Intelligent systems are faced with the problem of securing a principled (ideally, veridical) relationship between the world and its internal representation. I propose a unified approach to visual representation, addressing both the needs of superordinate and basic-level categorization and of identification of specific instances of familiar categories. According to the proposed theory, a shape is represented by its similarity to a number of reference shapes, measured in a high-dimensional space of elementary features. This amounts to embedding the stimulus in a (...) low-dimensional proximal shape space. That space turns out to support representation of distal shape similarities which is veridical in the sense of Shepard's (1968) notion of second-order isomorphism (i.e., correspondence between distal and proximal similarities among shapes, rather than between distal shapes and their proximal representations). Furthermore, a general expression for similarity between two stimuli, based on comparisons to reference shapes, can be used to derive models of perceived similarity ranging from continuous, symmetric, and hierarchical, as in the multidimensional scaling models (Shepard, 1980), to discrete and non-hierarchical, as in the general contrast models (Tversky, 1977; Shepard and Arabie, 1979). (shrink)

Computationalist theories of mind require brain symbols, that is, neural events that represent kinds or instances of kinds. Standard models of computation require multiple inscriptions of symbols with the same representational content. The satisfaction of two conditions makes it easy to see how this requirement is met in computers, but we have no reason to think that these conditions are satisfied in the brain. Thus, if we wish to give computationalist explanations of human cognition, without committing ourselvesa priori to a (...) strong and unsupported claim in neuroscience, we must first either explain how we can provide multiple brain symbols with the same content, or explain how we can abandon standard models of computation. It is argued that both of these alternatives require us to explain the execution of complex tasks that have a cognition-like structure. Circularity or regress are thus threatened, unless noncomputationalist principles can provide the required explanations. But in the latter case, we do not know that noncomputationalist principles might not bear most of the weight of explaining cognition. Four possible types of computationalist theory are discussed; none appears to provide a promising solution to the problem. Thus, despite known difficulties in noncomputationalist investigations, we have every reason to pursue the search for noncomputationalist principles in cognitive theory. (shrink)

I focus on and criticize John Searle's argument against a classical computational view of the mind according to which the attribution of syntax is observer relative (in Searle, Rediscovery of Mind, MIT Press, 1992). Searle's argument is interesting inasmuch as it differs from his previous and more well-known argument that syntax is not sufficient for semantics. This argument aims to undercut even the syntax as something that exists only in the eye of the beholder. I show that Searle's argument rests (...) on premises that go unsupported. (shrink)

I identify three lessons from Kenneth Craik’s landmark book “The Nature of Explanation” for contemporary debates surrounding the existence, extent, and nature of mental representation: first, an account of mental representations as neural structures that function analogously to public models; second, an appreciation of prediction as the central component of intelligence in demand of such models; and third, a metaphor for understanding the brain as an engineer, not a scientist. I then relate these insights to discussions surrounding the representational status (...) of predictive processing – which, I argue, provides a contemporary vindication of Craik’s extremely prescient “hypothesis on the nature of thought.”. (shrink)

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...) solved by bio-cognitive systems, chiefly regulation and prediction. We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of dynamical models as a central postulate in neurocognitive research. (shrink)

In a recent article in this journal (Adams and Aizawa 1992), Fred Adams and Ken Aizawa argued that Jerry Fodor's proposed naturalistic sufficient condition for meaning is unsatisfactory. In this paper, I respond to Adams and Aizawa, noting that (1) they have overestimated the importance of their “pathologies” objection, perhaps as a consequence of misunderstanding Fodor's asymmetric dependency condition, (2) they have misunderstood Fodor's asymmetric dependency condition in formulating their Twin Earth objection, and (3) they have, in addition to under (...) describing their “clear counterexample” to Fodor's proposal, in fact identified a satisfactory Fodorian rejoiner to their objection. I conclude that Fodor's proposal is, for all Adams and Aizawa have shown, adequate as a naturalistic theory of content. (shrink)

Cognitive science, at least as done by many philosophers, seeks to develop what one might call a content-based theory of cognition. These theorists generally seek to predict/explain cognition by employing generalizations between contentful states like beliefs and desires. In his book, From Folk Psychology To Cognitive Science, Stephen Stich argues that cognitive science should not attempt to employ content-based theories in its explanations of human (and other) behavior. For the most part Stich directs his arguments towards belief/desire psychology. Some of (...) his arguments, however, are directed at content-based theories in general. In this paper, I will be concerned with one of these more general arguments—the principle of autonomy argument.The conclusion of the principle of autonomy argument is that the predictions and explanations generated by content-based theories, in so far as they are possible, do not fall within the domain of prediction and explanation in cognitive science. (shrink)

This paper examines the nomic covariationist strategy of using idealization to define representation. While the literature has focused upon the possibility of defining ideal conditions for perception, I argue that nomic covariationist appeals to idealization are pseudoscientific and contrary to a foundational and empirically well-supported methodological presupposition in cognitive science. Moreover, one major figure in this camp fails to come to grips with its role and its problems in mainstream science. Thus he forwards a false dichotomy of the sciences and (...) treats idealization as a blank check written by scientists on an unknown bank. Finally, I consider and reject alternative formulations of the nomic covariationist's idealization strategy. (shrink)

Psychological individualism is motivated by two taxonomic principles: (i) that psychological states are individuated by their causal powers, and (ii) that causal powers supervene upon intrinsic physiological state. I distinguish two interpretations of individualism--the 'orthodox' and the 'alternative'--each of which is consistent with these motivating principles. I argue that the alternative interpretation is legitimately individualistic on the grounds that it accurately reflects the actual taxonomic practices of bona fide individualistic sciences. The classification of homeobox genes in developmental genetics provides an (...) illustration. When applied to the taxonomy of psychological kinds, alternative individualism has some surprising consequences. In particular, externalist taxonomies of thought are consistent with the alternative interpretation, and hence consistent with individualism. I conclude, on this basis, that the individualism/externalism dispute which has long preoccupied philosophy of psychology is an empty one. (shrink)

The distinction between analog and digital representation is reexamined; it emerges that a more fundamental distinction is that between symbolic and analog simulation. Analog simulation is analyzed in terms of a (near) isomorphism of causal structures between a simulating and a simulated process. It is then argued that a core concept, naturalistic analog simulation, may play a role in a bottom-up theory of adaptive behavior which provides an alternative to representational analyses. The appendix discusses some formal conditions for naturalistic analog (...) simulation. (shrink)

The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.

Can theories of mental imagery, conscious mental contents, developed within cognitive science throw light on the obscure (but culturally very significant) concept of imagination? Three extant views of mental imagery are considered: quasi‐pictorial, description, and perceptual activity theories. The first two face serious theoretical and empirical difficulties. The third is (for historically contingent reasons) little known, theoretically underdeveloped, and empirically untried, but has real explanatory potential. It rejects the “traditional” symbolic computational view of mental contents, but is compatible with recentsituated (...) cognition and active visionapproaches in robotics. This theory is developed and elucidated. Three related key aspects of imagination (non‐discursiveness, creativity, and seeing as) raise difficulties for the other theories. Perceptual activity theory presents imagery as non‐discursive and relates it closely to seeing as. It is thus well placed to be the basis for a general theory of imagination and its role in creative thought. (shrink)

I argue against theories that attempt to reduce scientific representation to similarity or isomorphism. These reductive theories aim to radically naturalize the notion of representation, since they treat scientist's purposes and intentions as non-essential to representation. I distinguish between the means and the constituents of representation, and I argue that similarity and isomorphism are common but not universal means of representation. I then present four other arguments to show that similarity and isomorphism are not the constituents of scientific representation. I (...) finish by looking at the prospects for weakened versions of these theories, and I argue that only those that abandon the aim to radically naturalize scientific representation are likely to be successful. (shrink)

The notion of “intentionality” is much invoked in various foundational theories of meaning, being very often equated with “meaning”, “content” and “reference”. In this paper, I propose and develop a basic distinction between two concepts and, more fundamentally, properties of intentionality: intentionality-T and intentionality-C. Representationalism is then defined as the position according to which intentionality-T can be reduced to intentionality-C, in the form of representational states. Nonrepresentationalism is rejecting this reduction, and argues that intentionality-T is more fundamental than intentionality-C. Non-representationalism (...) allows for a new layered view of the relations between cognitive intentionality and linguistic intentionality; this view is presented at the end of the paper. (shrink)

The purpose of the paper is twofold. It first aims at clarifying and developing an important tension within enactivism concerning the relations between intentionality and content, once representationalism has been abandoned. In which sense(s) do enactivists (still) say that intentionality is contentful and not contentful? Secondly, it puts this tension in perspective with two paradigmatic ways of defining the relations between intentional states and their objects: Husserl’s theory of intentionality in the Logical Investigations, and Charles Sanders Peirce’s triadic semiotics.

In this paper I will tackle three issues. First, I aim to briefly outline the backbone of semantic minimalism, while focusing on the idea of ‘liberal truth conditions’ developed by Emma Borg in her book ‘Minimal Semantics’. Secondly, I will provide an account of the three principal views in legal interpretation: intentionalism, textualism and purposivism. All of them are based on a common denominator labelled by lawyers ‘literal meaning’. In the paper I suggest a novel way of viewing this common (...) denominator as almost identical to the Borgian ‘liberal truth conditions’, at least at a conceptual level. In the third section I will focus on the conceptual similarities between the two ideas. I intend to depict that, although legal theorists do not admit it explicitly, they treat literal legal meaning as minimal propositional content that can be ascribed liberal truth conditions. There are two main objections to liberal truth conditions: their under-determinacy and unintuitive character. Both objections can be applied to ‘literal meaning’. However, the idea of liberal truth conditions gives an adequate account of what lawyers call literal meaning and is helpful in explaining the mechanism of understanding of provisions and reasons leading to the necessity of statutory interpretation. (shrink)

This paper sets out a view about the explanatory role of representational content and advocates one approach to naturalising content – to giving a naturalistic account of what makes an entity a representation and in virtue of what it has the content it does. It argues for pluralism about the metaphysics of content and suggests that a good strategy is to ask the content question with respect to a variety of predictively successful information processing models in experimental psychology and cognitive (...) neuroscience; and hence that data from psychology and cognitive neuroscience should play a greater role in theorising about the nature of content. Finally, the contours of the view are illustrated by drawing out and defending a surprising consequence: that individuation of vehicles of content is partly externalist. (shrink)

An interesting feature of some sets of representations is that their structure mirrors the structure of the items they represent. Founding an account of representational content on isomorphism, homomorphism or structural resemblance has proven elusive, however, largely because these relations are too liberal when the candidate structure over representational vehicles is unconstrained. Furthermore, in many cases where there is a clear isomorphism, it is not relied on in the way the representations are used. That points to a potential resolution: that (...) an isomorphism must be used, hence usable, if it is to be an ingredient in a theory of content. This paper argues that the class of exploitable isomorphisms can indeed play a content-constituting role. (shrink)

This paper challenges two orthodox theses: (a) that computational processes must be algorithmic; and (b) that all computed functions must be Turing-computable. Section 2 advances the claim that the works in computability theory, including Turing's analysis of the effective computable functions, do not substantiate the two theses. It is then shown (Section 3) that we can describe a system that computes a number-theoretic function which is not Turing-computable. The argument against the first thesis proceeds in two stages. It is first (...) shown (Section 4) that whether a process is algorithmic depends on the way we describe the process. It is then argued (Section 5) that systems compute even if their processes are not described as algorithmic. The paper concludes with a suggestion for a semantic approach to computation. (shrink)

An underlying assumption in computational approaches in cognitive and brain sciences is that the nervous system is an input–output model of the world: Its input–output functions mirror certain relations in the target domains. I argue that the input–output modelling assumption plays distinct methodological and explanatory roles. Methodologically, input–output modelling serves to discover the computed function from environmental cues. Explanatorily, input–output modelling serves to account for the appropriateness of the computed function to the explanandum information-processing task. I compare very briefly the (...) modelling explanation to mechanistic and optimality explanations, noting that in both cases the explanations can be seen as complementary rather than contrastive or competing. (shrink)

In Representation Reconsidered , William Ramsey suggests that the notion of structural representation is posited by classical theories of cognition, but not by the ‘newer accounts’ (e.g. connectionist modeling). I challenge the assertion about the newer accounts. I argue that the newer accounts also posit structural representations; in fact, the notion plays a key theoretical role in the current computational approaches in cognitive neuroscience. The argument rests on a close examination of computational work on the oculomotor system.

The semantic view of computation is the claim that semantic properties play an essential role in the individuation of physical computing systems such as laptops and brains. The main argument for the semantic view rests on the fact that some physical systems simultaneously implement different automata at the same time, in the same space, and even in the very same physical properties. Recently, several authors have challenged this argument. They accept the premise of simultaneous implementation but reject the semantic conclusion. (...) In this paper, I aim to explicate the semantic view and to address these objections. I first characterize the semantic view and distinguish it from other, closely related views. Then, I contend that the master argument for the semantic view survives the counter-arguments against it. One counter-argument is that computational individuation is not forced to choose between the implemented automata but rather always picks out a more basic computational structure. My response is that this move might undermine the notion of computational equivalence. Another counter-argument is that while computational individuation is forced to rely on extrinsic features, these features need not be semantic. My reply is that the semantic view better accounts for these extrinsic features than the proposed non-semantic alternatives. (shrink)

What does it mean to say that a physical system computes or, specifically, to say that the nervous system computes? One answer, endorsed here, is that computing is a sort of modeling. I trace this line of answer in the conceptual and philosophical work conducted over the last 3 decades by researchers associated with the University of California, San Diego. The linkage between their work and the modeling notion is no coincidence: the modeling notion aims to account for the computational (...) approach in neuroscience, and UCSD has been home to central studies in neurophilosophy, connectionism, and computational neuroscience. (shrink)

The paper begins with the assumption that psychological event tokens are identical to or constituted from physical events. It then articulates a familiar apparent problem concerning the causal role of psychological properties. If they do not reduce to physical properties, then either they must be epiphenomenal or any effects they cause must also be caused by physical properties, and hence be overdetermined. It then argues that both epiphenomenalism and over‐determinationism are prima facie perfectly reasonable and relatively unproblematic views. The paper (...) proceeds to argue against Kim’s (Kim, 2000, 2005) attempt to articulate a plausible version of reductionism. It is then argued that psychological properties, along with paradigmatically causally efficacious macro‐properties, such as toughness, are causally inefficacious in respect of their possessor’s typical effects, because they are insufficiently distinct from those effects. It is finally suggested that the distinction between epiphenomenalism and overdeterminationism may be more terminological than real. (shrink)

The paper begins with the assumption that psychological event tokens are identical to or constituted from physical events. It then articulates a familiar apparent problem concerning the causal role of psychological properties. If they do not reduce to physical properties, then either they must be epiphenomenal or any effects they cause must also be caused by physical properties, and hence be overdetermined. It then argues that both epiphenomenalism and over‐determinationism are prima facie perfectly reasonable and relatively unproblematic views. The paper (...) proceeds to argue against Kim’s (Kim, 2000, 2005) attempt to articulate a plausible version of reductionism. It is then argued that psychological properties, along with paradigmatically causally efficacious macro‐properties, such as toughness, are causally inefficacious in respect of their possessor’s typical effects, because they are insufficiently distinct from those effects. It is finally suggested that the distinction between epiphenomenalism and overdeterminationism may be more terminological than real. (shrink)

After briefly discussing the relevance of the notions computation and implementation for cognitive science, I summarize some of the problems that have been found in their most common interpretations. In particular, I argue that standard notions of computation together with a state-to-state correspondence view of implementation cannot overcome difficulties posed by Putnam's Realization Theorem and that, therefore, a different approach to implementation is required. The notion realization of a function, developed out of physical theories, is then introduced as a replacement (...) for the notional pair computation-implementation. After gradual refinement, taking practical constraints into account, this notion gives rise to the notion digital system which singles out physical systems that could be actually used, and possibly even built. (shrink)

I present an account of mental representation based upon the ‘SINBAD’ theory of the cerebral cortex. If the SINBAD theory is correct, then networks of pyramidal cells in the cerebral cortex are appropriately described as representing, or more specifically, as modelling the world. I propose that SINBAD representation reveals the nature of the kind of mental representation found in human and animal minds, since the cortex is heavily implicated in these kinds of minds. Finally, I show how SINBAD neurosemantics can (...) provide accounts of misrepresentation, equivocal representation, twin cases, and Frege cases. (shrink)

This paper engages critically with anti-representationalist arguments pressed by prominent enactivists and their allies. The arguments in question are meant to show that the “as-such” and “job-description” problems constitute insurmountable challenges to causal-informational theories of mental content. In response to these challenges, a positive account of what makes a physical or computational structure a mental representation is proposed; the positive account is inspired partly by Dretske’s views about content and partly by the role of mental representations in contemporary cognitive scientific (...) modeling. (shrink)

Causal theories of mental content (CTs) ground certain aspects of a concept's meaning in the causal relations a concept bears to what it represents. Section 1 explains the problems CTs are meant to solve and introduces terminology commonly used to discuss these problems. Section 2 specifies criteria that any acceptable CT must satisfy. Sections 3, 4, and 5 critically survey various CTs, including those proposed by Fred Dretske, Jerry Fodor, Ruth Garrett Millikan, David Papineau, Dennis Stampe, Dan Ryder, and the (...) author himself. The final section considers general objections to the causal approach. (shrink)

The medium‐independence of computational descriptions has shaped common conceptions of computational explanation. So long as our goal is to explain how a system successfully carries out its computations, then we only need to describe the abstract series of operations that achieve the desired input–output mapping, however they may be implemented. It is argued that this abstract conception of computational explanation cannot be applied to so‐called real‐time computing systems, in which meeting temporal deadlines imposed by the systems with which a device (...) interfaces are constitutive of the computing tasks that a device performs. Instead, real‐time computing reveals the need for alternative conceptions of computational explanation, as well as computational implementation, that eschew medium‐independence. (shrink)

Sensorimotor psychology studies the mental processes that control goal-directed bodily motion. Recently, sensorimotor psychologists have provided empirically successful Bayesian models of motor control. These models describe how the motor system uses sensory input to select motor commands that promote goals set by high-level cognition. I highlight the impressive explanatory benefits offered by Bayesian models of motor control. I argue that our current best models assign explanatory centrality to a robust notion of mental representation. I deploy my analysis to defend intentional (...) realism, to rebut eliminativism and instrumentalism regarding mental representation, and to explore the relation between intentionality and normativity. (shrink)

The formal conception of computation (FCC) holds that computational processes are not sensitive to semantic properties. FCC is popular, but it faces well-known difficulties. Accordingly, authors such as Block and Peacocke pursue a ?semantically-laden? alternative, according to which computation can be sensitive to semantics. I argue that computation is insensitive to semantics within a wide range of computational systems, including any system with ?derived? rather than ?original? intentionality. FCC yields the correct verdict for these systems. I conclude that there is (...) only one promising strategy for semantically-laden theorists: identify special computational systems that help generate their own semantic properties, and then show that computation within those systems is semantically-laden. Unfortunately, the few existing discussions that pursue this strategy are problematic. (shrink)

For the past 40 years, philosophers have generally assumed that a key to understanding mental representation is to develop a naturalistic theory of representational content. This has led to an outlook where the importance of content has been heavily inflated, while the significance of the representational vehicles has been somewhat downplayed. However, the success of this enterprise has been thwarted by a number of mysterious and allegedly non-naturalizable, irreducible dimensions of representational content. The challenge of addressing these difficulties has come (...) to be known as the “hard problem of content” (Hutto & Myin, 2012), and many think it makes an account of representation in the brain impossible. In this essay, I argue that much of this is misguided and based upon the wrong set of priorities. If we focus on the functionality of representational vehicles (as recommended by teleosemanticists) and remind ourselves of the quirks associated with many functional entities, we can see that the allegedly mysterious and intractable aspects of content are really just mundane features associated with many everyday functional kinds. We can also see they have little to do with content and more to do with representation function. Moreover, we can begin to see that our explanatory priorities are backwards: instead of expecting a theory of content to be the key to understanding how a brain state can function as a representation, we should instead expect a theory of neural representation function to serve as the key to understanding how content occurs naturally. (shrink)

In this article, I develop an account of the use of intentional predicates in cognitive neuroscience explanations. As pointed out by Maxwell Bennett and Peter Hacker, intentional language abounds in neuroscience theories. According to Bennett and Hacker, the subpersonal use of intentional predicates results in conceptual confusion. I argue against this overly strong conclusion by evaluating the contested language use in light of its explanatory function. By employing conceptual resources from the contemporary philosophy of science, I show that although the (...) use of intentional predicates in mechanistic explanations sometimes leads to explanatorily inert claims, intentional predicates can also successfully feature in mechanistic explanations as tools for the functional analysis of the explanandum phenomenon. Despite the similarities between my account and Daniel Dennett's intentional-stance approach, I argue that intentional stance should not be understood as a theory of subpersonal causal explanation, and therefore cannot be used to assess the explanatory role of intentional predicates in neuroscience. Finally, I outline a general strategy for answering the question of what kind of language can be employed in mechanistic explanations. (shrink)

According to the multiple realization argument, mental states or processes can be realized in diverse and heterogeneous physical systems; and that fact implies that mental state or process kinds cannot be identified with particular kinds of physical states or processes. More specifically, mental processes cannot be identified with brain processes. Moreover, the argument provides a general model for the autonomy of the special sciences. The multiple realization argument is widely influential, but over the last thirty years it has also faced (...) serious objections. Despite those objections, most philosophers regard the fact of multiple realization and the cogency of the multiple realization argument as plainly correct. Why is that? What is it about the multiple realization argument that makes it so resilient? One reason is that the multiple realization argument is deeply intertwined with a view that minds are, in some sense, computational. But we argue that the sense in which minds are computational does not support the conclusion that they are ipso facto multiply realized. We argue that the sense in which brains compute does not imply that brains implement multiply realizable computational processes, and it does not provide a general model for the autonomy of the special sciences. (shrink)

According to the received view in philosophy of mind, mental states or properties are _realized_ by brain states or properties but are not identical to them. This view is often called _realization_ _physicalism_. Carl Gillett has recently defended a detailed formulation of the realization relation. However, Gillett’s formulation cannot be the relation that realization physicalists have in mind. I argue that Gillett’s “dimensioned” view of realization fails to apply to a textbook case of realization. I also argue Gillett counts as (...) realization some cases that should not count if realization physicalism is to be distinguished from its competitors in the usual ways. I conclude that the relation described by Gillett cannot be realization. (shrink)

The view that the relationship between minds and brains can be thought of on the model of software and hardware is pervasive. The most common versions of the view, known as functionalism in philosophy of mind, hold that minds are realized by brains.

In this paper we defend structural representations, more specifically neural structural representation. We are not alone in this, many are currently engaged in this endeavor. The direction we take, however, diverges from the main road, a road paved by the mathematical theory of measure that, in the 1970s, established homomorphism as the way to map empirical domains of things in the world to the codomain of numbers. By adopting the mind as codomain, this mapping became a boon for all those (...) convinced that a representation system should bear similarities with what was being represented, but struggled to find a precise account of what such similarities mean. The euforia was brief, however, and soon homomorphism revealed itself to be affected by serious weaknesses, the primary one being that it included systems embarrassingly alien to representations. We find that the defense attempts that have followed, adopt strategies that share a common format: valid structural representations come as “homomorphism plus X”, with various “X”, provided in descriptive format only. Our alternative direction stems from the observation of the overlooked departure from homomorphism as used in the theory of measure and its later use in mental representations. In the former case, the codomain or the realm of numbers, is the most suited for developing theorems detailing the existence and uniqueness of homomorphism for a wide range of empirical domains. In the latter case, the codomain is the realm of the mind, possibly more vague and more ill-defined than the empirical domain itself. The time is ripe for articulating the mapping between represented domains and the mind in formal terms, by exploiting what is currently known about coding mechanisms in the brain. We provide a sketch of a possible development in this direction, one that adopts the theory of neural population coding as codomain. We will show that our framework is not only not in disagreement with the “plus X” proposals, but can lead to natural derivation of several of the “X”. (shrink)

Some philosophers have conflated functionalism and computationalism. I reconstruct how this came about and uncover two assumptions that made the conflation possible. They are the assumptions that (i) psychological functional analyses are computational descriptions and (ii) everything may be described as performing computations. I argue that, if we want to improve our understanding of both the metaphysics of mental states and the functional relations between them, we should reject these assumptions. # 2004 Elsevier Ltd. All rights reserved.

I offer an explication of the notion of computer, grounded in the practices of computability theorists and computer scientists. I begin by explaining what distinguishes computers from calculators. Then, I offer a systematic taxonomy of kinds of computer, including hard-wired versus programmable, general-purpose versus special-purpose, analog versus digital, and serial versus parallel, giving explicit criteria for each kind. My account is mechanistic: which class a system belongs in, and which functions are computable by which system, depends on the system's mechanistic (...) properties. Finally, I briefly illustrate how my account sheds light on some issues in the history and philosophy of computing as well as the philosophy of mind. (shrink)