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Статті в журналах з теми "Cognition Mathematical models"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 5 лютого 2022

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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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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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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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11

Estrada-Rojo, Francisco, Ricardo Jesús Martínez-Tapia, Francisco Estrada-Bernal, Marina Martínez-Vargas, Adán Perez-Arredondo, Luis Flores-Avalos, and Luz Navarro. "Models used in the study of traumatic brain injury." Reviews in the Neurosciences 29, no. 2 (February 23, 2018): 139–49. http://dx.doi.org/10.1515/revneuro-2017-0028.

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AbstractTraumatic brain injury (TBI) is a contemporary health problem and a leading cause of mortality and morbidity worldwide. Survivors of TBI frequently experience disabling long-term changes in cognition, sensorimotor function, and personality. A crucial step in understanding TBI and providing better treatment has been the use of models to mimic the event under controlled conditions. Here, we describe the known head injury models, which can be classified as whole animal (in vivo),in vitro, and mathematical models. We will also review the ways in which these models have advanced the knowled

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12

Baltieri, Manuel, and Christopher Buckley. "PID Control as a Process of Active Inference with Linear Generative Models." Entropy 21, no. 3 (March 7, 2019): 257. http://dx.doi.org/10.3390/e21030257.

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In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates

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13

D’Alessandro, Marco, Giuseppe Gallitto, Antonino Greco, and Luigi Lombardi. "A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data." Brain Sciences 10, no. 3 (March 1, 2020): 138. http://dx.doi.org/10.3390/brainsci10030138.

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Анотація:

Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a join

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14

Grabauskienė, Vaiva, and Oksana Mockaitytė-Rastenienė. "AN EXPRESSION OF MATHEMATICAL CONNECTIONS IN MULTIPLICATION-RELATED THINKING IN THIRD AND FOURTH GRADES OF PRIMARY SCHOOL." ŠVIETIMAS: POLITIKA, VADYBA, KOKYBĖ / EDUCATION POLICY, MANAGEMENT AND QUALITY 11, no. 1 (August 25, 2019): 9–29. http://dx.doi.org/10.48127/spvk-epmq/19.11.09.

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Анотація:

Mathematical comprehension is closely related to a cognition of mathematical connections. A multiplication is a mathematical operation characterized by complex mathematical connections. Students are early introduced with the multiplication. Therefore, in primary school, not so developed cognition of mathematical connections may become a reason for difficulties in Maths. A functionality of concept is based on a view to a multiplication. The analysis scientific literature revealed that a thinking of multiplication can be either additive or multiplicative. Additionally, the multiplication learnin

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15

Afraimovich, Valentin S., Todd R. Young, and Mikhail I. Rabinovich. "Hierarchical Heteroclinics in Dynamical Model of Cognitive Processes: Chunking." International Journal of Bifurcation and Chaos 24, no. 10 (October 2014): 1450132. http://dx.doi.org/10.1142/s0218127414501326.

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Combining the results of brain imaging and nonlinear dynamics provides a new hierarchical vision of brain network functionality that is helpful in understanding the relationship of the network to different mental tasks. Using these ideas it is possible to build adequate models for the description and prediction of different cognitive activities in which the number of variables is usually small enough for analysis. The dynamical images of different mental processes depend on their temporal organization and, as a rule, cannot be just simple attractors since cognition is characterized by transien

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16

Kornbrot, Diana. "The Utility of Categorising Multidimensional Mathematical Models in Psychology. Review of Multidimensional Models of Perception and Cognition, by F. Gregory Ashby." Journal of Mathematical Psychology 38, no. 3 (September 1994): 392–406. http://dx.doi.org/10.1006/jmps.1994.1028.

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17

Toulopoulou, T., X. Zhang, S. Cherny, R. Straub, K. Berman, D. Dickinson, P. Sham, and D. Weinberger. "Polygenic risk profile score increases schizophrenia liability mostly through cognition pathways: mathematical causation models with polygenic risk." European Neuropsychopharmacology 27 (October 2017): S885—S886. http://dx.doi.org/10.1016/s0924-977x(17)31582-1.

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18

Temperley, David. "Probabilistic Models of Melodic Interval." Music Perception 32, no. 1 (September 1, 2014): 85–99. http://dx.doi.org/10.1525/mp.2014.32.1.85.

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Two probabilistic models of melodic interval are compared. In the Markov model, the “interval probability” of a note is defined by the corpus frequency of its melodic interval (the interval to the previous note), conditioned on the previous one or two intervals; in the Gaussian model, the interval probability is a simple mathematical function of the size of the note’s melodic interval and its position in relation to the range of the melody. In both models, this interval probability is then multiplied by the probability of the note’s scale degree to yield its actual probability. The two models

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19

Cobb, Paul. "Review: A Double-Edged Sword." Journal for Research in Mathematics Education 20, no. 2 (March 1989): 213–18. http://dx.doi.org/10.5951/jresematheduc.20.2.0213.

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The notion of intuition frequently crops up in accounts of mathematical experiences (e.g., Davis & Hersh, 1981), and we have an intuitive idea of what is meant. As Fischbein notes, “intuition is generally seen as a primary phenomenon which may be described but which is not reducible to more elementary components” (p. ix). To rectify this situation, Fischbein presents a theory of mathematical and scientific intuition. In doing so, he synthesizes empirical research on problem solving, images and models, beliefs, and developmental stages of intelligence, drawing on examples from the history o

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20

Salimpoor, Valorie N., Catie Chang, and Vinod Menon. "Neural Basis of Repetition Priming during Mathematical Cognition: Repetition Suppression or Repetition Enhancement?" Journal of Cognitive Neuroscience 22, no. 4 (April 2010): 790–805. http://dx.doi.org/10.1162/jocn.2009.21234.

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We investigated the neural basis of repetition priming (RP) during mathematical cognition. Previous studies of RP have focused on repetition suppression as the basis of behavioral facilitation, primarily using word and object identification and classification tasks. More recently, researchers have suggested associative stimulus-response learning as an alternate model for behavioral facilitation. We examined the neural basis of RP during mathematical problem solving in the context of these two models of learning. Brain imaging and behavioral data were acquired from 39 adults during novel and re

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21

Jones, Matt, and Bradley C. Love. "Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition." Behavioral and Brain Sciences 34, no. 4 (August 2011): 169–88. http://dx.doi.org/10.1017/s0140525x10003134.

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AbstractThe prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assu

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22

Perlovsky, Leonid. "Language and Cognition Interaction Neural Mechanisms." Computational Intelligence and Neuroscience 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/454587.

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How language and cognition interact in thinking? Is language just used for communication of completed thoughts, or is it fundamental for thinking? Existing approaches have not led to a computational theory. We develop a hypothesis that language and cognition are two separate but closely interacting mechanisms. Language accumulates cultural wisdom; cognition develops mental representations modeling surrounding world and adapts cultural knowledge to concrete circumstances of life. Language is acquired from surrounding language “ready-made” and therefore can be acquired early in life. This early

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23

Mark, D. M., and A. U. Frank. "Experiential and Formal Models of Geographic Space." Environment and Planning B: Planning and Design 23, no. 1 (February 1996): 3–24. http://dx.doi.org/10.1068/b230003.

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In this paper human experience and perception of phenomena and relations in space are studied. This focus is in contrast to previous work where space and spatial relations were examined as objective phenomena of the world. This study leads in turn to a goal: to identify models of space that can be used both in cognitive science and in the design and implementation of geographic information systems (GISs). Experiential models of the world are based on sensorimotor and visual experiences with environments, and form in individual minds, as the associated bodies and senses experience their worlds.

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24

Raghubar, Kimberly P., Marcia A. Barnes, Mary Prasad, Chad P. Johnson, and Linda Ewing-Cobbs. "Mathematical Outcomes and Working Memory in Children With TBI and Orthopedic Injury." Journal of the International Neuropsychological Society 19, no. 3 (November 20, 2012): 254–63. http://dx.doi.org/10.1017/s1355617712001312.

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AbstractThis study compared mathematical outcomes in children with predominantly moderate to severe traumatic brain injury (TBI;n= 50) or orthopedic injury (OI;n=47) at 2 and 24 months post-injury. Working memory and its contribution to math outcomes at 24 months post-injury was also examined. Participants were administered an experimental cognitive addition task and standardized measures of calculation, math fluency, and applied problems; as well as experimental measures of verbal and visual-spatial working memory. Although children with TBI did not have deficits in foundational math fact ret

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25

Schubert, Anna-Lena, and Gidon T. Frischkorn. "Neurocognitive Psychometrics of Intelligence: How Measurement Advancements Unveiled the Role of Mental Speed in Intelligence Differences." Current Directions in Psychological Science 29, no. 2 (February 13, 2020): 140–46. http://dx.doi.org/10.1177/0963721419896365.

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More intelligent individuals typically show faster reaction times. However, individual differences in reaction times do not represent individual differences in a single cognitive process but in multiple cognitive processes. Thus, it is unclear whether the association between mental speed and intelligence reflects advantages in a specific cognitive process or in general processing speed. In this article, we present a neurocognitive-psychometrics account of mental speed that decomposes the relationship between mental speed and intelligence. We summarize research employing mathematical models of

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26

Gao, Yuan. "Rethinking the Formalism-Substantivism Debate in Social Science: A Perspective from Recent Developments in Economic Methodology." Modern China 47, no. 1 (May 28, 2020): 3–25. http://dx.doi.org/10.1177/0097700420924603.

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Taking as its point of departure the 1960s formalism-substantivism debate in social science methodology, this article argues that what is distinctive about the new development of formalism in economics since then is mainly the prevalence of using “complete models”—tractable, manipulable, and fully specified mathematical objects—to construct and express theories. The objective of complete models is not to establish general laws, but to formulate auxiliary devices of cognition to facilitate the explanation of targeted aspects of the empirical world; not to create idealistic or ideological discou

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27

Studenova, Tamara Iurevna. "Evolution of approaches to the organization of the education system." Moscow University Pedagogical Education Bulletin, no. 2 (June 29, 2016): 53–66. http://dx.doi.org/10.51314/2073-2635-2016-2-53-66.

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Given are the principles of building teaching models when the genetic approach to the formation of mathematical concepts is employed, as well as the classification of teaching models founded on the historical stages of cognition development. The genetic approach to the formation of concepts has been developed and discussed in the works of our leading psychologists V.V. Davidov, D.B. Elconin, P.Y. Galperin, L.F. Oвukhova and others. The given study in accordance with the above mentioned theoretical assumptions tackles the formation of concepts from a psychosemeiotic point of view. This approach

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28

Burgin, Mark. "Triadic Structures in Interpersonal Communication." Information 9, no. 11 (November 16, 2018): 283. http://dx.doi.org/10.3390/info9110283.

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Communication, which is information exchange between systems, is one of the basic information processes. To better understand communication and develop more efficient communication tools, it is important to have adequate and concise, static and dynamic, structured models of communication. The principal goal of this paper is explication of the communication structures, formation of their adequate mathematical models and description of their dynamic interaction. Exploring communication in the context of structures and structural dynamics, we utilize the most fundamental structure in mathematics,

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29

Wang, Haibo, Naiqi Jiang, Ting Pan, Haiqing Si, Yao Li, and Wenjing Zou. "Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments." Journal of Advanced Transportation 2020 (November 12, 2020): 1–16. http://dx.doi.org/10.1155/2020/5640784.

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Cognitive load is generated by pilots in the process of information cognition about aircraft control, and it is closely related to flight safety. Cognitive load is the physiological and psychological need that a pilot produces when completing a mission. Therefore, it is meaningful to study the dynamic identification of the cognitive load of the pilot under the complex human-aircraft-environment interaction. In this paper, the airfield traffic pattern flight simulation experiment was designed and used to obtain the ECG physiological and NASA-TLX psychological data. The wavelet transform preproc

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30

Hordijk, Wim, and Mike Steel. "Autocatalytic Networks at the Basis of Life’s Origin and Organization." Life 8, no. 4 (December 8, 2018): 62. http://dx.doi.org/10.3390/life8040062.

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Life is more than the sum of its constituent molecules. Living systems depend on a particular chemical organization, i.e., the ways in which their constituent molecules interact and cooperate with each other through catalyzed chemical reactions. Several abstract models of minimal life, based on this idea of chemical organization and also in the context of the origin of life, were developed independently in the 1960s and 1970s. These models include hypercycles, chemotons, autopoietic systems, (M,R)-systems, and autocatalytic sets. We briefly compare these various models, and then focus more spe

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31

Pissanetzky, Sergio, and Felix Lanzalaco. "Black-box Brain Experiments, Causal Mathematical Logic, and the Thermodynamics of Intelligence." Journal of Artificial General Intelligence 4, no. 3 (December 1, 2013): 10–43. http://dx.doi.org/10.2478/jagi-2013-0005.

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Abstract Awareness of the possible existence of a yet-unknown principle of Physics that explains cognition and intelligence does exist in several projects of emulation, simulation, and replication of the human brain currently under way. Brain simulation projects define their success partly in terms of the emergence of non-explicitly programmed biophysical signals such as self-oscillation and spreading cortical waves. We propose that a recently discovered theory of Physics known as Causal Mathematical Logic (CML) that links intelligence with causality and entropy and explains intelligent behavi

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32

Chen, Qiuying, and Hongwei Mo. "A Brain-Inspired Goal-Oriented Robot Navigation System." Applied Sciences 9, no. 22 (November 14, 2019): 4869. http://dx.doi.org/10.3390/app9224869.

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Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture Unified Network (SPAUN) neural model and neural navigation mechanism, we developed a brain-like biologically plausible mathematical model and applied it to robotic spatial navigation tasks. The proposed cognitive navigation framework adopts a one-dimensional ring attractor to model the head-d

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33

Radanliev, Petar, David De Roure, Kevin Page, Max Van Kleek, Omar Santos, La’Treall Maddox, Pete Burnap, Eirini Anthi, and Carsten Maple. "Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars." Safety in Extreme Environments 2, no. 3 (October 2020): 219–30. http://dx.doi.org/10.1007/s42797-021-00025-1.

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AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The pa

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34

Butt, Muhammad A., Faisal Riaz, Yasir Mehmood, and Somyyia Akram. "REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles." SIMULATION 97, no. 9 (April 10, 2021): 601–17. http://dx.doi.org/10.1177/00375497211004721.

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Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cogni

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35

Baek, Sori, Amy L. Daitch, Pedro Pinheiro-Chagas, and Josef Parvizi. "Neuronal Population Responses in the Human Ventral Temporal and Lateral Parietal Cortex during Arithmetic Processing with Digits and Number Words." Journal of Cognitive Neuroscience 30, no. 9 (September 2018): 1315–22. http://dx.doi.org/10.1162/jocn_a_01296.

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Past research has identified anatomically specific sites within the posterior inferior temporal gyrus (PITG) and the intraparietal sulcus (IPS) areas that are engaged during arithmetic processing. Although a small region of the PITG (known as the number form area) is selectively engaged in the processing of numerals, its surrounding area is activated during both digit and number word processing. In eight participants with intracranial electrodes, we compared the timing and selectivity of electrophysiological responses in the number form area-surround and IPS regions during arithmetic processin

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Miranda, R. C. R. "Identifying Conditions to Implement Strategic Knowledge Management in Brazilian Corporations — SKM Math Model Application." Journal of Information & Knowledge Management 08, no. 01 (March 2009): 67–77. http://dx.doi.org/10.1142/s021964920900221x.

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This is a step forward in researching about Strategic Knowledge Management — SKM. Firstly, an overview of SKM is presented and indicating the three integrative models: conceptual model, mathematical model and systemic model. After that, the SKM math model is deeply described in order to establish a framework to the study. The research objective was to evaluate conditions to implement SKM in Brazilian corporations. Thus, a methodology of four steps was applied: formulating a questionnaire, considering variables related to systemic factors — cognition, technology, organisational culture, manager

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Ferrari, Camilla, and Sandro Sorbi. "The complexity of Alzheimer’s disease: an evolving puzzle." Physiological Reviews 101, no. 3 (July 1, 2021): 1047–81. http://dx.doi.org/10.1152/physrev.00015.2020.

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The history of Alzheimer’s disease (AD) started in 1907, but we needed to wait until the end of the century to identify the components of pathological hallmarks and genetic subtypes and to formulate the first pathogenic hypothesis. Thanks to biomarkers and new technologies, the concept of AD then rapidly changed from a static view of an amnestic dementia of the presenium to a biological entity that could be clinically manifested as normal cognition or dementia of different types. What is clearly emerging from studies is that AD is heterogeneous in each aspect, such as amyloid composition, tau

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Eckstein, Shulamith G., and Michal Shemesh. "Mathematical models of cognitive development." British Journal of Mathematical and Statistical Psychology 45, no. 1 (May 1992): 1–18. http://dx.doi.org/10.1111/j.2044-8317.1992.tb00974.x.

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Wong, Pauline P., Georges Monette, and Neil I. Weiner. "Mathematical models of cognitive recovery." Brain Injury 15, no. 6 (June 1, 2001): 519–30. http://dx.doi.org/10.1080/02699050010005995.

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Pauline P. Wong, Georges Monette, N. "Mathematical models of cognitive recovery." Brain Injury 15, no. 6 (January 2001): 519–30. http://dx.doi.org/10.1080/02699050116774.

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Fujii, Keisuke. "Data-Driven Analysis for Understanding Team Sports Behaviors." Journal of Robotics and Mechatronics 33, no. 3 (June 20, 2021): 505–14. http://dx.doi.org/10.20965/jrm.2021.p0505.

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Анотація:

Understanding the principles of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. The rules regarding the real-world biological multi-agent behaviors such as those in team sports are often largely unknown due to their inherently higher-order interactions, cognition, and body dynamics. Estimation of the rules from data, i.e., via data-driven approaches such as machine learning, provides an effective way to analyze such behaviors. Although most data-driven models have non-linear structures and high predictive performances, it is some

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Pinheiro-Chagas, Pedro, Amy Daitch, Josef Parvizi, and Stanislas Dehaene. "Brain Mechanisms of Arithmetic: A Crucial Role for Ventral Temporal Cortex." Journal of Cognitive Neuroscience 30, no. 12 (December 2018): 1757–72. http://dx.doi.org/10.1162/jocn_a_01319.

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Анотація:

Elementary arithmetic requires a complex interplay between several brain regions. The classical view, arising from fMRI, is that the intraparietal sulcus (IPS) and the superior parietal lobe (SPL) are the main hubs for arithmetic calculations. However, recent studies using intracranial electroencephalography have discovered a specific site, within the posterior inferior temporal cortex (pITG), that activates during visual perception of numerals, with widespread adjacent responses when numerals are used in calculation. Here, we reexamined the contribution of the IPS, SPL, and pITG to arithmetic

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Li, Wenjun, Lidong Tan, and Ciyun Lin. "Modeling driver behavior in the dilemma zone based on stochastic model predictive control." PLOS ONE 16, no. 2 (February 24, 2021): e0247453. http://dx.doi.org/10.1371/journal.pone.0247453.

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Driver behavior is considered one of the most important factors in the genesis of dilemma zones and the safety of driver-vehicle-environment systems. An accurate driver behavior model can improve the traffic signal control efficiency and decrease traffic accidents in signalized intersections. This paper uses a mathematical modeling method to study driver behavior in a dilemma zone based on stochastic model predictive control (SMPC), along with considering the dynamic characteristics of human cognition and execution, aiming to provide a feasible solution for modeling driver behavior more accura

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Labunskaya, V. A., and E. V. Kapitanova. "Self-Assessment and Appearance Evaluation in Student Group as Predictors in Relationships of Interpersonal Significance." Social Psychology and Society 7, no. 1 (2016): 72–87. http://dx.doi.org/10.17759/sps.2016070106.

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The paper attempts to integrate the concept of relationships of interpersonal significance, approaches to the correlation between physical appearance and life satisfaction, as well as several concepts of interpersonal cognition, self-assessment and evaluations of other people’s physical appearance. It introduces the concept of “insignificant/significant assessor of appearance” and argues that among the factors that turn a group member into the “insignificant/significant assessor of appearance” are evaluations, self-evaluations and group evaluations of physical appearance. The research describe

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Datsii, O., N. Datsii, O. Zborovska, L. Ivashova, M. Cherkashyna, and K. Ingram. "Financing of environmental programs for industrial waste management in times of crisis." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 1 (2021): 130–36. http://dx.doi.org/10.33271/nvngu/2021-1/130.

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Purpose. To conduct an analysis of funding from state and regional budgets for environmental needs in general and itemized as waste management, to identify correlations of data and to build on their basis a mathematical apparatus for forecasting the financing of environmental needs in the event of a budget crisis. Methodology. The results of the study were obtained using general and special methods of cognition. Methods of comparative analysis, content analysis and logical generalization were used to analyze the financing of environmental needs in general as well as waste management, in partic

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Khmara, D. S., and V. N. Samotuga. "Development of E-Commerce in the Field of Small and Medium Enterprises." Economics and Management 27, no. 6 (July 23, 2021): 426–36. http://dx.doi.org/10.35854/1998-1627-2021-6-426-436.

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Aim. The presented study aims to produce a set of tools for modeling the development of internet companies.Tasks. The authors show the competitive advantages of small and medium enterprises; identify the main features of the modern business environment; determine the structure of modern e-business; identify Internet factors affecting a company that implements Internet technologies in its commercial activities; identify the main factors that make the Internet market attractive for small and medium enterprises and determine their readiness to implement e-business; provide an overview of governme

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O'Donnell, Timothy J., Marc D. Hauser, and W. Tecumseh Fitch. "Using mathematical models of language experimentally." Trends in Cognitive Sciences 9, no. 6 (June 2005): 284–89. http://dx.doi.org/10.1016/j.tics.2005.04.011.

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Lamberts, Koen. "Array Models of Cognition." Journal of Mathematical Psychology 40, no. 3 (September 1996): 271–74. http://dx.doi.org/10.1006/jmps.1996.0025.

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Elliott, T., C. I. Howarth, and N. R. Shadbolt. "Axonal Processes and Neural Plasticity: A Reply." Neural Computation 10, no. 3 (April 1, 1998): 549–54. http://dx.doi.org/10.1162/089976698300017656.

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We examine the claim that a class of sprouting-and-retraction models is mathematically equivalent to a fixed-anatomy model. We accept, subject to important caveats, a narrow mathematical equivalence of the energy functions in both classes of model. We argue that this narrow equivalence of energy functions does not, however, entail equivalence of the models. Indeed, the claim of complete model equivalence hides significant dynamical differences between the approaches, which we discuss. We also disagree that our work demonstrates that subtractive constraint enforcement is natural in fixed-anatom

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Zak, Michail. "Physical models of cognition." International Journal of Theoretical Physics 33, no. 5 (May 1994): 1113–61. http://dx.doi.org/10.1007/bf01882756.

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