I critically examine some provocative arguments that John Searle presents in his book The Rediscovery of Mind to support the claim that the syntactic states of a classical computational system are "observer relative" or "mind dependent" or otherwise less than fully and objectively real. I begin by explaining how this claim differs from Searle's earlier and more well-known claim that the physical states of a machine, including the syntactic states, are insufficient to determine its semantics. In contrast, his more recent (...) claim concerns the syntax, in particular, whether a machine actually has symbols to underlie its semantics. I then present and respond to a number of arguments that Searle offers to support this claim, including whether machine symbols are observer relative because the assignment of syntax is arbitrary, or linked to universal realizability, or linked to the sub-personal interpretive acts of a homunculus, or linked to a person's consciousness. I conclude that a realist about the computational model need not be troubled by such arguments. Their key premises need further support. (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)

It has recently become quite fashionable for one to defend a functionalist theory of mental states. However, as with most trends, functionalism has come under fire in certain philosophical quarters. In this paper, I shall not take up the question of whether any version of functionalism is true. I shall instead discuss a particular objection to a specific brand of functionalism.

It has recently become quite fashionable for one to defend a functionalist theory of mental states. However, as with most trends, functionalism has come under fire in certain philosophical quarters. In this paper, I shall not take up the question of whether any version of functionalism is true. I shall instead discuss a particular objection to a specific brand of functionalism.

By following the arguments developed by Vygotsky and employing the cultural-historical activity theory (CHAT) in addition to dialectical logic, this paper attempts to investigate the interaction between psychology and artificial intelligence (AI) to confront the epistemological and methodological challenges encountered in AI research. The paper proposes that AI is facing an epistemological and methodological crisis inherited from psychology based on dualist ontology. The roots of this crisis lie in the duality between rationalism and objectivism or in the mind-body rupture that (...) has governed the production of scientific thought and the proliferation of approaches. In addition, by highlighting the sociohistorical conditions of AI, this paper investigates the historical characteristics of the shift of the crisis from psychology to AI. Additionally, we examine the epistemological and methodological roots of the main challenges encountered in AI research by noting that empiricism is the dominant tendency in the field. Empiricism gives rise to methodological and practical challenges, including challenges related to the emergence of meaning, abstraction, generalization, the emergence of symbols, concept formation, functional reflection of reality, and the emergence of higher psychological functions. Furthermore, through discussing attempts to formalize dialectical logic, the paper, based on contradiction formation, proposes a qualitative epistemological, methodological, and formal alternative by using a preliminary algorithmic model that grasps the formation of meaning as an essential ability for the qualitative reflection of reality and the emergence of other mental functions. (shrink)

The commentary argues that we cannot be sure that human consciousness has survival value and that in order to understand the origins and, perhaps, the function of consciousness, we should examine the behavioural and neural precursors to consciousness in nonhumans. An example is given of research on the role of context in decisions regarding fleeing from probable predators in the Mongolian gerbil.

Gray's account is remarkable in its depth and scope but too little attention is paid to poor correspondences with the literature on hippocampal/subicular damage, the theta rhythm, and novelty detection. An alternative account, focusing on hippocampal involvement in organizing memories in a way that makes them accessible to conscious recollection but not in access to consciousness per se, avoids each of these limitations.

Introduction: representationalismMost theorists of cognition endorse some version of representationalism, which I will understand as the view that the human mind is an information-using system, and that human cognitive capacities are representational capacities. Of course, notions such as ‘representation’ and ‘information-using’ are terms of art that require explication. As a first pass, representations are “mediating states of an intelligent system that carry information” (Markman and Dietrich 2001, p. 471). They have two important features: (1) they are physically realized, and so (...) have causal powers; (2) they are intentional, in other words, they have meaning or representational content. This presumes a distinction between a representational vehicle—a physical state or structure that has causal powers and is responsible for producing behavior—and its content. Consider the following characterization of a device that computes the addition functionReaders will recognize the similarity t. (shrink)

This paper argues that many so-called digital technologies can be construed as notational technologies, explored through the example of Monegraph, an art and digital asset management platform built on top of the blockchain system originally developed for the cryptocurrency bitcoin. As the paper characterizes it, a notational technology is the performance of syntactic notation within a field of reference, a technologized version of what Nelson Goodman called a “notational system.” Notational technologies produce abstracted entities through positive and reliable, or constitutive, (...) tests of socially acceptable meaning. Accordingly, this account deviates from typical narratives of blockchains, instead demonstrating that blockchain technologies are effective at managing digital assets because they produce abstracted identities through the performance of notation. Since notational technologies rely on configurations of socially acceptable meaning, this paper also provides a philosophical account of how blockchain technologies are socially embedded. (shrink)

In his book The rediscovery of the mind John Searle claims that unconscious mental states (1) have first-person "aspectual shape", but (2) that their ontology is purely third-person. He attempts to eliminate the obvious inconsistency by arguing that the aspectual shape of unconscious mental states consists in their ability to cause conscious first-person states. However, I show that this attempted solution fails insofar as it covertly acknowledges that unconscious states lack the aspectual shape required for them to play a role (...) in psychological explanation. (shrink)

Roger Schank's research in AI takes seriously the ideas that understanding natural language involves mapping its expressions into an internal representation scheme and that these internal representations have a syntax appropriate for computational operations. It therefore falls within the computational approach to the study of mind. This paper discusses certain aspects of Schank's approach in order to assess its potential adequacy as a (partial) model of cognition. This version of the Language of Thought hypothesis encounters some of the same difficulties (...) that arise for Fodor's account. (shrink)

A theory of information is developed in which the informational content of a signal (structure, event) can be specified. This content is expressed by a sentence describing the condition at a source on which the properties of a signal depend in some lawful way. Information, as so defined, though perfectly objective, has the kind of semantic property (intentionality) that seems to be needed for an analysis of cognition. Perceptual knowledge is an information-dependent internal state with a content corresponding to the (...) information producing it. This picture of knowledge captures most of what makes knowledge an important cpistcmological notion. It also avoids many of the problems infecting traditional justificational accounts of knowledge (knowledge as [justified, true belief those (unlike knowledge) having a content that can be either true or false (e.g., belief) – are described in terms of the way internal (presumably neural) structures acquire during learning a certain information-carrying role. The content of these structures (whether true or false) is identified with the kind of information they were developed to carry. (shrink)

Can animals think? In this paper I address the proposal that many animals, including insects such as honeybees, have genuine thoughts. I consider one prominent version of this view that claims that honeybees can represent and process information about their environments in a way that satisfies the main hallmarks of human conceptual thought. I shall argue, however, that this view fails to provide convincing grounds for accepting that animals possess concepts. More precisely, I suggest that two important aspects of conceptual (...) thought, viz., concept individuation and the generality constraint, are not satisfied. (shrink)

Cognitive science is held, not only by its practitioners, to offer something distinctively new in the philosophy of mind. This novelty is seen as the product of two factors. First, philosophy of mind takes itself to have well and truly jettisoned the ‘old paradigm’, the theory of the mind as embodied soul, easily and completely known through introspection but not amenable to scientific inquiry. This is replaced by the ‘new paradigm’, the theory of mind as neurally-instantiated computational mechanism, relatively opaque (...) to introspection and the proper subject of detailed empirical investigation. Second, in the constitutive disciplines of cognitive science we have for the first time the theoretical, experimental and technological resources to begin this investigation. My concern here is to show that, despite its scientific and philosophical sophistication, the new paradigm is in certain striking ways very similar to the old paradigm and that Wittgenstein's criticisms of the former apply to much of the latter. (shrink)

John searle has recently claimed to have dissolved what daniel dennett calls 'hume's problem'--The question whether the explanation of behavior by appeal to mental representations can be done without circularity or infinite regress. Searle argues that a careful analysis of the concept of an intentional state shows that mental representations do not require intentional "homunculi" to explain how intentional states have their contents, And, Hence dennett's worry is groundless. I argue that searle's conceptual analysis of intentional states, Even if correct, (...) Provides no clue of an answer to the worry underlying hume's problem. (shrink)

This bold and brilliant book asks the ultimate question of the life sciences: How did the human mind acquire its incomparable power? In seeking the answer, Merlin Donald traces the evolution of human culture and cognition from primitive apes to the era of artificial intelligence, and presents an original theory of how the human mind evolved from its presymbolic form. In the emergence of modern human culture, Donald proposes, there were three radical transitions. During the first, our bipedal but still (...) apelike ancestors acquired "mimetic" skill—the ability to represent knowledge through voluntary motor acts—which made Homo erectus successful for over a million years. The second transition—to "mythic" culture —coincided with the development of spoken language. Speech allowed the large-brained Homo sapiens to evolve a complex preliterate culture that survives in many parts of the world today. In the third transition, when humans constructed elaborate symbolic systems ranging from cuneiforms, hieroglyphics, and ideograms to alphabetic languages and mathematics, human biological memory became an inadequate vehicle for storing and processing our collective knowledge. The modern mind is thus a hybrid structure built from vestiges of earlier biological stages as well as new external symbolic memory devices that have radically altered its organization. According to Donald, we are symbol-using creatures, more complex than any that went before us, and we may have not yet witnessed the final modular arrangement of the human mind. (shrink)

"The article is devoted to the ethical aspects of artificial intelligence functioning. The problem of the safe coexistence of man and artificial intelligence is taking on increasing importance. The definition of artificial intelligence and the explanation of the difference between weak, strong artificial intelligence and super-intelligence are given. The first ethical problem of artificial intelligence functioning is the existential question of human redundancy due to the spread of artificial intelligence. The article emphasizes that artificial intelligence, on the one hand, frees (...) a person from certain areas of activity, facilitating their existence, on the other hand, it makes them superfluous in production and in personal life. The second is the problem of responsibility for the actions of artificial intelligence. The article argues for the necessity of human responsibility for the actions of artificial intelligence because artificial intelligence does not have consciousness. The third problem is the intensification of the phenomenon of stupidity in society. Artificial intelligence creates the conditions for the increasing cleverness of some segments of the population and stupidity of others, which can result in even greater mental and property inequality. Keywords: intelligence, artificial intelligence, ethics, responsibility, legal responsibility, human existential state, problem of redundancy, stupidity. ". (shrink)

En este trabajo se analiza la cuestión de si el volcado de la mente en una máquina (mind uploading), en caso de ser alguna vez tecnológicamente posible, mantendría o destruiría la identidad personal de quien experimentara el volcado. Se verá cómo podría contestarse a la cuestión en función de los criterios para el mantenimiento de la identidad personal que se asuman. No hay una respuesta única, puesto que la identidad personal se mantendría o no en función de los supuestos aceptados. (...) No obstante, para la mayor parte de las concepciones filosóficas sobre la identidad personal, ésta no se mantendría en un proceso de volcado de la mente en una máquina. Si esto es así, habría que concluir que, desde diversos enfoques, dicho volcado sería equivalente a la desaparición del sujeto, es decir, a su muerte, y solo desde posiciones muy limitadas podría defenderse lo contrario. (shrink)

There is a prevalent notion among cognitive scientists and philosophers of mind that computers are merely formal symbol manipulators, performing the actions they do solely on the basis of the syntactic properties of the symbols they manipulate. This view of computers has allowed some philosophers to divorce semantics from computational explanations. Semantic content, then, becomes something one adds to computational explanations to get psychological explanations. Other philosophers, such as Stephen Stich, have taken a stronger view, advocating doing away with semantics (...) entirely. This paper argues that a correct account of computation requires us to attribute content to computational processes in order to explain which functions are being computed. This entails that computational psychology must countenance mental representations. Since anti-semantic positions are incompatible with computational psychology thus construed, they ought to be rejected. Lastly, I argue that in an important sense, computers are not formal symbol manipulators. (shrink)

This paper is a review of the work that has been carried out on machine consciousness. A clear overview of this diverse field is achieved by breaking machine consciousness down into four different areas, which are used to understand its aims, discuss its relationship with other subjects and outline the work that has been carried out so far. The criticisms that have been made against machine consciousness are also covered, along with its potential benefits, and the work that has been (...) done on analysing systems for signs of consciousness. Some of the social and ethical issues raised by machine consciousness are examined at the end of the paper. (shrink)

Abstract The notion of representation lies at the crossroads of questions about the nature of belief and knowledge, meaning, and intentionality. But there is some hope that it might be simpler than all those. If we could understand it clearly, it might then help to explicate those more difficult notions. In this paper, my central aim is to find a principled criterion, along lines that make biological sense, for deciding just when it becomes theoretically plausible to ascribe to some process (...) or state a representational role. I shall be especially concerned with some differences, in this regard, between classical and connectionist models. The relation between ?standard? artificial intelligence and connectionism turns out to illustrate a ?first in, last out? principle: What we most easily understand (and so can program) is what we have most recently invented; tasks we ourselves perform best, by contrast, are a lot harder to understand. Classical AI has modelled the former; connectionism tries to tackle the latter. I end with some speculations about the possible implications of these considerations for our understanding of understanding. (shrink)

It is usually accepted that deductions are non-informative and monotonic, inductions are informative and nonmonotonic, abductions create hypotheses but are epistemically irrelevant, and both deductions and inductions can’t provide new insights. In this article, I attempt to provide a more cohesive view of the subject with the following hypotheses: (1) the paradigmatic examples of deductions, such as modus ponens and hypothetical syllogism, are not inferential forms, but coherence requirements for inferences; (2) since any reasoner aims to be coherent, any inference (...) must be deductive; (3) a coherent inference is an intuitive process where the premises should be taken as sufficient evidence for the conclusion, which on its turn should be viewed as necessary evidence for the premises in some modal range; (4) inductions, properly understood, are abductions, but there are no abductions beyond the fact that in any inference the conclusion should be regarded as necessary evidence for the premises; (5) monotonicity is not only compatible with the retraction of past inferences given new information, but it is a requirement for it; (6) this explanation of inferences holds true for discovery processes, predictions and trivial inferences. (shrink)

[This is an earlier (1997), much longer and more detailed version of my entry on LOTH in the _Stanford Encyclopedia of Philosophy_] The Language of Thought Hypothesis (LOTH) is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that (...) has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)