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  1. Modern Thought Dynamics.Ilexa Yardley - 2024 - Https://Medium.Com/the-Circular-Theory/.
  2. Нужда и польза.Andrej Poleev - 2023 - Enzymes 21.
    Насколько дефекты культурного окружения людей сбивают их с толку, поскольку находятся в противоречии с их биологическими, т.е. жизненно важными потребностями, я хотел бы проиллюстрировать на примере сна, приснившегося мне в ночь с 13 на 14 апреля 2023 года.
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  3. Subjectology • Субъектология.Andrej Poleev - 2023 - Enzymes 21.
    Subjectology (from Latin subject and logos) studies the internal states of living and nonliving systems capable of symbolic representation of any real content, i.e. to display sensory perceptible information and to transform it into world pictures, the elements of which are symbols whose meaning or sense is determined in the context of the symbolic representation. Субъектология (от лат. subject и logos) изучает внутренние состояния живых и неживых систем, способных к символической репрезентации какого–либо реального содержания, т.е. к отображению чувственно воспринимаемой информации (...)
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  4. A Defense of Meaning Eliminativism: A Connectionist Approach.Tolgahan Toy - 2022 - Dissertation, Middle East Technical University
    The standard approach to model how human beings understand natural languages is the symbolic, compositional approach according to which the meaning of a complex expression is a function of the meanings of its constituents. In other words, meaning plays a fundamental role in the model. In this work, because of the polysemous, flexible, dynamic, and contextual structure of natural languages, this approach is rejected. Instead, a connectionist model which eliminates the concept of meaning is proposed.
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  5. Schemas versus symbols: A vision from the 90s.Michael A. Arbib - 2021 - Journal of Knowledge Structures and Systems 2 (1):68-74.
    Thirty years ago, I elaborated on a position that could be seen as a compromise between an "extreme," symbol-based AI, and a "neurochemical reductionism" in AI. The present article recalls aspects of the espoused framework of schema theory that, it suggested, could provide a better bridge from human psychology to brain theory than that offered by the symbol systems of A. Newell and H. A. Simon.
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  6. Towards Knowledge-driven Distillation and Explanation of Black-box Models.Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello - 2021 - In Proceedings of the Workshop on Data meets Applied Ontologies in Explainable {AI} {(DAO-XAI} 2021) part of Bratislava Knowledge September {(BAKS} 2021), Bratislava, Slovakia, September 18th to 19th, 2021. CEUR 2998.
    We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to target (...)
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  7. Tokenization: The Key to Philosophy, Physics, and Psychology.Ilexa Yardley - 2021 - Intelligent Design Center.
    Zero and one are the circumference and diameter of an always-conserved circle. Explaining everything in philosophy, physics, and psychology. This produces a completely tokenized 'reality' with important implications for governmental and financial systems. As is, already, happening, in the exploding 'world' of NFT ('crypto' 'currency' in general) based on the statement and the diagram, and the notion of identity (knowledge as power).
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  8. The quantization error in a Self-Organizing Map as a contrast and color specific indicator of single-pixel change in large random patterns.Birgitta Dresp-Langley - 2019 - Neural Networks 120:116-128..
    The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series and in time series of satellite images. Here, the functional properties of the quantization error in SOM are explored further to show that the metric is capable of reliably discriminating between the finest differences in local contrast intensities and contrast signs. While this capability of the QE is (...)
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  9. The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments.Antonio Lieto, Christian Lebiere & Alessandro Oltramari - 2018 - Cognitive Systems Research:1-42.
    In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build arti cial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the (...)
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  10. Computational Dynamics of Natural Information Morphology, Discretely Continuous.Gordana Dodig-Crnkovic - 2017 - Philosophies 2 (4):23.
    This paper presents a theoretical study of the binary oppositions underlying the mechanisms of natural computation understood as dynamical processes on natural information morphologies. Of special interest are the oppositions of discrete vs. continuous, structure vs. process, and differentiation vs. integration. The framework used is that of computing nature, where all natural processes at different levels of organisation are computations over informational structures. The interactions at different levels of granularity/organisation in nature, and the character of the phenomena that unfold through (...)
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  11. Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation.Antonio Lieto, Antonio Chella & Marcello Frixione - 2017 - Biologically Inspired Cognitive Architectures 19:1-9.
    During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are (...)
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  12. HeX and the single anthill: playing games with Aunt Hillary.J. M. Bishop, S. J. Nasuto, T. Tanay, E. B. Roesch & M. C. Spencer - 2015 - In Vincent Müller (ed.), Fundamental Issues of Artificial Intelligence. Springer. pp. 367-389.
    In a reflective and richly entertaining piece from 1979, Doug Hofstadter playfully imagined a conversation between ‘Achilles’ and an anthill (the eponymous ‘Aunt Hillary’), in which he famously explored many ideas and themes related to cognition and consciousness. For Hofstadter, the anthill is able to carry on a conversation because the ants that compose it play roughly the same role that neurons play in human languaging; unfortunately, Hofstadter’s work is notably short on detail suggesting how this magic might be achieved1. (...)
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  13. Review of Fenstad's "Grammar, Geometry & Brain". [REVIEW]Erich Rast - 2014 - Studia Logica 102 (1):219-223.
    In this small book logician and mathematician Jens Erik Fenstad addresses some of the most important foundational questions of linguistics: What should a theory of meaning look like and how might we provide the missing link between meaning theory and our knowledge of how the brain works? The author’s answer is twofold. On the one hand, he suggests that logical semantics in the Montague tradition and other broadly conceived symbolic approaches do not suffice. On the other hand, he does not (...)
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  14. Dietmar Heinke and Eirini Mavritsaki (eds): Computational Modelling in Behavioural Neuroscience. [REVIEW]Juan Felipe Martinez Florez - 2012 - Minds and Machines 22 (1):57-60.
    Dietmar Heinke and Eirini Mavritsaki (eds): Computational Modelling in Behavioural Neuroscience Content Type Journal Article Category Book Review Pages 57-60 DOI 10.1007/s11023-011-9265-8 Authors Juan Felipe Martinez Florez, Institute of Psychology, Universidad del Valle, Campus Universitario Melndez, Ed. 388, Of. 4017, Cali, Colombia Journal Minds and Machines Online ISSN 1572-8641 Print ISSN 0924-6495 Journal Volume Volume 22 Journal Issue Volume 22, Number 1.
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  15. Searching for General Principles in Cognitive Performance: Reply to Commentators.Damian G. Stephen & Guy Van Orden - 2012 - Topics in Cognitive Science 4 (1):94-102.
    The commentators expressed concerns regarding the relevance and value of non-computational non-symbolic explanations of cognitive performance. But what counts as an “explanation” depends on the pre-theoretical assumptions behind the scenes of empirical science regarding the kinds of variables and relationships that are sought out in the first place, and some of the present disagreements stem from incommensurate assumptions. Traditional cognitive science presumes cognition to be a decomposable system of components interacting according to computational rules to generate cognitive performances (i.e., component-dominant (...)
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  16. Qu’est-ce que l’informatique.Franck Varenne - 2009 - Paris: Librairie Philosophique Vrin.
    Que peut bien etre l'informatique pour nous envahir a ce point? Se fondant sur des travaux recents de philosophie de l'informatique, ce livre revient sur la notion de Machine de Turing et sur la These de Church: l'ordinateur peut-il tout simuler? (le vivant, l'esprit). Eclairant les notions de computation et d'abstraction a la lumiere de celles de simulation et d'ontologie, il montre en quoi l'informatique n'est ni simplement une branche des mathematiques, ni une technologie de l'information, mais une technologie des (...)
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  17. What the <0.70, 1.17, 0.99, 1.07> is a Symbol?Istvan S. N. Berkeley - 2008 - Minds and Machines 18 (1):93-105.
    The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...)
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  18. Semantic cognition or data mining?Denny Borsboom & Ingmar Visser - 2008 - Behavioral and Brain Sciences 31 (6):714-715.
    We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.
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  19. Is there a future for AI without representation?Vincent C. Müller - 2007 - Minds and Machines 17 (1):101-115.
    This paper investigates the prospects of Rodney Brooks’ proposal for AI without representation. It turns out that the supposedly characteristic features of “new AI” (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: “New AI” is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents—Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of (...)
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  20. Moving the goal posts: A reply to Dawson and Piercey. [REVIEW]Istvan S. N. Berkeley - 2006 - Minds and Machines 16 (4):471-478.
    Berkeley [Minds Machines 10 (2000) 1] described a methodology that showed the subsymbolic nature of an artificial neural network system that had been trained on a logic problem, originally described by Bechtel and Abrahamsen [Connectionism and the mind. Blackwells, Cambridge, MA, 1991]. It was also claimed in the conclusion of this paper that the evidence was suggestive that the network might, in fact, count as a symbolic system. Dawson and Piercey [Minds Machines 11 (2001) 197] took issue with this latter (...)
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  21. How do connectionist networks compute?Gerard O'Brien & Jonathan Opie - 2006 - Cognitive Processing 7 (1):30-41.
    Although connectionism is advocated by its proponents as an alternative to the classical computational theory of mind, doubts persist about its _computational_ credentials. Our aim is to dispel these doubts by explaining how connectionist networks compute. We first develop a generic account of computation—no easy task, because computation, like almost every other foundational concept in cognitive science, has resisted canonical definition. We opt for a characterisation that does justice to the explanatory role of computation in cognitive science. Next we examine (...)
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  22. Conceptual spaces as a framework for knowledge representation.Peter Gardenfors - 2004 - Mind and Matter 2 (2):9-27.
    The dominating models of information processes have been based on symbolic representations of information and knowledge. During the last decades, a variety of non-symbolic models have been proposed as superior. The prime examples of models within the non-symbolic approach are neural networks. However, to a large extent they lack a higher-level theory of representation. In this paper, conceptual spaces are suggested as an appropriate framework for non- symbolic models. Conceptual spaces consist of a number of 'quality dimensions' that often are (...)
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  23. Refocusing the question: Can there be skillful coping without propositional representations or brain representations? [REVIEW]Hubert L. Dreyfus - 2002 - Phenomenology and the Cognitive Sciences 1 (4):413-25.
  24. Brave new modeling: Cellular automata and artificial neural networks for mastering complexity in economics.Janette Aschenwald, Stefan Fink & Gottfried Tappeiner - 2001 - Complexity 7 (1):39-47.
  25. Understanding neural complexity: A role for reduction. [REVIEW]John Bickle - 2001 - Minds and Machines 11 (4):467-481.
    Psychoneural reduction is under attack again, only this time from a former ally: cognitive neuroscience. It has become popular to think of the brain as a complex system whose theoretically important properties emerge from dynamic, non-linear interactions between its component parts. ``Emergence'' is supposed to replace reduction: the latter is thought to be incapable of explaining the brain qua complex system. Rather than engage this issue at the level of theories of reduction versus theories of emergence, I here emphasize a (...)
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  26. The view from here: The nonsymbolic structure of spatial representation.Patricia S. Churchland, Ilya B. Farber & Will Peterman - 2001 - In Joao Branquinho (ed.), The Foundations of Cognitive Science. Oxford: Clarendon Press.
  27. What the #$*%! is a Subsymbol?István S. N. Berkeley - 2000 - Minds and Machines 10 (1):1-14.
    In 1988, Smolensky proposed that connectionist processing systems should be understood as operating at what he termed the `subsymbolic' level. Subsymbolic systems should be understood by comparing them to symbolic systems, in Smolensky's view. Up until recently, there have been real problems with analyzing and interpreting the operation of connectionist systems which have undergone training. However, recently published work on a network trained on a set of logic problems originally studied by Bechtel and Abrahamsen (1991) seems to offer the potential (...)
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  28. The other hard problem: How to bridge the gap between subsymbolic and symbolic cognition.Axel Cleeremans - 1998 - Behavioral and Brain Sciences 21 (1):22-23.
    The constructivist notion that features are purely functional is incompatible with the classical computational metaphor of mind. I suggest that the discontent expressed by Schyns, Goldstone and Thibaut about fixed-features theories of categorization reflects the growing impact of connectionism, and show how their perspective is similar to recent research on implicit learning, consciousness, and development. A hard problem remains, however: How to bridge the gap between subsymbolic and symbolic cognition.
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  29. Quantification without variables in connectionism.John A. Barnden & Kankanahalli Srinivas - 1996 - Minds and Machines 6 (2):173-201.
    Connectionist attention to variables has been too restricted in two ways. First, it has not exploited certain ways of doing without variables in the symbolic arena. One variable-avoidance method, that of logical combinators, is particularly well established there. Secondly, the attention has been largely restricted to variables in long-term rules embodied in connection weight patterns. However, short-lived bodies of information, such as sentence interpretations or inference products, may involve quantification. Therefore short-lived activation patterns may need to achieve the effect of (...)
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  30. Superpositional connectionism: A reply to Marinov. [REVIEW]Andy Clark - 1993 - Minds and Machines 3 (3):271-81.
    Marinov''s critique I argue, is vitiated by its failure to recognize the distinctive role of superposition within the distributed connectionist paradigm. The use of so-called subsymbolic distributed encodings alone is not, I agree, enough to justify treating distributed connectionism as a distinctive approach. It has always been clear that microfeatural decomposition is both possible and actual within the confines of recognizably classical approaches. When such approaches also involve statistically-driven learning algorithms — as in the case of ID3 — the fundamental (...)
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  31. On the spuriousness of the symbolic/subsymbolic distinction.Marin S. Marinov - 1993 - Minds and Machines 3 (3):253-70.
    The article criticises the attempt to establish connectionism as an alternative theory of human cognitive architecture through the introduction of thesymbolic/subsymbolic distinction (Smolensky, 1988). The reasons for the introduction of this distinction are discussed and found to be unconvincing. It is shown that thebrittleness problem has been solved for a large class ofsymbolic learning systems, e.g. the class oftop-down induction of decision-trees (TDIDT) learning systems. Also, the process of articulating expert knowledge in rules seems quite practical for many important domains, (...)
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  32. Subsymbolic computation and the chinese room.David J. Chalmers - 1992 - In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 25--48.
    More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of (...)
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  33. The Symbolic and Connectionist Paradigms: Closing the Gap.John Dinsmore (ed.) - 1992 - Lawrence Erlbaum.
    This book records the thoughts of researchers -- from both computer science and philosophy -- on resolving the debate between the symbolic and connectionist...
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  34. Symbolic parsing via subsymbolic rules.Stan C. Kwasny & Kanaan A. Faisal - 1992 - In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 209--236.
  35. Gibsonian representations and connectionist symbol-processing: Prospects for unification.Gary Hatfield - 1990 - Psychological Research 52:243-52.
    Not long ago the standard view in cognitive science was that representations are symbols in an internal representational system or language of thought and that psychological processes are computations defined over such representations. This orthodoxy has been challenged by adherents of functional analysis and by connectionists. Functional analysis as practiced by Marr is consistent with an analysis of representation that grants primacy to a stands for conception of representation. Connectionism is also compatible with this notion of representation; when conjoined with (...)
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  36. Treating connectionism properly: Reflections on Smolensky.Jay F. Rosenberg - 1990 - Psychological Research 52.
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  37. On the proper treatment of connectionism.Paul Smolensky - 1988 - Behavioral and Brain Sciences 11 (1):1-23.
    A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...)
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  38. Connectionist, symbolic, and the brain.Paul Smolensky - 1987 - AI Review 1:95-109.
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  39. Artificial intelligence: Subcognition as computation.Douglas R. Hofstadter - 1983 - In Fritz Machlup (ed.), The Study of Information: Interdisciplinary Messages. Wiley.
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  40. The Study of Information: Interdisciplinary Messages.Fritz Machlup (ed.) - 1983 - Wiley.
    A collection of articles by leading authorities presenting an interdisciplinary approach to key issues of information science. Debates how information science affects various fields.
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  41. A subsymbolic symbolic model for learning sequential navigation.Ron Sun Todd Peterson - unknown
    To deal with reactive sequential decision tasks we present a learning model Clarion which is a hybrid connectionist model consisting of both localist and dis tributed representations based on the two level ap proach proposed in Sun The model learns and utilizes procedural and declarative knowledge tapping into the synergy of the two types of processes It uni es neural reinforcement and symbolic methods to perform on line bottom up learning Experiments in various situations are reported that shed light on (...)
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