Abstract: According to the Veridicality Thesis, information requires truth. On this view, smoke carries information about there being a fire only if there is a fire, the proposition that the earth has two moons carries information about the earth having two moons only if the earth has two moons, and so on. We reject this Veridicality Thesis. We argue that the main notions of information used in cognitive science and computer (...) class='Hi'>science allow A to have information about the obtaining of p even when p is false. (shrink)

Since the cognitive revolution, it has become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theorists of (...) cognition be explicit and careful in choosing notions of computation and information and connecting them together.Keywords: Computation; Information processing; Computationalism; Computational theory of mind; Cognitivism. (shrink)

The skills of thinking, reading conceptions and reading practice (literacy levels) found in textbook activities for the sixth year of Primary Education in Social Sciences and Spanish Language in Spain are analyzed in this paper. A mixed methodology is used to triangulate the data, integrating the critical analysis of discourse and two types of statistical analysis: descriptive (frequencies and percentages) and inferential (χ 2, ANOVA, and the Mann-Whitney U Test). The results inform us of both the permanence and (...) the strengthening of the design of an activity oriented toward the development of traditional conceptions of linguistic-cognitive reading. In the framework of education for global citizenship, the conclusion is the need for student reading practices that begin with the principles of critical literacy directed at the acquisition of social, critical, and creative thinking skills. (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)

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)

There are currently considerable confusion and disarray about just how we should view computationalism, connectionism and dynamicism as explanatory frameworks in cognitive science. A key source of this ongoing conflict among the central paradigms in cognitive science is an equivocation on the notion of computation simpliciter. ‘Computation’ is construed differently by computationalism, connectionism, dynamicism and computational neuroscience. I claim that these central paradigms, properly understood, can contribute to an integrated cognitive science. Yet, before this (...) claim can be defended, a better understanding of ‘computation’ is required. ‘Digital computation’ is an ambiguous concept. It is not just the classical dichotomy between analogue and digital computation that is the basis for the equivocation on ‘computation’ simpliciter in cognitive science, but also the diversity of extant accounts of digital computation. There are many answers on what it takes for a system to perform digital computation. Answers to this problem range from Turing machine computation, through the formal manipulation of symbols, the execution of algorithms and others, to the strong-pancomputational thesis, according to which every physical system computes every Turing-computable function. Despite some overlap among them, extant accounts of concrete digital computation are non-equivalent, thus, rendering ‘digital computation’ ambiguous. The objective of this dissertation is twofold. First, it is to promote a clearer understanding of concrete digital computation. Accordingly, my main thesis is that not only are extant accounts of concrete digital computation non-equivalent, but most of them are inadequate. I show that these accounts are not just intensionally different (this is quite trivially the case), but also extensionally distinct. In the course of examining several key accounts of concrete digital computation, I propose the instructional information processing account, according to which digital computation is the processing of discrete data in accordance with finite instructional information. The second objective is to establish the foundational role of computation in cognitive science whilst rejecting the purported representational nature of computation. (shrink)

The primary goal of this essay is to provide a comprehensive overview and analysis of the various relations between material artifacts and the embodied mind. A secondary goal of this essay is to identify some of the trends in the design and use of artifacts. First, based on their functional properties, I identify four categories of artifacts co-opted by the embodied mind, namely (1) embodied artifacts, (2) perceptual artifacts, (3) cognitive artifacts, and (4) affective artifacts. These categories can (...) overlap and so some artifacts are members of more than one category. I also identify some of the techniques (or skills) we use when interacting with artifacts. Identifying these categories of artifacts and techniques allows us to map the landscape of relations between embodied minds and the artifactual world. Second, having identified categories of artifacts and techniques, this essay then outlines some of the trends in the design and use of artifacts, focussing on neuroprosthetics, brain-computer interfaces, and personalisation algorithms nudging their users towards particular epistemic paths of information consumption. (shrink)

Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron system in terms (...) of predictive coding. (shrink)

This special issue is a refreshing contrast to the intuitively influential notion of language as an internal system. This internal approach to language is going strong in some segments of the cognitive sciences. As an assumption, internalism drives much empirical work on language, and it is the basis of prominent theories of language – its nature (e.g. an internalised computational system), its evolution (e.g. a single still-unknown mutation), and its function (e.g. thinking, not communication). -/- Radical fundamentalist versions of (...) these theories are no longer in the main stream, however, despite the attention they may garner by forceful exposition (e.g. Chomsky 2011a). A fuller canvassing of the cognitive sciences – obviously outside the scope of the current presentation – would probably reveal that most researchers, even those who study aspects of language isolated in individual participants, would allow for an intrinsic social characteristic to language. I would go so far as to guess that they would place this social character on explanatory par with other structural or information-processing features that are studied in the lab. And despite what is averred by Chomsky (2011a), this social character in human cognition has been proposed in many domains, from vision (Balcetis & Lassiter 2010) to memory (Barnier et al. 2008). Humans are intrinsically social in a way that distinguishes them from any other primate species, and this sociality seems to be weaved into many cognitive processes (Castiello et al. 2010; Tomasello 2009). -/- But “social” is not the same thing as “distributed,” by the content of this issue. The latter may subsume the former. The distributed approach discussed in this special issue is not “simply” social – it does not just propose an added static feature of any synchronic language context (as noted in Jennings & Thompson this issue). The approach instead regards this social characteristic as just one part of a broader dynamic distributed process that constitutes language, through different kinds of inter-individual coordination at many levels of spatial and temporal scale (Cowley this issue; Fusaroli & Tylén this issue). (shrink)

Dasein is one of several twentieth-century notions which paint a portrait of the “post-Cartesian subject.” Critics of cognitivism such as Dreyfus (1992) have invoked Dasein in arguing that computational models cannot be sufficient to account for situated cognition. Van Gelder (1995) argues that dynamic systems theory provides an empirical model of cognition as practical activity which avoids the Cartesianism implicit in the computational approach. I assess Van Gelder’s claim for dynamic systems as a model of being-in-the-world. Contra Van Gelder, (...) I argue that the force of the “Dasein objection” is that the significance of a mental process, whether representational or not, depends on a lived background of value. While dynamic systems can help model the diachronic interplay between organism and environment, the semantic context for this interplay is no more accounted for here than in traditional computer models. (shrink)

Information processing theories in psychology give rise to executive theories of consciousness. Roughly speaking, these theories maintain that consciousness is a centralized processor that we use when processing novel or complex stimuli. The computational assumptions driving the executive theories are closely tied to the computer metaphor. However, those who take the metaphor serious — as I believe psychologists who advocate the executive theories do — end up accepting too particular a notion of a computing device. In this essay, (...) I examine the arguments from theoretical computational considerations that cognitive psychologists use to support their general approach in order to show that they make unwarranted assumptions about the processing attributes of consciousness. I then go on to examine the assumptions behind executive theories which grow out of the computer metaphor of cognitive psychology and conclude that we may not be the sort of computational machine cognitive psychology assumes and that cognitive psychology''s approach in itself does not buy us anything in developing theories of consciousness. Hence, the state space in which we may locate consciousness is vast, even within an information processing framework. (shrink)

In recent years the philosophy of information has emerged as an important area of research in philosophy. However, until now information’s philosophical history has been largely overlooked. Information and the History of Philosophy is the first comprehensive investigation of the history of philosophical questions around information, including work from before the Common Era to the twenty-first century. It covers scientific and technology-centred notions of information; views of human information processing, as well as socio-political (...) topics such as the control and use of information in societies. Organised into five parts, nineteen chapters by an international team of contributors cover the following topics and more: Information before 500 CE, including ancient Chinese, Greek and Roman approaches to information Early theories of information processing, sources of information and cognition Information and computation in Leibniz, visualised scientific information, copyright and social reform The nineteenth century, including biological information, knowledge economies, and information’s role in empire and eugenics Recent and contemporary philosophy of information, including racialized information, Shannon information, and the very idea of an information revolution. Information and the History of Philosophy is a landmark publication in this emerging field. As such, it is essential reading for students and researchers in the history of philosophy, philosophy of science and technology, and library and information studies. It is also a valuable resource for those working in subjects such as the history of science, media and communication studies, and intellectual history. (shrink)

When cognitive scientists are looking for the neural basis of consciousness or the computational processes underlying vision, what are they looking to find? I argue for a new account of this explanatory project in cognitive science (and the special sciences more generally) on which it is best understood on close analogy with causal explanation in the special sciences. Causal explanations cite causal difference-makers: they explain how certain events causally depend on other events. Generative explanations cite generative difference-makers: (...) they explain how certain events obtain in virtue of (and so are generated by) other events. I show how that this generative difference-making account (GDM) extends well regarded developments in philosophical work on causal explanation, helps to usefully clarify and expands the hypothesis space of a key project in cognitive science – that of understanding how underlying nonmental processes give rise to the mental, including consciousness. In section 2, I develop GDM by close analogy with causal explanation, and locate both of them as species of a more general project in the special sciences: that of discovering what makes a difference to what. In section 3, I motivate GDM by contrasting it with other rival accounts, including correlationist, identity, and grounding accounts, as well as Carl Craver’s constitutive relevance account. In section 4, I tie all of the strands of the preceding discussion together to show how GDM provides a new and compelling response to the so-called hard problem of consciousness. (shrink)

Various notions of plausibility are used in cognitive science to argue for or against the “goodness of theories.” However, plausibility remains poorly understood and difcult to analyze. We review debates in the philosophy of science on uses of plausibility in the assessment of novel scientifc theories as well as recent attempts to formalize, reform, or eliminate specifc notions of plausibility. Although these discussions highlight important concerns behind plausibility claims, they fail to identify viable (...) class='Hi'>notions of plausibility that are sufciently diferent from other criteria of “good theory,” such as prior probability or external coherence. We survey uses of plausibility in linguistics and cognitive science, confrming that plausibility is often a proxy for other criteria of good theory. We argue that the need remains for concepts of plausibility that can be employed to assess the quality of proposals at the early stages of theory development when other criteria are not yet applicable. We identify two such notions: one relating to formal constraints on theories and another capturing initial epistemic consensus, if not necessarily convergence on the truth, about the target system in a community of inquiry. We briefy assess the specifcity and added value of these notions of plausibility relative to other criteria for good theory. (shrink)

The state of computing science and, particularly, software engineering and knowledge engineering is generally considered immature. The best starting point for achieving a mature engineering discipline is a solid scientific theory, and the primary reason behind the immaturity in these fields is precisely that computing science still has no such agreed upon underlying theory. As theories in other fields of science do, this paper formally establishes the fundamental elements and postulates making up a first attempt at (...) a theory in this field, considering the features and peculiarities of computing science. The fundamental elements of this approach are informons and holons, and it is a general and comprehensive theory of software engineering and knowledge engineering that related disciplines (e.g., information systems) can particularise and/or extend to take benefit from it (Lakatos’ concepts of core theory and protective belt theories). (shrink)

The recent trend in cognitive robotics experiments on language learning, symbol grounding, and related issues necessarily entails a reduction of sensorimotor aspects from those provided by a human body to those that can be realized in machines, limiting robotic models of symbol grounding in this respect. Here, we argue that there is a need for modeling work in this domain to explicitly take into account the richer human embodiment even for concrete concepts that prima facie relate merely to simple (...) actions, and illustrate this using distributional methods from computational linguistics which allow us to investigate grounding of concepts based on their actual usage. We also argue that these techniques have applications in theories and models of grounding, particularly in machine implementations thereof. Similarly, considering the grounding of concepts in human terms may be of benefit to future work in computational linguistics, in particular in going beyond “grounding” concepts in the textual modality alone. Overall, we highlight the overall potential for a mutually beneficial relationship between the two fields. (shrink)

Cognitive science uses the notion of computational information processing to explain cognitive information processing. Some philosophers have argued that anything can be described as doing computational information processing; if so, it is a vacuous notion for explanatory purposes.An attempt is made to explicate the notions of cognitive information processing and computational information processing and to specify the relationship between them. It is demonstrated that the resulting notion of computational information (...) processing can only be realized in a restrictive class of dynamical systems called physical notational systems (after Goodman's theory of notationality), and that the systems generally appealed to by cognitive science-physical symbol systems-are indeed such systems. Furthermore, it turns out that other alternative conceptions of computational information processing, Fodor's (1975) Language of Thought and Cummins' (1989) Interpretational Semantics appeal to substantially the same restrictive class of systems. (shrink)

Information technology, that assortment of technology that enables the conversion of data into information, has had an enormous impact on the field of public administration and its theoretical foundation. This article explores five of them. It begins with a discussion of one of the primary impacts of information technology on public administration theory: the development of systems theory and its descendants including the study of complex systems, chaos, and complexity theory. The importance of information technology (...) in decision-making is explored next. Does information technology free us from the limits of bounded rationality or are we simply overwhelmed by the volume of information available via new sources, such as the Internet and the World Wide Web? The third role examined is the use of information technology as a research tool to make previously intractable problems solvable. Computer capability has enormously advanced the theoretical underpinnings of public administration. Fourth, the significance of information technology as a change agent that calls for revision of other theoretical postulates is investigated. As information technology has diffused into public organizations, how has it called into question what we previously thought about the foundation of bureaucracy? Finally, learning by doing and the manner through which practice informs theory is considered. (shrink)

Coherence considerations play an important role in science and in everyday reasoning. However, it is unclear what exactly is meant by coherence of information and why we prefer more coherent information over less coherent information. To answer these questions, we first explore how to explicate the dazzling notion of ``coherence'' and how to measure the coherence of an information set. To do so, we critique prima facie plausible proposals that incorporate normative principles such as ``Agreement'' (...) or ``Dependence'' and then argue that the coherence of an information set is best understood as an indicator of the truth of the set under certain conditions. Using computer simulations, we then show that a new probabilistic measure of coherence that combines aspects of the two principles above, but without strictly satisfying either principle, performs particularly well in this regard. (shrink)

There is a general conception of levels in philosophy which says that the world is arrayed into a hierarchy of levels and that there are different modes of analysis that correspond to each level of this hierarchy, what can be labelled the ‘Hierarchical Correspondence View of Levels” (or HCL). The trouble is that despite its considerable lineage and general status in philosophy of science and metaphysics the HCL has largely escaped analysis in specific domains of inquiry. The goal of (...) this paper is to take up a recent call to domain-specificity by examining the role of the HCL in cognitive science. I argue that the HCL is, in fact, a conception of levels that has been employed in cognitive science and that cognitive scientists should avoid its use where possible. The argument is that the HCL is problematic when applied to cognitive science specifically because it fails to distinguish two important kinds of shifts used when analysing information processing systems: shifts in grain and shifts in analysis. I conclude by proposing a revised version of the HCL which accommodates the distinction. (shrink)

Hubert L. Dreyfus's engagement with other thinkers has always been driven by his desire to understand certain basic questions about ourselves and our world. The philosophers on whom his teaching and research have focused are those whose work seems to him to make a difference to the world. The essays in this volume reflect this desire to "make a difference"—not just in the world of academic philosophy, but in the broader world. Dreyfus has helped to create a culture of reflection—of (...) questioning the deep premises that inform and shape work in artificial intelligence and cognitive science. He has also been the primary introducer and interpreter of Martin Heidegger's work to the world of information technology. The essays in this volume represent the fruitful application of deep philosophical analysis to the concerns of our modern technological world. The sections are Coping and Intentionality; Computers and Cognitive Science; and "Applied Heidegger." In addition to cognitive science and artificial intelligence, topics include everyday skills, religion, business practices, and medical care. The book concludes with Dreyfus's responses to the essays. Contributors Daniel Andler, Patricia Benner, Albert Borgmann, Harry Collins, George Downing, Fernando Flores, Sean Kelly, Joseph Rouse, Theodore R. Schatzki, John Searle, Robert C. Solomon, Charles Spinosa, David Stern, Charles Taylor, Terry Winograd, Mark Wrathall. (shrink)

In the dissertation we study the complexity of generalized quantifiers in natural language. Our perspective is interdisciplinary: we combine philosophical insights with theoretical computer science, experimental cognitive science and linguistic theories. -/- In Chapter 1 we argue for identifying a part of meaning, the so-called referential meaning (model-checking), with algorithms. Moreover, we discuss the influence of computational complexity theory on cognitive tasks. We give some arguments to treat as cognitively tractable only those problems which can (...) be computed in polynomial time. Additionally, we suggest that plausible semantic theories of the everyday fragment of natural language can be formulated in the existential fragment of second-order logic. -/- In Chapter 2 we give an overview of the basic notions of generalized quantifier theory, computability theory, and descriptive complexity theory. -/- In Chapter 3 we prove that PTIME quantifiers are closed under iteration, cumulation and resumption. Next, we discuss the NP-completeness of branching quantifiers. Finally, we show that some Ramsey quantifiers define NP-complete classes of finite models while others stay in PTIME. We also give a sufficient condition for a Ramsey quantifier to be computable in polynomial time. -/- In Chapter 4 we investigate the computational complexity of polyadic lifts expressing various readings of reciprocal sentences with quantified antecedents. We show a dichotomy between these readings: the strong reciprocal reading can create NP-complete constructions, while the weak and the intermediate reciprocal readings do not. Additionally, we argue that this difference should be acknowledged in the Strong Meaning hypothesis. -/- In Chapter 5 we study the definability and complexity of the type-shifting approach to collective quantification in natural language. We show that under reasonable complexity assumptions it is not general enough to cover the semantics of all collective quantifiers in natural language. The type-shifting approach cannot lead outside second-order logic and arguably some collective quantifiers are not expressible in second-order logic. As a result, we argue that algebraic (many-sorted) formalisms dealing with collectivity are more plausible than the type-shifting approach. Moreover, we suggest that some collective quantifiers might not be realized in everyday language due to their high computational complexity. Additionally, we introduce the so-called second-order generalized quantifiers to the study of collective semantics. -/- In Chapter 6 we study the statement known as Hintikka's thesis: that the semantics of sentences like ``Most boys and most girls hate each other'' is not expressible by linear formulae and one needs to use branching quantification. We discuss possible readings of such sentences and come to the conclusion that they are expressible by linear formulae, as opposed to what Hintikka states. Next, we propose empirical evidence confirming our theoretical predictions that these sentences are sometimes interpreted by people as having the conjunctional reading. -/- In Chapter 7 we discuss a computational semantics for monadic quantifiers in natural language. We recall that it can be expressed in terms of finite-state and push-down automata. Then we present and criticize the neurological research building on this model. The discussion leads to a new experimental set-up which provides empirical evidence confirming the complexity predictions of the computational model. We show that the differences in reaction time needed for comprehension of sentences with monadic quantifiers are consistent with the complexity differences predicted by the model. -/- In Chapter 8 we discuss some general open questions and possible directions for future research, e.g., using different measures of complexity, involving game-theory and so on. -/- In general, our research explores, from different perspectives, the advantages of identifying meaning with algorithms and applying computational complexity analysis to semantic issues. It shows the fruitfulness of such an abstract computational approach for linguistics and cognitive science. (shrink)

The impact of new advanced technology on issues that concern meaningful information and its relation to studies of intelligence constitutes the main topic of the present paper. The advantages, disadvantages and implications of the synthetic methodology developed by cognitive scientists, according to which mechanical models of the mind, such as computer simulations or self-organizing robots, may provide good explanatory tools to investigate cognition, are discussed. A difficulty with this methodology is pointed out, namely the use of meaningless (...) information to explain intelligent behavior that incorporates meaningful information. In this context, it is inquired what are the contributions of cognitive science to contemporary studies of intelligent behavior and how technology may play a role in the analysis of the relationships established by organisms in their natural and social environments. (shrink)

How is technology changing the way people remember? This book explores the interplay of memory stored in the brain and outside of the brain, providing a thorough interdisciplinary review of the current literature, including relevant theoretical frameworks from across a variety of disciplines in the sciences, arts, and humanities. It also presents the findings of a rich and novel empirical data set, based on a comprehensive survey on the shifting interplay of internal and external memory in the 21st century. Results (...) reveal a growing symbiosis between the two forms of memory in our everyday lives. The book presents a new theoretical framework for understanding the interplay of internal and external memory, and their complementary strengths. It concludes with a guide to important dimensions, questions, and methods for future research. Memory and Technology will be of interest to researchers, professors, and students across the disciplines of psychology, philosophy, library and information science, human factors, media and cultural studies, anthropology and archaeology, photography, and cognitive rehabilitation, as well as anyone interested in how technology is affecting human memory. _____ "This is a novel book, with interesting and valuable data on an important, meaningful topic, as well as a gathering of multidisciplinary and interdisciplinary ideas...The research is accurately represented and inclusive. As a teaching tool, I can envision graduate seminars in different disciplines drawing on the material as the basis for teaching and discussions." Dr. Linda A. Henkel, Fairfield University "This book documents the achievements of a vibrant scientific project – you feel the enthusiasm of the authors for their research. The organization of the manuscript introduces the reader into a comparatively new field the same way as pioneering authors have approached it." Prof. Dr. Wolfgang Schönpflug, Freie Universität Berlin. (shrink)

This special issue on what some regard as a crisis of replicability in cognitive science (i.e. the observation that a worryingly large proportion of experimental results across a number of areas cannot be reliably replicated) is informed by three recent developments. -/- First, philosophers of mind and cognitive science rely increasingly on empirical research, mainly in the psychological sciences, to back up their claims. This trend has been noticeable since the 1960s (see Knobe, 2015). This development (...) has allowed philosophers to draw on a wider range of relevant resources, but it also makes them vulnerable to relying on claims that may not survive further scrutiny. If we have reasons to believe that a large proportion of findings in the psychological sciences cannot be reliably replicated, this would be a problem for philosophers who use such findings in their work. Second, philosophers are increasingly designing and carrying out their own experiments to back up claims, or to test claims earlier made from the armchair, for example, on the perceived permissibility of diverting trolleys or on the nature of free will. This growing field of experimental philosophy has diversified the intellectual field in philosophy, but may also be vulnerable to issues of replicability that philosophers did not face before. -/- Third, the recent evidence of apparently widespread non-replicability in the social sciences (and other fields) has forced philosophers of science to grapple with long standing questions from their field from a new perspective. To what extent does replicability matter for theory construction? How do the notions of replicability and scientific progress interact? How can normative insights from philosophy of science be used in order to improve scientific practice? -/- It is with these three developments in mind—the increasing importance of empirically-informed philosophy, of experimental philosophy, and of philosophy of science around replicability—that this special issue has been conceived. (shrink)

The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanistic explanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate (...) analysis of processes at the level at which cognitive theories attempt to function: One is drawn from too low a level, the other from too high a level. If there is a distinctly cognitive level, then we still need to determine what are the basic organizational principles at that level. (shrink)

_Abstract_: In this paper we carve out a _reformist_ agenda within the debate on the foundations of cognitive science, incorporating some important ideas from the 4E cognition literature into the computational-representational framework. We are deeply sympathetic to this reformist program since we think that, despite strong criticism of the concept of computation and the related notion of representation, computational models should still be at the core of the study of mind. At the same time, we recognize the need (...) for a liberalization of the computational and representational framework that can address deep dissatisfaction with the anti-biologism and radical internalism of classical cognitive science. However, reform is a difficult task, so in this article we focus on two open questions within the reformist agenda. The first concerns the possibility of combining mechanistic-computational and dynamical explanations. The second concerns related changes in the notion of representation and its use (with special attention to Andy Clark’s radical predictive processing). _Keywords_: Continuum of Representational Genera;_ _Enactivism; Predictive Processing; Radical Embodied Cognition Thesis; Representationalism _ Due problemi aperti nell’agenda riformista della filosofia della scienza cognitiva _ _Riassunto_: In questo lavoro identifichiamo un’agenda _riformista_ nel dibattito sui fondamenti della scienza cognitiva che incorpora alcune idee centrali provenienti dalla letteratura sulla cognizione 4E all’interno di una cornice computazionalista e rappresentazionalista. Tale agenda considera il quadro computazionalista e rappresentazionalista ancora imprescindibile ai fini dello studio integrato della mente e del cervello, ma ne persegue una liberalizzazione nell’intento di renderlo idoneo ad accogliere alcuni importanti spunti emersi dalla letteratura sulla cognizione delle 4E. Tuttavia, riformare è un compito difficile. In questo articolo ci concentriamo su due problemi aperti nell’agenda riformista. Il primo riguarda la possibilità di mettere assieme le spiegazioni meccaniciste e computazionaliste con quelle dinamiche. Il secondo riguarda i cambiamenti relativi alla nozione di rappresentazione e al suo impiego (con particolare attenzione all’elaborazione predittiva radicale di Andy Clark). _Parole chiave_: Continuum dei generi rappresentazionali;_ _Elaborazione predittiva; Enattivismo; Tesi della cognizione incarnata radicale; Rappresentazionalismo. (shrink)

Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize (...) probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma–Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information‐theoretic formalism. (shrink)

We characterize abstraction in computer science by first comparing the fundamental nature of computer science with that of its cousin mathematics. We consider their primary products, use of formalism, and abstraction objectives, and find that the two disciplines are sharply distinguished. Mathematics, being primarily concerned with developing inference structures, has information neglect as its abstraction objective. Computer science, being primarily concerned with developing interaction patterns, has information hiding as its abstraction objective. (...) We show that abstraction through information hiding is a primary factor in computer science progress and success through an examination of the ubiquitous role of information hiding in programming languages, operating systems, network architecture, and design patterns. (shrink)

Computational physical systems may exhibit indeterminacy of computation (IC). Their identified physical dynamics may not suffice to select a unique computational profile. We consider this phenomenon from the point of view of cognitive science and examine how computational profiles of cognitive systems are identified and justified in practice, in the light of IC. To that end, we look at the literature on the underdetermination of theory by evidence and argue that the same devices that can be successfully (...) employed to confirm physical hypotheses can also be used to rationally single out computational profiles, notwithstanding IC. (shrink)

Science is a form of distributed analysis involving both individual work that produces new knowledge and collaborative work to exchange information with the larger community. There are many particular ways in which individual and community can interact in science, and it is difficult to assess how efficient these are, and what the best way might be to support them. This paper reports on a series of experiments in this area and a prototype implementation using a research platform (...) called CACHE. CACHE both supports experimentation with different structures of interaction between individual and community cognition and serves as a prototype for computational support for those structures. We particularly focus on CACHE‐BC, the Bayes community version of CACHE, within which the community can break up analytical tasks into “mind‐sized” units and use provenance tracking to keep track of the relationship between these units. (shrink)

Hubert L. Dreyfus's engagement with other thinkers has always been driven by his desire to understand certain basic questions about ourselves and our world. The philosophers on whom his teaching and research have focused are those whose work seems to him to make a difference to the world. The essays in this volume reflect this desire to "make a difference"--not just in the world of academic philosophy, but in the broader world.Dreyfus has helped to create a culture of reflection--of questioning (...) the deep premises that inform and shape work in artificial intelligence and cognitive science. He has also been the primary introducer and interpreter of Martin Heidegger's work to the world of information technology. The essays in this volume represent the fruitful application of deep philosophical analysis to the concerns of our modern technological world.The sections are Coping and Intentionality; Computers and Cognitive Science; and "Applied Heidegger." In addition to cognitive science and artificial intelligence, topics include everyday skills, religion, business practices, and medical care. The book concludes with Dreyfus's responses to the essays.Contributors : Daniel Andler, Patricia Benner, Albert Borgmann, Harry Collins, George Downing, Fernando Flores, Sean Kelly, Joseph Rouse, Theodore R. Schatzki, John Searle, Robert C. Solomon, Charles Spinosa, David Stern, Charles Taylor, Terry Winograd, Mark Wrathall. (shrink)

Cognitive scientists use computational models to represent the results of their experimental work and to guide further research. Neither of these claims is particularly controversial, but the philosophical and evidentiary statuses of these models are hotly debated. To clarify the issues, I return to Newell and Simon’s 1972 exposition on the computational approach; they herald its ability to describe mental operations despite that the neuroscience of the time could not. Using work on visual imagery (cf. imagination) as a guide, (...) I examine the extent to which this holds true today. Does contemporary neuroscience contain mechanisms capable of describing experimental results in imagery? I argue that it does not, first by exploring foundational achievements in imagery research then by showing that their neural basis cannot be specified. Newell and Simon’s methodological position accordingly stands, even 50 years later. Computational — as opposed to physiological — descriptions must be retained to characterize and study mental phenomena, even as we learn high-level details of their implementation via brain data. (shrink)

In this paper I argue that whether or not a computer can be built that passes the Turing test is a central question in the philosophy of mind. Then I show that the possibility of building such a computer depends on open questions in the philosophy of computer science: the physical Church-Turing thesis and the extended Church-Turing thesis. I use the link between the issues identified in philosophy of mind and philosophy of computer science (...) to respond to a prominent argument against the possibility of building a machine that passes the Turing test. Finally, I respond to objections against the proposed link between questions in the philosophy of mind and philosophy of computer science. (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)

Important aspects of human cognition are considered in terms of patterning, which we claim represents a shift from focusing on what is present to what is absent. We make use of Deacon’s notion of absentials and apply it to the patterning that underscores human cognition. Several important aspects of human cognition are considered that represent a shift from focusing on what is present to what is absent, namely, language as representing the transition from percept to concept-based thinking, mathematical grouping and (...) patterning of items into sets that gave rise to verbal language, as well as imaginative thinking which is so critical for the development of the arts, mathematics and science. The connection between information and absence is also examined, in which we claim that information is an absential, paralleling an idea of Deacon’s. (shrink)

The use of brain scanning now dominates the cognitive sciences, but important questions remain to be answered about what, exactly, scanning can tell us. One corner of cognitive science that has been transformed by the use of neuroimaging, and that a scanning enthusiast might point to as proof of scanning's importance, is the study of face perception. Against this view, we argue that the use of scanning has, in fact, told us rather little about the information (...) processing underlying face perception and that it is not likely to tell us much more. (shrink)

This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important and innovative changes in theories and concepts. Gathering revised contributions presented at the international conference on Model-Based Reasoning, held on October 24–26 2018 in Seville, Spain, the book is divided into three main parts. The first focuses on models, reasoning, and representation. It highlights key theoretical concepts from an applied perspective, and addresses issues concerning information visualization, (...) experimental methods, and design. The second part goes a step further, examining abduction, problem solving, and reasoning. The respective papers assess different types of reasoning, and discuss various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies, this part is intended to foster new discussions and stimulate new ideas. All in all, the book provides researchers and graduate students in the fields of applied philosophy, epistemology, cognitive science, and artificial intelligence alike with an authoritative snapshot of the latest theories and applications of model-based reasoning. (shrink)

Let’s consider the following paradox (Fodor [1989], Jackson and Petit [1988] [1992], Drestke [1988], Block [1991], Lepore and Loewer [1987], Lewis [1986], Segal and Sober [1991]): i) The intentional content of a thought (or any other intentional state) is causally relevant to its behavioural (and other) effects. ii) Intentional content is nothing but the meaning of internal representations. But, iii) Internal processors are only sensitive to the syntactic structures of internal representations, not their meanings. Therefore it seems that if we (...) want to defend the idea -absolutely plausible from an intuitive point of view- that mental / intentional states are causally responsible for behavioural outputs and we want to do it on the physicalist basis of any scientific methodology, we will have to give up the conviction that such intentional states qua intentional, i. e. as having a particular meaning, are the ones causally responsible for our behaviour. The path that takes us to mental epiphenomenalism is clear: 1) the causal powers of any event are completely determined by its physical properties; 2) although intentional properties supervene on physical properties, they can’t be identified with them; 3) intentional properties, as intentional, are not causally responsible for behaviour, because they don’t take part in the causal powers of the states to which they belong, i. e., intentional properties are epiphenomenal. Let’s consider now a different yet parallel position to the one just described. There is an important debate in cognitive science about whether the class of mechanisms to which we belong and which the computational modelling project of cognitive processes refers to is best represented by classical or connectionist approaches (McClelland, Rumelhart et. al [1986], Smolensky [1987] [1988], Fodor and Pylyshyn [1988], Pinker and Prince [1988], Clark [1989], Ramsey, Stich and Rumelhart [1991], Clark and Karmiloff-Smith [forthcoming]).In classical, serial processing models, information is encoded in terms of rules that have a linguistic character. In connectionist or parallel distributed processing (PDP) models, the causal relationships among the units that constitute the system determine how the information is processes by the network, although these units don’t have a direct semantic interpretation. But, for both, classical and connectionist models, all the computations can be explained without any reference to the content of the processed information, i.e., in both cases the properties that seem to be responsible for the system’s behaviour are ultimately physical properties nor intentional ones. This situation mirrors thus, within cognitive science, the philosophical discussion concerning the causal efficacy of semantic properties. Now, if in this debate we opt for the classical paradigm, there is a way of finding a solution to the computational version of the epiphenomenalism paradox. This solution is based mainly on the notion of supervenience or, more precisely, on the notion of mereological supervenience (Kim [1984] [1988])1 . The idea of intentional properties supervening on physical properties makes sense within the classical context because there exists an easily isolable supervenience base comprising the syntactic items in the socalled language of thought. But, what happens if we opt for the connectionist paradigm?. The situation here doesn’t seem to favour the use of the same supervenience strategy. For it has been argued (Ramsey, Stich and Garon [1991]) that beliefs, desires, and other mental states are nor, in the connectionist paradigm, individuable as weight or activation states of the system. This is because information is encoded by the network in distributed and superpositional representations, i.e., there are no straightforwardly isolable vehicles at the physical level that can be identified as the articulated supervenience base on which the semantic properties supervene2 . If this is true, then the connectionist not only loses the battle against epiphenomenalism but more drastically, seems to offer a standing invitation to eliminativism, since talk of beliefs and desires, etc. now seems to be floating free of any acceptable scientific underpinning, i.e., she has lost the necessary theoretical apparatus for supporting the intuitive idea that propositional attitudes -beliefs, desires and any mental states with semantic content- are physically realized. This second line of argumentation doesn’t take us to a paradox but to a dilemma: either we accept eliminativism, if connectionist hypothesis are correct or we defend the causal efficacy of mental states with semantic content by showing that, after all, connectionist networks are not plausible cognitive models (Davies [1991]). From my point of view, however, both lines of argumentation need to be revised. The aim of this paper is to find an account of the causal efficacy of content that avoids the aforementioned epiphenomenalist objections and that doesn’t require the discovery of inner symbols in the computational modelling of such contentful mental states. In short, the aim is to find a meeting point where a philosophical story about content and cause and the connectionist computational model can be brought together (Cfr. Clark [1989]). (shrink)

Several philosophers have expressed concerns with some recent uses of the term ‘cognition’. Underlying a number of these concerns are claims that cognition is only located in the brain and that no compelling case has been made to use ‘cognition’ in any way other than as a cause of behavior that is representational in nature. These concerns center on two primary misapprehensions: First, that some adherents of dynamical cognitive science think DCS implies the thesis of extended cognition (...) and the rejection of representation, and second, that cognition is mistakenly equated with behavior. We make three points in response to these claims: First, there is no thoroughly entrenched conception of cognition as distinct from behavior that is being illegitimately disregarded. Second, we present Shapiro’s exposition of dynamical systems theory as revealing a misunderstanding of the way that dynamical models are used in explanations of cognition and related phenomena. Accordingly, a proper conception of DCS’s methods facilitates an appreciation of extended cognition as a legitimate phenomenon of scientific investigation. Finally, we demonstrate that practicing cognitive scientists and psychologists are far more pluralistic in the phenomena they apply ‘cognition’ to than is suggested by some. At the heart of our disagreement with these concerned folks is that although we think it likely that some cognitive phenomena are representational, non-extended, and only in-between the ears, we also think there are good conceptual and empirical reasons to believe that many cognitive phenomena are non-representational, extended, and not confined to the brain. (shrink)

It is common in cognitive science to equate computation (and in particular digital computation) with information processing. Yet, it is hard to find a comprehensive explicit account of concrete digital computation in information processing terms. An information processing account seems like a natural candidate to explain digital computation. But when ‘information’ comes under scrutiny, this account becomes a less obvious candidate. Four interpretations of information are examined here as the basis for an (...) class='Hi'>information processing account of digital computation, namely Shannon information, algorithmic information, factual information and instructional information. I argue that any plausible account of concrete computation has to be capable of explaining at least the three key algorithmic notions of input, output and procedures. Whist algorithmic information fares better than Shannon information, the most plausible candidate for an information processing account is instructional information. (shrink)

A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners’ memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants (...) repeatedly heard tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners’ expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session. (shrink)

Lexical ambiguity is pervasive in language, and the nature of the representations of an ambiguous word's multiple meanings is yet to be fully understood. With a special focus on Chinese characters, the present study first established that native speaker's perception about a character's number of meanings was heavily influenced by the availability of its distinct word formations, while whether these meanings would be perceived to be closely related was driven by further conceptual analysis. These notions were operationalized as two (...) computed metrics, which assessed the degree of dispersion across individual word formations and the degree of propinquity across clusters of word formations, respectively, in a distributional semantic space. The observed correlations between the computed and the perceived metrics indicated that the utility of word formations to tap into meaning representations of Chinese characters was indeed cognitively plausible. The results have demonstrated the extent to which distributional semantics could inform about meaning representations of Chinese characters, which has theoretical implications for the representation of ambiguous words more generally. (shrink)

Most of this work is concerned with two theories that underlie cognitive science; theories which I call "the representational theory of intentionality" and "the computational theory of cognition" . While the representational theory of intentionality asserts that mental states are about the world in virtue of a representation relation between the world and the state, the computational theory of cognition asserts that humans and others perform cognitive tasks by computing functions on these representations. CTC draws upon a (...) rich analogy between the mind and digital computers, portraying cognizers as receiving input and generating outputs . ;Since researchers within cognitive science--especially within cognitive psychology, computer science, and philosophy of mind--share a common theoretic background in their adherence to the combination of RTI and CTC, what I call "computationalism" most of the work done in philosophy of mind has been aimed at either refuting or further articulating these two theories. This work is strongly within that tradition. ;RTI and CTC combine to create an explanatory framework for explanation in cognitive science. In this work, I seek to delineate the exact structure of this framework, to determine its components and their overall relationships. In my mind, this involves making implicit aspects of computational explanation explicit, providing an overall rationale for the various components and their relationships, and defending that rationale against criticism. ;In particular, the project of this work is to argue that computationalist explanations of cognition within cognitive science need to make appeals to both representation and knowledge; to distinguish the respective roles of knowledge and of representation in an overall picture of computational explanations, and to develop and argue for definitions of representation and knowledge that would allow those concepts to play their respective roles within computational explanations in cognitive science. ;The first half of this work , therefore, is devoted to theories of representation, especially to the need for, and role of representation in computational explanations. The first half of the work culminates in chapter three with a definition of representation and a picture of the structure of computational explanations. The second half of the project then becomes to generate a definition of knowledge that defines knowledge in non-epistemic language amenable to computationalism and that carries the epistemic burdens placed upon knowledge by its use in computational explanations, as well as to offer a limited defence of that definition within the framework of traditional epistemology. (shrink)

A central tenet of cognitive linguistics is that adults’ knowledge of language consists of a structured inventory of constructions, including various two-argument constructions such as the active, the passive and “fronting” constructions. But how do speakers choose which construction to use for a particular utterance, given constraints such as discourse/information structure and the semantic fit between verb and construction? The goal of the present study was to build a computational model of this phenomenon for two-argument constructions in Mandarin. (...) First, we conducted a grammaticality judgment study with 60 native speakers which demonstrated that, across 57 verbs, semantic affectedness – as determined by further 16 native speakers – predicted each verb’s relative acceptability in the bei-passive and ba-active constructions, but not the Notional Passive and SVO Active constructions. Second, in order to simulate acquisition of these competing constraints, we built a computational model that learns to map from corpus-derived input to an output representation corresponding to these four constructions. The model was able to predict judgments of the relative acceptability of the test verbs in the ba-active and bei-passive constructions obtained in Study 1, with model-human correlations in the region of r = 0.5 and r = 0.3, respectively. Surprisingly, these correlations increased when lexical verb identity was removed; perhaps because this information leads to over-fitting of the training set. These findings suggest the intriguing possibility that acquiring constructions involves forgetting as a mechanism for abstracting across certain fine-grained lexical details and idiosyncrasies. (shrink)

Machine generated contents note: Part 1 - The Constituent Disciplines of Cognitive Science -- Philosophical Epistemology -- Glossary -- 1.0 What is Philosophical Epistemology? -- 1.1 The reduced history of Philosophy Part I - The Classical Age -- 1.2 Mind and World - The problem of objectivity -- 1.3 The reduced history of Philosophy Part II - The twentieth century -- 1.4 The philosophy of Cognitive Science -- 1.5 Mind in Philosophy: summary -- 1.6 The Nolanian (...) Framework (so far) -- Psychology -- 2.0 Why is Psychology so difficult? -- 2.1 A brief history of Experimental Psychology -- 2.2 Methodologies in Psychology -- 2.3 Perception -- 2.4 Memory -- 2.5 Mind in Psychology -- Linguistics -- 3.0 Introduction -- 3.1 Why Linguistics? -- 3.2 Computation and Linguistics -- 3.3 The main grammatical theories -- 3.4 Language development and linguistics -- 3.5 Toward a definition of context -- 3.6 The multifarious uses of Language -- 3.7 Linguistics and Computational Linguistics -- 3.8 Language and other symbol systems -- 3.9 On the notion of context -- 3.10 Mind in Linguistics: summary -- Neuroscience -- 4.0 The constituent disciplines of Neuroscience -- 4.1 The methodology of Neuroscience -- 4.2 Gross Neuroanatomy -- 4.3 Some relevant findings -- 4.4 Connectionism (PDP) -- 4.5 The victory of Neuroscience? -- 4.6 Mind in Neuroscience: summary -- Artificial Intelligence -- 5.0 Introduction -- 5.1 Al and Cognitive Science -- 5.2 Skeptics and their techniques -- 5.3 Al as Computer Science -- 5.4 Al as software -- 5.5 The current methodological debate -- 5.6 Context, syntax and semantics -- 5.7 Mind in Al -- 5.8 Texts on Al -- Etholoqy and Ethnoscience -- 6.1 Etology -- 6.2 Ethnoscience -- 6.3 Mind in Ethology arid Ethnoscience -- Part II - A New Foundation for Cognitive Science -- - Symbol Systems -- 7.1 Characteristics of symbol systems -- 7.2 Context and the layers of symbol systems -- 7.3 Mind and symbol systems -- Consciousness and Selfhood -- 8.0 Introduction -- 8.1 Cognitive views -- 8.2 What is at stake? -- 8.3 Consciousness as treated in Philosophy -- 8.4 The Development of Selfhood -- 8.5 The minimal requirements for this theory -- 8.6 Self as a filter -- 8.7 Self and motivation -- 8.8 Conclusions -- 8.9 Recent developments -- Cognitive Science and the Search for Mind -- 9.1 Introduction -- 9.2 Review -- 9.3 A Theory of Mind anyone? -- 9.4 Foundational considerations -- 9.5 Coda: the Nolanian Framework. (shrink)

The computational paradigm, which has dominated psychology and artificial intelligence since the cognitive revolution, has been a source of intense debate. Recently, several cognitive scientists have argued against this paradigm, not by objecting to computation, but rather by objecting to the notion of representation. Our analysis of these objections reveals that it is not the notion of representation per se that is causing the problem, but rather specific properties of representations as they are used in various psychological (...) theories. Our analysis suggests that all theorists accept the idea that cognitive processing involves internal information-carrying states that mediate cognitive processing. These mediating states are a superordinate category of representations. We discuss five properties that can be added to mediating states and examine their importance in various cognitive models. Finally, three methodological lessons are drawn from our analysis and discussion. (shrink)

Cognitive science continues to make a compelling case for having a coherent, unique, and fundamental subject of inquiry: What is the nature of minds, where do they come from, and how do they work? Central to this inquiry is the notion of agents that have goals, one of which is their own persistence, who use dynamically constructed knowledge to act in the world to achieve those goals. An agentive perspective explains why a special class of systems have a (...) cluster of co‐occurring capacities that enable them to exhibit adaptive behavior in a complex environment: perception, attention, memory, representation, planning, and communication. As an intellectual endeavor, cognitive science may not have achieved a hard core of uncontested assumptions that Lakatos (1978) identifies as emblematic of a successful research program, but there are alternative conceptions according to which cognitive science has been successful. First, challenges of the early, core tenet of “Mind as Computation” have helped put cognitive science on a stronger foundation—one that incorporates relations between minds and their environments. Second, even if a full cross‐disciplinary theoretic consensus is elusive, cognitive science can inspire distant, deep, and transformative connections between pairs of fields. To be intellectually vital, cognitive science need not resemble a traditional discipline with its associated insularity and unchallenged assumptions. Instead, there is strength and resilience in the diverse perspectives and methods that cognitive science assembles together. This interdisciplinary enterprise is fragile and perhaps inherently unstable, as the looming absorption of cognitive science into psychology shows. Still, for many researchers, the excitement and benefits of triangulating on the nature of minds by integrating diverse cases cannot be secured by a stable discipline with an uncontested core of assumptions. (shrink)

Theory of Mind (ToM), the cognitive capacity to attribute internal mental states to oneself and others, is a crucial component of social skills. Its formal study has become important, witness recent research on reasoning and information update by intelligent agents, and some proposals for its formal modelling have put forward settings based on Epistemic Logic (EL). Still, due to intrinsic idealisations, it is questionable whether EL can be used to model the high-order cognition of ‘real’ agents. This (...) manuscript proposes a framework that takes visibility, memory, perspective and communication as the main factors underpinning mental attributions. This is more in-line with findings in cognitive science and allows us to model well-known False-Belief Tasks that are typically used in testing people's ToM. We discuss some of the framework's technical features, arguing why it does justice to empirical observations. (shrink)

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