universitario Melndez, Ed. 388, Of. 4017, Cali, Colombia Journal Minds and Machines Online ISSN 1572-8641 Print ISSN 0924-6495 Journal Volume Volume 22 Journal Issue Volume 22, Number 1.

Jerry Fodor presents a new development of his famous Language of Thought hypothesis, which has since the 1970s been at the centre of interdisciplinary debate about how the mind works. Fodor defends and extends the groundbreaking idea that thinking is couched in a symbolic system realized in the brain. This idea is central to the representational theory of mind which Fodor has established as a key reference point in modern philosophy, psychology, and cognitive science. The foundation stone of our present (...) cognitive science is Turing's suggestion that cognitive processes are not associations but computations; and computation requires a language of thought. So the latest on the Language of Thought hypothesis, from its progenitor, promises to be a landmark in the study of the mind. LOT 2 offers a more cogent presentation and a fuller explication of Fodor's distinctive account of the mind, with various intriguing new features. The central role of compositionality in the representational theory of mind is revealed: most of what we know about concepts follows from the compositionality of thoughts. Fodor shows the necessity of a referentialist account of the content of intentional states, and of an atomistic account of the individuation of concepts. Not least among the new developments is Fodor's identification and persecution of pragmatism as the leading source of error in the study of the mind today. LOT 2 sees Fodor advance undaunted towards the ultimate goal of a theory of the cognitive mind, and in particular a theory of the intentionality of cognition. No one who works on the mind can ignore Fodor's views, expressed in the coruscating and provocative style which has delighted and disconcerted countless readers over the years. (shrink)

It is a sad duty to report the death of Joseph Goguen (1941-2006) on July 3rd, shortly after a three-day Festschrift Symposium, organized by colleagues from across the world, to mark his 65th birthday and to celebrate his retirement from the University of California at San Diego.

This paper deals with the question: how is computation best individuated? 1. The semantic view of computation: computation is best individuated by its semantic properties.

This paper deals with the question: What are the criteria that an adequate theory of computation has to meet? (1) Smith’s answer: it has to meet the empirical criterion (i.e. doing justice to computational practice), the conceptual criterion (i.e. explaining all the underlying concepts) and the cognitive criterion (i.e. providing solid grounds for computationalism). (2) Piccinini’s answer: it has to meet the objectivity criterion (i.e. identifying computation as a matter of fact), the explanation criterion (i.e. explaining the computer’s behaviour), the (...) right things compute criterion, the miscomputation criterion (i.e. accounting for malfunctions), the taxonomy criterion (i.e. distinguishing between different classes of computers) and the empirical criterion. (3) Von Neumann’s answer: it has to meet the precision and reliability of computers criterion, the single error criterion (i.e. addressing the impacts of errors) and the distinction between analogue and digital computers criterion. (4) “Everything” computes answer: it has to meet the implementation theory criterion by properly explaining the notion of implementation. (shrink)

This discussion deals with the question: What are the criteria that an adequate theory of computation has to meet? 1. Smith's answer: an adequate theory of computation has to meet the empirical criterion – it has to do justice to computational practice, the conceptual criterion – it has to explain all the underlying concepts and the cognitive criterion – it has to provide solid grounds for computationalism. 2. Fodor & Pylyshyn's answer: an adequate theory of computation has to meet the (...) semantic level criterion – it has to explain the semantics of computation, the symbol level criterion – it has to explain the information processing aspect and the physical level criterion – it has to explain the underlying physical realization. 3. Piccinini's answer: an adequate theory of computation has to meet the objectivity criterion – it has to identify computation as a matter of fact, the explanation criterion – it has to explain the computer's behaviour, the right things compute criterion, the miscomputation criterion – it has to account for malfunctions, the taxonomy criterion – it has to distinguish between different classes of computers and the empirical criterion. 4. Von Neumann's answer: an adequate theory of computation has to meet the precision and reliability of computers criterion, the single error criterion – it has to address the impacts of errors to computation and the distinction between analogue & digital computers criterion. 5. “Everything” computes answer: an adequate theory of computation has to meet the implementation theory criterion – it has to properly explain the notion of implementation. There's a widespread tendency to compare minds to computers, but a better understanding of computation is required beforehand. I outline some of the competing answers and argue that Smith's criteria are inadequate and over demanding. My aim is to show why he's eventually concluded that an adequate theory of computation is unlikely. (shrink)

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental (...) program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories. (shrink)

In the study of cognitive processes, limitations on computational resources (computing time and memory space) are usually considered to be beyond the scope of a theory of competence, and to be exclusively relevant to the study of performance. Starting from considerations derived from the theory of computational complexity, in this paper I argue that there are good reasons for claiming that some aspects of resource limitations pertain to the domain of a theory of competence.

This book explores how people's subjective, felt experiences of their bodies in action provide part of the fundamental grounding for human cognition and language. Cognition is what occurs when the body engages the physical and cultural world and must be studied in terms of the dynamical interactions between people and the environment. Human language and thought emerge from recurring patterns of embodied activity that constrain ongoing intelligent behavior. We must not assume cognition to be purely internal, symbolic, computational, and disembodied, (...) but seek out the gross and detailed ways that language and thought are inextricably shaped by embodied action. Embodiment and Cognitive Science describes the abundance of empirical evidence from many disciplines, including work on perception, concepts, imagery and reasoning, language and communication, cognitive development, and emotions and consciousness, that support the idea that the mind is embodied. (shrink)

In this paper I discuss Searle's claim that the computational properties of a system could never cause a system to be conscious. In the first section of the paper I argue that Searle is correct that, even if a system both behaves in a way that is characteristic of conscious agents (like ourselves) and has a computational structure similar to those agents, one cannot be certain that that system is conscious. On the other hand, I suggest that Searle's intuition that (...) it is “empirically absurd” that such a system could be conscious is unfounded. In the second section I show that Searle's attempt to show that a system's computational states could not possibly cause it to be conscious is based upon an erroneous distinction between computational and physical properties. On the basis of these two arguments, I conclude that, supposing that the behavior of conscious agents can be explained in terms of their computational properties, we have good reason to suppose that a system having computational properties similar to such agents is also conscious. (shrink)

A new view of the functional role of the left anterior cortex in language use is proposed. The experimental record indicates that most human linguistic abilities are not localized in this region. In particular, most of syntax (long thought to be there) is not located in Broca's area and its vicinity (operculum, insula, and subjacent white matter). This cerebral region, implicated in Broca's aphasia, does have a role in syntactic processing, but a highly specific one: It is the neural home (...) to receptive mechanisms involved in the computation of the relation between transformationally moved phrasal constituents and their extraction sites (in line with the Trace-Deletion Hypothesis). It is also involved in the construction of higher parts of the syntactic tree in speech production. By contrast, basic combinatorial capacities necessary for language processing – for example, structure-building operations, lexical insertion – are not supported by the neural tissue of this cerebral region, nor is lexical or combinatorial semantics. The dense body of empirical evidence supporting this restrictive view comes mainly from several angles on lesion studies of syntax in agrammatic Broca's aphasia. Five empirical arguments are presented: experiments in sentence comprehension, cross-linguistic considerations (where aphasia findings from several language types are pooled and scrutinized comparatively), grammaticality and plausibility judgments, real-time processing of complex sentences, and rehabilitation. Also discussed are recent results from functional neuroimaging and from structured observations on speech production of Broca's aphasics. Syntactic abilities are nonetheless distinct from other cognitive skills and are represented entirely and exclusively in the left cerebral hemisphere. Although more widespread in the left hemisphere than previously thought, they are clearly distinct from other human combinatorial and intellectual abilities. The neurological record (based on functional imaging, split-brain and right-hemisphere-damaged patients, as well as patients suffering from a breakdown of mathematical skills) indicates that language is a distinct, modularly organized neurological entity. Combinatorial aspects of the language faculty reside in the human left cerebral hemisphere, but only the transformational component (or algorithms that implement it in use) is located in and around Broca's area. Key Words: agrammatism; aphasia; Broca's area; cerebral localization; dyscalculia; functional neuroanatomy; grammatical transformation; modularity; neuroimaging; syntax; trace deletion. (shrink)

The 'Conscious Pilot' is a new model of the neural correlate of consciousness (NCC) consistent with the Orch OR model. The basic idea is that spatiotemporal envelopes of dendritic gamma synchrony move through the brain's neuronal networks. The movement is sideways to neurocomputational flow, occurring via dendritic dendritic gap junction electrical synapses. A conscious pilot moving around an airplane while it flies on auto pilot is used as a metaphor for dendritic synchrony moving through the brain's neurocomputational networks, conveying conscious (...) experience and choice to otherwise non conscious cognitive modes. (shrink)

According to "computationalism" (Newell, 1980; Pylyshyn 1984; Dietrich 1990), mental states are computational states, so if one wishes to build a mind, one is actually looking for the right program to run on a digital computer. A computer program is a semantically interpretable formal symbol system consisting of rules for manipulating symbols on the basis of their shapes, which are arbitrary in relation to what they can be systematically interpreted as meaning. According to computationalism, every physical implementation of the right (...) symbol system will have mental states. (shrink)

Harnad accepts the picture of computation as formalism, so that any implementation of a program - thats any implementation - is as good as any other; in fact, in considering claims about the properties of computations, the nature of the implementing system - the interpreter - is invisible. Let me refer to this idea as 'Computationalism'. Almost all the criticism, claimed refutation by Searle's argument, and sharp contrasting of this idea with others, rests on the absoluteness of this separation between (...) a computational system and its implementation. (shrink)

Critique of Computationalism as merely projecting hermeneutics (i.e., meaning originating from the mind of an external interpreter) onto otherwise intrinsically meaningless symbols.

What Robots Can and Can't Be (hereinafter Robots) is, as Selmer Bringsjord says "intended to be a collection of formal-arguments-that-border-on-proofs for the proposition that in all worlds, at all times, machines can't be minds" (Bringsjord, forthcoming). In his (1994) "Précis of What Robots Can and Can't Be" Bringsjord styles certain of these arguments as proceeding "repeatedly . . . through instantiations of" the "simple schema".

Harnad accepts the picture of computation as formalism, so that any implementation of a program - thats any implementation - is as good as any other; in fact, in considering claims about the properties of computations, the nature of the implementing system - the interpreter - is invisible. Let me refer to this idea as 'Computationalism'. Almost all the criticism, claimed refutation by Searle's argument, and sharp contrasting of this idea with others, rests on the absoluteness of this separation between (...) a computational system and its implementation. (shrink)

Although cognitive psychology is still dominated by computational theories, there is an emerging emphasis on dynamical aspects of cognition. Examples are provided supporting the increased use of dynamically inspired models by psychologists. Despite measurement and model verification problems in the direct use of dynamical system theoretic technology, van Gelder's general approach to cognition is recommended.

The emergence of cognitive science as a multi-disciplinary investigation into the nature of mind has historically revolved around the core assumption that the central ‘cognitive’ aspects of mind are computational in character. Although there is some disagreement and philosophical speculation concerning the precise formulation of this ‘core assumption’ it is generally agreed that computationalism in some form lies at the heart of cognitive science as it is currently conceived. Von Eckardt’s recent work on this topic is useful in enabling us (...) to get a sense of the scope of the computational assumption. She makes clear that there are two rather different ways in which we could understand cognitive science’s commitment to computationalism and hence two ways to understand the claim that the ‘mind is a computer’, by appeal to either (1) A mathematical theory of computability or (2) A theory of data-processing or informationprocessing. Importantly, she also argues that although there are many aspects of claim that the ‘mind is a computer’ that can be nicely captured by Boyd’s account of the way scientific metaphors are employed, not to direct attention to the hitherto unnoticed, but to encourage investigation of the unknown. Nonetheless, cognitive scientists are not making the claim that the ‘mind is a computer’ in a metaphorical sense. If Von Eckhardt is correct, when cognitive scientists assume the ‘mind is a computer’ and give a sense to the notion of the computer in the sense of (2) above, they are making a literal claim about the nature of mind (Von Eckardt, 1993, p. 116). And as she points out that if one reads (2) in a theoretically committed way then there is no a priori reason to exclude the organic brain from the list of entities that might fall under the description of being a ‘computer’. Important, we can truly describe it as a data-processing (or information-processing) device. What is useful about Von Eckardt’s general analysis of computationalism’s core assumption is that it provides a clear angle from which to view the flaws of computationalism. This paper defends the claim that if there is an account of information adequate to capture those aspects of mind that we regard as essential to mentality it is one that requires us to surrender the idea that the mind is a computer.. (shrink)

Understanding consciousness is a truly multidisciplinary project, attracting intense interest from researchers and theorists from diverse backgrounds. Thus, we now have computational scientists, neuroscientists, and philosophers all engaged in the same effort. This book draws together the work of leading researchers around the world, providing insights from these three general perspectives. The work is highlighted by a rare look at work being conducted by Japanese researchers.

How do people interleave attention when multitasking? One dominant account is that the completion of a subtask serves as a cue to switch tasks. But what happens if switching solely at subtask boundaries led to poor performance? We report a study in which participants manually dialed a UK-style telephone number while driving a simulated vehicle. If the driver were to exclusively return his or her attention to driving after completing a subtask (i.e., using the single break in the xxxxx-xxxxxx representational (...) structure of the number), then we would expect to see a relatively poor driving performance. In contrast, our results show that drivers choose to return attention to steering control before the natural subtask boundary. A computational modeling analysis shows that drivers had to adopt this strategy to meet the required performance objective of maintaining an acceptable lateral position in the road while dialing. Taken together these results support the idea that people can strategically control the allocation of attention in multitask settings to meet specific performance criteria. (shrink)

Abstract This paper explains how mathematical computation can be constructed from weaker recursive patterns typical of natural languages. A thought experiment is used to describe the formalization of computational rules, or arithmetical axioms, using only orally-based natural language capabilities, and motivated by two accomplishments of ancient Indian mathematics and linguistics. One accomplishment is the expression of positional value using versified Sanskrit number words in addition to orthodox inscribed numerals. The second is Panini’s invention, around<br>the fifth century BCE, of a formal (...) grammar for spoken Sanskrit, expressed in oral verse extending ordinary Sanskrit, and using recursive methods rediscovered in the twentieth century. The Sanskrit positional number compounds and Panini’s formal system are construed as linguistic grammaticalizations relying on tacit cognitive models of symbolic form. The thought experiment shows that universal computation can be constructed from natural language structure and skills, and shows why intentional capabilities needed for language use play a role in computation across all<br>media. The evolution of writing and positional number systems in Mesopotamia is used to transfer the thought experiment of “oral arithmetic” to inscribed computation. The thought experiment and historical evidence combine to show how and why mathematical computation is a cognitive technology extending generic symbolic skills associated with language structure, usage, and change. (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

Arbib et al.'s comprehensive review of neural organization, over-relies on modernist concepts and restricts our understanding of brain and behavior. Reliance on terms like coding, transformation, and representation perpetuates a “black-box approach” to the study of the brain. Recognition is due to the authors for attempting to introduce postmodern concepts such as chaos and self-organization to the study of neural organization. However, confusion occurs in the implementation of “biologically rooted” schema theory in which schemas are viewed as computer programs. The (...) inclusion of an additional functional level between structure and dynamics is unnecessary in a postmodernist perspective of brain and behavior. (shrink)

Artificial neural networks have weaknesses as models of cognition. A conventional neural network has limitations of computational power. The localist representation is at least equal to its competition. We contend that locally connected neural networks are perfectly capable of storing and retrieving the individual features, but the process of reconstruction must be otherwise explained. We support the localist position but propose a “hybrid” model that can begin to explain cognition in anatomically plausible terms.

Computationalism, a specie of functionalism, posits that a mental state like pain is realized by a ‘core’ computational state within a particular causal network of such states. This entails that what is realized by the core state is contingent on events remote in space and time, which puts computationalism at odds with the locality principle of physics. If computationalism is amended to respect locality, then it posits that a type of phenomenal experience is determined by a single type of computational (...) state. But a computational state, considered by itself, is of no determinate type—it has no particular symbolic content, since it could be embedded in any of an infinite number of algorithms. Hence, if locality is respected, then the type of experience that is realized by a computational state, or whether any experience at all is realized, is under-determined by the computational nature of the state. Accordingly, Block’s absent and inverted qualia arguments against functionalism find support in the locality principle of physics. If computationalism denies locality to avoid this problem, then it cannot be considered a physicalist theory since it would entail a commitment to phenomena, like teleological causation and action-at-a-distance, that have long been rejected by modern science. The remaining theoretical alternative is to accept the locality principle for macro events and deny that formal, computational operations are sufficient to realize a phenomenal mental state. (shrink)

My purpose in this brief paper is to consider the implications of a radically different computer architecure to some fundamental problems in the foundations of Cognitive Science. More exactly, I wish to consider the ramifications of the 'Gödel-Minds-Machines' controversy of the late 1960s on a dynamically changing computer architecture which, I venture to suggest, is going to revolutionize which 'functions' of the human mind can and cannot be modelled by (non-human) computational automata. I will proceed on the presupposition that the (...) reader is familiar with some of the fundamentals of computational theory and mathematical logic. (shrink)

The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scienti c claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog computation (...) and digital computation are not essentially di erent, and so arguments such as Searle's hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scienti c matter of fact. (4) A substantive scienti c question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it's all too easy to become ensnared. (shrink)

L. Albertazzi, G. J. van Tonder, and D. Vishwanath (eds): Perception Beyond Inference: The Information Content of Visual Processes Content Type Journal Article Pages 53-55 DOI 10.1007/s11023-011-9253-z Authors Lorenzo Magnani, Department of Philosophy and Computational Philosophy Laboratory, University of Pavia, Pavia, Italy Journal Minds and Machines Online ISSN 1572-8641 Print ISSN 0924-6495 Journal Volume Volume 22 Journal Issue Volume 22, Number 1.

What I call semiotic brains are brains that make up a series of signs and that are engaged in making or manifesting or reacting to a series of signs: through this semiotic activity they are at the same time engaged in “being minds” and so in thinking intelligently. An important effect of this semiotic activity of brains is a continuous process of disembodiment of mind that exhibits a new cognitive perspective on the mechanisms underling the semiotic emergence of meaning processes. (...) Indeed at the roots of sophisticated thinking abilities there is a process of disembodiment of mind that presents a new cognitive perspective on the role of external models, representations, and various semiotic materials. Taking advantage of Turing’s comparison between “unorganized” brains and “logical” and “practical” machines” this paper illustrates the centrality to cognition of the disembodiment of mind from the point of view of the interplay between internal and external representations, both mimetic and creative. The last part of the paper describes the concept of mimetic mind I have introduced to shed new cognitive and philosophical light on the role of computational modeling and on the decline of the so-called Cartesian computationalism. (shrink)

Missing from Quartz & Sejnowski's (Q&S's) unique and valuable effort to relate cognitive development to neural constructivism is an examination of the global emergent properties of adding new neural circuits. Such emergent properties can be studied with computational models. Modeling with generative connectionist networks shows that synaptogenic mechanisms can account for progressive increases in children's representational power.

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research (...) exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. (shrink)

We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.

Technical hitches mar van Gelder's proposed map of the conceptual landscape, particularly with respect to descriptive levels and the trio of instantiation, realisation, and implementation. However, for all the formal quibbles, van Gelder is onto something important; the tension he notes between computationalism and a dynamical alternative threatens to transform the way we conduct cognitive science research.

Thomas & Karmiloff-Smith (T&K-S) provide evidence from computational modeling against modular assumptions of “Residual Normality” (RN) in developmental disorders. Even though I agree with their criticism, I find their choice of empirical evidence disappointing. Cognitive neuroscience cannot as yet provide a complete understanding of most developmental disorders, but what is known is more than enough to debunk the idea of RN.

The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to symbol grounding. (...) In this case, no symbol grounding could take place since any grounding presupposes intentional cognitive processes. So, whether computing in the mind is over meaningless or over meaningful symbols, computationalism implies semantic nativism. (shrink)

This paper reports a computer program to generate novel designs for the arrangement of furniture within a simulated room. It is based on the engagement-reflection computer model of the creative processes. During engagement the system generates material in the form of sequences of actions (e.g. change the colours of the walls, include some furniture in the room, modify their colour, and so on) guided by content and knowledge constraints. During reflection, the system evaluates the composition produced so far and, if (...) it is necessary, modifies it. We discuss the implementation of the system and some of its most salient features, especially the use of a computational model for creativity in the terrain of design. We argue that this kind of model opens new possibilities for the simulation of the design processes as well as the development of tools. (shrink)

Top-down feedback does not benefit speech recognition; on the contrary, it can hinder it. No experimental data imply that feedback loops are required for speech recognition. Feedback is accordingly unnecessary and spoken word recognition is modular. To defend this thesis, we analyse lexical involvement in phonemic decision making. TRACE (McClelland & Elman 1986), a model with feedback from the lexicon to prelexical processes, is unable to account for all the available data on phonemic decision making. The modular Race model (Cutler (...) & Norris 1979) is likewise challenged by some recent results, however. We therefore present a new modular model of phonemic decision making, the Merge model. In Merge, information flows from prelexical processes to the lexicon without feedback. Because phonemic decisions are based on the merging of prelexical and lexical information, Merge correctly predicts lexical involvement in phonemic decisions in both words and nonwords. Computer simulations show how Merge is able to account for the data through a process of competition between lexical hypotheses. We discuss the issue of feedback in other areas of language processing and conclude that modular models are particularly well suited to the problems and constraints of speech recognition. Key Words: computational modeling; feedback; lexical processing; modularity; phonemic decisions; reading; speech recognition; word recognition. (shrink)

One of the most impressive feats in robotics was the 2005 victory by a driverless Volkswagen Touareg in the DARPA Grand Challenge. This paper discusses what can be learned about the nature of representation from the car’s successful attempt to navigate the world. We review the hardware and software that it uses to interact with its environment, and describe how these techniques enable it to represent the world. We discuss robosemantics, the meaning of computational structures in robots. We argue that (...) the car constitutes a refutation of semantic arguments against the possibility of strong artificial intelligence. (shrink)

Concerning the question about consciousness, Georges Rey argues that it does not exist from the success of computational theory of human mind. Everything that such a theory requires can be fulfilled by machines which do not have consciousness. So, according to theoretical parsimony, we do not have to attribute consciousness even to human beings. I wish to offer reasons why we should not doubt the existence of consciousness by showing that computational explanations can be explanations of just one part of (...) an aspect of the human mind. Consciousness is also an explanandum rather than an explanans, and the possible reference of “what it is like” expression. Epistemic situation regarding possible accesses to consciousness is also considered. (shrink)

Theories of mind draw on processes that represent mental states and their computational connections; simulation, in addition, draws on processes that replicate (Heal 1986 ) a sequence of mental states. Moreover, mental simulation can be triggered by input from imagination instead of real perceptions. To avoid confusion between mental states concerning reality and those created in simulation, imagined contents must be quarantined. Goldman bypasses this problem by giving pretend states a special role to play in simulation (Goldman 2006 ). We (...) argue that this path leads to the resurgence of the threat of collapse (Davies 1994 ), diluting the principled distinction between simulation and theory use. Exploration of a related method of real-mental states operating in a pretend mode leads to a factually untenable model. Our main goal here is to raise this problem as a challenge for Goldman’s reconfigured simulation theory. Only at the end we will briefly sketch a possible alternative way of quarantine that preserves the replicative element of simulation and avoids collapse. Figure 1 provides a guide to our argument. (shrink)