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Journal articles on the topic 'Cognitive computation'

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Published: 10 December 2022

Last updated: 28 January 2023

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1

Magnani, Lorenzo. "Eccentric Computational Embodiments: Cognitive Domestication of External Entities." Proceedings 47, no. 1 (May 15, 2020): 36. http://dx.doi.org/10.3390/proceedings2020047036.

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Eco-cognitive computationalism sees computation in context, adopting the intellectual visions advanced by the cognitive science perspectives on embodied, situated, and distributed cognition. It is in this framework that we can fruitfully study the relevance in recent computer science devoted to the simplification of cognitive and motor tasks generated in organic entities by the morphological aspects. Ignorant bodies can be cognitively “domesticated” to become useful “mimetic bodies'', which originate eccentric new computational embodiments capable of rendering an involved computation simpler and more efficient. On the basis of these considerations, we will also see how the concept of computation changes, being related to historical and contextual factors, so that the “emergence'' of new kinds of computations can be epistemologically clarified, such as the one regarding morphological computation. Finally, my presentation will introduce and discuss the concept of overcomputationalism, as intertwined with the traditional concepts of pancognitivism, paniformationalism, and pancomputationalism, seeing them in a more naturalized intellectual disposition, more appropriate to the aim of bypass ontological or metaphysical overstatements.

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2

Magnani, Lorenzo. "Eccentric Computational Embodiments: Cognitive Domestication of External Entities." Proceedings 47, no. 1 (May 15, 2020): 36. http://dx.doi.org/10.3390/proceedings47010036.

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Abstract:

Eco-cognitive computationalism sees computation in context, adopting the intellectual visions advanced by the cognitive science perspectives on embodied, situated, and distributed cognition. It is in this framework that we can fruitfully study the relevance in recent computer science devoted to the simplification of cognitive and motor tasks generated in organic entities by the morphological aspects. Ignorant bodies can be cognitively “domesticated” to become useful “mimetic bodies'', which originate eccentric new computational embodiments capable of rendering an involved computation simpler and more efficient. On the basis of these considerations, we will also see how the concept of computation changes, being related to historical and contextual factors, so that the “emergence'' of new kinds of computations can be epistemologically clarified, such as the one regarding morphological computation. Finally, my presentation will introduce and discuss the concept of overcomputationalism, as intertwined with the traditional concepts of pancognitivism, paniformationalism, and pancomputationalism, seeing them in a more naturalized intellectual disposition, more appropriate to the aim of bypass ontological or metaphysical overstatements.

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3

Bishop, John Mark. "A Cognitive Computation Fallacy? Cognition, Computations and Panpsychism." Cognitive Computation 1, no. 3 (May 30, 2009): 221–33. http://dx.doi.org/10.1007/s12559-009-9019-6.

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4

Magnani, Lorenzo. "Disseminated Computation, Cognitive Domestication of New Ignorant Substrates, and Overcomputationalization." Proceedings 47, no. 1 (May 7, 2020): 29. http://dx.doi.org/10.3390/proceedings2020047029.

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Eco-cognitive computationalism considers computation in the context of following some of the main tenets advanced by the recent cognitive science views on embodied, situated and distributed cognition. It is in the framework of this eco-cognitive perspective that we can usefully analyze the recent attention in computer science devoted to the importance of the simplification of cognitive and motor tasks caused in organic entities by the morphological features: ignorant bodies can be domesticated to become useful “mimetic bodies”, that is to be able to render an intertwined computation simpler, resorting to that “simplexity” of animal embodied cognition, which represents one of the main qualities of organic agents. Through eco-cognitive computationalism we can clearly acknowledge that the concept of computation changes, depending on historical and contextual causes and we can build an epistemological view that illustrates the “emergence” of new kinds of computations, such as the one regarding morphological computation. This new perspective shows how the computational domestication of ignorant entities can originate new unconventional cognitive embodiments. I also introduce the concept of overcomputationalism, showing that my proposed framework helps us see the related concepts of pancognitivism, paniformationalism and pancomputationalism in a more naturalized and prudent perspective, avoiding the excess of old-fashioned ontological or metaphysical overstatements.

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5

Magnani, Lorenzo. "Disseminated Computation, Cognitive Domestication of New Ignorant Substrates, and Overcomputationalization." Proceedings 47, no. 1 (May 7, 2020): 29. http://dx.doi.org/10.3390/proceedings47010029.

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Abstract:

Eco-cognitive computationalism considers computation in the context of following some of the main tenets advanced by the recent cognitive science views on embodied, situated and distributed cognition. It is in the framework of this eco-cognitive perspective that we can usefully analyze the recent attention in computer science devoted to the importance of the simplification of cognitive and motor tasks caused in organic entities by the morphological features: ignorant bodies can be domesticated to become useful “mimetic bodies”, that is to be able to render an intertwined computation simpler, resorting to that “simplexity” of animal embodied cognition, which represents one of the main qualities of organic agents. Through eco-cognitive computationalism we can clearly acknowledge that the concept of computation changes, depending on historical and contextual causes and we can build an epistemological view that illustrates the “emergence” of new kinds of computations, such as the one regarding morphological computation. This new perspective shows how the computational domestication of ignorant entities can originate new unconventional cognitive embodiments. I also introduce the concept of overcomputationalism, showing that my proposed framework helps us see the related concepts of pancognitivism, paniformationalism and pancomputationalism in a more naturalized and prudent perspective, avoiding the excess of old-fashioned ontological or metaphysical overstatements.

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6

Faix, Marvin, Emmanuel Mazer, Raphaël Laurent, Mohamad Othman Abdallah, Ronan Le Hy, and Jorge Lobo. "Cognitive Computation." International Journal of Software Science and Computational Intelligence 9, no. 3 (July 2017): 37–58. http://dx.doi.org/10.4018/ijssci.2017070103.

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Probabilistic programming allows artificial systems to better operate with uncertainty, and stochastic arithmetic provides a way to carry out approximate computations with few resources. As such, both are plausible models for natural cognition. The authors' work on the automatic design of probabilistic machines computing soft inferences, with an arithmetic based on stochastic bitstreams, allowed to develop the following compilation toolchain: given a high-level description of some general problem, formalized as a Bayesian Program, the toolchain automatically builds a low-level description of an electronic circuit computing the corresponding probabilistic inference. This circuit can then be implemented and tested on reconfigurable logic. This paper describes two circuits as validating examples. The first one implements a Bayesian filter solving the problem of Pseudo Noise sequence acquisition in telecommunications. The second one implements decision making in a sensorimotor system: it allows a simple robot to avoid obstacles using Bayesian sensor fusion.

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7

Taylor, J. G. "Cognitive Computation." Cognitive Computation 1, no. 1 (January 23, 2009): 4–16. http://dx.doi.org/10.1007/s12559-008-9001-8.

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8

Smolensky, Paul. "Symbolic functions from neural computation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1971 (July 28, 2012): 3543–69. http://dx.doi.org/10.1098/rsta.2011.0334.

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Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and formulated computational systems in which meaningful concepts are encoded by symbols which are the objects of computation. Cognition has been carved into parts, each a function defined over such symbols. This paper reports on a research program aimed at computing these symbolic functions without computing over the symbols. Symbols are encoded as patterns of numerical activation over multiple abstract neurons, each neuron simultaneously contributing to the encoding of multiple symbols. Computation is carried out over the numerical activation values of such neurons, which individually have no conceptual meaning. This is massively parallel numerical computation operating within a continuous computational medium. The paper presents an axiomatic framework for such a computational account of cognition, including a number of formal results. Within the framework, a class of recursive symbolic functions can be computed. Formal languages defined by symbolic rewrite rules can also be specified, the subsymbolic computations producing symbolic outputs that simultaneously display central properties of both facets of human language: universal symbolic grammatical competence and statistical, imperfect performance.

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9

Magnani, Lorenzo. "Conceptualizing Machines in an Eco-Cognitive Perspective." Philosophies 7, no. 5 (August 25, 2022): 94. http://dx.doi.org/10.3390/philosophies7050094.

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Eco-cognitive computationalism explores computing in context, adhering to some of the key ideas presented by modern cognitive science perspectives on embodied, situated, and distributed cognition. First of all, when physical computation is seen from the perspective of the ecology of cognition it is possible to clearly understand the role Turing assigned to the process of “education” of the machine, paralleling it to the education of human brains, in the invention of the Logical Universal Machine. It is this Turing’s emphasis on education that furnishes the justification of the conceptualization of machines as “domesticated ignorant entities”, that is proposed in this article. I will show that conceptualizing machines as dynamically active in distributed physical entities of various kinds suitably transformed so that data can be encoded and decoded to obtain appropriate results sheds further light on my eco-cognitive perspective. Furthermore, it is within this intellectual framework that I will usefully analyze the recent attention in computer science devoted to the importance of the simplification of cognitive and motor tasks caused in organic entities thanks to morphological features: ignorant bodies can be computationally domesticated to make an intertwined computation simpler, relying on the “simplexity” of animal embodied cognition, which represents one of the main qualities of organic agents. Finally, eco-cognitive computationalism allows us to clearly acknowledge that the concept of computation evolves over time as a result of historical and contextual factors, and we can construct an epistemological view that depicts the “emergence” of new types of computations that exploit new substrates. This new viewpoint demonstrates how the computational domestication of ignorant entities might result in the emergence of novel unconventional cognitive embodiments.

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10

Dodig-Crnkovic, Gordana. "Cognition as Morphological/Morphogenetic Embodied Computation In Vivo." Entropy 24, no. 11 (October 31, 2022): 1576. http://dx.doi.org/10.3390/e24111576.

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Cognition, historically considered uniquely human capacity, has been recently found to be the ability of all living organisms, from single cells and up. This study approaches cognition from an info-computational stance, in which structures in nature are seen as information, and processes (information dynamics) are seen as computation, from the perspective of a cognizing agent. Cognition is understood as a network of concurrent morphological/morphogenetic computations unfolding as a result of self-assembly, self-organization, and autopoiesis of physical, chemical, and biological agents. The present-day human-centric view of cognition still prevailing in major encyclopedias has a variety of open problems. This article considers recent research about morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, free energy principle, cognition as Bayesian learning, active inference, and related topics, offering new theoretical and practical perspectives on problems inherent to the old computationalist cognitive models which were based on abstract symbol processing, and unaware of actual physical constraints and affordances of the embodiment of cognizing agents. A better understanding of cognition is centrally important for future artificial intelligence, robotics, medicine, and related fields.

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11

Sprevak, Mark. "Computation and cognitive science." Studies in History and Philosophy of Science Part A 41, no. 3 (September 2010): 223–26. http://dx.doi.org/10.1016/j.shpsa.2010.07.011.

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12

Hussain, Amir. "Cognitive Computation: An Introduction." Cognitive Computation 1, no. 1 (February 20, 2009): 1–3. http://dx.doi.org/10.1007/s12559-009-9013-z.

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13

van der Velde, Frank, and Marc de Kamps. "Toward a synthesis of dynamical systems and classical computation." Behavioral and Brain Sciences 21, no. 5 (October 1998): 652–53. http://dx.doi.org/10.1017/s0140525x98501732.

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Cognitive agents are dynamical systems but not quantitative dynamical systems. Quantitative systems are forms of analogue computation, which is physically too unreliable as a basis for cognition. Instead, cognitive agents are dynamical systems that implement discrete forms of computation. Only such a synthesis of discrete computation and dynamical systems can provide the mathematical basis for modeling cognitive behavior.

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14

Arsenijevic, Boban. "From Spatial Cognition to Language." Biolinguistics 2, no. 1 (March 30, 2008): 003–23. http://dx.doi.org/10.5964/bioling.8615.

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The evolution of language has been linked in the recent research to the evolution of a number of different capacities, from the theory of mind to the type-recursive computation. In this paper, I examine the possibility that language has evolved from the capacity of spatial computation. Similarities, but also certain differences, between the two capacities are outlined and discussed, including the following. From the aspect of neuro-cognitive science, it cannot stay unnoticed that some of the central computations both in the language faculty and in the spatial cognition are located in the same brain area - the hippocampus. On the cognitive side, direct counterparts of the central components of the language faculty can be identified within the domain of spatial cognition. In particular, this is argued for the recursive computation and its categorial base, for the use of two types of information, the descriptive and the geometric, in establishing reference, for the process of update of a mental representation of the relevant context based on the sensory input, and for several other aspects. Since humans and other vertebrates have spatial cognitive capacities of approximately the same nature and complexity, this narrows down the set of possible answers to the question what distinguishes humans and their language faculty from the cognitive capacities present in other species. The hypothesis proposed is that this difference is three-fold, and involves: 1) the domain-general use of the otherwise similar computational capacities as opposed to the use in animals which is bound to the spatial domain, and perhaps one or two others; 2) the serialization of the computations of the descriptive and the geometric means of reference in humans, resulting in a combined aggregate information, as opposed to a strict separation in other animals and 3) the increased use and importance of the update of the relevant mental representation of the context by a group of humans rather than just an individual.

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15

Müller, Vincent C., and Matej Hoffmann. "What Is Morphological Computation? On How the Body Contributes to Cognition and Control." Artificial Life 23, no. 1 (February 2017): 1–24. http://dx.doi.org/10.1162/artl_a_00219.

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The contribution of the body to cognition and control in natural and artificial agents is increasingly described as “offloading computation from the brain to the body,” where the body is said to perform “morphological computation.” Our investigation of four characteristic cases of morphological computation in animals and robots shows that the “offloading” perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that facilitates control, (2) morphology that facilitates perception, and the rare cases of (3) morphological computation proper, such as reservoir computing, where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control—how it contributes to the overall orchestration of intelligent behavior.

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16

Dodig-Crnkovic, Gordana. "Morphological, Natural, Analog, and Other Unconventional Forms of Computing for Cognition and Intelligence." Proceedings 47, no. 1 (May 7, 2020): 30. http://dx.doi.org/10.3390/proceedings2020047030.

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According to the currently dominant view, cognitive science is a study of mind and intelligence focused on computational models of knowledge in humans. It is described in terms of symbol manipulation over formal language. This approach is connected with a variety of unsolvable problems, as pointed out by Thagard. In this paper, I argue that the main reason for the inadequacy of the traditional view of cognition is that it detaches the body of a human as the cognizing agent from the higher-level abstract knowledge generation. It neglects the dynamical aspects of cognitive processes, emotions, consciousness, and social aspects of cognition. It is also uninterested in other cognizing agents such as other living beings or intelligent machines. Contrary to the traditional computationalism in cognitive science, the morphological computation approach offers a framework that connects low-level with high-level approaches to cognition, capable of meeting challenges listed by Thagard. To establish this connection, morphological computation generalizes the idea of computation from symbol manipulation to natural/physical computation and the idea of cognition from the exclusively human capacity to the capacity of all goal-directed adaptive self-reflective systems, living organisms as well as robots. Cognition is modeled as a layered process, where at the lowest level, systems acquire data from the environment, which in combination with the already stored data in the morphology of an agent, presents the basis for further structuring and self-organization of data into information and knowledge.

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17

Dodig-Crnkovic, Gordana. "Morphological, Natural, Analog, and Other Unconventional Forms of Computing for Cognition and Intelligence." Proceedings 47, no. 1 (May 7, 2020): 30. http://dx.doi.org/10.3390/proceedings47010030.

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According to the currently dominant view, cognitive science is a study of mind and intelligence focused on computational models of knowledge in humans. It is described in terms of symbol manipulation over formal language. This approach is connected with a variety of unsolvable problems, as pointed out by Thagard. In this paper, I argue that the main reason for the inadequacy of the traditional view of cognition is that it detaches the body of a human as the cognizing agent from the higher-level abstract knowledge generation. It neglects the dynamical aspects of cognitive processes, emotions, consciousness, and social aspects of cognition. It is also uninterested in other cognizing agents such as other living beings or intelligent machines. Contrary to the traditional computationalism in cognitive science, the morphological computation approach offers a framework that connects low-level with high-level approaches to cognition, capable of meeting challenges listed by Thagard. To establish this connection, morphological computation generalizes the idea of computation from symbol manipulation to natural/physical computation and the idea of cognition from the exclusively human capacity to the capacity of all goal-directed adaptive self-reflective systems, living organisms as well as robots. Cognition is modeled as a layered process, where at the lowest level, systems acquire data from the environment, which in combination with the already stored data in the morphology of an agent, presents the basis for further structuring and self-organization of data into information and knowledge.

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18

Crutchfield, James P. "Dynamical embodiments of computation in cognitive processes." Behavioral and Brain Sciences 21, no. 5 (October 1998): 635. http://dx.doi.org/10.1017/s0140525x98291734.

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Dynamics is not enough for cognition, nor it is a substitute for information-processing aspects of brain behavior. Moreover, dynamics and computation are not at odds, but are quite compatible. They can be synthesized so that any dynamical system can be analyzed in terms of its intrinsic computational components.

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19

Esposito, Anna, Alessandro Vinciarelli, Simon Haykin, Amir Hussain, and Marcos Faundez-Zanuy. "Cognitive Computation Special Issue on Cognitive Behavioural Systems." Cognitive Computation 3, no. 3 (August 20, 2011): 417–18. http://dx.doi.org/10.1007/s12559-011-9107-2.

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20

Fiorini, Rodolfo A. "Towards Advanced Quantum Cognitive Computation." International Journal of Software Science and Computational Intelligence 9, no. 1 (January 2017): 1–19. http://dx.doi.org/10.4018/ijssci.2017010101.

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Computational information conservation theory (CICT) can help us to develop competitive applications and even advanced quantum cognitive computational application and systems towards deep computational cognitive intelligence. CICT new awareness of a discrete HG (hyperbolic geometry) subspace (reciprocal space, RS) of coded heterogeneous hyperbolic structures, underlying the familiar Q Euclidean (direct space, DS) system surface representation can open the way to holographic information geometry (HIG) to recover lost coherence information in system description and to develop advanced quantum cognitive systems. This paper is a relevant contribution towards an effective and convenient “Science 2.0” universal computational framework to achieve deeper cognitive intelligence at your fingertips and beyond.

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21

Barrett, H. Clark. "Enzymatic Computation and Cognitive Modularity." Mind and Language 20, no. 3 (June 2005): 259–87. http://dx.doi.org/10.1111/j.0268-1064.2005.00285.x.

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22

DONG, ANDY. "Special Issue: Design computing and cognition." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, no. 4 (November 2005): 227–28. http://dx.doi.org/10.1017/s0890060405050158.

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The field of research in design computing and cognition focuses on computational theories and systems that enact design. Design computing and cognition produces a unifying framework to model and explain design beyond the description of “design computing and cognition,” as in “design computing” and “design cognition” as two cognate disciplines. Research in design computing and cognition recognizes not only the essential relationship between human cognitive processes as models of computation but also how models of computation inspire conceptual realizations of human cognition in design. The articles in this Special Issue address the concomitant key areas of research in design computing and cognition: computational models of design, computational representations in design, computational design systems, and design cognition. The computationally inspired perspectives, metaphors, models, and theories that the papers deliver create a base for computing and cognition to (re)shape design practice and its role in design science and inquiry.

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23

Clark, Andy, Zenon W. Pylyshyn, and Alvin T. Goldman. "Computation and Cognition: Toward a Foundation for Cognitive Science." Philosophical Quarterly 38, no. 153 (October 1988): 526. http://dx.doi.org/10.2307/2219716.

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24

Ward, Nigel. "Computation and cognition: Toward a foundation for cognitive science." Artificial Intelligence 33, no. 3 (November 1987): 415–17. http://dx.doi.org/10.1016/0004-3702(87)90045-2.

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25

Mackworth, Alan K. "Computation and cognition: Toward a fundation for cognitive science." Artificial Intelligence 38, no. 2 (March 1989): 239–40. http://dx.doi.org/10.1016/0004-3702(89)90060-x.

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26

Stefik, Mark. "Computation and cognition: Toward a foundation of cognitive science." Artificial Intelligence 38, no. 2 (March 1989): 241–47. http://dx.doi.org/10.1016/0004-3702(89)90061-1.

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27

Piccinini, Gualtiero, and Sonya Bahar. "Neural Computation and the Computational Theory of Cognition." Cognitive Science 37, no. 3 (November 5, 2012): 453–88. http://dx.doi.org/10.1111/cogs.12012.

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28

HAMANN, HEIKO, and HEINZ WÖRN. "EMBODIED COMPUTATION." Parallel Processing Letters 17, no. 03 (September 2007): 287–98. http://dx.doi.org/10.1142/s0129626407003022.

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The traditional computational devices and models, such as the von Neumann architecture or the Turing machine, are strongly influenced by concepts of central control and perfection. The standard models of computation seem to cover the reality of computation only partially and lack, in particular, in the ability to describe more natural forms of computation. In this paper we propose the concept of embodied computation, a straight forward advancement of well known concepts such as amorphous computing, emergent phenomena and embodied cognitive science. Many embodied microscopic computational devices form a single macroscopic device of embodied computation. The solution to computational problems emerges from a huge amount of local interactions. The system's memory is the sum of the positional information and possibly of the internal states. Such systems are very robust and allow different methodologies to analyze computation. To back this theoretic concept some results based on simulations are given and potential benefits of this approach are discussed.

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29

Ernst, Udo, David Rotermund, and Klaus Pawelzik. "Efficient Computation Based on Stochastic Spikes." Neural Computation 19, no. 5 (May 2007): 1313–43. http://dx.doi.org/10.1162/neco.2007.19.5.1313.

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The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons. We present a generative network model for which we derive biologically plausible algorithms that perform spike-by-spike updates of the neuron's internal states and adaptation of its synaptic weights from maximizing the likelihood of the observed spike patterns. Paradigmatic computational tasks demonstrate the online performance and learning efficiency of our framework. The potential relevance of our approach as a model for cortical computation is discussed.

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30

Pylyshyn, Zenon. "On computation and cognition: Toward a foundation of cognitive science." Artificial Intelligence 38, no. 2 (March 1989): 248–51. http://dx.doi.org/10.1016/0004-3702(89)90062-3.

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31

Mikhailov, Igor F. "Computational Knowledge Representation in Cognitive Science." Epistemology & Philosophy of Science 56, no. 3 (2019): 138–52. http://dx.doi.org/10.5840/eps201956355.

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Cognitive research can contribute to the formal epistemological study of knowledge representation inasmuch as, firstly, it may be regarded as a descriptive science of the very same subject as that, of which formal epistemology is a normative one. And, secondly, the notion of representation plays a constitutive role in both disciplines, though differing therein in shades of its meaning. Representation, in my view, makes sense only being paired with computation. A process may be viewed as computational if it adheres to some algorithm and is substrate-independent. Traditionally, psychology is not directly determined by neuroscience, sticking to functional or dynamical analyses in the what-level and skipping mechanistic explanations in the how-level. Therefore, any version of computational approach in psychology is a very promising move in connecting the two scientific realms. On the other hand, the digital and linear computational approach of the classical cognitive science is of little help in this way, as it is not biologically realistic. Thus, what is needed there on the methodological level, is a shift from classical Turing-style computationalism to a generic computational theory that would comprehend the complicated architecture of neuronal computations. To this end, the cutting-edge cognitive neuroscience is in need of а satisfactory mathematical theory applicable to natural, particularly neuronal, computations. Computational systems may be construed as natural or artificial devices that use some physical processes on their lower levels as atomic operations for algorithmic processes on their higher levels. A cognitive system is a multi-level mechanism, in which linguistic, visual and other processors are built on numerous levels of more elementary operations, which ultimately boil down to atomic neural spikes. The hypothesis defended in this paper is that knowledge derives not only from an individual computational device, such as a brain, but also from the social communication system that, in its turn, may be presented as a kind of supercomputer of the parallel network architecture. Therefore, a plausible account of knowledge production and exchange must base on some mathematical theory of social computations, along with that of natural, particularly neuronal, ones.

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32

Valiant, Leslie G. "A neuroidal architecture for cognitive computation." Journal of the ACM 47, no. 5 (September 2000): 854–82. http://dx.doi.org/10.1145/355483.355486.

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33

Horowitz, Amir. "Computation, External Factors, and Cognitive Explanations." Philosophical Psychology 20, no. 1 (February 2007): 65–80. http://dx.doi.org/10.1080/09515080601085856.

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34

Gregson, Robert A. M. "Nonlinear computation and dynamic cognitive generalities." Behavioral and Brain Sciences 20, no. 4 (December 1997): 688–89. http://dx.doi.org/10.1017/s0140525x97271608.

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Although one can endorse the complexity of the data and processes that Phillips & Singer (P&S) review, their mathematical suggestions can be compared critically with cases in nonlinear psychophysics, where the theoretician is faced with analogous problems. Owing to P&S's failure adequately to recognise both the intricate properties of nonlinear dynamics in networks and the constraints of metabolic demands on the temporal generation of patterns in biological nets their conclusions fail to meet the problems they properly address.

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35

Lloyd, Robert, and Hartwell Hooper. "URBAN COGNITIVE MAPS: COMPUTATION AND STRUCTURE." Professional Geographer 43, no. 1 (February 1991): 15–28. http://dx.doi.org/10.1111/j.0033-0124.1991.00015.x.

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36

Forbus, Kenneth D., Chen Liang, and Irina Rabkina. "Representation and Computation in Cognitive Models." Topics in Cognitive Science 9, no. 3 (June 21, 2017): 694–718. http://dx.doi.org/10.1111/tops.12277.

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37

Piccinini, Gualtiero. "Computation and Representation in Cognitive Neuroscience." Minds and Machines 28, no. 1 (February 27, 2018): 1–6. http://dx.doi.org/10.1007/s11023-018-9461-x.

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38

Oren, Sigal. "Economics and computation meets cognitive biases." ACM SIGecom Exchanges 20, no. 1 (July 2022): 67–69. http://dx.doi.org/10.1145/3572885.3572892.

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39

Clohessy, Anne Boylan, Michael I. Posner, Mary K. Rothbart, and Shaun P. Vecera. "The Development of Inhibition of Return in Early Infancy." Journal of Cognitive Neuroscience 3, no. 4 (October 1991): 345–50. http://dx.doi.org/10.1162/jocn.1991.3.4.345.

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The posterior visual spatial attention system involves a number of separable computations that allow orienting to visual locations. We have studied one of these computations, inhibition of return, in 3-, 4-, 6-, 12-, and 18--month-old infants and adults. Our results indicate that this computation develops rapidly between 3 and 6 months, in conjunction with the ability to program eye movements to specific locations. These findings demonstrate that an attention computation involving the mid-brain eye movement system develops after the third month of life. We suggest how this development might influence the infant's ability to represent and expect visual objects.

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Turnbull, William. "Review of Computation and cognition: Toward a foundation for cognitive science." Canadian Psychology/Psychologie canadienne 27, no. 1 (January 1986): 85–87. http://dx.doi.org/10.1037/h0084465.

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Wang, Yingxu. "On the Mathematical Theories and Cognitive Foundations of Information." International Journal of Cognitive Informatics and Natural Intelligence 9, no. 3 (July 2015): 42–64. http://dx.doi.org/10.4018/ijcini.2015070103.

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A recent discovery in computer and software sciences is that information in general is a deterministic abstract quantity rather than a probability-based property of the nature. Information is a general form of abstract objects represented by symbolical, mathematical, communication, computing, and cognitive systems. Therefore, information science is one of the contemporary scientific disciplines collectively known as abstract sciences such as system, information, cybernetics, cognition, knowledge, and intelligence sciences. This paper presents the cognitive foundations, mathematical models, and formal properties of information towards an extended theory of information science. From this point of view, information is classified into the categories of classic, computational, and cognitive information in the contexts of communication, computation, and cognition, respectively. Based on the three generations of information theories, a coherent framework of contemporary information is introduced, which reveals the nature of information and the fundamental principles of information science and engineering.

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Wilson, Robert A. "What Computations (Still, Still) Can't Do: Jerry Fodor on Computation and Modularity." Canadian Journal of Philosophy Supplementary Volume 30 (2004): 407–25. http://dx.doi.org/10.1080/00455091.2004.10717612.

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Jerry Fodor's The Mind Doesn't Work That Way (2000; hereafter Mind) purports to do a number of things. To name three: First, it aims to show what is problematic about recent evolutionary psychology, especially as popularized in Steven Pinker's How the Mind Works (1997). Fodor's particular target here is the rose-coloured view of evolutionary psychology as offering a “new synthesis” in integrating computational psychology with evolutionary theory. Second, Fodor's book poses a series of related, in-principle problems for any cognitive theory that revolve around the putative tension between the local nature of computational processing and the global nature of at least some cognitive processing. And third, it reiterates Fodor's earlier argument, in The Modularity of Mind, for the hopelessness of trying to extend the notion of modularity from “input systems” to “central systems.“

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Aizawa, Kenneth. "Computation in cognitive science: it is not all about Turing-equivalent computation." Studies in History and Philosophy of Science Part A 41, no. 3 (September 2010): 227–36. http://dx.doi.org/10.1016/j.shpsa.2010.07.013.

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Igel, Christian. "Learning ∈ Artificial Intelligence ∩ Cognitive Technologies ∩ Neural Computation ∩ …" KI - Künstliche Intelligenz 26, no. 3 (May 17, 2012): 209–12. http://dx.doi.org/10.1007/s13218-012-0205-4.

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Kadvany, John. "Indistinguishable from Magic: Computation is Cognitive Technology." Minds and Machines 20, no. 1 (February 2010): 119–43. http://dx.doi.org/10.1007/s11023-010-9185-z.

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Day, Matthew. "Religion, Off-Line Cognition and the Extended Mind." Journal of Cognition and Culture 4, no. 1 (2004): 101–21. http://dx.doi.org/10.1163/156853704323074778.

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AbstractThis essay argues that the "classical" or "standard" computation model of an enviroment of thought may hamstring the nascent cognitive science of religion by masking the ways in which the bare biological brain is prosthetically extended and embedded in the surrounding landscape. The motivation for distinsuishing between the problem-solving profiles of the basic brain and the brain-plus-scaffolding is that in many domains non-biological artifacts support and augment biological modes of computation - often allowing us to overcome some of the brain's native computation limitations. The recognition that in some contexts not all of the relevant computational machinery fits inside the head suggests that we should reconsider the possible role(s) and significance of material culture in religious cognition. More specifically, the broad spectrum of rituals, music, relics, scriptures, statues and buildings typically associated with religious traditions may be more than quaint ethnographic window dressing. Rather than thin cultural wrap arounds that decorate the real cognitive processes going on underneath, these elements could represent central components of the relevant machinery of religious thought. By introducing tangible features of the world that can be physically manipulated and tracked in real-time, for example, the cognitive scaffolding that religious material culture affords seems tailor-made for allowing people to exchange the intricate "off-line" problems that arise from dealing with invisible, counter-intuitive supernatural agents for the kinds of "on-line" cognitive tasks they are naturally good at doing (i.e., recognizing patterns, modeling simple worldly dynamics, and manipulating objects).

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der van Velde, Frank. "Association and computation with cell assemblies." Behavioral and Brain Sciences 18, no. 4 (December 1995): 643–44. http://dx.doi.org/10.1017/s0140525x0004036x.

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AbstractThe cell assembly is an important concept for cognitive psychology. Cognitive processing will to a large extent depend on the relations that can exist between different assemblies. A potential relation between assemblies can already be seen in the occurrence of (classical) conditioning. However, the resulting associations between assemblies only produce behavioristic processing or so-called regular computation. Higher-level cognitive abilities most likely result from nonregular computation. I discuss the possibility of this form of computation in terms of cell assemblies.

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Ballard, Dana H., Mary M. Hayhoe, Polly K. Pook, and Rajesh P. N. Rao. "Deictic codes for the embodiment of cognition." Behavioral and Brain Sciences 20, no. 4 (December 1997): 723–42. http://dx.doi.org/10.1017/s0140525x97001611.

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To describe phenomena that occur at different time scales, computational models of the brain must incorporate different levels of abstraction. At time scales of approximately 1/3 of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level – more abstract than that used to capture natural phenomena but less abstract than what is traditionally used to study high-level cognitive processes such as reasoning. At this “embodiment level,” the constraints of the physical system determine the nature of cognitive operations. The key synergy is that at time scales of about 1/3 of a second, the natural sequentiality of body movements can be matched to the natural computational economies of sequential decision systems through a system of implicit reference called deictic in which pointing movements are used to bind objects in the world to cognitive programs. This target article focuses on how deictic bindings make it possible to perform natural tasks. Deictic computation provides a mechanism for representing the essential features that link external sensory data with internal cognitive programs and motor actions. One of the central features of cognition, working memory, can be related to moment-by-moment dispositions of body features such as eye movements and hand movements.

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Chrisley, Ronald L. "What might dynamical intentionality be, if not computation?" Behavioral and Brain Sciences 21, no. 5 (October 1998): 634–35. http://dx.doi.org/10.1017/s0140525x98281738.

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(1) Van Gelder's concession that the dynamical hypothesis is not in opposition to computation in general does not agree well with his anticomputational stance. (2) There are problems with the claim that dynamic systems allow for nonrepresentational aspects of computation in a way in which digital computation cannot. (3) There are two senses of the “cognition is computation” claim and van Gelder argues against only one of them. (4) Dynamical systems as characterized in the target article share problems of universal realizability with formal notions of computation, but differ in that there is no solution available for them. (5) The dynamical hypothesis cannot tell us what cognition is, because instantiating a particular dynamical system is neither necessary nor sufficient for being a cognitive agent.

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Haugeland, John. "Computation and Cognition: Toward a Foundation for Cognitive Science. Zenon W. Pylyshyn." Philosophy of Science 54, no. 2 (June 1987): 309–11. http://dx.doi.org/10.1086/289381.

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