The mathematical theory of communication.

Brian Skyrms offers a fascinating demonstration of how fundamental signals are to our world. He uses various scientific tools to investigate how meaning and communication develop. Signals operate in networks of senders and receivers at all levels of life, transmitting and processing information. That is how humans and animals think and interact.

Luciano Floridi presents a book that will set the agenda for the philosophy of information. PI is the philosophical field concerned with the critical investigation of the conceptual nature and basic principles of information, including its dynamics, utilisation, and sciences, and the elaboration and application of information-theoretic and computational methodologies to philosophical problems. This book lays down, for the first time, the conceptual foundations for this new area of research. It does so systematically, by pursuing three goals. Its metatheoretical goal (...) is to describe what the philosophy of information is, its problems, approaches, and methods. Its introductory goal is to help the reader to gain a better grasp of the complex and multifarious nature of the various concepts and phenomena related to information. Its analytic goal is to answer several key theoretical questions of great philosophical interest, arising from the investigation of semantic information. (shrink)

This chapter contains section titles: Introduction Response Functions Information and Singular Causation The Functions of Sensory Representations The Contents of Sensory Representations: The Problem of Error The Contents of Sensory Representation: The Distality Problem.

Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...) this paper, we address this unsatisfactory state of affairs by presenting a general and theory-neutral account of computation and information processing. We also apply our framework by analyzing the relations between computation and information processing on one hand and classicism and connectionism on the other. We defend the relevance to cognitive science of both computation, in a generic sense that we fully articulate for the first time, and information processing, in three important senses of the term. Our account advances some foundational debates in cognitive science by untangling some of their conceptual knots in a theory-neutral way. By leveling the playing field, we pave the way for the future resolution of the debates’ empirical aspects. (shrink)

The reference class problem arises when we want to assign a probability to a proposition (or sentence, or event) X, which may be classified in various ways, yet its probability can change depending on how it is classified. The problem is usually regarded as one specifically for the frequentist interpretation of probability and is often considered fatal to it. I argue that versions of the classical, logical, propensity and subjectivist interpretations also fall prey to their own variants of the reference (...) class problem. Other versions of these interpretations apparently evade the problem. But I contend that they are all “no-theory” theories of probability - accounts that leave quite obscure why probability should function as a guide to life, a suitable basis for rational inference and action. The reference class problem besets those theories that are genuinely informative and that plausibly constrain our inductive reasonings and decisions. I distinguish a “metaphysical” and an “epistemological” reference class problem. I submit that we can dissolve the former problem by recognizing that probability is fundamentally a two-place notion: conditional probability is the proper primitive of probability theory. However, I concede that the epistemological problem remains. (shrink)

In this lucid portrayal of human behavior, Fred Dretske provides an original account of the way reasons function in the causal explanation of behavior.

"According to informational semantics, if it's necessary that a creature can't distinguish Xs from Ys, it follows that the creature can't have a concept that applies to Xs but not Ys." (Jerry Fodor, The Elm and the Expert, p.32).

Some philosophers have claimed that it is meaningless or paradoxical to consider the probability of a probability. Others have however argued that second-order probabilities do not pose any particular problem. We side with the latter group. On condition that the relevant distinctions are taken into account, second-order probabilities can be shown to be perfectly consistent. May the same be said of an infinite hierarchy of higher-order probabilities? Is it consistent to speak of a probability of a probability, and of a (...) probability of a probability of a probability, and so on, {\em ad infinitum}? We argue that it is, for it can be shown that there exists an infinite system of probabilities that has a model. In particular, we define a regress of higher-order probabilities that leads to a convergent series which determines an infinite-order probability value. We demonstrate the consistency of the regress by constructing a model based on coin-making machines. (shrink)

Abstract: According to the Veridicality Thesis, information requires truth. On this view, smoke carries information about there being a fire only if there is a fire, the proposition that the earth has two moons carries information about the earth having two moons only if the earth has two moons, and so on. We reject this Veridicality Thesis. We argue that the main notions of information used in cognitive science and computer science allow A to have information about the obtaining of (...) p even when p is false. (shrink)

We offer a novel theory of information that differs from traditional accounts in two respects: (i) it explains information in terms of counterfactuals rather than conditional probabilities, and (ii) it does not make essential reference to doxastic states of subjects, and consequently allows for the sort of objective, reductive explanations of various notions in epistemology and philosophy of mind that many have wanted from an account of information.

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)

The propensity interpretation of probability was introduced by Popper ([1957]), but has subsequently been developed in different ways by quite a number of philosophers of science. This paper does not attempt a complete survey, but discusses a number of different versions of the theory, thereby giving some idea of the varieties of propensity. Propensity theories are classified into (i) long-run and (ii) single-case. The paper argues for a long-run version of the propensity theory, but this is contrasted with two single-case (...) propensity theories, one due to Miller and the later Popper, and the other to Fetzer. The three approaches are compared by examining how they deal with a key problem for the propensity approach, namely the relationship between propensity and causality and Humphreys' paradox. (shrink)

There is, indeed, a form of informational semantics that has this verificationist implication. The original definition of information given in Dretske's Knowledge and the Flow of Information (1981, hereafter KFI), when employed as a base for a theory of intentional representation or "content," has this implication. I will argue that, in fact, most of what an animal needs to know about its environment is not available as natural information of this kind. It is true, I believe, that there is one (...) fundamental kind of perception that depends on this kind of natural information, but more sophisticated forms of inner representation do not. It is unclear, however, exactly what "natural information" is supposed to mean, certainly in Fodor's, and even in Dretske's writing. In many places, Dretske seems to employ a softer notion than the one he originally defines. I will propose a softer view of natural information that is, I believe, at least hinted at by Dretske, and show that it does not have verificationist consequences. According to this soft informational semantics, a creature can perfectly well have a representation of Xs without being able to discriminate Xs from Ys. (shrink)

In his essay "Consumers Need Information: Supplementing Teleosemantics with an Input Condition" (this issue) Nicholas Shea argues, with support from the work of Peter Godfrey-Smith (1996), that teleosemantics, as David Papinau and I have articulated it, cannot explain why "content attribution can be used to explain successful behavior." This failure is said to result from defining the intentional contents of representations by reference merely to historically normal conditions for success of their "outputs," that is, of their uses by interpreting or (...) consuming mechanisms, bypassing the more traditional focus, of those who would naturalize intentional content, on causal or informational inputs. Shea proposes to "add an input condition to teleosemantics," requiring that simple representations must carry "correlational information." I am grateful to Shea for his paper, as it presents me with an opportunity to clarify two fairly central features of my position on intentional content, one of which seems to have been overlooked in the literature (Millikan 1993a), the other of which I have stated previously only in a confusing way (Millikan 2004, Chapters 3-4). The first clarification concerns the general form that I take explanation by reference to intentional states to have. The second concerns my description of "locally recurrent natural information," why this kind of information is needed in place of Shea's "correlational information" to explain what feeds simple representational systems, and why no reference to natural information is needed to account for the success of behaviors by reference to the truth of representations that motivate them. (shrink)

This paper examines the relationship between a family of concepts involving reliable correlation, and a family of concepts involving adaptation and biological function, as these concepts are used in the naturalistic semantic theory of Dretske's "Explaining Behavior." I argue that Dretske's attempt to marry correlation and function to produce representation fails, though aspects of his failure point the way forward to a better theory.