Daniel Dennett's theory of intentionality has much to recommend it. Nevertheless, it could be significantly strengthened by addressing the causes of intentionality, that is, the mechanisms undelying intentional behavior. I will discuss three problems that a causal theory of intentionality could alleviate: attribution of rationality (or optimality), alternatives to sentential representation, and directedness of consciousness.
The issue of symbol grounding is not essentially different in analog and digital computation. The principal difference between the two is that in analog computers continuous variables change continuously, whereas in digital computers discrete variables change in discrete steps (at the relevant level of analysis). Interpretations are imposed on analog computations just as on digital computations: by attaching meanings to the variables and the processes defined over them. As Harnad (2001) claims, states acquire intrinsic meaning through their relation to the (...) real (physical) environment, for example, through transduction. However, this is independent of the question of the continuity or discreteness of the variables or the transduction processes. (shrink)
The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scienti c claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog computation (...) and digital computation are not essentially di erent, and so arguments such as Searle's hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scienti c matter of fact. (4) A substantive scienti c question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it's all too easy to become ensnared. (shrink)
Images and Models. The distinction between models and images is treated briefly in JL&B (pp. 38, 93, 140), but four differences are described in Johnson-Laird (1983, esp. ch. 8). I'll argue that the distinction better treated a matter of degree than of kind.
Values are critical for intelligent behavior, since values determine interests, and interests determine relevance. Therefore we address relevance and its role in intelligent behavior in animals and machines. Animals avoid exhaustive enumeration of possibilities by focusing on relevant aspects of the environment, which emerge into the (cognitive) foreground, while suppressing irrelevant aspects, which submerge into the background. Nevertheless, the background is not invisible, and aspects of it can pop into the foreground if background processing deems them potentially relevant. Essential to (...) these ideas are questions of how contexts are switched, which defines cognitive/behavioral episodes, and how new contexts are created, which allows the efficiency of foreground/background processing to be extended to new behaviors and cognitive domains. Next we consider mathematical characterizations of the foreground/background distinction, which we treat as a dynamic separation of the concrete space into (approximately) orthogonal subspaces, which are processed differently. Background processing is characterized by large receptive fields which project into a space of relatively low dimension to accomplish rough categorization of a novel stimulus and its approximate location. Such background processing is partly innate and partly learned, and we discuss possible correlational (Hebbian) learning mechanisms. Foreground processing is characterized by small receptive fields which project into a space of comparatively high dimension to accomplish precise categorization and localization of the stimuli relevant to the context. We also consider mathematical models of valences and affordances, which are an aspect of the foreground. Cells processing foregound information have no fixed meaning (i.e., their meaning is contextual), so it is necessary to explain how the processing accomplished by foreground neurons can be made relative to the context. Thus we consider the properties of several simple mathematical models of how the contextual representation controls foreground processing. We show how simple correlational processes accomplish the contextual separation of foreground from background on the basis of differential reinforcement. That is, these processes account for the contextual separation of the concrete space into disjoint subspaces corresponding to the foreground and background. Since an episode may comprise the activation of several contexts (at varying levels of activity) we consider models, suggested by quantum mechanics, of foreground processing in superposition. That is, the contextual state may be a weighted superposition of several pure contexts, with a corresponding superposition of the foreground representations and the processes operating on them. This leads us to a consideration of the nature and origin of contexts. Although some contexts are innate, many are learned. We discuss a mathematical model of contexts which allows a context to split into several contexts, agglutinate from several contexts, or to constellate out of relatively acontextual processing. Finally, we consider the acontextual processing which occurs when the current context is no longer relevant, and may trigger the switch to another context or the formation of a new context. We relate this to the situation known as "breakdown" in phenomenology. (shrink)
For all animals, color is an indicator of the substance and state of objects, for which purpose reflectance is just one among many relevant optical properties. This broader meaning of color is confirmed by linguistic evidence. Rather than reducing color to a simple physical property, it is more realistic to embrace its full phenomenology.
I discuss neuroscientific and phenomenological arguments in support of Millikan's thesis. I then consider invariance as a unifying theme in perceptual and conceptual tracking, and how invariants may be extracted from the environment. Finally, some wider implications of Millikan's nondescriptionist approach to language are presented, with specific application to color terms.
The idea of a calculus or discrete formal system is central to traditional models of language, knowledge, logic, cognition and computation, and it has provided a unifying framework for these and other disciplines. Nevertheless, research in psychology, neuroscience, philosophy and computer science has shown the limited ability of this model to account for the flexible, adaptive and creative behavior exhibited by much of the animal kingdom. Promising alternate models replace discrete structures by structured continua and discrete rule-following by continuous dynamical (...) processes. However, we believe that progress in these alternate models is retarded by the lack of a unifying theoretical construct analogous to the discrete formal system. In this paper we outline the general characteristics of continuous formal systems (simulacra), which we believe will be a unifying element in future models of language, knowledge, logic, cognition and computation. Therefore, we discuss syntax, semantics, inference and computation in the context of continuous formal systems. In addition, we address an issue that the discrete models were inadequate to address: the gradual emergence of (approximately) discrete structures from a continuum. This is relevant to the emergence of linguistic structures, including semantics and syntax, and to the emergence of rule-like regularities in behavior. (shrink)
The principal problem of consciousness is how brain processes cause subjective awareness. Since this problem involves subjectivity, ordinary scientific methods, applicable only to objective phenomena, cannot be used. Instead, by parallel application of phenomenological and scientific methods, we may establish a correspondence between the subjective and the objective. This correspondence is effected by the construction of a theoretical entity, essentially an elementary unit of consciousness, the intensity of which corresponds to electrochemical activity in a synapse. Dendritic networks correspond to causal (...) dependencies between these subjective units. Therefore, the structure of conscious experience is derived from synaptic connectivity. This parallel phenomenal/neural analysis provides a framework for the investigation of a number of problems, including sensory inversions, the unity of consciousness, and the nature of nonhuman consciousness. (shrink)
The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scientific claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog computation and (...) digital computation are not essentially different, and so arguments such as Searle''s hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scientific matter of fact. (4) A substantive scientific question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it''s all too easy to become ensnared. (shrink)
In this commentary on Harnad's "Grounding Symbols in the Analog World with Neural Nets: A Hybrid Model," the issues of symbol grounding and analog (continuous) computation are separated, it is argued that symbol graounding is as important an issue for analog cognitive models as for digital (discrete) models. The similarities and differences between continuous and discrete computation are discussed, as well as the grounding of continuous representations. A continuous analog of the Chinese Room is presented.