Online research in philosophy

"Semantic Minimalists" holds that there are virtually no semantically context sensitive expressions in English. In particular, they claim that the semantics for terms like "red", "tall", "ready", "every", or "know" are not (contrary to many popular semantic theories) context sensitive. While minimalism strikes many as obviously false, it will be argued here that the view is more plausible than commonly assumed if one accepts the 'normative' conception of the relation between meaning and use characteristic of the literature on semantic externalism.

We sketch an account according to which the semantic concepts themselves are not pathological and the pathologies that attend the semantic predicates arise because of the intention to impose on them a role they cannot fulfill, that of expressing semantic concepts for a language that includes them. We provide a simplified model of the account and argue in its light that (i) a consequence is that our meaning intentions are unsuccessful, and such semantic predicates fail to express any concept, and that (ii) in light of this it is incorrect to characterize the pathology simply as semantic inconsistency; a more nuanced view of the problem is needed. We also show that the defects of the semantic predicates need not undercut the use of a truth theory in a compositional semantics for a language containing them because the meaning theory per se need not involve commitment to the axioms of the truth theory it exploits.

This paper addresses one of the fundamental problems of the philosophy of information: How does semantic information emerge within the underlying dynamics of the world?—the dynamical semantic information problem. It suggests that the canonical approach to semantic information that defines data before meaning and meaning before use is inadequate for pre-cognitive information media. Instead, we should follow a pragmatic approach to information where one defines the notion of information system as a special kind of purposeful system emerging within the underlying dynamics of the world and define semantic information as the currency of the system. In this way, systems operating with semantic information can be viewed as patterns in the dynamics—semantic information is a dynamical system phenomenon of highly organized systems. In the simplest information systems, the syntax, semantics, and pragmatics of the information medium are co-defined. It proposes a new more general theory of information semantics that focuses on the interface role of the information states in the information system—the interface theory of meaning. Finally, with the new framework, it addresses the debate between weakly semantic and strongly semantic accounts of information, siding with the strongly semantic view because the pragmatic account developed here is a better generalization of it.

Abstract It is widely assumed that the meaning of at least some types of expressions involves more than their reference to objects, and hence that there may be co-referential expressions which differ in meaning. It is also widely assumed that ?syntax does not suffice for semantics?, i.e. that we cannot account for the fact that expressions have semantic properties in purely syntactical or computational terms. The main goal of the paper is to argue against a third related assumption, namely that what is responsible for a difference in meaning between co-referential expressions is the computational difference in the cognitive functioning of the expressions. ?Intentional aspects? of expressions?those features which their meanings involve in addition to reference?cannot be syntacticized, since they are individuated not in terms of any cognitive feature, but rather in terms of those properties of the referents through which the expressions refer to them, and cognitive features cannot determine such properties in exactly the same sense as they cannot determine reference.

Meaning holists hold, roughly, that each representation in a linguistic or mental system depends semantically on every other representation in the system. The main difficulty for holism is the threat it poses to meaning stability--shared meaning between representations in two systems. If meanings are holistically dependent, then semantic differences anywhere seem to balloon into semantic differences everywhere. My positive aim is to show how holism, even at its most extreme, can accommodate and also increase meaning stability. My negative aim is to provide reasons for rejecting various nonholist proposals, at least for systems of mental representations.

In this paper I discuss the topics of mechanism and algorithmicity. I emphasise that a characterisation of algorithmicity such as the Turing machine is iterative; and I argue that if the human mind can solve problems that no Turing machine can, the mind must depend on some non-iterative principle — in fact, Cantor's second principle of generation, a principle of the actual infinite rather than the potential infinite of Turing machines. But as there has been theorisation that all physical systems can be represented by Turing machines, I investigate claims that seem to contradict this: specifically, claims that there are noncomputable phenomena. One conclusion I reach is that if it is believed that the human mind is more than a Turing machine, a belief in a kind of Cartesian dualist gulf between the mental and the physical is concomitant.

The hypothesis that perceptual mechanisms could have more representational and logical power than usually assumed is interesting and provocative, especially with regard to brain evolution. However, the importance of embodiment and grounding is exaggerated, and the implication that there is no highly abstract representation at all, and that human-like knowledge cannot be learned or represented without human bodies, is very doubtful. A machine-learning model, Latent Semantic Analysis (LSA) that closely mimics human word and passage meaning relations is offered as a counterexample.

Formal semantics is an approach to SEMANTICS1, the study of meaning, with roots in logic, the philosophy of language, and linguistics, and since the 1980’s a core area of linguistic theory. Characteristics of formal semantics to be treated in this article include the following: Formal semanticists treat meaning as mind-independent (though abstract), contrasting with the view of meanings as concepts “in the head” (see I-LANGUAGE AND E-LANGUAGE and MEANING EXTERNALISM AND INTERNALISM); formal semanticists distinguish semantics from knowledge of semantics (Lewis 1975, Cresswell 1978), which has consequences for the notion of semantic COMPETENCE. A central part of the meaning of a sentence on this approach is its TRUTH CONDITIONS, and most although not all formal semantics is model-theoretic, relating linguistic expressions to model-theoretically constructed semantic values cast in terms of truth, REFERENCE, and possible worlds. This sets formal semantics apart from approaches which view semantics as relating a sentence just to a representation on another linguistic “level” (LOGICAL FORM) or a representation in an innate LANGUAGE OF THOUGHT. The formal semanticist could accept such representations as an aspect of semantics but would insist on asking what the model-theoretic semantic interpretation of the given representationlanguage is (Lewis 1970). Formal semantics is centrally concerned with COMPOSITIONALITY at the SYNTAX-SEMANTICS INTERFACE, how the meanings of larger constituents are built up from the meanings of their parts on the basis of their syntactic structure, and with the relation between compositional SENTENCE MEANING and meaning in discourse.

Earlier, we have studied computations possible by physical systems and by algorithms combined with physical systems. In particular, we have analysed the idea of using an experiment as an oracle to an abstract computational device, such as the Turing machine. The theory of composite machines of this kind can be used to understand (a) a Turing machine receiving extra computational power from a physical process, or (b) an experimenter modelled as a Turing machine performing a test of a known physical theory T.