Clark and Chalmers propose that the mind extends further than skin and skull. If they are right, then we should expect this to have some effect on our way of knowing our own mental states. If the content of my notebook can be part of my belief system, then looking at the notebook seems to be a way to get to know my own beliefs. However, it is at least not obvious whether self-ascribing a belief by looking at my notebook (...) is a case of introspection the same way that knowing my non-extended beliefs is. Traditionally this sort of introspection is thought to be privileged and special in ways that the extended introspection case seems not to be. There is nothing privileged about looking at my notebook. Anyone could do it. The aim of the paper is to find out how to understand extended introspection and whether there is something privileged and special about knowing one’s own extended beliefs. Moreover, the notebook case has close analogs using twenty-first century technology. It seems possible to know our beliefs that are extended to smartphones, wearable technology or a cloud-based data store. First, I present the case of extended introspection. I then discuss whether it should be understood as ordinary introspection or as mind-reading. Both seem to be bad fits, which finally prompts an original account for extended introspection based on epistemic rules. (shrink)

This article presents a distinct sense of ‘mechanism’, which I call the functional sense of mechanism. According to this sense, mechanisms serve functions, and this fact places substantive restrictions on the kinds of system activities ‘for which’ there can be a mechanism. On this view, there are no mechanisms for pathology; pathologies result from disrupting mechanisms for functions. Second, on this sense, natural selection is probably not a mechanism for evolution because it does not serve a function. After distinguishing this (...) sense fromsimilar explications of ‘mechanism’, I argue that it is ubiquitous in biology and has valuable epistemic benefits. (shrink)

The view that special science properties are multiply realizable has been attacked in recent years by Shapiro, Bechtel and Mundale, Polger, and others. Focusing on psychological and neuroscientific properties, I argue that these attacks are unsuccessful. By drawing on interspecies physiological comparisons I show that diverse physical mechanisms can converge on common functional properties at multiple levels. This is illustrated with examples from the psychophysics and neuroscience of early vision. This convergence is compatible with the existence of general constraints on (...) the evolution of cognitive systems, and does not involve any ad hoc typing of coarse-grained higher level properties. The mechanisms that realize these common higher level properties are really distinct by the criteria laid down by critics of multiple readability. Finally, I present an account of how such functional properties might constitute special science kinds by playing a central explanatory role in a range of cognitive models. Behavioral science kinds in particular are the functionally defined constituents picked out by our most successful models of the multilevel systems and mechanisms that explain cognitive capacities. (shrink)

Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.

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.

In the article I discuss functionalist interpretations of Husserlian phenomenology. The first one was coined in the discussion between Hubert Dreyfus and Ronald McIntyre. They argue that Husserl’s phenomenology shares similarities with computational functionalism, and the key similarity is between the concept of noema and the concept of mental representation. I show the weaknesses of that reading and argue that there is another available functionalist reading of Husserlian phenomenology. I propose to shift perspective and approach the relation between phenomenology and (...) functionalism from a methodological perspective, specifically taking into account the functionalist explanatory strategy called functional analysis. I discuss the notion of function in Husserl’s works and Husserl’s idea of functional phenomenology. The key argument I develop is that in functional phenomenology we can find an explanatory strategy which is analogous to the strategy of functional decomposition used in functional analysis. I conclude that the proposed functionalist reading of phenomenology opens a new approach to the integration of phenomenology with cognitive sciences. (shrink)

Roughly speaking, computationalism says that cognition is computation, or that cognitive phenomena are explained by the agent‘s computations. The cognitive processes and behavior of agents are the explanandum. The computations performed by the agents‘ cognitive systems are the proposed explanans. Since the cognitive systems of biological organisms are their nervous 1 systems (plus or minus a bit), we may say that according to computationalism, the cognitive processes and behavior of organisms are explained by neural computations. Some people might prefer to (...) say that cognitive systems are ―realized‖ by nervous systems, and thus that—according to computationalism—cognitive computations are ―realized‖ by neural processes. In this paper, nothing hinges on the nature of the relation between cognitive systems and nervous systems, or between computations and neural processes. For present purposes, if a neural process realizes a computation, then that neural process is a computation. Thus, I will couch much of my discussion in terms of nervous systems and neural computation.1 Before proceeding, we should dispense with a possible red herring. Contrary to a common assumption, computationalism does not stand in opposition to connectionism. Connectionism, in the most general and common sense of the term, is the claim that cognitive phenomena are explained (at some level and at least in part) by the processes of neural networks. This is a truism, supported by most neuroscientific evidence. Everybody ought to be a connectionist in this general sense. The relevant question is, are neural processes computations? More precisely, are the neural processes to be found in the nervous systems of organisms computations? Computationalists say ―yes‖, anti-computationalists say ―no‖. This paper investigates whether any of the arguments on offer against computationalism have a chance at knocking it off.2 Ever since Warren McCulloch and Walter Pitts (1943) first proposed it, computationalism has been subjected to a wide range of objections.. (shrink)

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

In this paper, we evaluate the pragmatic turn towards embodied, enactive thinking in cognitive science, in the context of recent empirical research on the memory palace technique. The memory palace is a powerful method for remembering yet it faces two problems. First, cognitive scientists are currently unable to clarify its efficacy. Second, the technique faces significant practical challenges to its users. Virtual reality devices are sometimes presented as a way to solve these practical challenges, but currently fall short of delivering (...) on that promise. We address both issues in this paper. First, we argue that an embodied, enactive approach to memory can better help us understand the effectiveness of the memory palace. Second, we present design recommendations for a virtual memory palace. Our theoretical proposal and design recommendations contribute to solving both problems and provide reasons for preferring an embodied, enactive account over an information-processing treatment of the memory palace. (shrink)

Qualia have proved difficult to integrate into a broadly physicalistic worldview. In this paper, I argue that despite popular wisdom in the philosophy of mind, qualia’s intrinsicality is not sufficient for their non-reducibility. Second, I diagnose why philosophers mistakenly focused on intrinsicality. I then proceed to argue that qualia are categorical and end with some reflections on how the conceptual territory looks when we keep our focus on categoricity.

In this paper, the Author reviewed the typical objections against the claim that brains are computers, or, to be more precise, information-processing mechanisms. By showing that practically all the popular objections are based on uncharitable interpretations of the claim, he argues that the claim is likely to be true, relevant to contemporary cognitive science, and non-trivial.

Functionalism is a popular solution to the mind–body problem. It has a number of versions. We outline some of the major releases of functionalism, listing some of their important features as well as some of the bugs that plagued these releases. We outline how different versions are related. Many have been pessimistic about functionalism’s prospects, but most criticisms have missed the latest upgrades. We end by suggesting a version of functionalism that provides a complete account of the mind.

Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of deep learning, namely the extraction of successively (...) higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism. (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

This paper motivates taking seriously the possibility that brains are basically protean: that they make use of neural structures in inventive, on-the-fly improvisations to suit circumstance and context. Accordingly, we should not always expect cognition to divide into functionally stable neural parts and pieces. We begin by reviewing recent work in cognitive ontology that highlights the inadequacy of traditional neuroscientific approaches when it comes to divining the function and structure of cognition. Cathy J. Price and Karl J. Friston, and Colin (...) Klein identify the limitations of relying on forward and reverse inferences to cast light on the relation between cognitive functions and neural structures. There is reason to prefer Klein’s approach to that of Price and Friston’s. But Klein’s approach is neurocentric - it assumes that we ought to look solely at neural contexts to fix cognitive ontology. Using recent work on mindreading as a case study, we motivate adopting a radically different approach to cognitive ontology. Promoting the Protean Brain Hypothesis, we posit the possibility that we may need to look beyond the brain when deciding which functions are being performed in acts of cognition and in understanding how the brain contributes to such acts by adapting to circumstance. (shrink)

In a friendly interdisciplinary debate, we interrogate from several vantage points the question of “personhood” in light of contemporary and near-future forms of social AI. David J. Gunkel approaches the matter from a philosophical and legal standpoint, while Jordan Wales offers reflections theological and psychological. Attending to metaphysical, moral, social, and legal understandings of personhood, we ask about the position of apparently personal artificial intelligences in our society and individual lives. Re-examining the “person” and questioning prominent construals of that category, (...) we hope to open new views upon urgent and much-discussed questions that, quite soon, may confront us in our daily lives. (shrink)

Mechanisms, in the prominent biological sense of the term, are historical entities. That is, whether or not something is a mechanism for something depends on its history. Put differently, while your spontaneously-generated molecule-for-molecule double has a heart, and its heart pumps blood around its body, its heart does not have a mechanism for pumping, since it does not have the right history. My argument for this claim is that mechanisms have proper functions; proper functions are historical entities; so, mechanisms are (...) historical entities, too. This thesis runs against the mainstream new mechanist way of thinking about mechanisms, where mechanisms are generally thought of in an ahistorical way. After arguing for this thesis, I draw out some consequences for philosophy of science and metaphysics. (shrink)

To account for the explanatory role representations play in cognitive science, Egan’s deflationary account introduces a distinction between cognitive and mathematical contents. According to that account, only the latter are genuine explanatory posits of cognitive-scientific theories, as they represent the arguments and values cognitive devices need to represent to compute. Here, I argue that the deflationary account suffers from two important problems, whose roots trace back to the introduction of mathematical contents. First, I will argue that mathematical contents do not (...) satisfy important and widely accepted desiderata all theories of content are called to satisfy, such as content determinacy and naturalism. Secondly, I will claim that there are cases in which mathematical contents cannot play the explanatory role the deflationary account claims they play, proposing an empirical counterexample. Lastly, I will conclude the paper highlighting two important implications of my arguments, concerning recent theoretical proposals to naturalize representations via physical computation, and the popular predictive processing theory of cognition. (shrink)

I use Carl Gillett’s much heralded dimensioned theory of realization as a platform to develop a plausible part–whole theory. I begin with some basic desiderata for a theory of realization that its key terms should be defined and that it should be explanatory. I then argue that Gillett’s original theory violates these conditions because its explanatory force rests upon an unspecified “in virtue of” relation. I then examine Gillett’s later version that appeals instead to theoretical terms tied to “mechanisms.” Yet (...) I argue that it too violates the desiderata, since it defines realization for mechanisms in terms of two undefined ideas whose explanatory credentials have not been established—“implementation” and “grounds.” Thus I drop those ideas in favor of an explicit constraint that the parts and properties provide a mechanistic explanation. I also distinguish a special mechanistic theory from a preferred general theory that incorporates other kinds of part–whole explanations that target causal powers or capacities. The result is a theory that has the explanatory virtues of mechanistic theories as well as a broader scope desired by Gillett. I also compare the result to a similar idea from Robert Cummins that has been neglected in recent discussions of realization, namely, his general property analysis rather than his functional analysis. Finally, I defend the preferred general theory against possible objections that attempt to show a conflict between metaphysical demands on a theory of realization versus facts about good scientific explanation. (shrink)

The received view of computation is methodologically bifurcated: it offers different accounts of computation in the mathematical and physical cases. But little in the way of argument has been given for this approach. This article rectifies the situation by arguing that the alternative, a unified account, is untenable. Furthermore, once these issues are brought into sharper relief we can see that work remains to be done to illuminate the relationship between physical and mathematical computation.

Dynamical systems play a central role in explanations in cognitive neuroscience. The grounds for these explanations are hotly debated and generally fall under two approaches: non-mechanistic and mechanistic. In this paper, I first outline a neurodynamical explanatory schema that highlights the role of dynamical systems in cognitive phenomena. I next explore the mechanistic status of such neurodynamical explanations. I argue that these explanations satisfy only some of the constraints on mechanistic explanation and should be considered pseudomechanistic explanations. I defend this (...) argument against three alternative interpretations of the neurodynamical explanatory schema. The independent interpretation holds that neurodynamical explanations and mechanisms are independent. The constitutive interpretation holds that neurodynamical explanations are constitutive but otherwise non-mechanistic. Both the independent and constitutive interpretations fail to account for all the features of neurodynamical explanations. The partial interpretation assumes that the targets of dynamical systems models are mechanisms and so holds that neurodynamical explanations are incomplete because they lack mechanistic details. I contend instead that the targets of those models are dynamical systems distinct from mechanisms and defend this claim against several objections. I conclude with a defense of the pseudomechanistic interpretation and a discussion of the source of their explanatory power in relation to a causal-mechanical description of the world. (shrink)

In 1956 U. T. Place proposed that consciousness is a brain process. More attention should be paid to his word ‘process’. There is near-universal agreement that experiences are processive—as witnessed in the platitude that experiences are occurrent states. The abandonment of talk of brain processes has benefited functionalism, because a functional state, as it is usually conceived, cannot be a process. This point is dimly recognized in a well-known but little-discussed argument that conscious experiences cannot be functional states because the (...) former are occurrent, while the latter are dispositional. That argument fails, but it can be made sound if we reformulate it with the premise that occurrent states are processive. The only way for functionalists to meet the resulting challenge is to abandon the standard individuation of functional states in terms of purely abstract causal roles. (shrink)

The theory of extended mind has been applied by some to the study of religious cognition. Past efforts have mainly centered around how material culture, like bibles and rosaries, functions in the perspective of extended cognition. In the present paper, I shift focus to unite these works with research on socially extended mind and participatory theory and discuss the additional role of living and nonmaterial culture, including cultural norms, customs, institutions, social ritual, and social others in capturing a full-bodied view (...) of extended religious cognition. I apply these areas of theory to the study of dispositional belief, affective states and processes, and the self and defend their application within this religious context. (shrink)

(Penultimate draft) Embodied Cognition holds that bodily (e.g. sensorimotor) states and processes are directly involved in some higher-level cognitive functions (e.g. reasoning). This challenges traditional views of cognition according to which bodily states and processes are, at most, indirectly involved in higher-level cognition. Although some elements of Embodied Cognition have been integrated into mainstream cognitive science, others still face adamant resistance. In this paper, rather than straightforwardly defend Embodied Cognition against specific objections I will do the following. First, I will (...) present a concise account of embodied conceptual processing and highlight some of its advantages over non-embodied accounts, specifically focusing on the role of metaphors. Second, I will detail the influence of computational metaphors on theories of cognition and evaluation of these theories. Third, I will argue that embodied cognitive mechanisms, specifically those operating through computational metaphors, may drive some of the resistance to Embodied Cognition - and that Embodied Cognition is able to offer a uniquely compelling account of this. Ultimately, this will contribute to an improved understanding of Embodied Cognition, its explanatory power, and how it ought to be evaluated. Additionally, it will shed light on the role of metaphors in shaping philosophical thought and highlight the importance of these influences. (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)

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)

Technological artefacts have, in recent years, invited increasingly intimate ways of interaction. But surprisingly little attention has been devoted to how such interactions, like with wearable devices or household robots, shape our minds, cognitive capacities, and moral character. In this thesis, I develop an embodied, enactive account of mind--technology interaction that takes the reciprocal influence of artefacts on minds seriously. First, I examine how recent developments in philosophy of technology can inform the phenomenology of mind--technology interaction as seen through an (...) enactivist lens. Second, I show how an enactive account of remembering can improve operationalizations of the memory palace mnemonic through virtual reality devices. Third, I draw on virtue ethics to argue that an enactivist approach allows us to better grasp the morally shaping aspects of artefacts by looking at social robots. Fourth, I fend off an underlying metaphysical concern about enactivism by arguing that an embodied, enactive account is compatible with the multiple realization of cognitive processes. This principle is often seen as a crucial test favouring accounts such as extended functionalism over enactivism and I argue that some forms of enactivism pass this test as well. Finally, I conclude by considering what the future relationship between enactivism and functionalism may have in store for the study of mind--technology interaction. (shrink)

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

The purpose of this paper is to argue against the claim that morphological computation is substantially different from other kinds of physical computation. I show that some (but not all) purported cases of morphological computation do not count as specifically computational, and that those that do are solely physical computational systems. These latter cases are not, however, specific enough: all computational systems, not only morphological ones, may (and sometimes should) be studied in various ways, including their energy efficiency, cost, reliability, (...) and durability. Second, I critically analyze the notion of “offloading” computation to the morphology of an agent or robot, by showing that, literally, computation is sometimes not offloaded but simply avoided. Third, I point out that while the morphology of any agent is indicative of the environment that it is adapted to, or informative about that environment, it does not follow that every agent has access to its morphology as the model of its environment. (shrink)