Готові списки джерел за темами / Cognitive computation

Добірка наукової літератури з теми "Cognitive computation"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Cognitive computation"

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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Анотація:
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.

Повний текст джерела
Анотація:
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.

Повний текст джерела
Анотація:
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.

Повний текст джерела
Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
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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Дисертації з теми "Cognitive computation"

1

Mansinghka, Vikash Kumar. "Natively probabilistic computation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/47892.

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Анотація:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009.
Includes bibliographical references (leaves 129-135).
I introduce a new set of natively probabilistic computing abstractions, including probabilistic generalizations of Boolean circuits, backtracking search and pure Lisp. I show how these tools let one compactly specify probabilistic generative models, generalize and parallelize widely used sampling algorithms like rejection sampling and Markov chain Monte Carlo, and solve difficult Bayesian inference problems. I first introduce Church, a probabilistic programming language for describing probabilistic generative processes that induce distributions, which generalizes Lisp, a language for describing deterministic procedures that induce functions. I highlight the ways randomness meshes with the reflectiveness of Lisp to support the representation of structured, uncertain knowledge, including nonparametric Bayesian models from the current literature, programs for decision making under uncertainty, and programs that learn very simple programs from data. I then introduce systematic stochastic search, a recursive algorithm for exact and approximate sampling that generalizes a popular form of backtracking search to the broader setting of stochastic simulation and recovers widely used particle filters as a special case. I use it to solve probabilistic reasoning problems from statistical physics, causal reasoning and stereo vision. Finally, I introduce stochastic digital circuits that model the probability algebra just as traditional Boolean circuits model the Boolean algebra.
(cont.) I show how these circuits can be used to build massively parallel, fault-tolerant machines for sampling and allow one to efficiently run Markov chain Monte Carlo methods on models with hundreds of thousands of variables in real time. I emphasize the ways in which these ideas fit together into a coherent software and hardware stack for natively probabilistic computing, organized around distributions and samplers rather than deterministic functions. I argue that by building uncertainty and randomness into the foundations of our programming languages and computing machines, we may arrive at ones that are more powerful, flexible and efficient than deterministic designs, and are in better alignment with the needs of computational science, statistics and artificial intelligence.
by Vikash Kumar Mansinghka.
Ph.D.
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2

Sprevak, Mark Daniel. "Computation in mind and world : a realist account of computation in cognitive science." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613848.

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3

Jonas, Eric Michael. "Stochastic architectures for probabilistic computation." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87457.

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Анотація:
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 107-111).
The brain interprets ambiguous sensory information faster and more reliably than modern computers, using neurons that are slower and less reliable than logic gates. But Bayesian inference, which is at the heart of many models for sensory information processing and cognition, as well as many machine intelligence systems, appears computationally challenging, even given modern transistor speeds and energy budgets. The computational principles and structures needed to narrow this gap are unknown. Here I show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude. By connecting stochastic digital components according to simple mathematical rules, it is possible to rapidly, reliably and accurately solve many Bayesian inference problems using massively parallel, low precision circuits. I show that our circuits can solve problems of depth and motion perception, perceptual learning and causal reasoning via inference over 10,000+ latent variables in real time - a 1,000x speed advantage over commodity microprocessors - by exploiting stochasticity. I will show how this natively stochastic approach follows naturally from the probability algebra, giving rise to easy-to-understand rules for abstraction and composition. I have developed a compiler that automatically generate circuits for a wide variety of problems fixed-structure problems. I then present stochastic computing architectures for models that are viable even when constrained by silicon area and dynamic creation and destruction of random variables. These results thus expose a new role for randomness and Bayesian inference in the engineering and reverse-engineering of computing machines.
by Eric Jonas.
Ph. D.
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4

Ullman, Michael Thomas. "The computation of inflectional morphology." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12489.

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5

Ghahramani, Zoubin. "Computation and psychophysics of sensorimotor integration." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11123.

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6

Kell, Alexander James Eaton. "Hierarchy and invariance in auditory cortical computation." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/132746.

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Анотація:
Thesis: Ph. D. in Neuroscience, Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, June, 2019
Cataloged from the PDF version of thesis. "June 2019"--Hand written on title page.
Includes bibliographical references.
With ease, we recognize a friend's voice in a crowd, or pick out the first violin in a concerto. But the effortlessness of everyday perception masks its computational challenge. Perception does not occur in the eyes and ears - indeed, nearly half of primate cortex is dedicated to it. While much is known about peripheral auditory processing, auditory cortex remains poorly understood. This thesis addresses basic questions about the functional and computational organization of human auditory cortex through three studies. In the first study we show that a hierarchical neural network model optimized to recognize speech and music does so at human levels, exhibits a similar pattern of behavioral errors, and predicts cortical responses, as measured with fMRI. The multi-task optimization procedure we introduce produces separate music and speech pathways after a shared front end, potentially recapitulating aspects of auditory cortical functional organization. Within the model, different layers best predict primary and non-primary voxels, revealing a hierarchical organization in human auditory cortex. We then seek to characterize the representational transformations that occur across stages of the putative cortical hierarchy, probing for one candidate: invariance to realworld background noise. To measure invariance, we correlate voxel responses to natural sounds with and without real-world background noise. Non-primary responses are substantially more noise-invariant than primary responses. These results illustrate a representational consequence of the potential hierarchical organization of the auditory system. Lastly, we explore of the generality of deep neural networks as models of human hearing by simulating many psychophysical and fMRI experiments on the above-described neural network model. The results provide an extensive comparison of the performance characteristics and internal representations of a deep neural network with those of humans. We observe many similarities that suggest that the model replicates a broad variety of aspects of auditory perception. However, we also find discrepancies that suggest targets for future modeling efforts.
by Alexander James Eaton Kell.
Ph. D. in Neuroscience
Ph.D.inNeuroscience Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences
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7

Heirdsfield, Ann M. "Mental computation: The identification of associated cognitive, metacognitive and affective factors." Thesis, Queensland University of Technology, 2001. https://eprints.qut.edu.au/36637/1/36637_Digitised%20Thesis.pdf.

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The purpose of the study was to develop an explanation why some children are better at addition and subtraction mental computation than others. For the purposes of this thesis, mental computation was defined as "the process of carrying out arithmetic calculations without the aid of external devices" (Sowder, 1988, p.182). To reflect current views of mental computation as calculating with the head, rather than merely, in the head, the definition was extended to calculating using strategies with understanding (Anghileri, 1999). Thus, proficiency was not confined to accuracy, but also included flexibility of strategy choice. The study investigated the part played by number sense knowledge (e.g., numeration, number facts, estimation and effects of operations on number), metacognition, affects (e.g., beliefs, attitudes), and memory. The study showed that students proficient in mental computation (accurate and flexible) possessed integrated understandings of number facts (speed, accuracy, and efficient number facts), numeration, and number and operation. These proficient students also exhibited some metacognitive strategies and possessed reasonable short term memory and executive functioning. Where there was less knowledge and fewer connections between knowledge, students compensated in different ways, depending on their beliefs and what knowledge they possessed. Accurate and inflexible students used the teacher taught strategy of mental image of pen and paper algorithm in which strong beliefs were held. Combined with fast and accurate number facts and some numeration understanding, their familiarity with this strategy enabled the students to complete the mental computation tasks with accuracy. Working memory was sufficient to use an inefficient mental strategy accurately. The visuospatial scratchpad was used as a visual memory aid. The inaccurate and flexible students compensated for their poor number facts and minimal and disconnected knowledge base by using a variety of mental strategies in an endeavour to find one that would enable them to complete the calculation. Although their limited numeration understanding and memory (including central executive) were sufficient to support the development of some alternative strategies, these were not high level strategies. Finally, the inaccurate and inflexible students who exhibited deficient and disconnected understanding tried to compensate by using teacher-taught procedures (similar to the strategy employed by accurate and inflexible students), but they were unsuccessful, as they possessed no procedural understanding and also had poor working memory. Detailed analysis of students' knowledge was used to develop frameworks, which explained children's proficiency in addition and subtraction mental computation. The theoretical frameworks explained the influence of contributing factors and the relationships (if any) between them. The frameworks formed the basis of flowcharts, which explained the process in mental computation for each group of students. The importance of connected knowledge for proficient mental computation demonstrates the need for teaching practices to focus on the development of an extensive and integrated knowledge base. Students can and do formulate their own strategies, but do not always use them accurately. Therefore, students should be encouraged to formulate their own strategies but in a supportive environment that assists them to use strategies appropriately. Because of memory load, students should be permitted to use external memory aids (e.g. pen and paper) to assist mental computation. This has a second payoff in that efficient mental strategies are, at times, also efficient written strategies. By having students formulate mental strategies, they have to call upon number sense knowledge, thus acquiring connected knowledge while they develop computational procedures. This is in contrast to students using teacher-taught procedures, which require little connected knowledge.
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8

Wells, Andrew J. "The External Tape Hypothesis : a Turing machine based approach to cognitive computation." Thesis, London School of Economics and Political Science (University of London), 1994. http://etheses.lse.ac.uk/118/.

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The symbol processing or "classical cognitivist" approach to mental computation suggests that the cognitive architecture operates rather like a digital computer. The components of the architecture are input, output and central systems. The input and output systems communicate with both the internal and external environments of the cognizer and transmit codes to and from the rule governed, central processing system which operates on structured representational expressions in the internal environment. The connectionist approach, by contrast, suggests that the cognitive architecture should be thought of as a network of interconnected neuron-like processing elements (nodes) which operates rather like a brain. Connectionism distinguishes input, output and central or "hidden" layers of nodes. Connectionists claim that internal processing consists not of the rule governed manipulation of structured symbolic expressions, but of the excitation and inhibition of activity and the alteration of connection strengths via message passing within and between layers of nodes in the network. A central claim of the thesis is that neither symbol processing nor connectionism provides an adequate characterization of the role of the external environment in cognitive computation. An alternative approach, called the External Tape Hypothesis (ETH), is developed which claims, on the basis of Turing's analysis of routine computation, that the Turing machine model can be used as the basis for a theory which includes the environment as an essential part of the cognitive architecture. The environment is thought of as the tape, and the brain as the control of a Turing machine. Finite state automata, Turing machines, and universal Turing machines are described, including details of Turing's original universal machine construction. A short account of relevant aspects of the history of digital computation is followed by a critique of the symbol processing approach as it is construed by influential proponents such as Allen Newell and Zenon Pylyshyn among others. The External Tape Hypothesis is then developed as an alternative theoretical basis. In the final chapter, the ETH is combined with the notion of a self-describing Turing machine to provide the basis for an account of thinking and the development of internal representations.
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9

Aboalela, Rania Anwar. "An Assessment of Knowledge by Pedagogical Computation on Cognitive Level mapped Concept Graphs." Kent State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1496941747313396.

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10

Fayez, Almohanad Samir. "Design Space Decomposition for Cognitive and Software Defined Radios." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23180.

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Анотація:
Software Defined Radios (SDRs) lend themselves to flexibility and extensibility because they
depend on software to implement radio functionality. Cognitive Engines (CEs) introduce
intelligence to radio by monitoring radio performance through a set of meters and configuring
the underlying radio design by modifying its knobs. In Cognitive Radio (CR) applications,
CEs intelligently monitor radio performance and reconfigure them to meet it application
and RF channel needs. While the issue of introducing computational knobs and meters
is mentioned in literature, there has been little work on the practical issues involved in
introducing such computational radio controls.

This dissertation decomposes the radio definition to reactive models for the CE domain
and real-time, or dataflow models, for the SDR domain. By allowing such design space
decomposition, CEs are able to define implementation independent radio graphs and rely on
a model transformation layer to transform reactive radio models to real-time radio models
for implementation. The definition of knobs and meters in the CE domain is based on
properties of the dataflow models used in implementing SDRs. A framework for developing
this work is presented, and proof of concept radio applications are discussed to demonstrate
how CEs can gain insight into computational aspects of their radio implementation during
their reconfiguration decision process.

Ph. D.
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Книги з теми "Cognitive computation"

1

Wells, A. J. Rethinking Cognitive Computation. London: Macmillan Education UK, 2006. http://dx.doi.org/10.1007/978-1-137-06661-9.

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2

Fresco, Nir. Physical Computation and Cognitive Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41375-9.

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3

Pylyshyn, Zenon W. Computation and cognition: Toward a foundation for cognitive science. 2nd ed. Cambridge, Mass: MIT Press, 1985.

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4

Pylyshyn, Zenon W. Computation and cognition: Toward a foundation for cognitive science. Cambridge, Mass: MIT Press, 1989.

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5

Computation, dynamics, and cognition. New York: Oxford University Press, 1997.

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6

1928-, Itō Masao, Miyashita Y. 1949-, and Rolls Edmund T, eds. Cognition, computation, and consciousness. Oxford: Oxford University Press, 1997.

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7

Kryzhanovsky, Boris, Witali Dunin-Barkowski, and Vladimir Redko, eds. Advances in Neural Computation, Machine Learning, and Cognitive Research. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-66604-4.

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8

Spain) Neural Computation and Psychology Workshop (13th 2012 San Sebastián. Computational models of cognitive processes: Proceedings of the 13th Neural Computation and Psychology Workshop, San Sebastian, Spain, 12-14 July 2012. Edited by Mayor, Julien, editor of compilation and Gomez, Pablo (Pablo Alegria), editor of compilation. Hackensack,] New Jersey: World Scientific, 2014.

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9

Language, mind and computation. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2014.

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10

Wells, Andrew. Rethinking cognitive computation: Turing and the science of the mind. Basingstoke [England]: Palgrave Macmillan, 2006.

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Частини книг з теми "Cognitive computation"

1

Smith, Aaron C. T. "Computation." In Cognitive Mechanisms of Belief Change, 105–200. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-137-57895-2_3.

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Wells, A. J. "Ecological Functionalism: Computation." In Rethinking Cognitive Computation, 224–35. London: Macmillan Education UK, 2006. http://dx.doi.org/10.1007/978-1-137-06661-9_19.

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Wells, A. J. "Turing’s Analysis of Computation." In Rethinking Cognitive Computation, 74–87. London: Macmillan Education UK, 2006. http://dx.doi.org/10.1007/978-1-137-06661-9_6.

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Schweizer, Paul. "Cognitive Computation sans Representation." In Philosophical Studies Series, 65–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61043-6_4.

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Wang, Wenfeng, Hengjin Cai, Xiangyang Deng, Chenguang Lu, and Limin Zhang. "Cognitive Computation and Systems." In Interdisciplinary Evolution of the Machine Brain, 17–34. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4244-6_2.

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Stufflebeam, Robert S. "Representation and Computation." In A Companion to Cognitive Science, 636–48. Oxford, UK: Blackwell Publishing Ltd, 2017. http://dx.doi.org/10.1002/9781405164535.ch50.

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Giannopulu, Irini. "Introduction." In Cognitive Computation Trends, 1–3. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95558-2_1.

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Giannopulu, Irini. "The Mind." In Cognitive Computation Trends, 5–35. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95558-2_2.

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Giannopulu, Irini. "Dynamic Embrained Systems." In Cognitive Computation Trends, 37–121. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95558-2_3.

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Giannopulu, Irini. "Externalised Mind 1." In Cognitive Computation Trends, 123–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95558-2_4.

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Тези доповідей конференцій з теми "Cognitive computation"

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Maley, Corey. "Analog Computation in Computational Cognitive Neuroscience." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1178-0.

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Piccinini, Gualtiero. "Non-Computational Functionalism: Computation and the Function of Consciousness." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1022-0.

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Ciftcioglu, Ozer, and Michael S. Bittermann. "Generic cognitive computing for cognition." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7256942.

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Fiorini, Rodolfo A. "Quantum cognitive computation by CICT." In 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2016. http://dx.doi.org/10.1109/icci-cc.2016.7862085.

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Shao, Shuangjia, Guiming Luo, Jian Luo, and Xibin Zhao. "Circuit delay computation based on ITTPN." In 2013 12th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2013. http://dx.doi.org/10.1109/icci-cc.2013.6622282.

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Luo, Jian, Guiming Luo, and Yang Zhao. "Satisfiability degree computation for linear temporal logic." In Cognitive Computing (ICCI-CC). IEEE, 2011. http://dx.doi.org/10.1109/coginf.2011.6016168.

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Papadimitriou, Christos H., Santosh S. Vempala, Daniel Mitropolsky, Michael J. Collins, Wolfgang Maass, and Larry F. Abbott. "A Calculus for Brain Computation." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1381-0.

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Faix, Marvin, Emmanuel Mazer, Raphael Laurent, Mohamad Othman Abdallah, Ronan Le Hy, and Jorge Lobo. "Cognitive computation: A Bayesian machine case study." In 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2015. http://dx.doi.org/10.1109/icci-cc.2015.7259367.

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Anderson, J. A. "Cognitive computation: the Ersatz Brain project." In Fourth IEEE Conference on Cognitive Informatics, 2005. (ICCI 2005). IEEE, 2005. http://dx.doi.org/10.1109/coginf.2005.1532607.

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Ciftcioglu, Ozer, and Michael S. Bittermann. "Computational cognitive color perception." In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016. http://dx.doi.org/10.1109/cec.2016.7744068.

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Звіти організацій з теми "Cognitive computation"

1

Kosslyn, Stephen M. DURIP - Computational Modeling of Cognitive Processes. Fort Belvoir, VA: Defense Technical Information Center, March 1990. http://dx.doi.org/10.21236/ada219934.

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Faden, Alan I. Georgetown Institute for Cognitive and Computational Sciences. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada373779.

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Ledley, Robert S., and Alan I. Faden. Georgetown Institute for Cognitive and Computational Sciences. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada289775.

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4

Moore, Jr, and L. R. Cognitive Model Exploration and Optimization: A New Challenge for Computational Science. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada539438.

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Just, Marcel A., Patricia A. Carpenter, Cleotilde Gonzalez, and Javier Lerch. Integrated Cognitive Computational and Biological Assessment of Workload in Decision Making. Fort Belvoir, VA: Defense Technical Information Center, August 2003. http://dx.doi.org/10.21236/ada418079.

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Gray, Wayne D. Computational Cognitive Modeling of Adaptive Choice Behavior in a Dynamic Decision Paradigm. Fort Belvoir, VA: Defense Technical Information Center, February 2006. http://dx.doi.org/10.21236/ada444683.

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7

Lu, Zhong-Lin. Workshop on Cognitive Science from Cellular Mechanisms to Computational Theories (CS-2009). Fort Belvoir, VA: Defense Technical Information Center, May 2009. http://dx.doi.org/10.21236/ada533451.

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Castro, Carolina Robledo, Piedad Rocio Lerma-Castaño, and Luis Gerardo Pachón-Ospina. Rehabilitation programs based on computational systems: effects in the executive functions in young and middle adulthood: A scoping review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2022. http://dx.doi.org/10.37766/inplasy2022.10.0052.

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Анотація:
Review question / Objective: To identify empirical studies that measured the feasibility and effect of computer-based executive function stimulation and rehabilitation programs in the young and middle adult population. Background: Reviews that evaluate the effectiveness of computerized cognitive training programs on executive functions in different population groups have shown contradictory results, to a certain extent associated with the methodological characteristics of said studies (Gates et al., 2019; 2020); most of them These reviews have focused on older adults (Ten Brinke et al., 2020; Yoo et al., 2015) with stroke sequelae, and adults with cognitive impairment. These studies have found improvements in general cognitive function in older adults (Ten Brinke et al., 2020); however, the effect on executive functions have not been studied. Only one review was carried out on the average adult (Gates et al., 2019); the authors restricted the search to interventions with more than 12 weeks and only found one article with eligibility criteria. Their work concluded that computerized cognitive training in midlife demonstrated lasting effects on general cognitive function after 12 weeks of training and on memory after 24 weeks of training.
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Schunn, C. D. A Review of Human Spatial Representations Computational, Neuroscience, Mathematical, Developmental, and Cognitive Psychology Considerations. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada440864.

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RAYBOURN, ELAINE M., and JAMES C. FORSYTHE. Toward the Computational Representation of Individual Cultural, Cognitive, and Physiological State: The Sensor Shooter Simulation. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/786630.

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