Since the birth of computing as an academic discipline, the disciplinary identity of computing has been debated fiercely. The most heated question has concerned the scientific status of computing. Some consider computing to be a natural science and some consider it to be an experimental science. Others argue that computing is bad science, whereas some say that computing is not a science at all. This survey article presents viewpoints for and against computing as a science. Those viewpoints are analyzed against (...) basic positions in the philosophy of science. The article aims at giving the reader an overview, background, and a historical and theoretical frame of reference for understanding and interpreting some central questions in the debates about the disciplinary identity of computer science. The article argues that much of the discussion about the scientific nature of computing is misguided due to a deep conceptual uncertainty about science in general as well as computing in particular. (shrink)

The philosophy of information (PI) is a new area of research with its own field of investigation and methodology. This article, based on the Herbert A. Simon Lecture of Computing and Philosophy I gave at Carnegie Mellon University in 2001, analyses the eighteen principal open problems in PI. Section 1 introduces the analysis by outlining Herbert Simon's approach to PI. Section 2 discusses some methodological considerations about what counts as a good philosophical problem. The discussion centers on Hilbert's famous analysis (...) of the central problems in mathematics. The rest of the article is devoted to the eighteen problems. These are organized into five sections: problems in the analysis of the concept of information, in semantics, in the study of intelligence, in the relation between information and nature, and in the investigation of values. (shrink)

In Supersizing the Mind: Embodiment, Action, and Cognitive Extension (Clark, 2008), Andy Clark bolsters his case for the extended mind thesis and casts a critical eye on some related views for which he has less enthusiasm. To these ends, the book canvasses a wide range of empirical results concerning the subtle manner in which the human organism and its environment interact in the production of intelligent behavior. This fascinating research notwithstanding, Supersizing does little to assuage my skepticism about the hypotheses (...) of extended cognition and extended mind. In particular, Supersizing fails to make the case for the extended view as a revolutionary thesis in the theoretical foundations of cognitive science. (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)

The standard view of classical cognitive science stated that cognition consists in the manipulation of language-like structures according to formal rules. Since cognition is ‘linguistic’ in itself, according to this view language is just a complex communication system and does not influence cognitive processes in any substantial way. This view has been criticized from several perspectives and a new framework (Embodied Cognition) has emerged that considers cognitive processes as non-symbolic and heavily dependent on the dynamical interactions between the cognitive system (...) and its environment. But notwithstanding the successes of the embodied cognitive science in explaining low-level cognitive behaviors, it is still not clear whether and how it can scale up for explaining high-level cognition. In this paper we argue that this can be done by considering the role of language as a cognitive tool: i.e. how language transforms basic cognitive functions in the high-level functions that are characteristic of human cognition. In order to do that, we review some computational models that substantiate this view with respect to categorization and memory. Since these models are based on a very rudimentary form of non-syntactic ‘language’ we argue that the use of language as a cognitive tool might have been an early discovery in hominid evolution, and might have played a substantial role in the evolution of language itself. (shrink)

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)

Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence of a model for the process of practise in (...) current approaches is a significant contributor to brittleness. This paper will investigate how the process of practise relates to deeper forms of contextual representations of knowledge. While researchers and developers of knowledge based systems have often incorporated the notion of context they treat context as a static entity, neglecting many connectionists’ work in learning hidden and dynamic contexts. This paper argues that the omission of these higher forms of context is one of the fundamental problems in the application and interpretation of symbolic knowledge. Finally, these ideas for modelling context will lead to the reinterpretation of situation cognition which makes a significant step towards a philosophy of knowledge that could lead to the modelling of the process of practice. (shrink)

Computationalism has been the mainstream view of cognition for decades. There are periodic reports of its demise, but they are greatly exaggerated. This essay surveys some recent literature on computationalism. It concludes that computationalism is a family of theories about the mechanisms of cognition. The main relevant evidence for testing it comes from neuroscience, though psychology and AI are relevant too. Computationalism comes in many versions, which continue to guide competing research programs in philosophy of mind as well as psychology (...) and neuroscience. Although our understanding of computationalism has deepened in recent years, much work in this area remains to be done. (shrink)

The notion of representation has become ubiquitous throughout cognitive psychology, cognitive neuroscience and the cognitive sciences generally. This paper addresses the status of mental representations as entities that have been posited to explain cognition. I do so by examining similarities between mental representations and sense-data in both their characteristics and key arguments offered for each. I hope to show that more caution in the adoption and use of representations in explaining cognition is warranted. Moreover, by paying attention to problematic notions (...) of representations, a less problematic sense of representation might emerge. (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)

In this paper I argue that Turing’s responses to the mathematical objection are straightforward, despite recent claims to the contrary. I then go on to show that by understanding the importance of learning machines for Turing as related not to the mathematical objection, but to Lady Lovelace’s objection, we can better understand Turing’s response to Lady Lovelace’s objection. Finally, I argue that by understanding Turing’s responses to these objections more clearly, we discover a hitherto unrecognized, substantive thesis in his philosophical (...) thinking about the nature of mind. (shrink)

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...) We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines. (shrink)

Because intelligent agents employ physically embodied cognitive systems to reason about the world, their cognitive abilities are constrained by the laws of physics. Scientists have used digital computers to develop and validate theories of physically embodied cognition. Computational theories of intelligence have advanced our understanding of the nature of intelligence and have yielded practically useful systems exhibiting some degree of intelligence. However, the view of cognition as algorithms running on digital computers rests on implicit assumptions about the physical world that (...) are incorrect. Recently, the view is emerging of computing systems as goal-directed agents, evolving during problem solving toward improved world models and better task performance. A full realization of this vision requires a new logic for computing that incorporates learning from experience as an intrinsic part of the logic, and that permits full exploitation of the quantum nature of the physical world. This paper proposes a theory of physically embodied cognitive agents founded upon first-order logic, Bayesian decision theory, and quantum physics. An abstract architecture for a physically embodied cognitive agent is presented. The cognitive aspect is represented as a Bayesian decision theoretic agent; the physical aspect is represented as a quantum process; and these aspects are related through von Neumann’s principle of psycho-physical parallelism. Alternative metaphysical positions regarding the meaning of quantum probabilities and the role of efficacious choices by agents are discussed in relation to the abstract agent architecture. The concepts are illustrated with an extended example from the domain of science fiction. (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)

Online collection of papers by Devitt, Dretske, Guarino, Hochberg, Jackson, Petitot, Searle, Tye, Varzi and other leading thinkers on philosophy and the foundations of cognitive Science. Topics dealt with include: Wittgenstein and Cognitive Science, Content and Object, Logic and Foundations, Language and Linguistics, and Ontology and Mereology.

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)

According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (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)

On the occasion of a first conference on Cognitive Science, it seems appropriate to review the basis of common understanding between the various disciplines. In my estimate, the most fundamental contribution so far of artificial intelligence and computer science to the joint enterprise of cognitive science has been the notion of a physical symbol system, i.e., the concept of a broad class of systems capable of having and manipulating symbols, yet realizable in the physical universe. The notion of symbol so (...) defined is internal to this concept, so it becomes a hypothesis that this notion of symbols includes the symbols that we humans use every day of our lives. In this paper we attempt systematically, but plainly, to lay out the nature of physical symbol systems. Such a review is in ways familiar, but not thereby useless. Restatement of fundamentals is an important exercise. (shrink)

Let psychologism be the doctrine that whether behavior is intelligent behavior depends on the character of the internal information processing that produces it. More specifically, I mean psychologism to involve the doctrine that two systems could have actual and potential behavior _typical_ of familiar intelligent beings, that the two systems could be exactly alike in their actual and potential behavior, and in their behavioral dispositions and capacities and counterfactual behavioral properties (i.e., what behaviors, behavioral dispositions, and behavioral capacities they would (...) have exhibited had their stimuli differed)--the two systems could be alike in all these ways, yet there could be a difference in the information processing that mediates their stimuli and responses that determines that one is not at all intelligent while the other is fully intelligent. (shrink)

The objective of this work is to explore the notion of “action” and “agency” in artificial intelligence (AI). It employs a metaphysical notion of action and agency as an epistemological tool in the critique of the notion of “action” and “agency” in artificial intelligence. Hence, both a metaphysical and cognitive analysis is employed in the investigation of the quiddity and nature of action and agency per se, and how they are, by extension employed in the language and science of artificial (...) intelligence. The advent of the science of artificial intelligence and cognitive science, and the technological applications of artificial intelligence in the production of agents such as driverless cars and expert systems, have raised the question of moral, ethical and/or legal responsibility in AI agents. This has re-emphasized the importance of the philosophical discourse on the notions of action and agency, which in contemporary intellectual discourse are now perceived to be phenomena within the epistemic competence of the natural sciences. This paper argues that AI systems do not and cannot possess free agency and autonomy, thus cannot be morally and ethically responsible, hence, it recommends a socio-political response to the question of responsibility in AI. It is then the duty of individual nations, or the global community to define and enact policies on who shoulders the responsibility of actions executed by AIs. (shrink)

The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...) from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank’s computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI’s potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society. (shrink)

Recent advancements in artificial intelligence have fueled widespread academic discourse on the ethics of AI within and across a diverse set of disciplines. One notable subfield of AI ethics is machine ethics, which seeks to implement ethical considerations into AI systems. However, since different research efforts within machine ethics have discipline-specific concepts, practices, and goals, the resulting body of work is pestered with conflict and confusion as opposed to fruitful synergies. The aim of this paper is to explore ways to (...) alleviate these issues, both on a practical and theoretical level of analysis. First, we describe two approaches to machine ethics: the philosophical approach and the engineering approach and show how tensions between the two arise due to discipline specific practices and aims. Using the concept of disciplinary capture, we then discuss potential promises and pitfalls to cross-disciplinary collaboration. Drawing on recent work in philosophy of science, we finally describe how metacognitive scaffolds can be used to avoid epistemological obstacles and foster innovative collaboration in AI ethics in general and machine ethics in particular. (shrink)

It is the contention of this paper that ethics of work ought to be anthropological, and artificial intelligence (AI) research and development, which is the focus of work today, should be anthropological, that is, human-centered. This paper discusses the philosophical and theological implications of the development of AI research on the intrinsic nature of work and the nature of the human person. AI research and the implications of its development and advancement, being a relatively new phenomenon, have not been comprehensively (...) interrogated in the social and ethical teachings of the Catholic Church. This paper, therefore, proposes a path for this interrogation by expounding a discourse which is believed to be epistemically helpful in the developing discourse of AI in the ethical and social teachings of the Church. The advancement in the research on AI is not only redefining the meaning of work, but, even more so, it is questioning the metaphysical notion of the human person and the theological notion of work as an intrinsic part in the selfhood and dignity of the human person. (shrink)

In a brief overview this article discusses that, in the multifaceted field of cognitive sciences, the problem of consciousness and mind is far from being resolved. The article then argues that this problem is worked out in careful detail in Peirce’s work. Intelligence, hence mind, for Peirce, is distinct from consciousness. Without being dissociable, they are, in fact, distinct but complementary. Hence, Peirce’s ideas should be recovered not only for the sake of their relevance, but also because cognitivists have a (...) lot to learn from Peirce. (shrink)

Theologians often reflect on the ethical uses and impacts of artificial intelligence, but when it comes to artificial intelligence techniques themselves, some have questioned whether much exists to discuss in the first place. If the significance of computational operations is attributed rather than intrinsic, what are we to say about them? Ancient thinkers—namely Augustine of Hippo (lived 354–430)—break the impasse, enabling us to draw forth the moral and metaphysical significance of current developments like the “deep neural networks” that are responsible (...) for some of the most remarkable achievements of the past few years. First, Augustine’s theology of the natural world’s rationes seminales makes sense of neural networks’ success by explaining the world as a kaleidoscopic refraction of divine Wisdom rather than as a collection of discrete objects standing in crisp relations. Second, his account of interpretive judgment as a moral act bound up with love reveals how our training and use of AI relates to the Christian’s assimilation to Wisdom. The contingent meaning of the neural network reveals AI’s potential either for standing between us and the world or in some sense facilitating a Christian’s regathering of the created echoes of divine Wisdom throughout his or her journey into the Trinity. (shrink)

As physical entities that translate symbols into physical actions, computers offer insights into the nature of meaning and agency. • Physical symbol systems, generically known as agents, link abstractions to material actions. The meaning of a symbol is defined as the physical actions an agent takes when the symbol is encountered. • An agent has autonomy when it has the power to select actions based on internal decision processes. Autonomy offers a partial escape from constraints imposed by direct physical influences (...) such as gravity and the transfer of momentum. Swimming upstream is an example. • Symbols are names that can designate other entities. It appears difficult to explain the use of names and symbols in terms of more primitive functionality. The ability to use names and symbols, i.e., symbol grounding, may be a fundamental cognitive building block. • The standard understanding of causality—wiggling X results in Y wiggling—applies to both physical causes (e.g., one billiard ball hitting another) and symbolic causes (e.g., a traffic light changing color). Because symbols are abstract, they cannot produce direct physical effects. For a symbol to be a cause requires that the affected entity determine its own response. This is called autonomous causality. • This analysis of meaning and autonomy offers new perspectives on free will. (shrink)

Modern computing is generally taken to consist primarily of symbol manipulation. But symbols are abstract, and computers are physical. How can a physical device manipulate abstract symbols? Neither Church nor Turing considered this question. My answer is that the bit, as a hardware-implemented abstract data type, serves as a bridge between materiality and abstraction. Computing also relies on three other primitive—but more straightforward—abstractions: Sequentiality, State, and Transition. These physically-implemented abstractions define the borderline between hardware and software and between physicality and (...) abstraction. At a deeper level, asking how a physical device can interact with abstract symbols is the wrong question. The relationship between symbols and physical devices begins with the realization that human beings already know what it means to manipulate symbols. We build and program computers to do what we understand to be symbol manipulation. To understand what that means, consider a light switch. A light switch doesn’t turn a light on or off. Those are abstractions. Light switches don’t operate with abstractions. We build light switches, so that when flipped, the world is changed in such a way that we understand the light to be on or off. Similarly, we build computers to perform operations that we understand as manipulating symbols. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

In this paper, I review the objections against the claim that brains are computers, or, to be precise, information-processing mechanisms. By showing that practically all the popular objections are either based on uncharitable interpretation of the claim, or simply wrong, I argue that the claim is likely to be true, relevant to contemporary cognitive (neuro)science, and non-trivial.

There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.

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)

This essay is divided into two parts. In the first part (§2), I introduce the idea of practical meaning by looking at a certain kind of procedural systems — the motor system — that play a central role in computational explanations of motor behavior. I argue that in order to give a satisfactory account of the content of the representations computed by motor systems (motor commands), we need to appeal to a distinctively practical kind of meaning. Defending the explanatory relevance (...) of semantic properties in a computationalist explanation of motor behavior, my argument concludes that practical meanings play a central role in an adequate psychological theory of motor skill. In the second part of this essay (§3), I generalize and clarify the notion of practical meaning, and I defend the intelligibility of practical meanings against an important objection. (shrink)

The journal of Cognitive Computation is defined in part by the notion that biologically inspired computational accounts are at the heart of cognitive processes in both natural and artificial systems. Many studies of various important aspects of cognition (memory, observational learning, decision making, reward prediction learning, attention control, etc.) have been made by modelling the various experimental results using ever-more sophisticated computer programs. In this manner progressive inroads have been made into gaining a better understanding of the many components of (...) cognition. Concomitantly in both science and science fiction the hope is periodically re-ignited that a manmade system can be engineered to be fully cognitive and conscious purely in virtue of its execution of an appropriate computer program. However, whilst the usefulness of the computational metaphor in many areas of psychology and neuroscience is clear, it has not gone unchallenged and in this article I will review a group of philosophical arguments that suggest either such unequivocal optimism in computationalism is misplaced—computation is neither necessary nor sufficient for cognition—or panpsychism (the belief that the physical universe is fundamentally composed of elements each of which is conscious) is true. I conclude by highlighting an alternative metaphor for cognitive processes based on communication and interaction. (shrink)

This chapter describes the conceptual foundations of cognitive science during its establishment as a science in the 20th century. It is organized around the core ideas of individual agency as its basic explanans and information-processing as its basic explanandum. The latter consists of a package of ideas that provide a mathematico-engineering framework for the philosophical theory of materialism.

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)

Models of memory in cognitive science and philosophy have traditionally explained human remembering in terms of storage and retrieval. This tendency has been entrenched by reliance on computationalist explanations over the course of the twentieth century; even research programs that eschew computationalism in name, or attempt the revision of traditional models, demonstrate tacit commitment to computationalist assumptions. It is assumed that memory must be stored by means of an isomorphic trace, that memory processes must divide into conceptually distinct systems and (...) phases, and that human remembering consists in inner, cognitive processes that are implemented by distinct neural processes. This dissertation draws on recent empirical work, and on philosophical arguments from Ludwig Wittgenstein and others, to demonstrate that this latent computationalism in the study of memory is problematic, and that it can and should be eliminated. Cognitive psychologists studying memory have encountered numerous data in recent decades that belie archival models. In cognitive neuroscience, establishing the neural basis of storage and retrieval processes has proven elusive. A number of revised models on offer in memory science, that have taken these issues into account, fail to sufficiently extricate the archival framework. Several impasses in memory science are products of these underlying computationalist assumptions. Wittgenstein and other philosophers offer a number of arguments against the need for, and the efficacy of, the storage and retrieval of traces in human remembering. A study of these arguments clarifies the ways that these computationalist assumptions are presently impeding the science of memory, and provides ways forward in removing them. We can and should characterize and model human memory without invoking the storage and retrieval of traces. A range of work in connectionism, dynamical systems theory, and recent philosophical accounts of memory demonstrate how the science of memory can proceed without these assumptions, toward non-archival models of remembering. (shrink)

Cognitive sciences as an interdisciplinary field, involving scientific disciplines (such as computer science, linguistics, psychology, neuroscience, economics, etc.), philosophical disciplines (philosophy of language, philosophy of mind, analytic philosophy, etc.) and engineering (notably knowledge engineering), have a vast theoretical and practical content, some even conflicting. In this interdisciplinary context and on computational modeling, ontologies play a crucial role in communication between disciplines and also in a process of innovation of theories, models and experiments in cognitive sciences. We propose a model for (...) this process here. An ontological commitment is advocated as the framework of a scientific realism, which leads computational modeling to search for more realistic models, for a complex systems perspective of nature and cognition. In that way multiagent modeling of complex systems has been fulfilling an important role. (shrink)

In the twentieth century, no person epitomized more dramatically the “Renaissance mind” than Herbert A. Simon (1916–2001). In aworking life spanning over 60 years, Simon made seminal contributions to administrative theory, axiomatic foundations of physics, economics, sociology, econometrics, cognitive psychology, logic of scientific discovery, and artificial intelligence. Simon's life of the mind, thus, affords nothing less than a “laboratory” in which to observe and examine at close quarters the phenomenon ofmultidisciplinary creativity. In this paper, we attempt to shed some light (...) on the nature of Simon's creativity and the nature of his particular Renaissance mind. In particular, we have attempted here to articulate thecognitive styleunderlying Simon's multidisciplinary creativity. (shrink)

The development of computers as ‘mind tools’ has generated intriguing and provocative views about their potential human-like qualities. In this paper an attempt is made to explore the ‘real’ nature of computers by an examination of three widely different perspective, (1) the common-sense view of computers as tools; (2) the provocative view of computers as persons; and (3) the challenging view of computers as texts. In the course of the discussion an extended critique of the use of anthropomorphic terms in (...) relation to machines is conducted and the fundamental differences between human persons and machines is reasserted. (shrink)

This paper provides a glimpse of some different theoretical frameworks and empirical methods in the author's search for theories and practices that might improve the utility and usability of computer artifacts. The essay touches on some problematic aspects of currently accepted theories and techniques in the cognitive sciences, especially in their application to the field of human-computer interaction, and mentions some alternative conceptions based on a cultural-historical approach. The intent is to widen the nature of the debate about appropriate frameworks (...) for discussing human activities especially when we discuss design activities in the context of computer systems development. The paper concludes with some suggestions for more fruitful research directions that involve the active participation of those for whom the research is ostensibly being done, and a greater emphasis on understanding how artifact design and use are inextricably intertwined. (shrink)

The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...) disagreement is articulated. The goal here is to provide something like a ‘geography’ of the various notions of ‘symbol’ that have appeared in the various literatures, so as to highlight the key issues and to permit the focusing of attention upon the important dimensions. In particular, the relationship between ‘tokens’ and ‘symbols’ is addressed. The issue of designation is discussed in some detail. The distinction between simple and complex symbols is clarified and an apparently necessary condition for a system to be potentially symbol, or token bearing, is introduced. (shrink)

The question about the scientific nature of computing has been widely debated with no universal consensus reached about its disciplinary status. Positions vary from acknowledging computing as the science of computers to defining it as a synthetic engineering discipline. In this paper, we aim at discussing the nature of computing from a methodological perspective. We consider, in particular, the nature and role of experiments in this field, whether they can be considered close to the traditional experimental scientific method or, instead, (...) they possess peculiar and unique features. We argue that this experimental perspective should be taken into account when discussing the status of computing. We critically survey how the experimental method has been conceived and applied in computing, and some open issues that could be tackled with the aid of the history of science, the philosophy of science, and the philosophy of technology. (shrink)

One major fault line in foundational theories of cognition is between the so-called “representational” and “non-representational” theories. Is it possible to formulate an intermediate approach for a foundational theory of cognition by defining a conception of representation that may bridge the fault line? Such an account of representation, as well as an account of correspondence semantics, is offered here. The account extends previously developed agent-based pragmatic theories of semantic information, where meaning of an information state is defined by its interface (...) role, to a theory that accommodates a notion of representation and correspondence semantics. It is argued that the account can be used to develop an intermediate approach to cognition, by showing that the major sources of tension between “representational” and “non-representational” theories may be eased. (shrink)