A key failing in contemporary philosophy of mind is the lack of attention paid to evolutionary theory in its research projects. Notably, where evolution is incorporated into the study of mind, the work being done is often described as philosophy of cognitive science rather than philosophy of mind. Even then, whereas possible implications of the evolution of human cognition are taken more seriously within the cognitive sciences and the philosophy of cognitive science, its relevance for cognitive science has only been (...) appreciated relatively recently, and the approach still comes in for some major criticism from prominent theorists within the field. This paper explores some of the reasons for this state of affairs and finds that it might have less to do with due consideration and well-founded scepticism about the relevance of evolutionary theory to these disciplines and more to do with historical accident and faulty assumptions on the part of key theorists in these disciplines. It is also noted that where cognitive scientists are taking evolution into account in their work on the mind, they straying more and more into domains that used to fall exclusively under the purview of philosophy of mind as it is traditionally conceived – qualia, consciousness, perception, intentionality and so forth. The point is made that in ignoring the work being done on the evolution of mind, philosophy of mind runs the risk of becoming obsolete. (shrink)

The foundations of biology have been a topic of debate for the past few decades. The traditional perspective of the Modern Synthesis, which portrays organisms as passive entities with limited role in evolutionary theory, is giving way to a new paradigm where organisms are recognized as active agents, actively shaping their own phenotypic traits for adaptive purposes. Within this context, this article raises the question of whether contemporary biological theory is undergoing a cognitive revolution. This inquiry can be approached in (...) two ways: from a theoretical standpoint, exploring the centrality of the cognitive sciences in current theoretical biology; and from a historical perspective, examining the resemblance between the current state of theoretical biology and the Cognitive Revolution of the mid-20th century. Both inquiries yield affirmative answers, though important nuances will be emphasized. The cognitive sciences' explanatory framework is employed to elucidate the agentic characteristics of organisms, establishing a clear parallelism between the Cognitive Revolution and the present state of theoretical biology. (shrink)

A response to a recent critique by Cem Bozşahin of the theory of syntactic semantics as it applies to Helen Keller, and some applications of the theory to the philosophy of computer science.

This paper extends three decades of work arguing that researchers who discuss consciousness should not restrict themselves only to (adult) human minds, but should study (and attempt to model) many kinds of minds, natural and artificial, thereby contributing to our understanding of the space containing all of them. We need to study what they do or can do, how they can do it, and how the natural ones can be emulated in synthetic minds. That requires: (a) understanding sets of requirements (...) that are met by different sorts of minds, i.e. the niches that they occupy, (b) understanding the space of possible designs, and (c) understanding complex and varied relationships between requirements and designs. Attempts to model or explain any particular phenomenon, such as vision, emotion, learning, language use, or consciousness lead to muddle and confusion unless they are placed in that broader context. A methodology for making progress is summarised and a novel requirement proposed for a theory of how human minds work: the theory should support a single generic design for a learning, developing system that, in addition to meeting familiar requirements, should be capable of developing different and opposed philosophical viewpoints about consciousness, and the so-called hard problem. In other words, we need a common explanation for the mental machinations of mysterians, materialists, functionalists, identity theorists, and those who regard all such theories as attempting to answer incoherent questions. No designs proposed so far come close. (shrink)

Intersemiotic translation (IT) was defined by Roman Jakobson (The Translation Studies Reader, Routledge, London, p. 114, 2000) as “transmutation of signs”—“an interpretation of verbal signs by means of signs of nonverbal sign systems.” Despite its theoretical relevance, and in spite of the frequency in which it is practiced, the phenomenon remains virtually unexplored in terms of conceptual modeling, especially from a semiotic perspective. Our approach is based on two premises: (i) IT is fundamentally a semiotic operation process (semiosis) and (ii) (...) IT is a deeply iconic-dependent process. We exemplify our approach by means of literature to dance IT and we explore some implications for the development of a general model of IT. (shrink)

Given the personal acquaintance between Alan M. Turing and W. Ross Ashby and the partial proximity of their research fields, a comparative view of Turing’s and Ashby’s work on modelling “the action of the brain” (letter from Turing to Ashby, 1946) will help to shed light on the seemingly strict symbolic/embodied dichotomy: While it is clear that Turing was committed to formal, computational and Ashby to material, analogue methods of modelling, there is no straightforward mapping of these approaches onto symbol-based (...) AI and embodiment-centered views respectively. Instead, it will be demonstrated that both approaches, starting from a formal core, were at least partly concerned with biological and embodied phenomena, albeit in revealingly distinct ways. (shrink)

The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe that (...) it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI. (shrink)

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

I have argued elsewhere that non-sentential representations that are the close kin of scale models can be, and often are, realized by computational processes. I will attempt here to weaken any resistance to this claim that happens to issue from those who favor an across-the-board computational theory of cognitive activity. I will argue that embracing the idea that certain computers harbor nonsentential models gives proponents of the computational theory of cognition the means to resolve the conspicuous disconnect between the sentential (...) character of the data structures they posit and the nonsentential qualitative character of our perceptual experiences of corporeal (i.e., spatial, kinematic, and dynamic) properties. Along the way, I will question the viability of some externalist remedies for this disconnect, and I will explain why the computational theory put forward here falls quite clearly beyond the useful bounds of the Chinese-Room argument. (shrink)

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; (...) and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

What is the role of the environment, and of the information it provides, in cognition? More specifically, may there be a role for certain artefacts to play in this context? These are questions that motivate "4E" theories of cognition (as being embodied, embedded, extended, enactive). In his take on that family of views, Hajo Greif first defends and refines a concept of information as primarily natural, environmentally embedded in character, which had been eclipsed by information-processing views of cognition. He continues (...) with an inquiry into the cognitive bearing of some artefacts that are sometimes referred to as 'intelligent environments'. Without necessarily having much to do with Artificial Intelligence, such artefacts may ultimately modify our informational environments. -/- With respect to human cognition, the most notable effect of digital computers is not that they might be able, or become able, to think but that they alter the way we perceive, think and act. (shrink)

ArgumentNeuroscience research has created multiple versions of the human brain. The “social brain” is one version and it is the subject of this paper. Most image-based research in the field of social neuroscience is task-driven: the brain is asked to respond to a cognitive stimulus. The tasks are derived from theories, operational models, and back-stories now circulating in social neuroscience. The social brain comes with a distinctive back-story, an evolutionary history organized around three, interconnected themes: mind-reading, empathy, and the emergence (...) of self-consciousness. This paper focuses on how empathy has been incorporated into the social brain and redefined via parallel research streams, employing a shared, imaging technology. The concluding section describes how these developments can be understood as signaling the emergence of a new version of human nature and the unconscious. My argument is not that empathy in the social brain is a myth, but rather that it is served by a myth consonant with the canons of science. (shrink)

In the current dialogue of “science and religion,” it is widely assumed that the thoughts of Darwinists and that of atheists overlap. However, Jerry Fodor, a full-fledged atheist, recently announced a war against Darwinism with his atheistic campaign. Prima facie, this “civil war” might offer a chance for theists: If Fodor is right, Darwinistic atheism will lose the cover of Darwinism and become less tenable. This paper provides a more pessimistic evaluation of the situation by explaining the following: Fodor’s criticism (...) of adaptationism (as the backbone of Darwinism), viz., his refutation of any counterfactual-supporting laws on the macro-evolutionary level, implies that a law-maker is dispensable on this level. This will either encourage skepticism against the omniscience (at least that concerning the future of macro-evolution) of the Creator, or render the notion of God less appealing. (shrink)

Interdisciplinary research is frequently viewed as an important component of the research landscape through its innovative ability to integrate knowledge from different areas. However, support for interdisciplinary research is often strategic rhetoric, with policy-makers and universities frequently adopting practices that favour disciplinary performance. We argue that disciplinary and interdisciplinary research are complementary, and we develop a simple framework that demonstrates this for a semi-permanent interdisciplinary research field. We argue that the presence of communicating infrastructures fosters communication and integration between disciplines (...) and the interdisciplinary research field to generate innovative knowledge. We apply this to the experience of economic history in Australia in the second half of the twentieth century to demonstrate the life cycle of a semi-permanent interdisciplinary research field. (shrink)

Recent years have seen growing evidence of a fruitful engagement between phenomenology and cognitive science. This paper confronts an in-principle problem that stands in the way of this intellectual coalition, namely the fact that a tension exists between the transcendentalism that characterizes phenomenology and the naturalism that accompanies cognitive science. After articulating the general shape of this tension, I respond as follows. First, I argue that, if we view things through a kind of neo-McDowellian lens, we can open up a (...) conceptual space in which phenomenology and cognitive science may exert productive constraints on each other. Second, I describe some examples of phenomenological cognitive science that illustrate such constraints in action. Third, I use the mutually constraining relationship at work here as the platform from which to bring to light a domesticated version of the transcendental and a minimal form of naturalism that are compatible with each other. (shrink)

Simon’s bounded rationality , the first scientific research program to seriously take the cognitive limitations of decision makers into account, has often been conflated with his more restricted concept of satisficing—choosing an alternative that meets or exceeds specified criteria, but that is not guaranteed to be unique or in any sense “the best.” Proponents of optimization often dismiss bounded rationality out of hand with the following “hallway syllogism” : bounded rationality “boils down to” satisficing; satisficing is “simply” a theory of (...) search for alternatives that takes into account the costs of computation. Hence, bounded rationality is “just a minor tweak” on optimal search theory. (shrink)

Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...) the functional properties of the system, which may not coincide with its mechanistic organization. I describe these techniques and show that despite being non-mechanistic, these cognitive models can satisfy the normative constraints on good explanations. (shrink)

Wants and desires are central to ordinary experience and to aesthetic, philosophical, and theological thought. Yet despite a burgeoning interest in the history of emotions research, their history as objects of scientific study has received little attention. This historiographical neglect mirrors a real one, with the retreat of introspection in the positivist human sciences of the early 20th century culminating in the relative marginalization of questions of psychic interiority. This article therefore seeks to explain an apparent paradox: the attempt to (...) develop a comprehensive theory of ‘why … we want what we want’ in the 1940s by esteemed American ‘neo-behaviorist’ psychologist Edward Tolman – a proponent of a methodology famous for its prohibition on appeals to unobservable mental phenomena. Though chiefly known today for his theory of ‘cognitive maps’, Tolman also sought to map the contours of desire as such, integrating Freudian and behaviorist models of the ‘drives’ to develop a complex iconography of the universal structures of motivation. Close attention to Tolman's striking maps offers a compelling limit case for what could and could not be captured within an anti-mentalist framework, and illuminates an important precursor to theories of motivational ‘affect’ in the postwar cognitive and neurosciences. His work upsets a standard chronology that centers on the ‘cognitive revolution’ of the 1960s, and points to the significance of psychoanalysis to an earlier turn to cognitivism. Tolman concluded his theory pointed ‘in the direction of more socialism’ – a reminder of the politically labile anti-essentialism of behaviorism's commitment to mental plasticity. (shrink)

There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between models and (...) targets are picked out, how modeling inferences are justified, and the metaphysical status of models. (shrink)

In this paper, I will focus on the extension theories of technology. I will identify four influential positions that have been put forward: (1) technology as an extension of the human organism, (2) technology as an extension of the lived body and the senses, (3) technology as an extension of our intentions and desires, and (4) technology as an extension of our faculties and capabilities. I will describe and critically assess these positions one by one and highlight their advantages and (...) their shortcomings and limitations. Along the way, I will explicate some of the differences and similarities between the various approaches. I conclude the paper with some suggestions for future research directions that will be beneficial for advancing theory building and that will drive forward the philosophical refinement of extension theory. (shrink)

Cognitive Science is a promising field of research that deals with one of the most fundamental questions ever: how do beings know? However, despite the long and extensive tradition of the field it has not yet become an area of knowledge with scientific identity. This is primarily due to three reasons: the lack of boundaries in defining the object of study, i.e. cognition, the lack of a precise, robust and consistent scientific methodology and results, and the inner problems derived from (...) its interdisciplinary nature. This paper presents a background review, a theoretical frame and a humble reflection on these topics in order to arouse the internal debate among readers once more. (shrink)

To which extent is it justified to adopt Kant as a godfather of cognitive science? To prepare the stage for an answer of this question, we need to set aside Kant’s general transcendental approach to the mind which is radically anti-empiricist and instead turn our attention to his specific topics and claims regarding the mind which are often not focus of Kant’s epistemological investigations. If someone is willing to take this stance, it turns out that there are many bridges connecting (...) Kant with contemporary cognitive science. We investigate possible bridges suggested in the literature between some of Kant’s central claims about consciousness, mental content, and functions of mind, and some specific treatments of these topics in contemporary philosophy of mind and cognitive science. While doing so, we offer additional arguments for some proposed bridges, reconstruct others and completely destroy still other bridges by demonstrating that some suggested links between Kant and cognitive science remain only apparent. (shrink)

Our primary goal in this article is to discuss the cross-talk between biological and cultural factors that become manifested in the individual brain development, neural wiring, neurochemical homeostasis, and behavior. We will show that behavioral propensities are the product of both cultural and biological factors and an understanding of these interactive processes can provide deep insights into why people behave the way they do. This interdisciplinary perspective is offered in an effort to generate dialog and empirical work among scholars interested (...) in merging aspects of anthropology and neuroscience, and anticipates that biological and cultural anthropology converge. We discuss new theoretical developments, hypothesis-testing strategies, and cross-disciplinary methods of observation and data collection. We believe that the exigency of integrating anthropology and the neurosciences is indisputable and anthropology's role in an emerging interdisciplinary science of human behavior will be critical because its focus is, and has always been, on human biological and cultural systems. (shrink)

An intuitive view is that creativity involves bringing together what is already known and familiar in a way that produces something new. In cognitive science, this intuition is typically formalized in terms of computational processes that combine or associate internally represented information. From this computationalist perspective, it is hard to imagine how non-representational approaches in embodied cognitive science could shed light on creativity, especially when it comes to abstract conceptual reasoning of the kind scientists so often engage in. The present (...) article offers an entry point to addressing this challenge. The scientific project of embodied cognitive science is a continuation of work in the functionalist tradition in psychology developed over a century ago by William James and John Dewey, among others. The focus here is on how functionalist views on the nature of mind, thought, and experience offer an alternative starting point for cognitive science in general, and for the cognitive science of scientific creativity in particular. The result may seem paradoxical. On the one hand, the article claims that the functionalist conceptual framework motivates rejecting mainstream cognitive views of creativity as the combination or association of ideas. On the other hand, however, the strategy adopted here—namely, revisiting ideas from functionalist psychology to inform current scientific theorizing—can itself be described as a process of arriving at new, creative ideas from combinations of old ones. As is shown here, a proper understanding of cognition in light of the functionalist tradition resolves the seeming tension between these two claims. (shrink)

The Lisp programming language is often described as the first functional programming language and also as an important early AI language. In the history of functional programming, however, it occupies a rather anomalous position, as the circumstances of its development do not fit well with the widely accepted view that functional languages have been developed through a theoretically-inspired project of deriving practical programming languages from the lambda calculus. This paper examines the origins of Lisp in the early AI programming work (...) of the mid-to-late 1950s, and in particular in the work of Allen Newell, Cliff Shaw and Herbert Simon. Their 1956 program, the Logic Theory Machine, introduced new ideas about data and program structures that were articulated in response to perceived limitations in existing programming technique. Later writers, notably John Backus, have described these features as constituting a “programming language style” distinct from the traditional style that preceded it. The paper examines the origins of the earlier style in practices of manual computation, analyses the key technical differences between it and the style first manifested in the Logic Theory Machine, and concludes that programming practice and experience play a large and underappreciated role in the development of programming styles and languages. (shrink)

The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to symbol grounding. (...) In this case, no symbol grounding could take place since any grounding presupposes intentional cognitive processes. So, whether computing in the mind is over meaningless or over meaningful symbols, computationalism implies semantic nativism. (shrink)

According to the hypotheses of distributed and extended cognition, remembering does not always occur entirely inside the brain but is often distributed across heterogeneous systems combining neural, bodily, social, and technological resources. These ideas have been intensely debated in philosophy, but the philosophical debate has often remained at some distance from relevant empirical research, while empirical memory research, in particular, has been somewhat slow to incorporate distributed/extended ideas. This situation, however, appears to be changing, as we witness an increasing level (...) of interaction between the philosophy and the empirical research. In this editorial, we provide a high-level historical overview of the development of the debates around the hypotheses of distributed and extended cognition, as well as relevant theory and empirical research on memory, considering both the role of memory in theoretical debates around distributed/extended ideas and strands of memory research that resonate with those ideas; we emphasize recent trends towards increased interaction, including new empirical paradigms for investigating distributed memory systems. We then provide an overview of the special issue itself, drawing out a number of general implications from the contributions, and conclude by sketching promising directions for future research on distributed memory. (shrink)

Enactive and ecological approaches to cognitive science both claim a “mutuality” between agents and their environments – that they have a complementary nature and should be addressed as a single whole system. Despite this apparent agreement, each offers criticisms of the other on precisely this point – enactivists claiming that ecological psychologists over-emphasise the environment, while the complementary criticism, of agent-centred constructivism, is levelled by ecological psychologists at enactivists. In this paper I suggest that underlying the confusion between the two (...) approaches is the complexity of agency, which comes in different forms, at different scales or levels of analysis. Cognitive science has not theorised the relationship between these different forms in a sufficiently disciplined manner, and a task therefore remains of finding a way to map the complex territory of agency. (shrink)

The past decade has seen an explosion of work on the history of the human sciences during the Cold War. This work, however, does not engage with one of the leading human sciences of the period: linguistics. This article begins to rectify this knowledge gap by investigating the influence of linguistics and its concept of study, language, on American public, political and intellectual life during the postwar and early Cold War years. I show that language emerged in three frameworks in (...) this period: language as tool, language as weapon, and language as knowledge. As America stepped onto the international stage, language and linguistics were at the forefront: the military poured millions of dollars into machine translation, American diplomats were required to master scores of foreign languages, and schoolchildren were exposed to language-learning on a scale never before seen in the United States. Together, I argue, language and linguistics formed a critical part of the rise of American leadership in the new world order — one that provided communities as dispersed as the military, the diplomatic corps, scientists and language teachers with a powerful way of tackling the problems they faced. To date, linguistics has not been integrated into the broader framework of Cold War human sciences. In this article, I aim to bring both language, as concept, and linguistics, as discipline, into this framework. In doing so, I pave the way for future work on the history of linguistics as a human science. (shrink)

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

This article outlines the emergence of ABA (Applied Behaviour Analysis) in the mid-20th century, and the current popularity of ABA in the anglophone world. I draw on the work of earlier historians to highlight the role of Ole Ivar Lovaas, the most influential practitioner of ABA. I argue that reception of his initial work was mainly positive, despite concerns regarding its efficacy and use of physical aversives. Lovaas’ work, however, was only cautiously accepted by medical practitioners until he published results (...) in 1987. Many accepted the results as validation of Lovaas’ research, though both his methods and broader understanding of autism had shifted considerably since his early work in the 1960s. The article analyses the controversies surrounding ABA since the early 1990s, considering in particular criticisms made by autistic people in the ‘neurodiversity movement’. As with earlier critics, some condemn the use of painful aversives, exemplified in the campaign against the use of shock therapy at the Judge Rotenberg Center. Unlike earlier, non-autistic critics, however, many in this movement reject the ideological goals of ABA, considering autism a harmless neurological difference rather than a pathology. They argue that eliminating benign autistic behaviour through ABA is impermissible, owing to the individual psychological harm and the wider societal impact. Finally, I compare the claims made by the neurodiversity movement with those made by similar 20th- and 21st-century social movements. (shrink)

Artificial intelligence (AI) is one of the main drivers of what has been described as the “Fourth Industrial Revolution”, as well as the most innovative technology developed to date. It is a pervasive transformative innovation, which needs a new approach. In 2017, the European Parliament introduced the notion of the “electronic person”, which sparked huge debates in philosophical, legal, technological, and other academic settings. The issues related to AI should be examined from an interdisciplinary perspective. In this paper, we examine (...) this legal innovation—that has been proposed by the European Parliament—from not only legal but also technological points of view. In the first section, we define AI and analyse its main characteristics. We argue that, from a technical perspective, it appears premature and probably inappropriate to introduce AI personhood now. In the second section, justifications for the European Parliament’s proposals are explored in contrast with the opposing arguments that have been presented. As the existing mechanisms of liability could be insufficient in scenarios where AI systems cause harm, especially when algorithms of AI learn and evolve on their own, there is a need to depart from traditional liability theories. (shrink)

This paper criticizes the role of intuition-based ascriptions of cognition that are closely related to the ascription of mind. This practice hinders the explication of a clear and stable target domain for the cognitive sciences. To move forward, the proposal is to cut the notion of cognition free from such ascriptions and the intuition-based judgments that drive them. Instead, cognition is reinterpreted and developed as a scientific concept that is tied to a material domain of research. In this reading, cognition (...) becomes a changeable theoretical concept that can and must be adapted to the findings within this target domain. Taking humans as the best-established existing example of the relevant material target domain, this central case is extended to include all living systems. To clarify what it is about living systems that warrants their role as cognitive target domain, the new concept of cobolism is introduced as a complement to metabolism. Cobolism refers to the systematic ways in which each living system encompasses structures, processes and external events that maintain the fundamental metabolic processes that constitute the core of each living system. Cobolism is perfectly general, applies to bacterial and human cases alike, and provides a general format to describe wildly different cognitive organizations. It provides a clear target for the cognitive sciences to work on, turning them into what we can call the cognitive life sciences. (shrink)

Cultural psychologists often describe the relationship between mind and culture as ‘dynamic.’ In light of this, we provide two desiderata that a theory about encultured minds ought to meet: the theory ought to reflect how cultural psychologists describe their own findings and it ought to be thoroughly naturalistic. We show that a realist theory of causal powers — which holds that powers are causally-efficacious and empirically-discoverable — fits the bill. After an introduction to the major concepts in cultural psychology and (...) describing causal power realism, we use a case study — the effects of pathogen prevalence on culture and cognition — to show the explanatory capacities of the powers framework. (shrink)

The paper takes as its starting point Donna Haraway’s suggestion, “The actors are cyborg, nature is coyote, and the geography is elsewhere”. It discusses first the understanding of the cyborg promoted by Haraway as illustrating an ontological non-humanist disposition, rather than a periodizing claim. The second part of the paper examines some instances of low-tech cyborg identities, which have emerged in developing countries (elsewhere) as a consequence of development initiatives. The paper argues that the quite literal attempts to develop cyborgs (...) in such countries gives rise to developments not foreseen or controllable by the development industries. If cyborg identities are developing and minds and bodies shaped in the frictions between culture, technology, economy, and development projects and activities then what are the implications for cognitive studies. In the final part of the paper this question is considered and it is suggested that cognitive studies would do well to expand their analytical foci to take into account cyborg bodies and minds found “elsewhere”. (shrink)

Philosophy of science has undergone a naturalistic turn, moving away from traditional idealized concerns with the logical structure of scientific theories and toward focusing on real-world scientific practice, especially in domains such as modeling and experimentation. As part of this shift, recent work has explored how the project of philosophically understanding science as a natural phenomenon can be enriched by drawing from different fields and disciplines, including niche construction theory in evolutionary biology, on the one hand, and ecological and enactive (...) views in embodied cognitive science, on the other. But these insights have so far been explored in separation from each other, without clear indication of whether they can work together. Moreover, the focus on particular practices, however insightful, has tended to lack consideration of potential further implications for a naturalized understanding of science as a whole (i.e., above and beyond those particular practices). Motivated by these developments, here we sketch a broad-ranging view of science, scientific practice and scientific knowledge in terms of ecological-enactive co-construction. The view we propose situates science in the biological, evolutionary context of human embodied cognitive activity aimed at addressing the demands of life. This motivates reframing theory as practice, and reconceptualizing scientific knowledge in ecological terms, as relational and world-involving. Our view also brings to the forefront of attention the fundamental link between ideas about the nature of mind, of science and of nature itself, which we explore by outlining how our proposal differs from more conservative, and narrower, conceptions of “cognitive niche construction.”. (shrink)

The article presents an epistemological and partially methodological analysis of cognitive science as a scientific discipline, created as a result of the transformations that took place in the philosophical and psychological concepts of the mind and cognition, which were carried out with the aid of tools and methods of modelling as well as through simulating human cognitive processes and consciousness. In order to describe this interdisciplinary science, and its positions, as well as the stages and directions of its development, it (...) makes use of the epistemological model formulated by Michel Foucault, in which he draws attention to social, ideological and technological conditions of scientific knowledge. The opinions of the leading creators and critics of cognitive science, such as George A. Miller, Howard Gardner, Margaret Boden and José Luis Bermúdez are referenced to and analyzed with the use of this model. The article shows the epistemologically and methodologically divergent status of cognitive science, as well as its cognitive and institutional conditions and challenges, which stand before it after half a century of intensive development. (shrink)

Will robots ever be able to learn like humans? To answer that question, one first needs to ask: what is learning? Hubert and Stuart Dreyfus had a point when they claimed that computers and robots would never be able to learn like humans because human learning, after an initial phase of rule-based learning, is uncertain, context sensitive and intuitive under contract F49620-C-0063 with the University of California) Berkeley, February 1980.. Washington, DC: Storming Media. https://www.stormingmedia.us/15/1554/A155480.html. Accessed 10 Oct 2017, 1980). I (...) would add that learning also builds on prior learning, and that from the outset, human learning is a socio-cultural materially grounded collective epistemology. This posthuman acknowledgement shifts the focus from the individual learner to learning within collective phenomena. Dreyfus and Dreyfus do not seem to emphasise the essentially social and cultural nature of the human condition. Learning theory, new materialism and postphenomenology have emphasised in different ways the materially based socio-cultural nature of human learning. They thereby point towards a ‘posthuman’ learning that is far from the machine-like or enhanced creature envisioned by singularists. Until robots are essentially social and ground their epistemologies in socio-cultural materiality, I suggest that human-like AI is not possible. (shrink)

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...) traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

The problem of epistemic opacity in Artificial Intelligence is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this claim, I first (...) contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system. While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties. (shrink)

Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing refer to (...) gender imitation in a test for machine intelligence? In this article, I shall address these questions by unveiling social, historical and epistemological roots of the so-called Turing test. I will draw attention to a historical fact that has been only scarcely observed in the secondary literature thus far, namely that Turing’s 1950 test emerged out of a controversy over the cognitive capabilities of digital computers, most notably out of debates with physicist and computer pioneer Douglas Hartree, chemist and philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson. Seen in its historical context, Turing’s 1950 paper can be understood as essentially a reply to a series of challenges posed to him by these thinkers arguing against his view that machines can think. Turing did propose gender learning and imitation as one of his various imitation tests for machine intelligence, and I argue here that this was done in response to Jefferson's suggestion that gendered behavior is causally related to the physiology of sex hormones. (shrink)

Virtue ethics seems to be a promising moral theory for understanding and interpreting the development and behavior of artificial moral agents. Virtuous artificial agents would blur traditional distinctions between different sorts of moral machines and could make a claim to membership in the moral community. Accordingly, we investigate the “machine question” by studying whether virtue or vice can be attributed to artificial intelligence; that is, are people willing to judge machines as possessing moral character? An experiment describes situations where either (...) human or AI agents engage in virtuous or vicious behavior and experiment participants then judge their level of virtue or vice. The scenarios represent different virtue ethics domains of truth, justice, fear, wealth, and honor. Quantitative and qualitative analyses show that moral attributions are weakened for AIs compared to humans, and the reasoning and explanations for the attributions are varied and more complex. On “relational” views of membership in the moral community, virtuous machines would indeed be included, even if they are indeed weakened. Hence, while our moral relationships with artificial agents may be of the same types, they may yet remain substantively different than our relationships to human beings. (shrink)

This article explores the proposition that the brain, normally seen as an organ of the human body, should be understood as a biologically based form of artificial intelligence, in the course of which the case is made for a new kind of ‘brain exceptionalism’. After noting that such a view was generally assumed by the founders of AI in the 1950s, the argument proceeds by drawing on the distinction between science—in this case neuroscience—adopting a ‘telescopic’ or a ‘microscopic’ orientation to (...) reality, depending on how it regards its characteristic investigative technologies. The paper concludes by recommending a ‘microscopic’ yet non-reductionist research agenda for neuroscience, in which the brain is seen as an underutilised organ whose energy efficiency is likely to outstrip that of the most powerful supercomputers for the foreseeable future. (shrink)

There is a small but growing community of researchers spanning a spectrum of disciplines which are united in rejecting the still dominant computationalist paradigm in favor of theenactive approach. The framework of this approach is centered on a core set of ideas, such as autonomy, sense-making, emergence, embodiment, and experience. These concepts are finding novel applications in a diverse range of areas. One hot topic has been the establishment of an enactive approach to social interaction. The main purpose of this (...) paper is to serve as an advanced entry point into these recent developments. It accomplishes this task in a twofold manner: it provides a succinct synthesis of the most important core ideas and arguments in the theoretical framework of the enactive approach, and it uses this synthesis to refine the current enactive approach to social interaction. A new operational definition of social interaction is proposed which not only emphasizes the cognitive agency of the individuals and the irreducibility of the interaction process itself, but also the need for jointly co-regulated action. It is suggested that this revised conception of ‘socio-cognitive interaction’ may provide the necessary middle ground from which to understand the confluence of biological and cultural values in personal action. (shrink)