Online research in philosophy

We introduce a general framework for solving the problem of a computer collecting and combining information from various sources. Unlike previous approaches to this problem, in our framework the sources are allowed to provide information about complex formulae too. This is enabled by the use of a new tool — non-deterministic logical matrices. We also consider several alternative plausible assumptions concerning the framework. These assumptions lead to various logics. We provide strongly sound and complete proof systems for all the basic logics induced in this way.

It's very interesting to see neurophysiological evidence brought to bear on the puzzling question of conscious experience. Many have observed that information-processing models of cognition seem to leave consciousness untouched; it is natural to hope that turning to neurophysiology might lead us to the Holy Grail. Still, I think there are reasons to be skeptical. There are good reasons to suppose that neurophysiological investigation contributes to cognitive explanation at best in virtue of constraining the information-processing structure of cognition. Of course this is a very large and significant role for it to play, but it may be over-optimistic to suppose that it can play some further explanatory role, taking us where information-processing theories cannot. If so, then neurophysiological accounts will be no more and no less successful at dealing with consciousness than information-processing accounts are.

Over the past few years numerous proposals have appeared that attempt to characterize consciousness in terms of what could be called its computational correlates: Principles of information processing with which to characterize the differences between conscious and unconscious processing. Proposed computational correlates include architectural specialization (such as the involvement of specific regions of the brain in conscious processing), properties of representations (such as their stability in time or their strength), and properties of specific processes (such as resonance, synchrony, interactivity, or information integration). In exactly the same way as one can engage in a search for the neural correlates of consciousness, one can thus search for the computational correlates of consciousness. The most direct way of doing is to contrast models of conscious versus unconscious information processing. In this paper, I review these developments and illustrate how computational modeling of specific cognitive processes can be useful in exploring and in formulating putative computational principles through which to capture the differences between conscious and unconscious cognition. What can be gained from such approaches to the problem of consciousness is an understanding of the function it plays in information processing and of the mechanisms that subtend it. Here, I suggest that the central function of consciousness is to make it possible for cognitive agents to exert ?exible, adaptive control over behavior. From this perspective, consciousness is best characterized as involving (1) a graded continuum de?ned over quality of representation, such that availability to consciousness and to cognitive control correlates with properties of representation, and (2) the implication of systems of meta-representations.

The productivity of (human) information processing as an economic activity is a question that is raising some interest. Using Marschak's evaluation framework, Radner and Stiglitz have shown that, under certain conditions, the production function of this activity has increasing marginal returns in its initial stage. This paper shows that, under slightly different conditions, this information processing function has repeated convexities with ongoing processing activity. Even for smooth changes in the signals' likelihoods, the function is only piecewise smooth with non-differentiable convexities at points of conditional changes of action. For linear likelihood functions the processing value proves to be piecewise linear with convexities at these levels.

The naturalistic voluntary control (VC) theory explains free will and consciousness in terms of each other. It is central to free voluntary control of action that one can control both what one is conscious of, and also what one is not conscious of. Furthermore, the specific cognitive ability or skill involved in voluntarily controlling whether information is processed consciously or unconsciously can itself be used to explain consciousness. In functional terms, it is whatever kind of cognitive processing occurs when a conscious state is voluntarily chosen. This leads to a bivalent view of cognitive processing in which there is voluntary choice either of non-routine (conscious) or routine (unconscious) kinds of processing. On this VC account, consciousness could not exist without its being possible to voluntarily choose a non-routine kind of processing.

It's very interesting to see neurophysiological evidence brought to bear on the puzzling question of conscious experience. Many have observed that information-processing models of cognition seem to leave consciousness untouched; it is natural to hope that turning to neurophysiology might lead us to the Holy Grail. Still, I think there are reasons to be skeptical. There are good reasons to suppose that neurophysiological investigation contributes to cognitive explanation at best in virtue of constraining the information-processing structure of cognition. Of course this is a very large and significant role for it to play, but it may be over-optimistic to suppose that it can play some further explanatory role, taking us where information-processing theories cannot. If so, then neurophysiological accounts will be no more and no less successful at dealing with consciousness than information-processing accounts are.

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.

The study of preconscious versus conscious processing has an extensive history in cognitive psychology, dating back to the writings of William James. Much of the experimental work on this issue has focused on perception, conceived of as input analysis, and on the relation of consciousness to attentional processing. The present paper examines when input analysis becomes conscious from the perspectives of cognitive modelling, methodology, and a more detailed understanding of what is meant by "conscious processing." Current evidence suggests that perception becomes conscious at a late-arising stage of focal-attentive processing concerned with information integration and dissemination. Reliable criteria for determining when perception becomes conscious combine the evidence of "first-person," phenomenological reports with "third-person" functional dissociations between preconscious and conscious processing. There are three, distinct senses in which a process may be said to be "conscious." It might be "conscious" (a) in the sense that one is conscious of the process, (b) in the sense that the operation of the process is accompanied by consciousness (of its results) and (c) in the sense that consciousness enters into or causally influences the process. Consciousness of familiar stimuli, rather than entering into input analysis, appears to follow it, in human information processing. Processes closely associated with the appearance of consciousness such as information integration and dissemination appear to operate unconsciously. Consequently, perception appears to be "conscious" only in sense (b).

Investigations of the function of consciousness in human information processing have focused mainly on two questions: (1) where does consciousness enter into the information processing sequence and (2) how does conscious processing differ from preconscious and unconscious processing. Input analysis is thought to be initially "preconscious," "pre-attentive," fast, involuntary, and automatic. This is followed by "conscious," "focal-attentive" analysis which is relatively slow, voluntary, and flexible. It is thought that simple, familiar stimuli can be identified preconsciously, but conscious processing is needed to identify complex, novel stimuli. Conscious processing has also been thought to be necessary for choice, learning and memory, and the organization of complex, novel responses, particularly those requiring planning, reflection, or creativity.

Cognitive science uses the notion of computational information processing to explain cognitive information processing. Some philosophers have argued that anything can be described as doing computational information processing; if so, it is a vacuous notion for explanatory purposes.An attempt is made to explicate the notions of cognitive information processing and computational information processing and to specify the relationship between them. It is demonstrated that the resulting notion of computational information processing can only be realized in a restrictive class of dynamical systems called physical notational systems (after Goodman's theory of notationality), and that the systems generally appealed to by cognitive science-physical symbol systems-are indeed such systems. Furthermore, it turns out that other alternative conceptions of computational information processing, Fodor's (1975) Language of Thought and Cummins' (1989) Interpretational Semantics appeal to substantially the same restrictive class of systems.