An important part of David Hume’s work is his attempt to put the natural sciences on a firmer foundation by introducing the scientific method into the study of human nature. This investigation resulted in a novel understanding of the mind, which in turn informed Hume’s critical evaluation of the scope and limits of the scientific method as such. However, while these latter reflections continue to influence today’s philosophy of science, his theory of mind is nowadays mainly of interest in terms (...) of philosophical scholarship. This paper aims to show that, even though Hume’s recognition in the cognitive sciences has so far been limited, there is an opportunity to reevaluate his work in the context of more recent scientific developments. In particular, it is argued that we can gain a better understanding of his overall philosophy by tracing the ongoing establishment of the enactive approach. In return, this novel interpretation of Hume’s ‘science of man’ is used as the basis for a consideration of the current and future status of the cognitive sciences. (shrink)

What is nontrivial digital computation? It is the processing of discrete data through discrete state transitions in accordance with finite instructional information. The motivation for our account is that many previous attempts to answer this question are inadequate, and also that this account accords with the common intuition that digital computation is a type of information processing. We use the notion of reachability in a graph to defend this characterization in memory-based systems and underscore the importance of instructional information for (...) digital computation. We argue that our account evaluates positively against adequacy criteria for accounts of computation. (shrink)

Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme (...) dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation. (shrink)

This paper deals with the question: what are the key requirements for a physical system to perform digital computation? Time and again cognitive scientists are quick to employ the notion of computation simpliciter when asserting basically that cognitive activities are computational. They employ this notion as if there was or is a consensus on just what it takes for a physical system to perform computation, and in particular digital computation. Some cognitive scientists in referring to digital computation simply adhere to (...) Turing’s notion of computability . Classical computability theory studies what functions on the natural numbers are computable and what mathematical problems are undecidable. Whilst a mathematical formalism of computability may perform a methodological function of evaluating computational theories of certain cognitive capacities, concrete computation in physical systems seems to be required for explaining cognition as an embodied phenomenon . There are many non-equivalent accounts of digital computation in physical systems. I examine only a handful of those in this paper: (1) Turing’s account ; (2) The triviality “account”; (3) Reconstructing Smith’s account of participatory computation ; (4) The Algorithm Execution account . My goal in this paper is twofold. First, it is to identify and clarify some of the underlying key requirements mandated by these accounts. I argue that these differing requirements justify a demand that one commits to a particular account when employing the notion of computation in regard to physical systems. Second, it is to argue that despite the informative role that mathematical formalisms of computability may play in cognitive science, they do not specify the relationship between abstract and concrete computation. (shrink)

Contemporary science seems to be caught in a strange predicament. On the one hand, it holds a firm and reasonable commitment to a healthy naturalistic methodology, according to which explanations of natural phenomena should never overstep the limits of the natural itself. On the other hand, contemporary science is also inextricably and now inevitably dependent on ever more complex technologies, especially Information and Communication Technologies, which it exploits as well as fosters. Yet such technologies are increasingly “artificialising” or “denaturalising” the (...) world, human experiences and interactions, as well as what qualifies as real. So the search for the ultimate explanation of the natural seems to rely upon, and promote, the development of the artificial, seen here as an instantiation of the non-natural. In this article, I would like to try and find a way out of this apparently strange predicament. I shall argue that the naturalisation of our knowledge of the world is either philosophically trivial, or mistaken. First, I shall distinguish between different kinds of naturalism. Second, I shall remind the reader that the kinds of naturalism that are justified today need to be protected and supported pragmatically, but they are no longer very interesting conceptually. We know how to win the argument. We just have to keep winning it. Whereas the kind of naturalism that is still interesting today is now in need of revision in order to remain acceptable. Such a kind of naturalism may be revised on the basis of a realistic philosophy of information, according to which knowing is a constructive activity, through which we do not merely represent the phenomena we investigate passively, but create more or less correct informational models of them, proactively and interactively. I shall conclude that the natural is in itself artefactual, and that the information revolution is disclosing a tension not between the natural and the non-natural, but a deeper one between a user’s and a producer’s interpretation of knowledge. The outcome is a philosophical view of knowledge and science in the information age that may be called constructionist and a revival of philosophy as a classic, foundationalist enterprise. (shrink)

This article offers an account and defence of constructionism, both as a metaphilosophical approach and as a philosophical methodology, with references to the so-called maker's knowledge tradition. Its main thesis is that Plato's “user's knowledge” tradition should be complemented, if not replaced, by a constructionist approach to philosophical problems in general and to knowledge in particular. Epistemic agents know something when they are able to build (reproduce, simulate, model, construct, etc.) that something and plug the obtained information into the correct (...) network of relations that account for it. Their epistemic expertise increases with the scope and depth of the questions that they are able to ask and answer. Thus, constructionism deprioritises mimetic, passive, and declarative knowledge that something is the case, in favour of poietic, interactive, and practical knowledge of something being the case. Metaphilosophically, constructionism suggests adding conceptual engineering to conceptual analysis as a fundamental method. (shrink)

Cognitive science has been dominated by the computational conception that cognition is computation across representations. To the extent to which cognition as computation across representations is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from their interpretation by human users. Strictly speaking, computers as such — apart (...) from human users — are not only incapable of cognition, but even incapable of computation, properly construed. If we want to understand the nature of thought, then we have to study thinking, not computing, because they are not the same thing. (shrink)

A debate over the theoretical capabilities of formal methods in computer science has raged for more than two years now. The function of this paper is to summarize the key elements of this debate and to respond to important criticisms others have advanced by placing these issues within a broader context of philosophical considerations about the nature of hardware and of software and about the kinds of knowledge that we have the capacity to acquire concerning their performance.

The purpose of this article is to start a dialogue between the so-called autopoietic enactivism and the semiotic pragmatism of C. S. Peirce, in order to re-examine both action and representation under a Peircean light. The focus lays on autopoietic enactivism because this approach offers a wider theoretical scope to cognition based on the continuity of life and mind, embodiment, dynamic and non-linear interaction between a system and its environment which are compatible ideas with Peirce’s semiotic pragmatism. The term ‘pragmatic’ (...) has been introduced in cognitive science to reinforce the idea that cognition is a form of practice and to help action-oriented viewpoints to escape representationalism. In this paper, I shall try to demonstrate that Peirce’s semiotic pragmatism can be a meaningful methodological path to guide a reconciliation between not only anti-Cartesianism and representation but also representation and action. In order to accomplish this purpose, Peirce’s account to action, habit, thought and mind will be addressed through some of the guiding principles of his semiotic—sign and sign action. What follows is the re-examining of the problem of representation—as refuted by autopoietic enactivism—under the light of Peirce’s semiotic pragmatism. (shrink)

Some philosophers search for the mark of the cognitive: a set of individually necessary and jointly sufficient conditions identifying all instances of cognition. They claim that the mark of the cognitive is needed to steer the development of cognitive science on the right path. Here, I argue that, at least at present, it cannot be provided. First (§2), I identify some of the factors motivating the search for a mark of the cognitive, each yielding a desideratum the mark is supposed (...) to satisfy (§2.1). I then (§2.2) highlight a number of tensions in the literature on the mark of the cognitive, suggesting they’re best resolved by distinguishing two distinct programs. The first program (§3) is that of identifying a mark of the cognitive capturing our everyday notion of cognition. I argue that such a program is bound to fail for a number of reasons: it is not clear whether such an everyday notion exists; and even if it existed, it would not be able to spell out individually necessary and jointly sufficient conditions for cognition; and even if it were able to spell them out, these conditions won’t satisfy the desiderata a mark of the cognitive should satisfy. The second program is that of identifying a mark of the cognitive spelling out a genuine scientific kind. But the current state of fragmentation of cognitive science, and the fact that it is splintered in a myriad of different research traditions, prevent us from identifying such a kind. And we have no reason to think that these various research traditions will converge, allowing us to identify a single mark. Or so, at least, I will argue in (§4). I then conclude the paper (§5) deflecting an intuitive objection, and exploring some of the consequences of the thesis I have defended. (shrink)

The purpose of this paper is to outline some new developments in a mature research program that sees administrative theory as cohering with natural science and uses a coherence theory of epistemic justification to shape the content and structure of administrative theory. Three main developments are discussed. First, the paper shows how to deal with the evaluation of theories where there is a demand that a theory needs to be context relevant, but also comprehensive. The solution is to allow context (...) to determine the scope of comprehensiveness. Second, the paper develops an argument structure employing a coherentist epistemology for how ethical claims can be incorporated into administrative theories. Finally, the paper, drawing on research in neuroscience, argues for the relevance of emotion in rational decision-making. Contrary to the belief that emotion compromises rationality, the paper argues that it is essential for rationality. (shrink)

Many philosophers and psychologists now argue that emotions play a vital role in reasoning. This paper explores one particular way of elucidating how emotions help reason which may be dubbed ?the search hypothesis of emotion?. After outlining the search hypothesis of emotion and dispensing with a red herring that has marred previous statements of the hypothesis, I discuss two alternative readings of the search hypothesis. It is argued that the search hypothesis must be construed as an account of what emotions (...) typically do, rather than as a definition of emotion. Even as an account of what emotions typically do, the search hypothesis can only be evaluated in the context of a specific theory of what emotions are. 1 Introduction 2 The search hypothesis of emotion 3 A red herring: the frame problem 4 The search problem 5 Two readings of the search hypothesis 6 Two final remarks 7 Conclusion. (shrink)

This paper examines the relationship between simulation and experiment. Many discussions of simulation, and indeed the term "numerical experiments," invoke a strong metaphor of experimentation. On the other hand, many simulations begin as attempts to apply scientific theories. This has lead many to characterize simulation as lying between theory and experiment. The aim of the paper is to try to reconcile these two points of viewto understand what methodological and epistemological features simulation has in common with experimentation, while at the (...) same time keeping a keen eye on simulation's ancestry as a form of scientific theorizing. In so doing, it seeks to apply some of the insights of recent work on the philosophy of experiment to an aspect of theorizing that is of growing philosophical interest: the construction of local models. (shrink)

Marr’s seminal distinction between computational, algorithmic, and implementational levels of analysis has inspired research in cognitive science for more than 30 years. According to a widely-used paradigm, the modelling of cognitive processes should mainly operate on the computational level and be targeted at the idealised competence, rather than the actual performance of cognisers in a specific domain. In this paper, we explore how this paradigm can be adopted and revised to understand mathematical problem solving. The computational-level approach applies methods from (...) computational complexity theory and focuses on optimal strategies for completing cognitive tasks. However, human cognitive capacities in mathematical problem solving are essentially characterised by processes that are computationally sub-optimal, because they initially add to the computational complexity of the solutions. Yet, these solutions can be optimal for human cognisers given the acquisition and enactment of mathematical practices. Here we present diagrams and the spatial manipulation of symbols as two examples of problem solving strategies that can be computationally sub-optimal but humanly optimal. These aspects need to be taken into account when analysing competence in mathematical problem solving. Empirically informed considerations on enculturation can help identify, explore, and model the cognitive processes involved in problem solving tasks. The enculturation account of mathematical problem solving strongly suggests that computational-level analyses need to be complemented by considerations on the algorithmic and implementational levels. The emerging research strategy can help develop algorithms that model what we call enculturated cognitive optimality in an empirically plausible and ecologically valid way. (shrink)

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)

Current cognitive architectures are either working at the abstract, symbolic level, or the low, emergent level related to neural modeling. The best way to understand phenomena is to see, or imagine them, hence the need for a geometric model of mental processes. Geometric models should be based on an intermediate level of modeling that describe mental states in terms of features relevant from the first-person perspective but also linked to neural events. Concepts should be represented as geometrical objects that have (...) sufficiently rich structures to show their properties and their relations to other concepts. The best way to create such geometrical representations of concepts is through the approximate description of the physical states of neural networks. The evolution of brain states is then represented as a trajectory linking successful concepts, and topological constraints on the shape of such trajectory define grammar and logic. (shrink)

In the 1960s, without realizing it, AI researchers were hard at work finding the features, rules, and representations needed for turning rationalist philosophy into a research program, and by so doing AI researchers condemned their enterprise to failure. About the same time, a logician, Yehoshua Bar-Hillel, pointed out that AI optimism was based on what he called the “first step fallacy”. First step thinking has the idea of a successful last step built in. Limited early success, however, is not a (...) valid basis for predicting the ultimate success of one’s project. Climbing a hill should not give one any assurance that if he keeps going he will reach the sky. Perhaps one may have overlooked some serious problem lying ahead. There is, in fact, no reason to think that we are making progress towards AI or, indeed, that AI is even possible, in which case claiming incremental progress towards it would make no sense. In current excited waiting for the singularity, religion and technology converge. Hard headed materialists desperately yearn for a world where our bodies no longer have to grow old and die. They will be transformed into information, like Google digitizes old books, and we will achieve the promise of eternal life. As an existential philosopher, however, I suggest that we may have to overcome the desperate desire to digitalize our bodies so as to achieve immortality, and, instead, face up to and maybe even enjoy our embodied finitude. (Paper from PT-AI 2011 conference. Volume ed. Vincent C. Müller). (shrink)

In this paper we introduce three methods to approach philosophical problems informationally: Minimalism, the Method of Abstraction and Constructionism. Minimalism considers the specifications of the starting problems and systems that are tractable for a philosophical analysis. The Method of Abstraction describes the process of making explicit the level of abstraction at which a system is observed and investigated. Constructionism provides a series of principles that the investigation of the problem must fulfil once it has been fully characterised by the previous (...) two methods. For each method, we also provide an application: the problem of visual perception, functionalism, and the Turing Test, respectively. (shrink)

Mental imagery (varieties of which are sometimes colloquially refered to as “visualizing,” “seeing in the mind's eye,” “hearing in the head,” “imagining the feel of,” etc.) is quasi-perceptual experience; it resembles perceptual experience, but occurs in the absence of the appropriate external stimuli. It is also generally understood to bear intentionality (i.e., mental images are always images of something or other), and thereby to function as a form of mental representation. Traditionally, visual mental imagery, the most discussed variety, was thought (...) to be caused by the presence of picturelike representations (mental images) in the mind, soul, or brain, but this is no longer universally accepted. (shrink)

The notion of a "mental representation" is, arguably, in the first instance a theoretical construct of cognitive science. As such, it is a basic concept of the Computational Theory of Mind, according to which cognitive states and processes are constituted by the occurrence, transformation and storage (in the mind/brain) of information-bearing structures (representations) of one kind or another.

Over the past thirty years, it is been common to hear the mind likened to a digital computer. This essay is concerned with a particular philosophical view that holds that the mind literally is a digital computer (in a specific sense of “computer” to be developed), and that thought literally is a kind of computation. This view—which will be called the “Computational Theory of Mind” (CTM)—is thus to be distinguished from other and broader attempts to connect the mind with computation, (...) including (a) various enterprises at modeling features of the mind using computational modeling techniques, and (b) employing some feature or features of production-model computers (such as the stored program concept, or the distinction between hardware and software) merely as a guiding metaphor for understanding some feature of the mind. This entry is therefore concerned solely with the Computational Theory of Mind (CTM) proposed by Hilary Putnam [1961] and developed most notably for philosophers by Jerry Fodor [1975, 1980, 1987, 1993]. The senses of ‘computer’ and ‘computation’ employed here are technical; the main tasks of this entry will therefore be to elucidate: (a) the technical sense of ‘computation’ that is at issue, (b) the ways in which it is claimed to be applicable to the mind, (c) the philosophical problems this understanding of the mind is claimed to solve, and (d) the major criticisms that have accrued to this view. (shrink)

We argue that the locomotion of organisms is better understood as a form of interaction with a subjective environment, rather than as a set of behaviors allegedly amenable to objective descriptions. An organism's interactions with its subjective environment are in turn understandable in terms of its cognitive architecture. We propose a large-scale classification of the possible types of cognitive architectures, giving a sketch of the subjective structure that each of them superimposes on space and of the relevant consequences on locomotion. (...) The classification comprises a main division between nonrepresentational and representational architectures and further subdivisions. (shrink)

Explicatures are not cancellable. Theoretical considerations.

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)

I propose a new approach to the constitutive problem of psychology ‘what is mind?’ The first section introduces modifications of the received scope, methodology, and evaluation criteria of unified theories of cognition in accordance with the requirements of evolutionary compatibility and of a mature science. The second section outlines the proposed theory. Its first part provides empirically verifiable conditions delineating the class of meaningful neural formations and modifies accordingly the traditional conceptions of meaning, concept and thinking. This analysis is part (...) of a theory of communication in terms of inter-level systems of primitives that proposes the communication-understanding principle as a psychological invariance. It unifies a substantial amount of research by systematizing the notions of meaning, thinking, concept, belief, communication, and understanding and leads to a minimum vocabulary for this core system of mental phenomena. Its second part argues that written human language is the key characteristic of the artificially natural human mind. Overall, the theory both supports Darwin’s continuity hypothesis and proposes that the mental gap is within our own species. (shrink)

Man’s intellectual capacity remains an enigma, as it is both the subject and the means of analysis. If one is to assume quantum-wave dualism in physics then the state of the world depends on the instruments we use for observation. The “paradoxical” nature of investigating human cognition may thus bear inherent limitations. However, studying cognitive models may be less of a seemingly inconsistent endeavor, if “contradictions” may be classified. In this brief exposition, a variety of aspects related to cognitive models (...) are discussed. The authors maintain that modeling the functional “paradoxical nature” of human cognition remains the greatest challenge. Therefore, consciousness aside, models of conscious systems, or rather conscious models of conscious systems, are the main objects of exploration. While intentional systems may seem a good starting point for such an exploration, they lack two important constructs: volition and reflexion. Both concepts, and especially volition, unlike rationality for example, are less discussed in the discourse of cognitive models. Although not devoted to volition or reflexion, this work proposes an increased research interest in these areas. (shrink)

This paper examines the central place of the list and the associated concept of an identifier within the scaffolding of contemporary institutional order. These terms are deliberately chosen to make strange and help unpack the constitutive capacity of information systems and information technology within and between contemporary organisations. We draw upon the substantial body of work by John Searle to help understand the place of lists in the constitution of order. To enable us to ground our discussion of the potentiality (...) and problematic associated with lists we describe a significant and modern instance of list-making, situated around the issue of digital identity management. The theoretical framework discussed allows us to better explain breakdowns in the institutional order characteristic of this domain. (shrink)

Logic has been a—disputed—ingredient in the emergence and development of the now very large field known as knowledge representation and reasoning. In this book (in progress), I select some central topics in this highly fruitful, albeit controversial, association (e.g., non-monotonic reasoning, implicit belief, logical omniscience, closed world assumption), identifying their sources and analyzing/explaining their elaboration in highly influential published work.

The paper focuses on mental imagery and concepts. First we discuss the possible reasons why the propositional view of representation was so successful among cognitive scientists interested in concepts. Then a novel perspective, the embodied view, is presented. Differently from the classic cognitivist view, this perspective acknowledges the importance of perceptual and motor imagery for concepts. According to the embodied perspective concepts are not given by propositional, abstract and amodal symbols but are grounded in sensorimotor processes. Neural and behavioral evidence (...) favouring this perspective is presented. The paper discusses the continuity, but also the differences, between the imagery view and the embodied view of conceptual representation. (shrink)

While we agree that the frame problem, as initially stated by McCarthy and Hayes (1969), is a problem that arises because of the use of representations, we do not accept the anti-representationalist position that the way around the problem is to eliminate representations. We believe that internal representations of the external world are a necessary, perhaps even a defining feature, of higher cognition. We explore the notion of dynamically created context-dependent representations that emerge from a continual interaction between working memory, (...) external input, and long-term memory. We claim that only this kind of representation, necessary for higher cognitive abilities such as counterfactualization, will allow the combinatorial explosion inherent in the frame problem to be avoided. (shrink)

Digital computers play a special role in cognitive science—they may actually be instances of the phenomenon they are being used to model. This paper surveys some of the main issues involved in understanding the relationship between digital computers and cognition. It sketches the role of digital computers within orthodox computational cognitive science, in the light of a recently emerging alternative approach based around dynamical systems.

Context: The enactive paradigm in the cognitive sciences is establishing itself as a strong and comprehensive alternative to the computationalist mainstream. However, its own particular historical roots have so far been largely ignored in the historical analyses of the cognitive sciences. Problem: In order to properly assess the enactive paradigm’s theoretical foundations in terms of their validity, novelty and potential future directions of development, it is essential for us to know more about the history of ideas that has led to (...) the current state of affairs. Method: The meaning of the disappearance of the field of cybernetics and the rise of second-order cybernetics is analyzed by taking a closer look at the work of representative figures for each of the phases: Rosenblueth, Wiener and Bigelow for the early wave of cybernetics, Ashby for its culmination, and von Foerster for the development of the second-order approach. Results: It is argued that the disintegration of cybernetics eventually resulted in two distinct scientific traditions, one going from symbolic AI to modern cognitive science on the one hand, and the other leading from second-order cybernetics to the current enactive paradigm. Implications: We can now understand that the extent to which the cognitive sciences have neglected their cybernetic parent is precisely the extent to which cybernetics had already carried the tendencies that would later find fuller expression in second-order cybernetics. (shrink)

More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of (...) human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power. (shrink)

I love information upon all subjects that come in my way, and especially upon those that are most important. Thus boldly declares Euphranor, one of the defenders of Christian faith in Berkley’s Alciphron (Berkeley, (1732), Dialogue 1, Section 5, Paragraph 6/10). Evidently, information has been an object of philosophical desire for some time, well before the computer revolution, Internet or the dotcompandemonium (see for example Dunn (2001) and Adams (2003)). Yet what does Euphranor love, exactly? What is information? The question (...) has received many answers in different fields. Unsurprisingly, several surveys do not even converge on a single, unified definition of information (see for example Braman 1989, Losee (1997), Machlup and Mansfield (1983), Debons and Cameron (1975), Larson and Debons (1983)). (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)

Previous work has shown that the Complex Conceptual Spaces − Single Observation Mathematical framework is a useful tool for event characterization. This mathematical framework is developed on the basis of Conceptual Spaces and uses integer linear programming to find the needed similarity values. The work of this paper is focused primarily on space event characterization. In particular, the focus is on the ranking of threats for malicious space events such as a kinetic kill. To make the Conceptual Spaces framework work, (...) the similarity values between the contents of observations on the one hand and the properties of the entities observed on the other needs to be found. This paper shows how to exploit Dempster-Shafer theory to implement a statistical approach for finding these similarities values. This approach will allow a user to identify the uncertainty involved in similarity value data, which can later be propagated through the developed mathematical model in order for the user to know the overall uncertainty in the observation-to-concept mappings needed for space event characterization. (shrink)

WikiSilo is a tool for theorizing across interdisciplinary fields such as Cognitive Science, and provides a vocabulary for talking about the problems of doing so. It can be used to demonstrate that a particular cognitive theory is complete and coherent at multiple levels of discourse, and commensurable with and relevant to a wider domain of cognition. WikiSilo is also a minimalist theory and methodology for effectively doing science. WikiSilo is simultaneously similar to and distinct, as well as integrated and separated (...) from Wikipedia™. This paper will introduce the advantages of WikiSilo for use in the Cognitive Sciences. (shrink)

Pancomputationalism is the view that everything is a computer. This, if true, poses some difficulties to the computational theory of cognition. In particular, the strongest version of it suggested by John Searle seems enough to trivialize computational cognitivists’ core idea on which our cognitive system is a computing system. The aim of this paper is to argue against Searle’s pancomputationalism. To achieve this, I will draw a line between realized computers and unrealized computers. Through this distinction, I expect that it (...) will become evident that Searle’s pancomputationalism should be understood in terms of unrealized computers, while the computational theory of cognition is concerned with realized computers. (shrink)

A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...) based on the idiosyncratic notions of symbol systems and the representational abilities they give rise to, in particular with respect to knowledge. While focusing on the period 1956-1982, this review cites both earlier and later literature and it attempts to make visible their potential relevance to today's greatest unifying AI challenge, to wit, the design of wholly autonomous artificial agents (a.k.a. robots) that are not only rational and ethical, but also self-conscious. (shrink)

In this thesis I explore how older people make use of, and interact with, their physical environment in home and near-by settings to manage cognitive situations, specifically prospective memory situations. Older adults have in past research been shown to perform better on prospective memory in real-life settings than what findings in laboratory-like settings predict. An explanation for this paradox is that older adults has a more developed skill of using the environment for prospective memory than younger adults. However, research investigating (...) this explanation has primarily been based on self-reports. I contribute to the understanding of this skill by doing two related things. First I introduce distributed cognition, a theoretical perspective that primarily has been used within professional and socio-technical environments, to the research field of prospective memory in everyday life. Second I present a cognitive ethnography conducted during two years across eight home, and near-by, environments and old-age retired persons, for which I have used theoretical concepts from distributed cognition to analyze observations. The analysis shows rich variations in how participants use common cultural cognitive tools, invent their own cognitive tools, deliberately and incidentally shape more or less functional spaces, make use of other physical features, orient themselves toward and make sense of cognitive resources. I complement both prospective memory and distributed cognition research by describing both the intelligent shaping and use of space. Furthermore, by taking a distributed cognitive perspective I show that prospective memory processes in home environments involve properties, and the management, of a multipurpose environment. Altogether this supports the understanding of distributed cognition as a perspective on all cognition. Distributed cognition is not a reflection of particular work practices, instead it is a formulation of the general features of human cognition. Prospective memory in everyday life can be understood as an ability persons have. However, in this thesis I show that prospective memory can also be understood as a process that takes place between persons, arrangements of space, and tools. (shrink)

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research (...) aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems. (shrink)

The paper focuses on the embodied view of cognition applied to language. First we discuss what we intend when we say that concepts are “embodied”. Then we briefly explain the notion of simulation, addressing also its neuro-physiological basis. In the main part of the paper we will focus on concepts mediated by language, presenting behavioral and neuro-physiological evidence of the action/perception systems activation during words and sentences comprehension.

I do not think that the world or the sciences would ever have suggested to me any philosophical problems. What has suggested philosophical problems to me is things which other philosophers have said about the world or the sciences. (G.E. Moore, 1942, p. 14).

This chapter offers a high-level overview of the philosophy of cognitive science and an introduction to the Oxford Handbook of Philosophy of Cognitive Science. The philosophy of cognitive science emerged out of a set of common and overlapping interests among philosophers and scientists who study the mind. We identify five categories of issues that illustrate the best work in this broad field: (1) traditional philosophical issues about the mind that have been invigorated by research in cognitive science, (2) issues regarding (...) the practice of cognitive science and its foundational assumptions, (3) issues regarding the explication and clarification of core concepts in cognitive science, (4) first-order empirical issues where philosophers participate in the interdisciplinary investigation of particular psychological phenomena, (5) traditional philosophical issues that aren’t about the mind but that can be informed by a better understanding of how the mind works. (shrink)