This is a review of Mind Design II: Philosophy, Psychology, and Artificial Intelligence, edited by John Haugeland, published by MIT Press in 1997.

The relation between computational and intentional psychology has always been a vexing issue. The worry is that if mental processes are computational, then these processes, which are defined over symbols, are sensitive solely to the non-semantic properties of symbols. If so, perhaps psychology could dispense with adverting in its laws to intentional/semantic properties of symbols. Stich, as is well-known, has made a great deal out of this tension and argued for a purely "syntactic" psychology by driving a wedge between a (...) semantic individuation of symbol tokens and their narrow functional individuation. If the latter can be carried out, he claimed, we do not need semantic typing. I argue that since a narrow functional individuation cannot type-identify symbol tokens across organisms, a semantic account of typing must be the only option given that interpersonal physical individuation of tokens is not to be taken seriously. (shrink)

According to the Computational/Representational Theory of Thought (CRTT ? Language of Thought Hypothesis, or LOTH), propositional attitudes, such as belief, desire, and the like, are triadic relations among subjects, propositions, and internal mental representations. These representations form a representational _system_ physically realized in the brain of sufficiently sophisticated cognitive organisms. Further, this system of representations has a combinatorial syntax and semantics, but the processes that operate on the representations are causally sensitive only to their syntax, not to their semantics. On (...) this approach, a first pass account of propositional attitudes is the following (cf. Field 1978: 37 and Fodor 1987: 17). (shrink)

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)

The role of content in computational accounts of cognition is a matter of some controversy. An early prominent view held that the explanatory relevance of content consists in its supervenience on the the formal properties of computational states (see, e.g., Fodor 1980). For reasons that derive from the familiar Twin Earth thought experiments, it is usually thought that if content is to supervene on formal properties, it must be narrow; that is, it must not be the sort of content that (...) determines reference and truth-conditions. An interesting alternative to this view has recently been proposed by Egan (1995). According to Egan, the explanatory role of content is such that contents must in general be broad to be explanatorily relevant. But Egan’s view involves a non-realist interpretation of content assignments. I will argue here that this non-realism about contents is undermotivated. A realist variation on her view of the explanatory role of content, however, would survive this criticism. This realist variation, I suggest, shares with the views of other commentators on Marr’s theory (e.g., Burge 1986; Shapiro 1993; forthcoming) certain commitments concerning the supervenience base of visual contents and processes. I will argue, however, that these commitments beg important questions regarding the individuation of cognitive states and processes. I conclude, contrary to Burge and Shapiro, that Marr’s theory does not favor anti-individualism. (shrink)

High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...) BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context. (shrink)

How can the human mind represent the external world? What is thought, and can it be studied scientifically? Does it help to think of the mind as a kind of machine? Tim Crane sets out to answer questions like these in a lively and straightforward way, presuming no prior knowledge of philosophy or related disciplines. Since its first publication in 1995, The Mechanical Mind has introduced thousands of people to some of the most important ideas in contemporary philosophy of mind. (...) Tim Crane explains some fundamental ideas that cut across philosophy of mind, artificial intelligence and cognitive science: what the mind-body problem is; what a computer is and how it works; what thoughts are and how computers and minds might have them. He examines different models of the mind from dualist to eliminativist, and questions whether there can be thought without language and whether the mind is subject to the same causal lsaws as natural phenomena. The result is a fascinating exploration of the theories and arguments surrounding the notions of thought and representation. The edition has been fully revised and updated, and includes a new chapter on consciousness and new sections on modularity and evolutionary psychology. There are also guides for further reading, a chronology and a new glossary of terms such as mentalese, connectionism and the homonculus fallacy. The Mechanical Mind is accessible to the general reader as well as students, and anyone interested in the mechanism of our minds. (shrink)

The confusion between cognitive states and the content of cognitive states that gives rise to psychologism also gives rise to reverse psychologism. Weak reverse psychologism says that we can study cognitive states by studying content – for instance, that we can study the mind by studying linguistics or logic. This attitude is endemic in cognitive science and linguistic theory. Strong reverse psychologism says that we can generate cognitive states by giving computers representations that express the content of cognitive states and (...) that play a role in causing appropriate behaviour. This gives us strong representational, classical AI (REPSCAI), and I argue that it cannot succeed. This is not, as Searle claims in his Chinese Room Argument, because syntactic manipulation cannot generate content. Syntactic manipulation can generate content, and this is abundantly clear in the Chinese Room scenano. REPSCAI cannot succeed because inner content is not sufficient for cognition, even when the representations that carry the content play a role in generating appropriate behaviour. (shrink)

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. (shrink)

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. (shrink)

The book focuses on relations between information and computation. Information is a basic structure of the world, while computation is a process of the dynamic change of information. In order for anything to exist for an individual, the individual must get information on it, either by means of perception or by re-organization of the existing information into new patterns and networks in the brain. With the advent of World Wide Web and a prospect of semantic web, the ways of information (...) supply for individuals, networks of humans and machines and for humanity as a whole are becoming strategically important in a number of ways. Information becomes pivotal for communication, research, education systems, government, businesses and basic functioning of everyday life. At the same time, information may be understood only if we understand its dynamics - time changes of informational structure, that is, we should understand information processing and its primary form - computation. As there is no information without (physical) representation, the dynamics of information is implemented on different levels of granularity by different physical processes, including the level of computation performed by computing machines. There are a lot of open problems of the nature of information and computation, as well as their relationships. How exactly is information dynamics implemented in computational systems, machines as well as living organisms? Are computers processing only data or information and knowledge as well? How does information processing relate to knowledge management and sciences, especially to science of information itself? What do we know of computational processes in machines and living organisms and how these processes are related? What can we learn from natural computational processes that can be useful for information systems and knowledge management? These and similar problems related to information and computation are treated in the book. (shrink)

John Searle believes that computational properties are purely formal and that consequently, computational properties are not intrinsic, empirically discoverable, nor causal; and therefore, that an entity’s having certain computational properties could not be sufficient for its having certain mental properties. To make his case, Searle’s employs an argument that had been used before him by Max Newman, against Russell’s structuralism; one that Russell himself considered fatal to his own position. This paper formulates a not-so-explored version of Searle’s problem with computational (...) cognitive science, and refutes it by suggesting how our understanding of computation is far from implying the structuralism Searle vitally attributes to it. On the way, I formulate and argue for a thesis that strengthens Newman’s case against Russell’s structuralism, and thus raises the apparent risk for computational cognitive science too. (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)

I review a widely accepted argument to the conclusion that the contents of our beliefs, desires and other mental states cannot be causally efficacious in a classical computational model of the mind. I reply that this argument rests essentially on an assumption about the nature of neural structure that we have no good scientific reason to accept. I conclude that computationalism is compatible with wide semantic causal efficacy, and suggest how the computational model might be modified to accommodate this possibility.

To move beyond vague platitudes about the importance of context in legal reasoning or natural language understanding, one must take account of ideas from artificial intelligence on how to represent context formally. Work on topics like prior probabilities, the theory-ladenness of observation, encyclopedic knowledge for disambiguation in language translation and pathology test diagnosis has produced a body of knowledge on how to represent context in artificial intelligence applications.

Readers choosing to download the article--one must use the "other links" tab--should please be so kind as to respect the author's wishes and also see the Erratum et Corrigendum which is locally available by simply clicking on the article title. -/- Cross-examination of witnesses is not an opportunity to explore the beliefs of those on the stand on even relevant matters. Rather, it is an opportunity to impeach evidence given by the witness during direct examination. That is possible in two (...) ways: questioning the credibility of the witness directly, for example by bringing up a perjury conviction or by asking what favors he received in expectation of his future testimony, or questioning the witness’ credibility indirectly, by trying to disassemble the story he has woven on examination-in-chief. We are concerned with formalizing this last process. Like the question of the whole truth, the questioner is allowed considerable latitude, but that latitude is bounded by the examination in-chief. Also like that question, any formalization will have to use a logic which models questions and answers, not just propositions. Such a logic is called an erotetic logic, and we review the basics of the erotetic logic we will be using and then present our formalization using that logic. (shrink)

According to Ramsey (Representation reconsidered, Cambridge University Press, New York, 2007), only classical cognitive science, with the related notions of input–output and structural representations, meets the job description challenge (the challenge to show that a certain structure or process serves a representational role at the subpersonal level). By contrast, connectionism and other nonclassical models, insofar as they exploit receptor and tacit notions of representation, are not genuinely representational. As a result, Ramsey submits, cognitive science is taking a U-turn from representationalism (...) back to behaviourism, thus presupposing that (1) the emergence of cognitivism capitalized on the concept of representation, and that (2) the materialization of nonclassical cognitive science involves a return to some form of pre-cognitivist behaviourism. We argue against both (1) and (2), by questioning Ramsey’s divide between classical and representational, versus nonclassical and nonrepresentational, cognitive models. For, firstly, connectionist and other nonclassical accounts have the resources to exploit the notion of a structural isomorphism, like classical accounts (the beefing-up strategy); and, secondly, insofar as input–output and structural representations refer to a cognitive agent, classical explanations fail to meet the job description challenge (the deflationary strategy). Both strategies work independently of each other: if the deflationary strategy succeeds, contra (1), cognitivism has failed to capitalize on the relevant concept of representation; if the beefing-up strategy is sound, contra (2), the return to a pre-cognitivist era cancels out. (shrink)

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 we attempt to develop a problem representation technique which enables the decomposition of a problem into subproblems such that their solution in sequence constitutes a strategy for solving the problem. An important issue here is that the subproblems generated should be easier than the main problem. We propose to represent a set of problem states by a statement which is true for all the members of the set. A statement itself is just a set of atomic statements (...) which are binary predicates on state variables. Then, the statement representing the set of goal states can be partitioned into its subsets each of which becomes a subgoal of the resulting strategy. The techniques involved in partitioning a goal into its subgoals are presented with examples. (shrink)

Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical underdetermination of the theory (...) by the data. Level t1 is clearly too underdetermined, T2 is vulnerable to a counterexample (Searle's Chinese Room Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3 is the appropriate target level for cognitive science. When it is reached, however, there will still remain more unanswerable questions than when Physics reaches its Grand Unified Theory of Everything (GUTE), because of the mind/body problem and the other-minds problem, both of which are inherent in this empirical domain, even though Turing hardly mentions them. (shrink)

Computational properties, it is standardly assumed, are to be sharply distinguished from semantic properties. Specifically, while it is standardly assumed that the semantic properties of a cognitive system are externally or non-individualistically individuated, computational properties are supposed to be individualistic and internal. Yet some philosophers (e.g., Tyler Burge) argue that content impacts computation, and further, that environmental factors impact computation. Oron Shagrir has recently argued for these theses in a novel way, and gave them novel interpretations. In this paper I (...) present a conception of computation in cognitive science that takes Shagrir's conception as its starting point, but further develops it in various directions and strengthens it. I argue that the explanatory role of computational properties emerges from the idea that syntactical properties and the relevant external factors presented by cognitive systems compose wide computational properties. I also elaborate upon the notion of content that is in play, and argue that it is contents of the kind that are ascribed by transparent interpretations of content ascriptions that impact computation. This fact enables the thesis that external factors impact computation to rebuff the challenge which concerns the claim that psychology must be individualistic. (shrink)

This paper surveys applications of logical methods in the cognitive sciences. Special attention is paid to non-monotonic logics and complexity theory. We argue that these particular tools have been useful in clarifying the debate between symbolic and connectionist models of cognition.

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)

For reasons internal to the concepts of thought and causality, a series of representations must be semantics-driven if that series is to add up to a single, unified thought. Where semantics is not operative, there is at most a series of disjoint representations that add up to nothing true or false, and therefore do not constitute a thought at all. There is necessarily a gulf between simulating thought, on the one hand, and actually thinking, on the other. It doesn't matter (...) how perfect the simulation is; nor does it matter how reliable the causal mechanism involved is. Where semantics is inert, there is no thought. In connection with this, this paper also argues that a popular doctrine - the so-called 'computational theory of mind' (CTM) - is based on a confusion. CTM is the view that thought-processes consist in 'computations', where a computation is defined as a 'form-driven' operation on symbols. The expression 'form-driven operation' is ambiguous, and may refer either to syntax-driven operations or to morphology-driven operations. Syntax-driven operations presuppose the existence of operations that are driven by semantic and extra-semantic knowledge. So CTM is false if the terms 'computation' and 'form-driven operation' are taken to refer to syntax-driven operations. So if CTM is to work, those expressions must be taken to refer to morphology-driven operations. But, as previously stated, an operation must be semantics-driven if it is to qualify as a thought. Thus CTM fails on every disambiguation of the expressions 'formal operation' and 'computation'. (shrink)

Philosophical Psychology, Volume 0, Issue 0, Page 1-7, Ahead of Print.

Why is interaction so simple? This article presents a theory of interaction based on the use of shared representations as coordination tools (e.g., roundabouts that facilitate coordination of drivers). By aligning their representations (intentionally or unintentionally), interacting agents help one another to solve interaction problems in that they remain predictable, and offer cues for action selection and goal monitoring. We illustrate how this strategy works in a joint task (building together a tower of bricks) and discuss its requirements from a (...) computational viewpoint. (shrink)

Humans and other animals are able not only to coordinate their actions with their current sensorimotor state, but also to imagine, plan and act in view of the future, and to realize distal goals. In this paper we discuss whether or not their future-oriented conducts imply (future-oriented) representations. We illustrate the role played by anticipatory mechanisms in natural and artificial agents, and we propose a notion of representation that is grounded in the agent’s predictive capabilities. Therefore, we argue that the (...) ability that characterizes and defines a true cognitive mind, as opposed to a merely adaptive system, is that of building representations of the non-existent, of what is not currently (yet) true or perceivable, of what is desired. A real mental activity begins when the organism is able to endogenously (i.e. not as the consequence of current perceptual stimuli) produce an internal representation of the world in order to select and guide its conduct goal-directed: the mind serves to coordinate with the future. (shrink)

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...) this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism on the other. We defend the relevance to cognitive science of both computation, in a generic sense that we fully articulate for the first time, and information processing, in three important senses of the term. Our account advances some foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects. (shrink)

Hilary Putnam, who may have been the first philosopher to advance the notion that the computer is an apt model for the mind, takes a radically new view of his...

It has been over thirty years since the publication of Jerry Fodor’s landmark book The Language of Thought (LOT 1). In LOT 2: The Language of Thought Revisited, Fodor provides an update on his thoughts concerning a range of topics that have been the focus of his work in the intervening decades. The Representational Theory of Mind (RTM), the central thesis of LOT 1, remains intact in LOT 2: mental states are relations between organisms and syntactically-structured mental representations, and mental (...) processes are computations defined over such representations. The differences between LOT 1 and LOT 2 are mostly differences of focus. Whereas LOT 1 had a number of targets—e.g. reductionism, behaviorism, empiricism, and operationalism—LOT 2 identifies “pragmatism” as the main enemy of the “Cartesian” kind of mentalism Fodor favors (pp. 11-12). Moreover, unlike LOT 1, a main aim of LOT 2 is to defend a theory of concepts that is atomistic and referentialist: lexical concepts lack structure, and their meaning is determined by their relation to the world and not by their relations to other concepts (pp. 16-20). In addition to new discussions of concepts and content, LOT 2 treats us to Fodor’s latest thoughts on compositionality, computationalism, nativism, nonconceptual content, and the causal theory of reference. Although those familiar with Fodor’s work over the last thirty years will find its main conclusions unsurprising, LOT 2 is nevertheless an exciting, breezily written book that’s full of stimulating arguments and (in standard Fodor style) immensely interesting digressions. In the Introduction, Fodor bundles together a number of distinct doctrines under “pragmatism”—e.g., that “knowing how is the paradigm cognitive state and it is prior to knowing that in the order of intentional explanation” (p. 10), and that “the distinctive function of the mind is guiding action” (p. 13). But it’s clear by Chapter 2 that his main target is “concept pragmatism,” according to which concepts are individuated by their inferential properties. Fodor’s “Cartesianism,” in contrast, has it that none of the epistemic properties of concepts are constitutive.. (shrink)

Introduction There are some exceptions, which we shall see below, but virtually all theories in psychology and cognitive science make use of the notion of representation. Arguably, folk psychology also traffics in representations, or is at least strongly suggestive of their existence. There are many different types of things discussed in the psychological and philosophical literature that are candidates for representation-hood. First, there are the propositional attitudes – beliefs, judgments, desires, hopes etc. (see Chapters 9 and 17 of this volume). (...) If the propositional attitudes are representations, they are person-level representations – the judgment that the sun is bright pertains to John, not a subpersonal part of John. By contrast, the representations of edges in V1 of the cerebral cortex that neuroscientists talk about and David Marr’s symbolic representations of “zero-crossings” in early vision (Marr 1982) are at the “sub-personal” level – they apply to parts or states of a person (e.g. neural parts or computational states of the visual system). Another important distinction is often made among perceptual, cognitive, and action-oriented representations (e.g. motor commands). Another contrast lies between “stored representations” (e.g. memories) and “active representations” (e.g. a current perceptual state). Related to this is the distinction between “dispositional representations” and “occurrent representations.” Beliefs that are not currently being entertained are dispositional, e.g. your belief that the United States is in North America - no doubt you had this belief two minutes ago, but you were not consciously accessing it until you read this sentence. Occurrent representations, by contrast, are active, conscious thoughts or perceptions. Which leads us to another important distinction: 1 between conscious and non-conscious mental representations, once a bizarre-sounding distinction that has become familiar since Freud (see Chapter 4 of this volume). I mention these distinctions at the outset to give you some idea of the range of phenomena we will be considering, and to set the stage for our central “problem of representation”: what is a mental representation, exactly, and how do we go about deciding whether there are any? We know there are public representations of various kinds: words, maps, and pictures, among others.. (shrink)

Allen Newell (1973) once observed that psychology researchers were playing “twenty questions with nature,” carving up human cognition into hundreds of individual phenomena but shying away from the difficult task of integrating these phenomena with unifying theories. We argue that research on cognitive control has followed a similar path, and that the best approach toward unifying theories of cognitive control is that proposed by Newell, namely developing theories in computational cognitive architectures. Threaded cognition, a recent theory developed within the ACT-R (...) cognitive architecture, offers promise as a unifying theory of cognitive control that addresses multitasking phenomena for both laboratory and applied task domains. (shrink)

The paper presents an extended argument for the claim that mental content impacts the computational individuation of a cognitive system (section 2). The argument starts with the observation that a cognitive system may simultaneously implement a variety of different syntactic structures, but that the computational identity of a cognitive system is given by only one of these implemented syntactic structures. It is then asked what are the features that determine which of implemented syntactic structures is the computational structure of the (...) system, and it is contended that these features are certain aspects of mental content. The argument helps (section 3) to reassess the thesis known as computational externalism, namely, the thesis that computational theories of cognition make essential reference to features in the individual's environment. It is suggested that the familiar arguments for computational externalism?which rest on thought experiments and on exegesis of Marr's theories of vision?are unconvincing, but that they can be improved. A reconstruction of the visex/audex thought experiment is offered in section 3.1. An outline of a novel interpretation of Marr's theories of vision is presented in section 3.2. The corrected arguments support the claim that computational theories of cognition are intentional. Computational externalism is still pending, however, upon the thesis that psychological content is extrinsic. (shrink)

Searle’s celebrated Chinese room thought experiment was devised as an attempted refutation of the view that appropriately programmed digital computers literally are the possessors of genuine mental states. A standard reply to Searle, known as the “robot reply” (which, I argue, reflects the dominant approach to the problem of content in contemporary philosophy of mind), consists of the claim that the problem he raises can be solved by supplementing the computational device with some “appropriate” environmental hookups. I argue that not (...) only does Searle himself casts doubt on the adequacy of this idea by applying to it a slightly revised version of his original argument, but that the weakness of this encoding-based approach to the problem of intentionality can also be exposed from a somewhat different angle. Capitalizing on the work of several authors and, in particular, on that of psychologist Mark Bickhard, I argue that the existence of symbol-world correspondence is not a property that the cognitive system itself can appreciate, from its own perspective, by interacting with the symbol and therefore, not a property that can constitute intrinsic content. The foundational crisis to which Searle alluded is, I conclude, very much alive. (shrink)

Many philosophers and psychologists who approach the issue of representation from a computational or measurement theoretical perspective end up having to deny the possibility of junk representations?representations present in an organism's head but that enter into no psychological processes or produce no behaviour. However, I argue, a more functional perspective makes the possibility of junk representations intuitively quite plausible?so much so that we may wish to question those views of representation that preclude the possibility of junk representations. I explore some (...) of the reasons we should care about the possibility of junk representations and conclude with some speculation about whether junk representations are in fact present in our heads. (shrink)

The question is, How does the brain make its mind? In Cognition, computation and consciousness [Ito et al. (Eds) (1997) Oxford & New York: Oxford University Press], a variety of noted theoreticians from the fields of cognitive psychology, computer science, and philosophy postulate answer-blueprints rather than full-blown explanatory solutions to this most nettlesome question. Coming to the problem from quite different starting points and perspectives, they nevertheless succeed in reaching consensus on the idea that the contingencies of the brain's evolution (...) have resulted in an organ that generates its mind by a complex process of information exchange among its constituents. Put in the vernacular, the brain produces its mind by having its parts, especially those most recently evolved, talk to each other. In this essay I take a critical look at proposals of several celebrated (neuro)scientists and philosophers in their specific areas of expertise. The underlying theme of brain component communication suggests the image of conversations in the cortex. From such cortical conversations arise selves (the mind/brain's I) and their stories and projects. This in turn suggests the idea that the brain is a stage where a Pirandello-like play is continually rehearsed. (shrink)

The distinction between mature and immature science is controversial. Laudan (1977) disavows the idea of immature science while Von Eckardt (1993) claims that cognitive science is just that (an immature science) and modifies Laudan's Research Tradition methodology to argue its rational pursuability . She uses the (Kuhnian) idea of a framework of shared characteristics (FSC) to identify the community of cognitive scientists. Diverse community assumptions pertaining specifically to human cognitive capacities (should) consolidate cognitive research efforts into a coherent and rationally (...) pursuable scientific endeavor. Von Eckardt maintains that the substantive assumptions about the computational and representational character of human cognitive capacities are central to the rational reconstruction of immature cognitive science in two ways. Descriptively, these assumptions are evident in the cognitive science community. Normatively, the assumptions satisfy justificatory conditions on the rational pursuit of this computational, cognitive science research tradition. Normativity is a problem for any naturalistic approach and thus for Von Eckardt's FSC. I critique the FSC strategy and present a modestly naturalistic alternative based on ideas of Goodman, Quine, and more recently, Philip Kitcher. I apply them to the “childhood afflictions” endemic to immaturity, scientific and otherwise. I test my critique of immature computational cognitive science by discussing two phenomena that, in my view, belong properly to any theory of human cognition but are noticeably absent from Von Eckardt's FSC reconstruction. I conclude that understanding the reasons why the FSC view fails can and should contribute to the development of a successful and complete theory of human cognition. (shrink)

The ‘received view’ about computation is that all computations must involve representational content. Egan and Piccinini argue against the received view. In this paper, I focus on Egan’s arguments, claiming that they fall short of establishing that computations do not involve representational content. I provide positive arguments explaining why computation has to involve representational content, and how the representational content may be of any type (e.g. distal, broad, etc.). I also argue (contra Egan and Fodor) that there is no need (...) for computational psychology to be individualistic. Finally, I draw out a number of consequences for computational individuation, proposing necessary conditions on computational identity and necessary and sufficient conditions on computational I/O equivalence of physical.. (shrink)

William Ramsey’s Representation Reconsidered is a superb, insightful analysis of the notion of mental representation in cognitive science. The book presents an original argument for a bold conclusion: partial eliminativism about mental representation in scientific psychology. According to Ramsey, once we examine the conditions that need to be satisfied for something to qualify as a representation, we can see those conditions are not fulfilled by the ‘representations’ posited by much of modern psychology. Cognitive science—or at least large swathes of it—has (...) no warrant for positing representations. The structure of Ramsey’s argument repeats a familiar eliminativist strategy (c.f. Churchland (1981); Stich (1983)).1 First step: argue that in order for something to be an X, it must satisfy a certain description D (say, beliefs must satisfy the description given in folk psychology). Second step: argue that to the best of our knowledge, nothing satisfies description D (e.g. folk psychology is false). Third step: conclude that since nothing satisfies description D, there are no Xs (no beliefs). Here is how the strategy is played out in the book. First, Ramsey argues for certain minimal conditions that a representation must satisfy (what he calls the ‘job description’). Second (this takes the bulk of the book), he considers the ways in which our best psychological theories use the notion of representation. Ramsey argues that none of these uses satisfy the job description associated with a genuine representation. (A wrinkle is that some representations—those posited by the classical computational theory of cognition—do qualify as true representations. But, Ramsey claims, classical theories are in a minority in cognitive science, and their hold on the field is shrinking.) Therefore, Ramsey concludes, in most of cognitive science, there are no mental representations. (shrink)

Relevance Theory is the influential theory of linguistic interpretation first championed by Dan Sperber and Deirdre Wilson. Relevance theorists have made important contributions to our understanding of a wide range of constructions, especially constructions that tend to receive less attention in semantics and philosophy of language. But advocates of Relevance Theory also have had a tendency to form a rather closed community, with an unwillingness to translate their own special vocabulary and distinctions into more neutral vernacular. Since Robyn Carston has (...) long been the advocate of Relevance Theory most able to communicate with a broader philosophical and linguistic audience, it is with particular interest that the emergence of her long-awaited volume, Thoughts and Utterances has been greeted. The volume exhibits many of the strengths, but also some of the weaknesses, of this well-known program. (shrink)

Advocates of the computational theory of mind claim that the mind is a computer whose operations can be implemented by various computational systems. According to these philosophers, the mind is multiply realisable because—as they claim—thinking involves the manipulation of syntactically structured mental representations. Since syntactically structured representations can be made of different kinds of material while performing the same calculation, mental processes can also be implemented by different kinds of material. From this perspective, consciousness plays a minor role in mental (...) activity. However, contemporary neuroscience provides experimental evidence suggesting that mental representations necessarily involve consciousness. Consciousness does not only enable individuals to become aware of their own thoughts, it also constantly changes the causal properties of these thoughts. In light of these empirical studies, mental representations appear to be intrinsically dependent on consciousness. This discovery represents an obstacle to any attempt to construct an artificial mind. (shrink)

The paper uses ideas from Machine Learning, Artificial Intelligence and Genetic Algorithms to provide a model of the development of a fight-or-flight response in a simulated agent. The modelled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structures which can be given a representational interpretation. It also shows how these may form the infrastructure for (...) closely-coupled agent/environment interaction. (shrink)

This is an encyclopedia entry and does not include an abstract.

The "New Synthesis" in cognitive science is committed to the computational theory of mind (CTM), massive modularity, nativism, and adaptationism. In The mind doesn't work that way , Jerry Fodor argues that CTM has problems explaining abductive or global inference, but that the New Synthesis offers no solution, since massive modularity is in fact incompatible with global cognitive processes. I argue that it is not clear how global human mentation is, so whether CTM is imperiled is an open question. Massive (...) modularity also lacks some of the invidious commitments Fodor ascribes to it. Furthermore, Fodor's anti-adaptationist arguments are in tension with his nativism about the contents of modular systems. The New Synthesis thus has points worth preserving. (shrink)