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Block, Ned

doi:10.1111/j.1475-4975.1987.tb00558.x

Cited by 438 publications

(221 citation statements)

References 37 publications

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“…However, there is a second, philosophical use of external meaning to refer to meaning that is causally connected to the external world without necessarily being mediated through the senses. In this tradition, both the perceptual and conceptual components to meaning would be considered internal because they are centered in a single person rather than world (Block, 1986;Miller & Johnson-Laird, 1976). The famous Putnam (1973) "twin earth" thought experiment is designed to show how the same internal, mental content can be associated with two different external referents.…”

Section: Externally Grounded Conceptsmentioning

confidence: 99%

Using relations within conceptual systems to translate across conceptual systems

Goldstone

2002

Cognition

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Section: Externally Grounded Conceptsmentioning

confidence: 99%

Using relations within conceptual systems to translate across conceptual systems

Goldstone

2002

Cognition

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“…But Putnam has repudiated this earlier attempt (e.g., Putnam, 1990Putnam, , 1992) and Kripke's model is a purely extensional endeavor which never attempted to characterize representation for the knowing agent (Block, 1986). Such carelessness about this central issue suggests that Vera and Simon take denotation to be unproblematic.…”

Section: Discussionmentioning

confidence: 99%

Topologies of learning and development

Bickhard

Campbell

1996

New Ideas in Psychology

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How systems can represent and how systems can learn are two central problems in the study of cognition. Conventional contemporary approaches to these problems are vitiated by a shared error in their presuppositions about representation. Consequently, such approaches share further errors about the sorts of architectures that are required to support either representation or learning. We argue that the architectural requirements for genuine representing systems lead to architectural characteristics that are necessary (though not sufficient) for heuristic learning and development. These architectural constraints, in turn, explain properties of the functioning of the central nervous system that remain inexplicable for standard approaches. Topologies of Learning and DevelopmentMark H. Bickhard Robert L. CampbellHow systems can represent and how systems can learn are two central problems in the study of cognition. Conventional contemporary approaches to these problems are vitiated by a shared error in their presuppositions about representation. Consequently, such approaches share further errors about the sorts of architectures that are required to support either representation or learning. We argue that the architectural requirements for genuine representing systems lead to architectural characteristics that are necessary (though not sufficient) for heuristic learning and development. These architectural constraints, in turn, explain properties of the functioning of the central nervous system that remain inexplicable for standard approaches.We will first outline some architectural requirements for models of learning and development, then present a model of representation that poses its own architectural requirements. The requirements for representation, it turns out, will also satisfy the requirements for learning and development.A major architectural requirement imposes itself on any system that can profit from past successes and failures -in other words, any system that can learn and get better at learning. That requirement is a functional topology in the space of possible constructive products of learning and development. The basic intuition is that, if previous problem-solving successes are to be functionally useful in attempts at solving new problems, there must be some way to "locate" and make use of the old problem representations and solutions that are "closest" to the new problem representations, or at least "nearby." Such closeness information constitutes a topology. Exploration of these topological requirements yields strong constraints on the nature and locus of learning and of development.Such topological considerations are not well addressed within standard architectural and mathematical approaches to computation. In finite state machines, Turing machines, or programming languages, topologies do not naturally arise. When Artificial Intelligence researchers encounter the constraints that yield the necessity for such topologies, the standard fix, as we shall discuss below, is to introduce feature-ba...

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“…Advocates of the vehicle-view can honour individualist convictions (see Block 1986). Still, it is more important to note that a commitment to externalism will quite naturally bring a commitment to the vehicle-view in its wake.…”

Section: Should We Hold That Mental and Linguistic Contents Are Determentioning

confidence: 99%

Concepts in Philosophy—a Rough Geography

Nimtz

Langkau

2010

Grazer Philos Stud

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Much recent work on concepts has been inspired by and is developed within the bounds of the representational theory of the mind often taken for granted by philosophers of mind, cognitive scientists, and psychologists alike (see e.g. Margolis and Laurence 1999; Stich and Warfield 1994). The contributions to this volume take a more encompassing perspective on the issue of concepts. Rather than modelling details of our representational architecture in line with the dominant paradigm, they explore three traditional issues concerning concepts. Inquiring into how language and the mind are interrelated, Brandom, Bermúdez, Nida-Rümelin and Racokzy ask: Is mastery of a language necessary for thought?Pondering whether drawing on concepts to explain our thinking requires us to adopt the representational paradigm of the mind in the first place, Kenny, Glock, and Saporiti are concerned with the question:  Do concepts reduce to abilities?Finally, in order to assess the prospects for philosophical reliance on conceptual analysis, Jackson, Nimtz, Spicer, and Textor discuss: Is the analysis of concepts a viable means to ascertain truths from the proverbial armchair?Needless to say, there is no consensus to be had on either issue.This introductory essay explores the backdrop to the debate our authors engage in. We will provide a rough geography of key ideas and issues shaping the overall debate on concepts within contemporary philosophy. We will proceed in two steps. In a first step, we will present and discuss key ideas shaping the assumptions and expectations concerning concepts philosophers harbour ( §1). Taken together, they form what we want to think of as the conventional picture of concepts. In a second step, we will focus on recent developments and divisive fundamental issues that have brought about tectonic changes in the philosophical views on concepts ( §2). These explain why the conventional picture has gone basically out of fashion and why the philosophical debate on concepts in general appears heterogeneous, and feels fragmented. Concepts in philosophy: some traditional ideasIn line with well-known instructive attempts to characterize the nature and role of concepts (see Fodor 1998, ch. 1, Margolis and Laurence 1999b, and especially Burge 1993), we present a list of popular ideas concerning concepts. Although items from our list are often presented as platitudes, we hold that it neither catalogues mere truisms, nor states a general consensus. The list rather registers key ideas as to what concept are, and what conceptsdo, that figure prominently in the contemporary debate. Apparently, few contemporary philosophers subscribe to the conventional picture of concepts in its entirety. As we shall see in §2, many philosophers embrace fundamental views about thought, language, and content which lead them to reinterpret or reject core ideas of the conventional picture.Talk of 'concepts' looms large in the philosophy of mind and the philosophy of language, and it plays an important role in epistemology. These disciplines,...

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Cited by 438 publications

References 37 publications

Using relations within conceptual systems to translate across conceptual systems

Using relations within conceptual systems to translate across conceptual systems

Topologies of learning and development

Concepts in Philosophy—a Rough Geography

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