The field of the philosophy of biology is flourishing in its aim to evaluate and rethink the view inherited from the previous century ---the Modern Synthesis. Different research areas and theories have come to the fore in the last decades in order to account for different biological phenomena that, in the first instance, fall beyond the explanatory scope of the Modern Synthesis. This thesis is anchored and motivated by this revolt in the philosophy of biology. -/- The central target in (...) this context is the possibility of naturalizing teleology, a classical nightmare for the history of biology itself. This requires, principally, understanding the causes of teleological explanations without assuming an unfashioned backward causation of sorts. As the riddles of teleological explanations are about their temporal dimension, I analyze different temporal scales of biological processes: evolutionary, developmental, and physiological. -/- The first one is the one defended in the context of the Modern Synthesis. As expected, one of the aims of this thesis is to evaluate the adequacy of an evolutionary account of teleological explanation. The scrutiny is negative. Evolutionary explanations in the context of the Modern Synthesis lack the necessary causal roots to naturalize teleology. Concerning the physiological scale, a long tradition pushed up by Kant and the organicist movement in the 20th century allows us to better understand how teleological explanations can be naturalized in physiological process. The key notions in this temporal scale are self-organization and the recursive, looped character of physiological process. While the physiological scale may be suitably accounted by contemporaries views, such as Autonomous Systems Theory, different central teleological phenomena remain unexplained from a purely physiological perspective. In particular, different issues concerning the (adaptive) construction of organism ---such as plasticity, robustness, variation, novelty, inheritance--- deserve an ontogenetic analysis. -/- The principal aim of this thesis is to provide a theory of teleological development that falls beyond the Modern Synthesis' framework and is prompted by different insights from the history of biology. I call it Agential Teleosemantics. It rests on two central pillars. First, that developmental processes, beyond any gene-centered stance, can be understood in informational terms; i.e. developmental processes are about the interaction of developmental resources conveying biological information. The second ingredient is agentivity, namely the idea that development is regulated by an agentive system according to the adaptiveness of the phenotypic outcomes produced. The role of agency in Agential Teleosemantic is equivalent to the role of genes in the Modern Synthesis: it is responsible for explaining the order and the adaptive complexity in the living realm. -/- The second target of this thesis regards the possibility of naturalizing intentional explanations in cognitive science. The central project involved in such an aim is known as teleosemantics. Classical teleosemantics however is etiological: it explains the teleofunctions of representational systems in terms of evolutionary processes. The different disputes in the contemporary philosophy of biology provide two insights to analyze teleosemantics in cognitive science. First, the challenges against the Modern Synthesis must be extended to the evolutionary approach of etiological teleosemantics. Second, as Agential Teleosemantics suggests an alternative source for teleofunctions ---ontogeny, I offer an attempt to integrate Agential Teleosemantics into cognitive science in order to provide an alternative teleosemantic project to understand intentional explanations in cognitive science. (shrink)

I argue that Sellars’s naturalization of Kant should be understood in terms of how he used behavioristic psychology and cybernetics. I first explore how Sellars used Edward Tolman’s cognitive-behavioristic psychology to naturalize Kant in the early essay “Language, Rules, and Behavior”. I then turn to Norbert Wiener’s understanding of feedback loops and circular causality. On this basis I argue that Sellars’s distinction between signifying and picturing, which he introduces in “Being and Being Known,” can be understood in terms of what (...) I call cybernetic behaviorism. I interpret picturing in terms of cycles of cybernetic behavior and signifying in terms of coordination between cybernetic behavior systems, or what I call triangulated cybernetic behavior. This leads to a formal, naturalistic understanding of personhood as the capacity to engage in triangulated cybernetic behavior. I conclude by showing that Sellars’s thought has the resources, which he did not exploit, for introducing the concept of second-order cybernetics. This suggests that Sellars’s philosophy of mind could be developed in the direction of autopoiesis and enactivism. (shrink)

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

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience must literally exist the system being modelled. Computational models are important tools to test a theory about how the collected data has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in (...) the model. While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling that combines dynamical systems theory mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science. (shrink)

Virtue ethics has many times been suggested as a promising recipe for the construction of artificial moral agents due to its emphasis on moral character and learning. However, given the complex nature of the theory, hardly any work has de facto attempted to implement the core tenets of virtue ethics in moral machines. The main goal of this paper is to demonstrate how virtue ethics can be taken all the way from theory to machine implementation. To achieve this goal, we (...) critically explore the possibilities and challenges for virtue ethics from a computational perspective. Drawing on previous conceptual and technical work, we outline a version of artificial virtue based on moral functionalism, connectionist bottom–up learning, and eudaimonic reward. We then describe how core features of the outlined theory can be interpreted in terms of functionality, which in turn informs the design of components necessary for virtuous cognition. Finally, we present a comprehensive framework for the technical development of artificial virtuous agents and discuss how they can be implemented in moral environments. (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)

This paper discusses the epistemic status of biology from the standpoint of the systemic approach to living systems based on the notion of biological autonomy. This approach aims to provide an understanding of the distinctive character of biological systems and this paper analyses its theoretical and epistemological dimensions. The paper argues that, considered from this perspective, biological systems are examples of emergent phenomena, that the biological domain exhibits special features with respect to other domains, and that biology as a discipline (...) employs some core concepts, such as teleology, function, regulation among others, that are irreducible to those employed in physics and chemistry. It addresses the claim made by Jacques Monod that biology as a science is marginal. It argues that biology is general insofar as it constitutes a paradigmatic example of complexity science, both in terms of how it defines the theoretical object of study and of the epistemology and heuristics employed. As such, biology may provide lessons that can be applied more widely to develop an epistemology of complex systems. (shrink)

By the late 1990s, several converging trends in economics, psychology, and neuroscience had set the stage for the birth of a new scientific field known as “neuroeconomics”. Without the availability of an extensive variety of experimental designs for dealing with individual and social decision-making provided by experimental economics and psychology, many neuroeconomics studies could not have been developed. At the same time, without the significant progress made in neuroscience for grasping and understanding brain functioning, neuroeconomics would have never seen the (...) light of day. The paper is an overview of the main significant advances in the knowledge of brain functioning by neuroscience that have contributed to the emergence of neuroeconomics and its rise over the past two decades. These advances are grouped over three non-independent topics referred to as the “emo-rational” brain, “social” brain, and “computational” brain. For each topic, it emphasizes findings considered as critical to the birth and development of neuroeconomics while highlighting some of prominent questions about which knowledge should be improved by future research. In parallel, it shows that the boundaries between neuroeconomics and several recent sub-fields of cognitive neuroscience, such as affective, social, and more generally, decision neuroscience, are rather porous. (shrink)

Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...) solution for the so-called NP-complete problems will also be a solution for any other such problems. Its artificial-intelligence analogue is the class of AI-complete problems, for which a complete mathematical formalization still does not exist. In this chapter we will focus on analysing computational classes to better understand possible formalizations of AI-complete problems, and to see whether a universal algorithm, such as a Turing test, could exist for all AI-complete problems. In order to better observe how modern computer science tries to deal with computational complexity issues, we present several different deep-learning strategies involving optimization methods to see that the inability to exactly solve a problem from a higher order computational class does not mean there is not a satisfactory solution using state-of-the-art machine-learning techniques. Such methods are compared to philosophical issues and psychological research regarding human abilities of solving analogous NP-complete problems, to fortify the claim that we do not need to have an exact and correct way of solving AI-complete problems to nevertheless possibly achieve the notion of strong AI. (shrink)

Background: Interdisciplinarity is one of the current trends in the scientific world today, that began with the uneasiness about the loss of the unity of science. This trend also opens possibilities for explaining complex phenomena more comprehensively and creating more advanced applications and implementations of scientific theories. One of the biggest challenges to conducting interdisciplinary research is theoretical integration, how can we combine theories from various disciplines such that the combination is fruitful? Method: This dissertation attempts to answer this challenge (...) by analyzing the intertheoretical connections of some theories from various disciplines for some real interdisciplinary research. The structuralist metatheory of science is applied as the basic theory to model the intertheoretical connections formally. This research begins with modeling the scientific theories in question before modeling the intertheoretical connections and some modifications needed. This research focuses on some researches in cognitive science that involve psychology, neuroscience, and artificial intelligence. The first research is the research conducted by van Veen et al., who research the activity of neurons in a brain's field called dorsal Anterior Cingulate Cortex during a phase of dissonance between cognitions. This research serves as a context for the analysis of intertheoretical reduction between the Festinger theory of cognitive dissonance and the Hawkins-Kandel computational neuroscientific theory. The other research is the consonance model of simulation built by Shultz and Lepper, which implements the Hopfield network to build a simulation of the cognitive dissonance. The third research is the connectionist model of simulation built by van Overwalle and Jordens, which implements the two-layers feed-forward perceptron and the delta rule as its learning rule to build a simulation of forced compliance dissonance. Result: Through this research, the author concludes that the structuralist metatheory of science can be applied for modeling and analyzing intertheoretical connections in the same discipline and between disciplines in real scientific research. The structuralist metatheory of science enables us to model and analyze the structure of the theories and their intertheoretical connections with great detail and brings very fruitful results. This research delivers some results not only for the structuralist theory science itself but also for the philosophy of science in general and interdisciplinary researches, especially cognitive science. First, by analyzing the models, a revision of the definition of intertheoretical specialization, and specialization of the concept of theory-holon according to the structuralist theory of science, called the V-pattern and strategy for combining scientific theories, are proposed. These V-pattern and strategy can serve as a tool for combining scientific theories. Second, unlike other approaches on intertheoretical reduction such as the GNS model, the structuralist metatheory of science does not intend to formulate a generalized model of reduction nor focus only on intertheoretical reduction. It provides us powerful tools for modeling various intertheoretical relations, including intertheoretical reduction, case by case. Although the intertheoretical reduction in the structuralist model is more epistemological than ontological, the structuralist models show how the reduction has empirical claims and intended applications by applying the r* function that maps the T-theoretical level to T-non-theoretical level. Third, related to the notion of the unity of science, this dissertation still sees that the unity of science is still a plausible and essential agenda for the philosophy of science and the scientific world in general. This dissertation's idea of the unity of science proposed does not assume essentialism, reductionism, and epistemological monism. This dissertation sees that the unity of science is closely related to scientific practice. Fourth, for interdisciplinary research, primarily cognitive science, this dissertation proposes an approach to model and analyze intertheoretical connections for any scientific research or any philosophical school in philosophy of science related to the idea of intertheoretical relation. This dissertation is the first example of such modeling and analysis. (shrink)

In the 70 years since Alan Turing’s ‘Computing Machinery and Intelligence’ appeared in Mind, there have been two widely-accepted interpretations of the Turing test: the canonical behaviourist interpretation and the rival inductive or epistemic interpretation. These readings are based on Turing’s Mind paper; few seem aware that Turing described two other versions of the imitation game. I have argued that both readings are inconsistent with Turing’s 1948 and 1952 statements about intelligence, and fail to explain the design of his game. (...) I argue instead for a response-dependence interpretation. This interpretation has implications for Turing’s view of free will: I argue that Turing’s writings suggest a new form of free will compatibilism, which I call response-dependence compatibilism. The philosophical implications of rethinking Turing’s test go yet further. It is assumed by numerous theorists that Turing anticipated the computational theory of mind. On the contrary, I argue, his remarks on intelligence and free will lead to a new objection to computationalism. (shrink)

This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism – from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete (...) abandonment of its assumptions in the study of cognition. Criticisms have mostly pointed to its explanatory inadequacy as a theory of cognition or to its biological implausibility as a theory of implementation, and critics often focused on specific shortcomings of some connectionist models and argued that they apply to connectionism in general. In this paper, we want to show that both types of critique are based on the assumption that the only valid explanations in cognitive science are instances of homuncular functionalism and that by removing this assumption and by adopting an alternative methodology – exploratory mechanistic strategy, we can reject most objections to connectionism as irrelevant, explain the progress of connectionist models despite their shortcomings and sketch the trajectory of their future development. By adopting mechanistic explanations and by criticizing functionalism, we will reject the objections of explanatory inadequacy, by characterizing connectionist models as generic rather than concrete mechanisms, we will reject the objections of biological implausibility, and by attributing the exploratory character to connectionist models we will show that practice of generalizing current to general failures of connectionism is unjustified. (shrink)

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

Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her (...) actions and subsequently updated her beliefs. Under this interpretation, the agent’s body is considered merely as a means to report the decision, or to acquire the relevant goods. However, embodied cognition argues that an agent’s body should be understood as a proper part of the decision-making pro- cess. Accepting this principle challenges a number of commonly held beliefs in the cognitive sciences, but may lead to a more unified account of decision-making. This thesis explores an embodied account of decision-making using a recent frame- work known as predictive processing. This framework has been proposed by some as a functional description of neural activity. However, if it is approached from an embodied perspective, it can also offer a novel account of decision-making that ex- tends the scope of our explanatory considerations out beyond the brain and the body. We explore work in the cognitive sciences that supports this view, and argue that decision theory can benefit from adopting an embodied and predictive perspective. (shrink)

The book is addressed to all readers interested in cognitive science, and especially in research combining a logical analysis with psychological, linguistic and neurobiological approaches. The publication is the result of a collaboration between the Department of Cognitive Science at University of Lodz and the Department of Cognitive Science at Lund University. It is intended to provide a comprehensive presentation of the key research issues undertaken in both Departments, including considerations on meaning, natural language and reasoning, linguistic as well as (...) numerical competence, the theory of decision making, modelling of conceptual representations, cognitive and game theoretic approach to social interactions. (shrink)

ABSTRACTWhile over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating “artificial minded intelligence” that can help to unify existing approaches and provide new avenues for research. I first outline (...) three desiderata that a framework for AMI research should satisfy. In Section 1, I further motivate the desiderata as well as the need for a new framework. Section 2 develops a general methodological approach, the “generative methodology,” and Section 3 develops a version of this methodology, the “Competence Framework for AI research”. (shrink)

The mainstream view in cognitive science is that computation lies at the basis of and explains cognition. Our analysis reveals that there is no compelling evidence or argument for thinking that brains compute. It makes the case for inverting the explanatory order proposed by the computational basis of cognition thesis. We give reasons to reverse the polarity of standard thinking on this topic, and ask how it is possible that computation, natural and artificial, might be based on cognition and not (...) the other way around. (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)

A critical survey of some attempts to define ‘computer’, beginning with some informal ones, then critically evaluating those of three philosophers, and concluding with an examination of whether the brain and the universe are computers.

The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that conceived (...) the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology. (shrink)

EEG signals are records of nonlinear solitary waves in human brains. The waves have several types (e.g., α, β, γ, θ, δ) in response to different levels of consciousness. They are classified into two groups: Group-1 consists of complex storm-like waves (α, β, and γ); Group-2 is composed of simple quasilinear waves (θ and δ). In order to elucidate the mechanism of EEG wave formation and propagation, this paper extends the Vlasov-Maxwell equations of Plasma Brain Dynamics (PBD) to a set (...) of two-fluid, self-similar, nonlinear solitary wave equations. Numerical simulations are performed for different EEG signals. Main results include: (1) The excitation and propagation of the EEG wave packets are dependent of electric and magnetic fields, brain aqua-ions, electron and ion temperatures, masses, and their initial fluid speeds; (2) Group-1 complex waves contain three ingredients: the high-frequency ion-acoustic (IA) mode, the intermediate-frequency lower-hybrid (LH) mode, and, the low-frequency ion-cyclotron (IC) mode; (3) Group-2 simple waves fall within the IA band, featured by one or a combination of the three envelopes: sinusoidal, sawtooth, and spiky/bipolar. The study proposes an alternative model to Quantum Brain Dynamics (QBD) by suggesting that the formation and propagation of the nonlinear solitary EEG waves in the brain have the same mechanism as that of the waves in space plasmas. (shrink)

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

We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we focus on views that attempt to render such ascriptions naturalistic by construing them in a deflationary or merely pragmatic way. We then contrast these views with staunchly realist views that attempt to naturalize intentionality by developing theories of content for representations in terms of information and biological function. (...) We echo several other philosophers by arguing that these theories over-generalize unless they are constrained by a theory of the functional role of representational vehicles. This leads to a discussion of the functional roles of representations, and how representations might be realized in the brain. We argue that there’s work to be done to identify a distinctively mental kind of representation. We close by sketching a way forward for the project of naturalizing intentionality. This will not be achieved simply by ascribing the content of mental states to generic neural representations, but by identifying specific neural representations that explain the puzzling intentional properties of mental states. (shrink)

Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead to unification are (...) analyzed in terms of the mechanistic account of explanation. Finally, I argue that theoretical unification is not an absolute requirement from the mechanistic perspective, and that strategies aiming at unification may be premature in fields where there are multiple conflicting explanatory models. (shrink)

For diverse reasons, the problem of phenomenal consciousness is persistently challenging. Mental terms are characteristically ambiguous, researchers have philosophical biases, secondary qualities are excluded from objective description, and philosophers love to argue. Adhering to a regime of efficient causes and third-person descriptions, science as it has been defined has no place for subjectivity or teleology. A solution to the “hard problem” of consciousness will require a radical approach: to take the point of view of the cognitive system itself. To facilitate (...) this approach, a concept of agency is introduced along with a different understanding of intentionality. Following this approach reveals that the autopoietic cognitive system constructs phenomenality through acts of fiat, which underlie perceptual completion effects and “filling in”—and, by implication, phenomenology in general. It creates phenomenality much as we create meaning in language, through the use of symbols that it assigns meaning in the context of an embodied evolutionary history that is the source of valuation upon which meaning depends. Phenomenality is a virtual representation to itself by an executive agent (the conscious self) tasked with monitoring the state of the organism and its environment, planning future action, and coordinating various sub- agencies. Consciousness is not epiphenomenal, but serves a function for higher organisms that is distinct from that of unconscious processing. While a strictly scientific solution to the hard problem is not possible for a science that excludes the subjectivity it seeks to explain, there is hope to at least psychologically bridge the explanatory gulf between mind and matter, and perhaps hope for a broader definition of science. (shrink)

For diverse reasons, the problem of phenomenal consciousness is persistently challenging. Mental terms are characteristically ambiguous, researchers have philosophical biases, secondary qualities are excluded from objective description, and philosophers love to argue. Adhering to a regime of efficient causes and third-person descriptions, science as it has been defined has no place for subjectivity or teleology. A solution to the “hard problem” of consciousness will require a radical approach: to take the point of view of the cognitive system itself. To facilitate (...) this approach, a concept of agency is introduced along with a different understanding of intentionality. Following this approach reveals that the autopoietic cognitive system constructs phenomenality through acts of fiat, which underlie perceptual completion effects and “filling in”—and, by implication, phenomenology in general. It creates phenomenality much as we create meaning in language, through the use of symbols that it assigns meaning in the context of an embodied evolutionary history that is the source of valuation upon which meaning depends. Phenomenality is a virtual representation to itself by an executive agent (the conscious self) tasked with monitoring the state of the organism and its environment, planning future action, and coordinating various sub- agencies. Consciousness is not epiphenomenal, but serves a function for higher organisms that is distinct from that of unconscious processing. While a strictly scientific solution to the hard problem is not possible for a science that excludes the subjectivity it seeks to explain, there is hope to at least psychologically bridge the explanatory gulf between mind and matter, and perhaps hope for a broader definition of science. (shrink)

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to (...) shed light on the dispute over the interpretation of dynamical models of the brain. _1_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter? (shrink)

This paper links the conjecture that the physical world is a virtual reality to the findings of modern physics. What is usually the subject of science fiction is here proposed as a scientific theory open to empirical evaluation. We know from physics how the world behaves, and from computing how information behaves, so whether the physical world arises from ongoing information processing is a question science can evaluate. A prima facie case for the virtual reality conjecture is presented. If a (...) photon is a pixel on a multi-dimensional grid that gives rise to space, the speed of light could reflect its refresh rate. If mass, charge and energy all arise from processing, the many conservation laws of physics could reduce to a single law of dynamic information conservation. If the universe is a virtual reality, then its big bang creation could be simply when the system was booted up. Deriving core physics from information processing could reconcile relativity and quantum theory, with the former how processing creates the space-time operating system and the latter how it creates energy and matter applications. (shrink)

I go deep into the biology of the human organism to argue that the psychological features and functions of persons are realized by cellular and molecular parallel distributed processing networks dispersed throughout the whole body. Persons supervene on the computational processes of nervous, endocrine, immune, and genetic networks. Persons do not go with brains.

This paper contains a discussion of striking similarities between influential philosophical concepts of the past and the approaches currently employed in selected areas of computer science. In particular, works of the Pythagoreans, Plato, Abelard, Ash’arites, Malebranche and Berkeley are presented and contrasted with such computer science ideas as digital computers, object-oriented programming, the modelling of an object’s actions and causality in virtual environments, and 3D graphics rendering. The intention of this paper is to provoke the computer science community to go (...) off the beaten path in order to find inspiration for the development of new approaches in software engineering. (shrink)

If we are eventually to understand the capability of higher organisms for perceptual recognition, generalization, recall, and thinking, we must first have answers to three fundamental questions: 1. How is information about the physical world sensed, or detected, by the biological system? 2. In what form is information stored, or remembered? 3. How does information contained in storage, or in memory, influence recognition and behavior? The first of these questions is in the.

The Church–Turing Thesis (CTT) is often employed in arguments for computationalism. I scrutinize the most prominent of such arguments in light of recent work on CTT and argue that they are unsound. Although CTT does nothing to support computationalism, it is not irrelevant to it. By eliminating misunderstandings about the relationship between CTT and computationalism, we deepen our appreciation of computationalism as an empirical hypothesis.

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.

In the popular imagination, the relevance of Turing's theoretical ideas to people producing actual machines was significant and appreciated by everybody involved in computing from the moment he published his 1936 paper ‘On Computable Numbers’. Careful historians are aware that this popular conception is deeply misleading. We know from previous work by Campbell-Kelly, Aspray, Akera, Olley, Priestley, Daylight, Mounier-Kuhn, Haigh, and others that several computing pioneers, including Aiken, Eckert, Mauchly, and Zuse, did not depend on Turing's 1936 universal-machine concept. Furthermore, (...) it is not clear whether any substance in von Neumann's celebrated 1945 ‘First Draft Report on the EDVAC’ is influenced in any identifiable way by Turing's work. This raises the questions: When does Turing enter the field? Why did the Association for Computing Machinery honor Turing by associating his name to ACM's most prestigious award, the Turing Award? Previous authors have.. (shrink)

Standard semantic information processing models—information in; information processed; information out —lend themselves to standard models of the functioning of the brain in terms, e.g., of threshold-switch neurons connected via classical synapses. That is, in terms of sophisticated descendants of McCulloch and Pitts models. I argue that both the cognition and the brain sides of this framework are incorrect: cognition and thought are not constituted as forms of semantic information processing, and the brain does not function in terms of passive input (...) processing units organized as neural nets. An alternative framework is developed that models cognition and thought not in terms of semantic information processing, and, correspondingly, models brain functional processes also not in terms of semantic information processing. As alternative to such models: I outline a pragmatist oriented, interaction based, model of representation; derive from this model a fundamental framework of constraints on how the brain must function; show that such a framework is in fact found in the brain, and develop the outlines of a broader model of how mental processes can be realized within this alternative framework. Part I of this discussion focuses on some criticisms of standard modeling frameworks for representation and cognition, and outlines an alternative interactivist, pragmatist oriented, model. In part II, the focus is on the fact that the brain does not, in fact, function in accordance with standard passive input processing models—e.g., information processing models. Instead, there are multiple endogenously active processes at multiple spatial and temporal scales across multiple kinds of cells. A micro-functional model that accounts for, and even predicts, these multi-scale phenomena in generating emergent representation and cognition is outlined. That is, I argue that the interactivist model of representation outlined offers constraints on how the brain should function that are in fact empirically found, and, in reverse, that the multifarious details of brain functioning entail the pragmatist representational model—a very strong interrelationship. In the sequel paper, starting with part III, this model is extended to address macro-functioning in the CNS. In part IV, I offer a discussion of an approach to brain functioning that has some similarities with, as well as differences from, the model presented here: sometimes called the predictive brain approach. (shrink)

We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain (...) cognition. To a large extent, practicing cognitive neuroscientists have already accepted this shift, but philosophical theory has not fully acknowledged and appreciated its significance. As a result, the explanatory framework underlying cognitive neuroscience has remained largely implicit. We explicate this framework and demonstrate its contrast with previous approaches. (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)

This Special Issue on Connectionist Models of Human Language Processing provides an opportunity for an appraisal both of specific connectionist models and of the status and utility of connectionist models of language in general. This introduction provides the background for the papers in the Special Issue. The development of connectionist models of language is traced, from their intellectual origins, to the state of current research. Key themes that arise throughout different areas of connectionist psycholinguistics are highlighted, and recent developments in (...) speech processing, morphology, sentence processing, language production, and reading are described. We argue that connectionist psycholinguistics has already had a significant impact on the psychology of language, and that connectionist models are likely to have an important influence on future research. (shrink)

I consider the role of cognitive modeling in cognitive science. Modeling, and the computers that enable it, are central to the field, but the role of modeling is often misunderstood. Models are not intended to capture fully the processes they attempt to elucidate. Rather, they are explorations of ideas about the nature of cognitive processes. In these explorations, simplification is essential—through simplification, the implications of the central ideas become more transparent. This is not to say that simplification has no downsides; (...) it does, and these are discussed. I then consider several contemporary frameworks for cognitive modeling, stressing the idea that each framework is useful in its own particular ways. Increases in computer power (by a factor of about 4 million) since 1958 have enabled new modeling paradigms to emerge, but these also depend on new ways of thinking. Will new paradigms emerge again with the next 1,000‐fold increase? (shrink)

We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous (...) signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation. (shrink)

In this paper I argue that it is finally time to move beyond the Nagelian framework and to break new ground in thinking about epistemic reduction in biology. I will do so, not by simply repeating all the old objections that have been raised against Ernest Nagel’s classical model of theory reduction. Rather, I grant that a proponent of Nagel’s approach can handle several of these problems but that, nevertheless, Nagel’s general way of thinking about epistemic reduction in terms of (...) theories and their logical relations is entirely inadequate with respect to what is going on in actual biological research practice. (shrink)

In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are (...) semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary. (shrink)

Critical realism, especially as developed by Roy Bhaskar, embodies at its heart systemic and holistic concepts such as totality, emergence, open systems, stratification, autopoiesis and holistic causality. These concepts have their own long history of development in disciplines such as systems thinking and cybernetics, but there is an absence in Bhaskar’s writings, and that absence is a lack of any reference to the corresponding systems literature. The purpose of this paper is threefold: (i) to demonstrate the extent of this correspondence; (...) (ii) to show that critical realism can benefit from an exposure to these other discourses; and (iii) to show that systems thinking too can gain philosophically from critical realism. (shrink)

Cognitive science is an interdisciplinary research endeavor focusing on human cognitive phenomena such as memory, language use, and reasoning. It emerged in the second half of the 20th century and is charting new directions at the beginning of the 21st century. This chapter begins by identifying the disciplines that contribute to cognitive science and reviewing the history of the interdisciplinary engagements that characterize it. The second section examines the role that mechanistic explanation plays in cognitive science, while the third focuses (...) on the importance of mental representations in specifically cognitive explanations. The fourth section considers the interdisciplinary nature of cognitive science and explores how multiple disciplines can contribute to explanations that exceed what any single discipline might accomplish. The conclusion sketches some recent developments in cognitive science and their implications for philosophers. (shrink)