It might be that intuitions are central to philosophy, and it might be that this is true because when philosophers give case‐based arguments for philosophical claims (in published philosophy), the case verdict is typically (a) an intuited proposition and (b) either left undefended or defended on the grounds that it is an intuited proposition. This paper remains neutral on these global issues, however, and instead focuses on whether there is a nontrivial (or many‐membered) class of case‐based arguments in philosophy in (...) which the case verdict is defended by appeal to background beliefs and not on the grounds that it is an intuited proposition. The paper argues that the answer is affirmative by examining seven such arguments that are referred to as “paradigm cases” of case‐based arguments in which the verdict is justified via an appeal to intuition. (shrink)

The cognitive phenomenology thesis has it that conscious cognitive states essentially exhibit a phenomenal character. Defenders of ‘conservatism’ about cognitive phenomenology think that the phenomenology of thought is reducible to sensory phenomenology. In contrast, proponents of ‘liberalism’ hold that there is a proprietary, sui generis cognitive phenomenology. Horgan develops a morph-sequence argument to argue for liberalism. The argument is based on the conceivability of a cognitive phenomenology zombie, i.e. a man who does not understand Chinese but shares the behavior and (...) sensory phenomenology with his twin who does understand Chinese. I argue that the conceivability of a cognitive phenomenology-zombie fails to settle the debate between conservatives and liberals. The roots of the ineffectiveness of the argument lie in the diverse readings of sensory phenomenology which flesh out the relation between sensory phenomenology and concepts differently but explain the conceivability of the scenario equally well. The lesson to learn is that to adjudicate the debate about cognitive phenomenology, we first have to clarify the notion of sensory phenomenology. (shrink)

As we rapidly approach the 50th year of the much‐celebrated ‘cognitive revolution’, it is worth reflecting on its widespread impact on individual disciplines and areas of multidisciplinary endeavour. Of specific concern in this paper is the example of the influence of cognitivism's equation of mind and computer in education. Within education, this paper focuses on a particular area of concern to which both mind and computer are simultaneously central: educational technology. It examines the profound and lasting effect of cognitive science (...) on our understandings of the educational potential of information and communication technologies, and further argues that recent and multiple ‘signs of discontent’, ‘crises’ and even ‘failures’ in cognitive science and psychology should result in changes in these understandings. It concludes by suggesting new directions that educational technology research might take in the light of this crisis of cognitivsm. (shrink)

Gray has expanded his account of schizophrenia to explain consciousness as well. His theory explains neither phenomenon adequately because he treats individual minds in isolation. The primary function of consciousness is to permit high level interactions with other conscious beings. The key symptoms of schizophrenia reflect a failure of this mechanism.

The Turing Test, originally proposed as a simple operational definition of intelligence, has now been with us for exactly half a century. It is safe to say that no other single article in computer science, and few other articles in science in general, have generated so much discussion. The present article chronicles the comments and controversy surrounding Turing's classic article from its publication to the present. The changing perception of the Turing Test over the last fifty years has paralleled the (...) changing attitudes in the scientific community towards artificial intelligence: from the unbridled optimism of 1960's to the current realization of the immense difficulties that still lie ahead. I conclude with the prediction that the Turing Test will remain important, not only as a landmark in the history of the development of intelligent machines, but also with real relevance to future generations of people living in a world in which the cognitive capacities of machines will be vastly greater than they are now. (shrink)

Do the dynamics of a physical system determine what function the system computes? Except in special cases, the answer is no: it is often indeterminate what function a given physical system computes. Accordingly, care should be taken when the question ‘What does a particular neuronal system do?’ is answered by hypothesising that the system computes a particular function. The phenomenon of the indeterminacy of computation has important implications for the development of computational explanations of biological systems. Additionally, the phenomenon lends (...) some support to the idea that a single neuronal structure may perform multiple cognitive functions, each subserved by a different computation. We provide an overarching conceptual framework in order to further the philosophical debate on the nature of computational indeterminacy and computational explanation. (shrink)

Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme (...) dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation. (shrink)

The convincing argument that Brette makes for the neural coding metaphor as imposing one view of brain behavior can be further explained through discourse analysis. Instead of a unified view, we argue, the coding metaphor's plasticity, versatility, and robustness throughout time explain its success and conventionalization to the point that its rhetoric became overlooked.

Because consciousness has an organizational, or functional, center, Gray supposes that there must be a corresponding physical center in the brain. He proposes further that since this center generates consciousness, ablating it would eliminate consciousness, while leaving behavior intact. But the center of consciousness is simply the product of the functional linkages among sensory input, memory, inner speech, and so on, and behavior.

Like everyone with a scientific bent of mind, Dennett thinks our capacity for meaningful language and states of mind is the product of evolution (Dennett [1987, ch. VIII]). But unlike many of this bent, he sees virtue in viewing evolution itself from the intentional stance. From this stance, ?Mother Nature?, or the process of evolution by natural selection, bestows intentionality upon us, hence we are not Unmeant Meaners. Thus, our intentionality is extrinsic, and Dennett dismisses the theories of meaning of (...) Dretske, Fodor, Burge, Putnam, and Kripke on the grounds that each requires that our mental states, unlike those of artifacts, have meaning intrinsically. I argue that we are Unmeant Meaners, incidentally defending Dretske et al., though my goal is to test the explanatory virtue of the intentional stance as applied to the evolution of intentionality. (shrink)

The aim of this paper is to offer an analysis of the notion of artificial moral agent (AMA) and of its impact on human beings’ self-understanding as moral agents. Firstly, I introduce the topic by presenting what I call the Continuity Approach. Its main claim holds that AMAs and human moral agents exhibit no significant qualitative difference and, therefore, should be considered homogeneous entities. Secondly, I focus on the consequences this approach leads to. In order to do this I take (...) into consideration the work of Bostrom and Dietrich, who have radically assumed this viewpoint and thoroughly explored its implications. Thirdly, I present an alternative approach to AMAs—the Discontinuity Approach—which underscores an essential difference between human moral agents and AMAs by tackling the matter from another angle. In this section I concentrate on the work of Johnson and Bryson and I highlight the link between their claims and Heidegger’s and Jonas’s sug-gestions concerning the relationship between human beings and technological products. In conclusion I argue that, although the Continuity Approach turns out to be a necessary postulate to the machine ethics project, the Discontinuity Approach highlights a relevant distinction between AMAs and human moral agents. On this account, the Discontinuity Approach generates a clearer understanding of what AMAs are, of how we should face the moral issues they pose, and, finally, of the difference that separates machine ethics from moral philosophy. (shrink)

I suggest that most discussions of intentional systems have overlooked an important aspect of living organisms: the intrinsic goal-directedness inherent in the behaviour of living eukaryotic cells. This goal directedness is nicely displayed by a normal cell’s ability to rearrange its own local material structure in response to damage, nutrient distribution or other aspects of its individual experience. While at a vastly simpler level than intentionality at the human cognitive level, I propose that this basic capacity of living things provides (...) a necessary building block for cognition and high-order intentionality, because the neurons that make up vertebrate brains, like most cells in our body, embody such capacities. I provisionally dub the capacities in question “nano-intentionality”: a microscopic form of “aboutness”. The form of intrinsic intentionality I propose is thoroughly materialistic, fully compatible with known biological facts, and derived non-mysteriously through evolution. Crucially, these capacities are not shared by any existing computers or computer components, and thus provide a clear, empirically-based distinction between brains and currently existing artificial information processing systems. I suggest that an appreciation of this aspect of living matter provides a potential route out of what may otherwise appear to be a hopeless philosophical quagmire confronting information-processing models of the mind. (shrink)

This paper aims to show that dominant conceptions of intelligence used in artificial intelligence (AI) are biased by normative assumptions that originate from the Global North, making it questionable if AI can be uncritically applied elsewhere without risking serious harm to vulnerable people. After the introduction in Sect. 1 we shortly present the history of IQ testing in Sect. 2, focusing on its multiple discriminatory biases. To determine how these biases came into existence, we define intelligence ontologically and underline its (...) constructed and culturally variable character. Turning to AI, specifically the Turing Test (TT), in Sect. 3, we critically examine its underlying intelligence conceptions. The test has been of central influence in AI research and remains an important point of orientation. We argue that both the test itself and how it is used in practice risk promoting a limited conception of intelligence which solely originated in the Global North. Hence, this conception should be critically assessed in relation to the different global contexts in which AI technologies are and will be used. In Sect. 4, we highlight how unequal power relations in AI research are a real threat, rather than just philosophical sophistry while considering the history of IQ testing and the TT’s practical biases. In the last section, we examine the limits of our account and identify fields for further investigation. Tracing colonial continuities in AI intelligence research, this paper points to a more diverse and historically aware approach to the design, development, and use of AI. (shrink)

The relationship between philosophy and research on artificial intelligence (AI) has been difficult since its beginning, with mutual misunderstanding and sometimes even hostility. By contrast, we show how an approach informed by both philosophy and AI can be productive. After reviewing some popular frameworks for computation and learning, we apply the AI methodology of “build it and see” to tackle the philosophical and psychological problem of characterizing perception as distinct from sensation. Our model comprises a network of very simple, but (...) interacting agents which have binary experiences of the “yes/no”-type and communicate their experiences with each other. When does such a network refer to a single agent instead of a distributed network of entities? We apply machine learning techniques to address the following related questions: i) how can the model explain stability of compound entities, and ii) how could the model implement a single task such as perceptual inference? We thereby find consistency with previous work on “interface” strategies from perception research. While this reflects some necessary conditions for the ascription of agency, we suggest that it is not sufficient. Here, AI research, if it is intended to contribute to conceptual understanding, would benefit from issues previously raised by philosophy. We thus conclude the article with a discussion of action-selection, the role of embodiment, and consciousness to make this more explicit. We conjecture that a combination of AI research and philosophy allows general principles of mind and being to emerge from a “quasi-empirical” investigation. (shrink)

Meaning has traditionally been regarded as a problem for philosophers and psychologists. Advances in cognitive science since the early 1960s, however, broadened discussions of meaning, or more technically, the semantics of perceptions, representations, and/or actions, into biology and computer science. Here, we review the notion of “meaning” as it applies to living systems, and argue that the question of how living systems create meaning unifies the biological and cognitive sciences across both organizational and temporal scales.

In this article I defend two theses. The first is that the centrality of recording in the social world is manifested through the production of documents, a phenomenon which has been present since the earliest phases of society and which has undergone an exponential growth through the technological developments of the last decades. The second is that the centrality of documents leads to a view of normativity according to which human beings are primarily passive receptors of rules manifested through documents. (...) We are not intentional producers of values. The latter, as I shall suggest in my conclusion, should be viewed as being ‘socially dependent’ rather than ‘socially constructed’. (shrink)

Perhaps personality traits substantially influence one’s philosophically relevant intuitions. This suggestion is not only possible, it is consistent with a growing body of empirical research: Personality traits have been shown to be systematically related to diverse intuitions concerning some fundamental philosophical debates. We argue that this fact, in conjunction with the plausible principle that almost all adequate philosophical views should take into account all available and relevant evidence, calls into question some prominent approaches to traditional philosophical projects. To this end, (...) we present the Philosophical Personality Argument (PPA). We explain how it supports the growing body of evidence challenging some of the uses of intuitions in philosophy, and we defend it from some criticisms of empirically based worries about intuitions in philosophy. We conclude that the current evidence indicates that the PPA is sound, and thus many traditional philosophical projects that use intuitions must become substantially more empirically oriented. (shrink)

This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand, and the philosophical and cultural critique of these attempts on the other, I will (...) argue that computation’s epistemic productions should be assessed vis-à-vis the logico-mathematical specificity of formal axiomatic systems. Such an assessment requires us to conceive automated modes of thought in such a way as to supersede the hope that machines might replicate human cognitive faculties, and to thereby acknowledge a form of onto-epistemological autonomy in automated ‘thinking’ processes. This involves moving beyond the view that machines might merely simulate humans. Machine thought should be seen as dramatically alien to human thought, and to the dimension of lived experience upon which the latter is predicated. Having stepped outside the simulative paradigm, the question ‘Can a machine think anything new?’ can then be reformulated. One should ask whether novel behaviour in computing might come not from the breaking of mechanical rules, but from following them: from doing what computers do already, and not what we might think they should be doing if we wanted them to imitate us. (shrink)

This paper discusses the nature of consciousness’ intrinsic intentionality from a transcendental-phenomenological viewpoint. In recent philosophy of mind the essentially intentional character of consciousness has become obscured because the latter is predominantly understood in terms of “qualia” or the “what-it-is-like-ness” of mental states and it is hard to see why such subjective “feels”, of all things, could bestow states with objective reference. As the paper attempts to demonstrate, this is an inadequate understanding of consciousness, which should instead be defined in (...) terms of presence. Consciousness essentially takes place as presence-of, i.e., consists in something coming to appearance. This presence-of is not only a fundamental, irreducible phenomenon, but also in a radical sense un-naturalisable. Naturalism only knows “nature”, as the world of objects, and the question of intentionality then seems to be how certain inner-worldly objects can be “representations” of other inner-worldly objects. In fact, no object is ever intrinsically “about” anything. This is exclusively the nature of subjectivity qua consciousness, which is not an object alongside other objects but rather exists as the manifestation of objects. (shrink)

With recent advancements in technology and computer science, we have reached a point where we can clearly state that thinking is no longer the exclusive privilege of living minds. Artificial intelligence can gather and process information in a manner fairly similar or even superior to our thinking process. AI can use this processed information in a reasoning process to make decisions and execute them. However, what makes our mind distinct from AI is the addition of “I,” that is, an entity (...) composed of two mental processes of awareness-based choice selection and discretionary selection of information for attention that are the fundamental core function of decision making and awareness processes, respectively. ABCS allows the coexistence of free will alongside—but unrestrained by—the chain of causality in the decision-making process with the help of awareness. “I” also allows for DSIA, which is a key step in the awareness process. This intertwined process is crucial for our consciousness. Similar to AI, the mind is devoid of any form of self-recognition without awareness. Self-awareness is a collaborative function of “I” and the mind. “I” is instrumental in the sense of self-awareness, but on its own, it is selfless. Additionally, “I” is the heart of the decision-making process. Therefore, AI is missing “I,” a selfless master of the mind. (shrink)