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Готові списки джерел за темами / Cognition Mathematical models

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Добірка наукової літератури з теми "Cognition Mathematical models"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 5 лютого 2022

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Статті в журналах з теми "Cognition Mathematical models"

1

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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Анотація:

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

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2

Sommer, Friedrich T., and Pentti Kanerva. "Can neural models of cognition benefit from the advantages of connectionism?" Behavioral and Brain Sciences 29, no. 1 (February 2006): 86–87. http://dx.doi.org/10.1017/s0140525x06379022.

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Cognitive function certainly poses the biggest challenge for computational neuroscience. As we argue, past efforts to build neural models of cognition (the target article included) had too narrow a focus on implementing rule-based language processing. The problem with these models is that they sacrifice the advantages of connectionism rather than building on them. Recent and more promising approaches for modeling cognition build on the mathematical properties of distributed neural representations. These approaches truly exploit the key advantages of connectionism, that is, the high representat

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3

Frischkorn, Gidon, and Anna-Lena Schubert. "Cognitive Models in Intelligence Research: Advantages and Recommendations for Their Application." Journal of Intelligence 6, no. 3 (July 17, 2018): 34. http://dx.doi.org/10.3390/jintelligence6030034.

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Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the int

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4

Moustafa, Ahmed A., Angela Porter, and Ahmed M. Megreya. "Mathematics anxiety and cognition: an integrated neural network model." Reviews in the Neurosciences 31, no. 3 (April 28, 2020): 287–96. http://dx.doi.org/10.1515/revneuro-2019-0068.

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AbstractMany students suffer from anxiety when performing numerical calculations. Mathematics anxiety is a condition that has a negative effect on educational outcomes and future employment prospects. While there are a multitude of behavioral studies on mathematics anxiety, its underlying cognitive and neural mechanism remain unclear. This article provides a systematic review of cognitive studies that investigated mathematics anxiety. As there are no prior neural network models of mathematics anxiety, this article discusses how previous neural network models of mathematical cognition could be

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5

Wagner, Roy. "Cognitive stories and the image of mathematics." THEORIA. An International Journal for Theory, History and Foundations of Science 33, no. 2 (June 20, 2018): 305. http://dx.doi.org/10.1387/theoria.17917.

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6

Аникеева, Ольга, and Olga Anikyeyeva. "Development of Socio-Historical Models as a Cognitive Process: A Cross-Disciplinary Analysis." Servis Plus 8, no. 2 (June 3, 2014): 4–9. http://dx.doi.org/10.12737/3886.

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The article analyses the problems of modeling as a means of socio-historical cognition. The major discrepancy lies in the fact that the practice of cognition, as well as change-oriented activity, frequently  employ modeling, while the principles and methods of model-development have not been clearly defined. The article considers the correlation between modeling and the conventional methods of historical research, and identifies the common and specific aspects of their implementation, the peculiarities of socio-historical modeling and its Junctions.  Modern science rega

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7

RAY, ASOK, SHASHI PHOHA, and SOUMIK SARKAR. "BEHAVIOR PREDICTION FOR DECISION AND CONTROL IN COGNITIVE AUTONOMOUS SYSTEMS." New Mathematics and Natural Computation 09, no. 03 (October 3, 2013): 263–71. http://dx.doi.org/10.1142/s1793005713400061.

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This paper presents an innovative concept of behavior prediction for decision and control in cognitive autonomous systems. The objective is to coordinate human–machine collaboration such that human operators can assess and enable autonomous systems to utilize their experiential and unmodeled domain knowledge and perception for mission execution. The concept of quantum probability is proposed to construct a unified mathematical framework for interfacing between models of human cognition and machine intelligence.

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8

Broekaert, Jan, Irina Basieva, Pawel Blasiak, and Emmanuel M. Pothos. "Quantum-like dynamics applied to cognition: a consideration of available options." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2106 (October 2, 2017): 20160387. http://dx.doi.org/10.1098/rsta.2016.0387.

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Quantum probability theory (QPT) has provided a novel, rich mathematical framework for cognitive modelling, especially for situations which appear paradoxical from classical perspectives. This work concerns the dynamical aspects of QPT, as relevant to cognitive modelling. We aspire to shed light on how the mind's driving potentials (encoded in Hamiltonian and Lindbladian operators) impact the evolution of a mental state. Some existing QPT cognitive models do employ dynamical aspects when considering how a mental state changes with time, but it is often the case that several simplifying assumpt

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9

GOPISETTI, NAGA-SAI-RAM, MARIA LEONILDE ROCHA VARELA, and JOSE MACHADO. "HUMAN COGNITION INSPIRED PROCEDURES FOR PART FAMILY FORMATION BASED ON NOVEL INSPECTION BASED CLUSTERING APPROACH." DYNA 96, no. 5 (September 1, 2021): 546–52. http://dx.doi.org/10.6036/9997.

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Анотація:

Human cognition based procedures are promising approaches for solving different kind of problems, and this paper addresses the part family formation problem inspired by a human cognition procedure through a graph-based approach, drawing on pattern recognition. There are many algorithms which consider nature inspired models for solving a broad range of problem types. However, there is a noticeable existence of a gap in implementing models based on human cognition, which are generally characterized by “visual thinking”, rather than complex mathematical models. Hence, the natural power of reasoni

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10

Reihenova, Austra. "MODELLING OF MATHEMATICAL PROCESSES AS A SCIENTIFIC COGNITION IN HIGH SCHOOL." SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference 3 (May 20, 2020): 516. http://dx.doi.org/10.17770/sie2020vol3.5016.

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Анотація:

The topicality of the article relates to the use of modelling in a real, complicated and complex process, with the need to forecast the progress and results of the occurrence. Article problem: In school, the focus is on building theoretical models, without real-life context. In real life, the problems are interdisciplinary, more difficult to define than in the theoretical model. The student should be able to transfer knowledge and concepts from one learning discipline in which he can deal with the problem to another. Mathematical modelling offers opportunities to connect and use knowledge from

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Дисертації з теми "Cognition Mathematical models"

1

Wong, Pauline P. "Mathematical models of cognitive recovery and impairment profile after severe traumatic brain injury." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0003/NQ43457.pdf.

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2

Navarro, Daniel. "Representing stimulus similarity." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09PH/09phn322.pdf.

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Анотація:

Bibliography: p. 209-233. Over the last 50 years, psychologists have developed a range of frameworks for similarity modelling, along with a large number of numerical techniques for extracting mental representations from empirical data. This thesis is concerned with the psychological theories used to account for similarity judgements, as well as the mathematical and statistical issues that surround the numerical problem of finding appropriate representations. It discusses, evaluates, and further develops three widely-adopted approaches to similarity modelling: spatial, featural and tree represe

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3

Izquierdo, Ángel Cabrera. "A functional analysis of categorization." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/30522.

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4

Cuppini, Cristiano <1977&gt. "Mathematical models of cognitive processes." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1690/.

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Анотація:

The research activity carried out during the PhD course was focused on the development of mathematical models of some cognitive processes and their validation by means of data present in literature, with a double aim: i) to achieve a better interpretation and explanation of the great amount of data obtained on these processes from different methodologies (electrophysiological recordings on animals, neuropsychological, psychophysical and neuroimaging studies in humans), ii) to exploit model predictions and results to guide future research and experiments. In particular, the research activity h

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5

Stone, Jason C. "The Formation of Self-Constructed Identity as Advanced Mathematical Thinker Among Some Female PhD Holders in Mathematics and the Relationship to the "Three-Worlds" Cognitive Model of Advanced Mathematical Thinking." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1436975429.

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6

Nefdt, Ryan Mark. "The foundations of linguistics : mathematics, models, and structures." Thesis, University of St Andrews, 2016. http://hdl.handle.net/10023/9584.

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Анотація:

The philosophy of linguistics is a rich philosophical domain which encompasses various disciplines. One of the aims of this thesis is to unite theoretical linguistics, the philosophy of language, the philosophy of science (particularly mathematics and modelling) and the ontology of language. Each part of the research presented here targets separate but related goals with the unified aim of bringing greater clarity to the foundations of linguistics from a philosophical perspective. Part I is devoted to the methodology of linguistics in terms of scientific modelling. I argue against both the Con

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7

Hassler, Ryan Scott. "Mathematical comprehension facilitated by situation models: Learning opportunities for inverse relations in elementary school." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/410935.

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Math & Science Education<br>Ph.D.<br>The Common Core State Standards call for more rigorous, focused, and coherent curriculum and instruction, has resulted in students being faced with more cognitively high-demanding tasks which involve forming connections within and between fundamental mathematical concepts. Because mathematical comprehension generally relates back to one’s ability to form connections to prior knowledge, this study sought to examine the extent to which current learning environments expose students to connection-making opportunities that may help facilitate mathematical unders

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8

Hollmann, Claudia. "A cognitive human behaviour model for pedestrian behaviour simulation." Thesis, University of Greenwich, 2015. http://gala.gre.ac.uk/13831/.

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Pedestrian behaviour simulation models are being developed with the intention to simulate human behaviour in various environments in both non-emergency and emergency situations. These models are applied with the objective to understand the underlying causes and dynamics of pedestrian behaviour and how the environment or the environment’s intrinsic procedures can be adjusted in order to provide an improvement of human comfort and safety. In order to realistically model pedestrian behaviour in complex environments, the specific human behaviour patterns which govern their behaviour need to be rep

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9

Warrick, Pamela Dianne. "Investigation of the PASS model (planning, attention, simultaneous, successive) of cognitive processing and mathematics achievement /." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487676261010362.

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10

Tolar, Tammy Daun. "A Cognitive Model of Algebra Achievement among Undergraduate College Students." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/epse_diss/47.

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Анотація:

Algebra has been called a gatekeeper because proficiency in algebra allows access to educational and economic opportunities. Many students struggle with algebra because it is cognitively demanding. There is little empirical evidence concerning which cognitive factors influence algebra achievement. The purpose of this study was to test a cognitive model of algebra achievement among undergraduate college students. Algebra achievement was defined as the ability to manipulate algebraic expressions which is a substantial part of many algebra curriculums. The model included cognitive factors that pa

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Книги з теми "Cognition Mathematical models"

1

Introduction to projective cognition: A mathematical approach. New York: Philosophical Library, 1986.

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2

Busemeyer, Jerome R. Quantum models of cognition and decision. Cambridge: Cambridge University Press, 2012.

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3

Busemeyer, Jerome R. Quantum models of cognition and decision. Cambridge: Cambridge University Press, 2012.

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4

Psicologia matematica: Spunti per una modellistica formale dei processi cognitivi. Roma: Aracne, 2006.

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5

Computational models of conditioning. New York: Cambridge University Press, 2010.

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6

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

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7

Cognitive sciences: Basic problems, new perspectives and implications for artificial intelligence. Orlando: Academic Press, 1986.

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8

Quantum theoretic machines: What is thought from the point of view of physics. Amsterdam: Elsevier, 2000.

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9

Human cognitive abilities: A survey of factor-analytic studies. Cambridge: Cambridge University Press, 1993.

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10

Principles of brain functioning: A synergetic approach to brain activity, behavior, and cognition. Berlin: Springer, 1996.

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Частини книг з теми "Cognition Mathematical models"

1

Poulsgaard, Kåre Stokholm, and Lambros Malafouris. "Models, Mathematics and Materials in Digital Architecture." In Cognition Beyond the Brain, 283–304. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49115-8_14.

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2

Matos, José Manuel. "Cognitive Models in Geometry Learning." In Mathematical Problem Solving and New Information Technologies, 93–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-58142-7_7.

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3

Guhe, Markus, Alan Smaill, and Alison Pease. "A Formal Cognitive Model of Mathematical Metaphors." In KI 2009: Advances in Artificial Intelligence, 323–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04617-9_41.

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4

Jacobs, Cassandra L. "Quantifying Context With and Without Statistical Language Models." In Handbook of Cognitive Mathematics, 1–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44982-7_17-1.

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5

Calderón, Francisca, and Jorge González. "Polytomous IRT Models Versus IRTree Models for Scoring Non-cognitive Latent Traits." In Springer Proceedings in Mathematics & Statistics, 113–25. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74772-5_11.

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6

Kohen, Zehavit, and Bracha Kramarski. "Promoting Mathematics Teachers’ Pedagogical Metacognition: A Theoretical-Practical Model and Case Study." In Cognition, Metacognition, and Culture in STEM Education, 279–305. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66659-4_13.

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7

Corbett, Albert, Megan McLaughlin, K. Christine Scarpinatto, and William Hadley. "Analyzing and Generating Mathematical Models: An Algebra II Cognitive Tutor Design Study." In Intelligent Tutoring Systems, 314–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45108-0_35.

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8

Acharya, B. D., and S. Joshi*. "Some Reflections on Discrete Mathematical Models in Behavioral, Cognitive and Social Sciences." In Proof, Computation and Agency, 277–307. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0080-2_16.

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9

Focardi, Stefano, and Silvano Toso. "Foraging and Social Behaviour of Ungulates: Proposals for a Mathematical Model." In Cognitive Processes and Spatial Orientation in Animal and Man, 295–304. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3531-0_24.

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10

Lim, Youn Seon, and Fritz Drasgow. "An Joint Maximum Likelihood Estimation Approach to Cognitive Diagnosis Models." In Springer Proceedings in Mathematics & Statistics, 335–50. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77249-3_28.

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Тези доповідей конференцій з теми "Cognition Mathematical models"

1

Trenado, C., L. Haab, D. J. Strauss, Alberto Cabada, Eduardo Liz, and Juan J. Nieto. "Mathematical Modeling of Neural Correlates of Cognition: The Case of Selective Attention and Habituation." In MATHEMATICAL MODELS IN ENGINEERING, BIOLOGY AND MEDICINE: International Conference on Boundary Value Problems: Mathematical Models in Engineering, Biology and Medicine. AIP, 2009. http://dx.doi.org/10.1063/1.3142947.

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2

Kawanishi, Shouki, Emi Matsunaga, Yoshiki Ujiie, and Yoshiyuki Matsuoka. "Mathematical Formulation of Macroscopic Feature for Digital Style Design." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49150.

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In style design, a mathematical formulation of a macroscopic feature, which emerges from the sum of the shape elements, is important. However, mathematically formulating a macroscopic feature is difficult using conventional microscopic shape information such as dimension and curvature. Because the evaluation of a macroscopic feature depends on the experience of the designer, constructing a mathematical model to formulate the macroscopic feature is highly desired. Herein, we examined the mathematical formulation of the macroscopic feature “complexity”, which affects the evaluation of important

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3

Yingxu Wang. "Mathematical models and properties of games." In Fourth IEEE Conference on Cognitive Informatics, 2005. (ICCI 2005). IEEE, 2005. http://dx.doi.org/10.1109/coginf.2005.1532644.

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4

Tan, Xinming, and Yingxu Wang. "Transforming RTPA Mathematical Models of System Behaviors Into C++." In 2006 5th IEEE International Conference on Cognitive Informatics. IEEE, 2006. http://dx.doi.org/10.1109/coginf.2006.365518.

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Matsushima, Masatomo, and Taro Okano. "Mathematical model of depression based on Cognitive theory." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5044130.

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6

Magri, Caterina, Andrew Marantan, L. Mahadevan, and Talia Konkle. "A mathematical model of real-world object shape predicts human perceptual judgments." In 2018 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2018. http://dx.doi.org/10.32470/ccn.2018.1107-0.

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Balagura, Kyrill, Helen Kazakova, Daliant Maximus, and Victoria Turygina. "Mathematical models of cognitive interaction identification in the social networks." In CENTRAL EUROPEAN SYMPOSIUM ON THERMOPHYSICS 2019 (CEST). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5114453.

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Danyadi, Zs, P. Foldesi, and L. T. Koczy. "Fuzzy search space for correction of cognitive biases in constructing mathematical models." In 2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom). IEEE, 2012. http://dx.doi.org/10.1109/coginfocom.2012.6422047.

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Han, Qi, Jun Peng, Sk Md Mizanur Rahman, Ahmad Almogran, Atif Alamri, Tengfei Weng, and Jin Liu. "A mathematical and simulation model on stability and parameters of multi-equilibrium points in CNNs." In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2017. http://dx.doi.org/10.1109/icci-cc.2017.8109767.

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Jiang, Y., J. Meng, and N. Jaffer. "A Novel Segmentation and Navigation Method for Polyps Detection using Mathematical Morphology and Active Contour Models." In 6th IEEE International Conference on Cognitive Informatics. IEEE, 2007. http://dx.doi.org/10.1109/coginf.2007.4341910.

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