Online research in philosophy

SNePS, the Semantic Network Processing System 45, 54], has been designed to be a system for representing the beliefs of a natural-language-using intelligent system (a \cognitive agent"). It has always been the intention that a SNePS-based \knowledge base" would ultimatelybe built, not by a programmeror knowledge engineer entering representations of knowledge in some formallanguage or data entry system, but by a human informing it using a natural language (NL) (generally supposed to be English), or by the system reading books or articles that had been prepared for human readers. Because of this motivation, the criteria for the development of SNePS have included: it should be able to represent anything and everything expressible in NL; it should be able to represent generic, as well as speci c information; it should be able to use the generic and the speci c information to reason and infer information implied by what it has been told; it cannot count on any particular order among the pieces of information it is given; it must continue to act reasonably even if the information it is given includes circular de nitions, recursive rules, and inconsistent information.

For many people, consciousness is one of the defining characteristics of mental states. Thus, it is quite surprising that consciousness has, until quite recently, had very little role to play in the cognitive sciences. Three very popular multi-authored overviews of cognitive science, Stillings et al. [33], Posner [26], and Osherson et al. [25], do not have a single reference to consciousness in their indexes. One reason this seems surprising is that the cognitive revolution was, in large part, a repudiation of behaviorism's proscription against appealing to inner mental events. When researchers turned to consider inner mental events, one might have expected them to turn to conscious states of mind. But in fact the appeals were to postulated inner events of information processing. The model for many researchers of such information processing is the kind of transformation of symbolic structures that occurs in a digital computer. By positing procedures for performing such transformation of incoming information, cognitive scientists could hope to account for the performance of cognitive agents. Artificial intelligence, as a central discipline of cognitive science, has seemed to impose some of the toughest tests on the ability to develop information processing accounts of cognition: it required its researchers to develop running programs whose performance one could compare with that of our usual standard for cognitive agents, human beings. As a result of this focus, for AI researchers to succeed, at least in their primary task, they did not need to attend to consciousness; they simply had to design programs that behaved appropriately (no small task in itself!). This is not to say that conscious was totally ignored by artificial intelligence researchers. Some aspect of our conscious experience seemed critical to the success of any information processing model. For example, conscious agents exhibit selective attention. Some information received through their senses is attended to; much else is ignored..

The Language of Thought program has a suicidal edge. Jerry Fodor, of all people, has argued that although LOT will likely succeed in explaining modular processes, it will fail to explain the central system, a subsystem in the brain in which information from the different sense modalities is integrated, conscious deliberation occurs, and behavior is planned. A fundamental characteristic of the central system is that it is “informationally unencapsulated” -- its operations can draw from information from any cognitive domain. The domain general nature of the central system is key to human reasoning; our ability to connect apparently unrelated concepts enables the creativity and flexibility of human thought, as does our ability to integrate material across sensory divides. The central system is the holy grail of cognitive science: understanding higher cognitive function is crucial to grasping how humans reach their highest intellectual achievements. But according to Fodor, the founding father of the LOT program and the related Computational Theory of Mind (CTM), the holy grail is out of reach: the central system is likely to be non-computational (Fodor 1983, 2000, 2008). Cognitive scientists working on higher cognitive function should abandon their efforts. Research should be limited to the modules, which for Fodor rest at the sensory periphery (2000).1 Cognitive scientists who work in the symbol processing tradition outside of philosophy would reject this pessimism, but ironically, within philosophy itself, this pessimistic streak has been very influential, most likely because it comes from the most well-known proponent of LOT and CTM. Indeed, pessimism about centrality has become assimilated into the mainstream conception of LOT. (Herein, I refer to a LOT that appeals to pessimism about centrality as the “standard LOT”). I imagine this makes the standard LOT unattractive to those philosophers with a more optimistic approach to what cognitive science can achieve..

Kognitionswissenschaften – in einem weiten Sinn – sind einfach alle die Wissen- schaften, die sich mit der Analyse und Erklärung kognitiver Leistungen und Fähig- keiten befassen. Wenn man jedoch von der Kognitionswissenschaft im Singular spricht, dann ist in der Regel mehr gemeint. Für die Kognitionswissenschaft ist nicht nur ein bestimmter Forschungsgegenstand charakteristisch, sondern auch ein be- stimmter Erklärungsansatz: der Informationsverarbeitungsansatz. Stillings et al. z.B. schreiben gleich auf der ersten Seite ihres 1987 erschienenen Buches Cognitive Science – An Introduction: „Cognitive scientists view the human mind as a complex system that receives, stores, retrieves, transforms, and transmits information.“ (Stil- lings et al. 1987: 1) Der Informationsverarbeitungsansatz führt jedoch sofort weiter zum Symbolverarbeitungsansatz. Denn offenbar kann ein System nur dann Informa- tionen empfangen, speichern und verarbeiten, wenn es über ein System von internen Repräsentationen oder Symbolen verfügt, über eine interne Sprache, in der diese In- formationen codiert sind. Zumindest ist das eine naheliegende Überlegung, die Peter Hacker so formuliert hat: „... if information is received, encoded, decoded, inter- preted and provides grounds for making plans, then there must be a language or sys- tem of representation in which this is all done.“ (Hacker 1987: 486f.) In der Tat ist die Annahme, daß es in kognitiven Systemen so etwas wie ein System interner Rep- räsentationen bzw. eine Sprache des Geistes1 gibt, die zentrale Grundannahme vieler neuerer Arbeiten in den Bereichen der Kognitionspsychologie und der kognitiven Neurobiologie. Für diese Wissenschaften hat diese Annahme den Status einer empi-.

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.

Shanker & King argue for a shift in the focus of ape language research from an emphasis on information processing to a dynamic systems approach. We differ from these authors in our understanding of how this “new paradigm” emerged and in our perceptions of its limitations. We see information processing and dynamic systems as complementary approaches in the study of communication.

In recent years we have seen a dramatic shift, in several different areas of communication studies, from an information-theoretic to a dynamic systems paradigm. In an information processing system, communication, whether between cells, mammals, apes, or humans, is said to occur when one organism encodes information into a signal that is transmitted to another organism that decodes the signal. In a dynamic system, all of the elements are continuously interacting with and changing in respect to one another, and an aggregate pattern emerges from this mutual co-action. Whereas the information-processing paradigm looks at communication as a linear, binary sequence of events, the dynamic systems paradigm looks at the relation between behaviors and how the whole configuration changes over time. One of the most dramatic examples of the significance of shifting from an information processing to a dynamic systems paradigm can be found in the debate over the interpretation of recent advances in ape language research (ALR). To some extent, many of the early ALR studies reinforced the stereotype that animal communication is functional and stimulus bound, precisely because they were based on an information-processing paradigm that promoted a static model of communicative development. But Savage-Rumbaugh's recent results with bonobos has introduced an entirely new dimension into this debate. Shifting the terms of the discussion from an information-processing to a dynamic systems paradigm not only highlights the striking differences between Savage-Rumbaugh's research and earlier ALR studies, but further, it sheds illuminating light on the factors that underpin the development of communication skills in great apes and humans, and the relationship between communicative development and the development of language. Key Words: apes; ape language research (ALR); brain development; co-regulation; communication; dynamic systems; language development; symbols.

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.

Whatever else language may be, it is complex and multifaceted. Shanker & King (S&K) have tried to contrast a dynamic interactive view of language with an information processing view. I take issue with two main claims: first, that the dynamic interactive view of language is a “new paradigm” in either animal research or human language studies; and second, that the dynamic systems language-as-dance view of language is in any way incompatible with an information-processing view of language. That some information is defined in coregulated social interaction guarantees the dancing. That all information is composed of relevant differences guarantees the information processing.