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 formal properties of information towards an extended theory of information science. From this point of view, information is classified into the categories of classic, computational, and cognitive information in the contexts of communication, computation, and cognition, respectively. Based on the three generations of information theories, a coherent framework of contemporary information is introduced, which reveals the nature of information and the fundamental principles of information science and engineering.
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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 representational power of distributed neural codes and similarity-based pattern recognition. The architectures for cognitive computing that emerge from these approaches are neural associative memories endowed with additional mapping operations to handle invariances and to form reduced representations of combinatorial structures.
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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 interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.
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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 adapted to simulate the neural and behavioral studies of mathematics anxiety. In other words, here we provide a novel integrative network theory on the links between mathematics anxiety, cognition, and brain substrates. This theoretical framework may explain the impact of mathematics anxiety on a range of cognitive and neuropsychological tests. Therefore, it could improve our understanding of the cognitive and neurological mechanisms underlying mathematics anxiety and also has important applications. Indeed, a better understanding of mathematics anxiety could inform more effective therapeutic techniques that in turn could lead to significant improvements in educational outcomes.
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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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Анотація:
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 regards a model as analogous to a protoimage (a fact, an event, a process), its symbolic representation, or an idealized pattern (actions, behavior). The article highlights the basic principles underlying the development of socio-historical models: a model is representational (reflecting the ontologicalfeatures of the protoimage), relevant, both conditional and autonomous, moreover, a model has its individual life cycle, with the existence and development of the model determined by its cognitive value. Modeling as a cognitive method emerged in response to the new perspective which viewed socio-historical processes as products of the meaningful activity of the agent. One of the most significant constituents of the model is its axiological motivation, which reflects the axiological system and the ideology immanent to the protoimage, and, thus, accounts for both the specifics and the essence of modeling. Another peculiarity and forte of modeling is the possibility and tolerance of the quantification of socio-historical processes, that is, translating qualitative characteristics onto the quantitative plane and developing mathematical models which lend themselves to mathematical study and interpretation, reducing ideological and axiological influences.
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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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Анотація:
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 assumptions are introduced. What kind of modelling flexibility does QPT dynamics offer without any simplifying assumptions and is it likely that such flexibility will be relevant in cognitive modelling? We consider a series of nested QPT dynamical models, constructed with a view to accommodate results from a simple, hypothetical experimental paradigm on decision-making. We consider Hamiltonians more complex than the ones which have traditionally been employed with a view to explore the putative explanatory value of this additional complexity. We then proceed to compare simple models with extensions regarding both the initial state (e.g. a mixed state with a specific orthogonal decomposition; a general mixed state) and the dynamics (by introducing Hamiltonians which destroy the separability of the initial structure and by considering an open-system extension). We illustrate the relations between these models mathematically and numerically. This article is part of the themed issue ‘Second quantum revolution: foundational questions’.
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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 reasoning - by detecting the patterns that mimic the natural human cognition - is used in this study as this paper is based on the partial implementation of graph theory in modelling and solving issues related to part machine grouping, regardless of their size. The obtained results have shown that most of the problems solved by using the proposed approach have provided interesting benchmark results when compared with previous results given by GRASP (Greedy Randomized Adaptive Search Procedure) heuristics. Keywords: Cellular manufacturing systems; part family formation; human cognition; inspection-based clustering.
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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 different disciplines. The aim of the article is to stimulate interest in the use of diverse learning approaches and forms, on the learning of mathematics as science, on its application in other scientific disciplines to address problems, on mathematics as a form of systemic thinking and on mathematical modelling as a learning method. The study used student test papers and open-ended questionnaires to collect data. The research used data triangulation method for data processing.
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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 representation.
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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 has been focused on two different projects: 1) the first one concerns the development of neural oscillators networks, in order to investigate the mechanisms of synchronization of the neural oscillatory activity during cognitive processes, such as object recognition, memory, language, attention; 2) the second one concerns the mathematical modelling of multisensory integration processes (e.g. visual-acoustic), which occur in several cortical and subcortical regions (in particular in a subcortical structure named Superior Colliculus (SC)), and which are fundamental for orienting motor and attentive responses to external world stimuli. This activity has been realized in collaboration with the Center for Studies and Researches in Cognitive Neuroscience of the University of Bologna (in Cesena) and the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA). PART 1. Objects representation in a number of cognitive functions, like perception and recognition, foresees distribute processes in different cortical areas. One of the main neurophysiological question concerns how the correlation between these disparate areas is realized, in order to succeed in grouping together the characteristics of the same object (binding problem) and in maintaining segregated the properties belonging to different objects simultaneously present (segmentation problem). Different theories have been proposed to address these questions (Barlow, 1972). One of the most influential theory is the so called “assembly coding”, postulated by Singer (2003), according to which 1) an object is well described by a few fundamental properties, processing in different and distributed cortical areas; 2) the recognition of the object would be realized by means of the simultaneously activation of the cortical areas representing its different features; 3) groups of properties belonging to different objects would be kept separated in the time domain. In Chapter 1.1 and in Chapter 1.2 we present two neural network models for object recognition, based on the “assembly coding” hypothesis. These models are networks of Wilson-Cowan oscillators which exploit: i) two high-level “Gestalt Rules” (the similarity and previous knowledge rules), to realize the functional link between elements of different cortical areas representing properties of the same object (binding problem); 2) the synchronization of the neural oscillatory activity in the γ-band (30-100Hz), to segregate in time the representations of different objects simultaneously present (segmentation problem). These models are able to recognize and reconstruct multiple simultaneous external objects, even in difficult case (some wrong or lacking features, shared features, superimposed noise). In Chapter 1.3 the previous models are extended to realize a semantic memory, in which sensory-motor representations of objects are linked with words. To this aim, the network, previously developed, devoted to the representation of objects as a collection of sensory-motor features, is reciprocally linked with a second network devoted to the representation of words (lexical network) Synapses linking the two networks are trained via a time-dependent Hebbian rule, during a training period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from linguistic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with some shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits). PART 2. The ability of the brain to integrate information from different sensory channels is fundamental to perception of the external world (Stein et al, 1993). It is well documented that a number of extraprimary areas have neurons capable of such a task; one of the best known of these is the superior colliculus (SC). This midbrain structure receives auditory, visual and somatosensory inputs from different subcortical and cortical areas, and is involved in the control of orientation to external events (Wallace et al, 1993). SC neurons respond to each of these sensory inputs separately, but is also capable of integrating them (Stein et al, 1993) so that the response to the combined multisensory stimuli is greater than that to the individual component stimuli (enhancement). This enhancement is proportionately greater if the modality-specific paired stimuli are weaker (the principle of inverse effectiveness). Several studies have shown that the capability of SC neurons to engage in multisensory integration requires inputs from cortex; primarily the anterior ectosylvian sulcus (AES), but also the rostral lateral suprasylvian sulcus (rLS). If these cortical inputs are deactivated the response of SC neurons to cross-modal stimulation is no different from that evoked by the most effective of its individual component stimuli (Jiang et al 2001). This phenomenon can be better understood through mathematical models. The use of mathematical models and neural networks can place the mass of data that has been accumulated about this phenomenon and its underlying circuitry into a coherent theoretical structure. In Chapter 2.1 a simple neural network model of this structure is presented; this model is able to reproduce a large number of SC behaviours like multisensory enhancement, multisensory and unisensory depression, inverse effectiveness. In Chapter 2.2 this model was improved by incorporating more neurophysiological knowledge about the neural circuitry underlying SC multisensory integration, in order to suggest possible physiological mechanisms through which it is effected. This endeavour was realized in collaboration with Professor B.E. Stein and Doctor B. Rowland during the 6 months-period spent at the Department of Neurobiology and Anatomy of the Wake Forest University School of Medicine (NC, USA), within the Marco Polo Project. The model includes four distinct unisensory areas that are devoted to a topological representation of external stimuli. Two of them represent subregions of the AES (i.e., FAES, an auditory area, and AEV, a visual area) and send descending inputs to the ipsilateral SC; the other two represent subcortical areas (one auditory and one visual) projecting ascending inputs to the same SC. Different competitive mechanisms, realized by means of population of interneurons, are used in the model to reproduce the different behaviour of SC neurons in conditions of cortical activation and deactivation. The model, with a single set of parameters, is able to mimic the behaviour of SC multisensory neurons in response to very different stimulus conditions (multisensory enhancement, inverse effectiveness, within- and cross-modal suppression of spatially disparate stimuli), with cortex functional and cortex deactivated, and with a particular type of membrane receptors (NMDA receptors) active or inhibited. All these results agree with the data reported in Jiang et al. (2001) and in Binns and Salt (1996). The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain the fundamental aspects of multisensory integration, and provides a biologically plausible hypothesis about the underlying circuitry.
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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 Conceptualist and Platonist (as well as Pluralist) interpretations of linguistic theory by means of three grades of mathematical involvement for linguistic grammars. Part II explores the specific models of syntactic and semantics by an analogy with the harder sciences. In Part III, I develop a novel account of linguistic ontology and in the process comment on the type-token distinction, the role and connection with mathematics and the nature of linguistic objects. In this research, I offer a structural realist interpretation of linguistic methodology with a nuanced structuralist picture for its ontology. This proposal is informed by historical and current work in theoretical linguistics as well as philosophical views on ontology, scientific modelling and mathematics.
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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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Анотація:
Math & Science Education
Ph.D.
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 understanding of elementary multiplicative inverses. As part of an embedded mixed-methods design, I analyzed curriculum materials, classroom instruction, and student assessments from four elementary mathematics teachers’ classrooms. A situation model perspective of comprehension was used for analysis. The aim of this study was thus to determine how instructional tasks, representations, and deep questions are used for connection-making, which is the foundation of a situation model that can be used for inference-making. Results suggest that student comprehension depends more on connection-making opportunities afforded by classroom teachers, rather than on learning opportunities found solely within a curriculum. This included instruction that focused on deeply unpacking side-by-side comparison type examples, situated examples in personal concrete contexts, used semi-concrete representations to illustrate structural relationships, promoted efficiency through the sequence of presented representations, and posed deep questions which supported students’ sense-making and emphasized the interconnectedness of mathematics. By analyzing these key aspects, this study contributes to research on mathematical understanding and provides a foundation for helping students facilitate transfer of prior knowledge into novel mathematical situation.
Temple University--Theses
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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 represented. It is thereby of importance to understand the causal chains between the surrounding conditions and the pedestrians’ behaviours: the impact of the environment’s purpose and facilities as well as the pedestrians’ individual goals on the pedestrians’ planning and route choice behaviour; the influence of emergent stimuli on the pedestrians’ plans and environment usage; the influence of the pedestrians’ environment usage under normal usage conditions on the pedestrians’ behaviour in response to a potential alarm event. In this thesis, a framework is developed for modelling advanced individual pedestrian behaviours and especially purpose-driven environment usage. The framework thereby aims to assist building and facility planners in improving a building’s layout in terms of pedestrian experience and walking routes. In this thesis, a comprehensive review on how individual pedestrian behaviour and the pedestrians’ environment usage are realised in current pedestrian behaviour simulation models has been undertaken. In addition, current theories on human decision making, goal-driven behaviour and emotion modelling have been surveyed from the research fields of artificial intelligence, virtual reality simulation, human psychology and human behavioural sciences. From this survey, theories suitable for this thesis’ cause have been identified and combined for the proposed Cognitive Pedestrian Agent Framework (CPAF). The proposed framework contains a sophisticated human decision making model, a multi-faceted individual knowledge representation, a model to realise situational and contextual awareness, and a novel realisation of a human path planning heuristic. The proposed framework has been demonstrated in the simulation of a building usage-cycle use case. Further, it has been outlined how the proposed framework could be used to model experiential alarm response behaviour.
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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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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 past research has shown relate to overall math achievement. Other goals were to compare a cognitive model of algebra achievement with a model of SAT-M performance and to test for gender differences in the model of algebra achievement. Structural equation modeling was used to test the direct and indirect effects of algebra experience, working memory, 3D spatial abilities, and computational fluency on algebra achievement. Algebra experience had the strongest direct effect on algebra achievement. Combined direct and indirect effects of computational fluency were as strong as the direct effect of algebra experience. While 3D spatial abilities had a direct effect on algebra achievement, working memory did not. Working memory did have a direct effect on computational fluency and 3D spatial abilities. The total effects of 3D spatial abilities and working memory on algebra achievement were moderate. There were differences in the cognitive models of algebra achievement and SAT-M. SAT-M scores were highly related to 3D spatial abilities, but moderately related to algebra experience. There were also gender differences in the cognitive model of algebra achievement. Working memory was highly related to computational fluency for males, but was not related to computational fluency for females. This study adds to the large body of evidence that working memory plays a role in computational abilities throughout development. The evidence that working memory affects higher level math achievement indirectly through computational fluency and 3D spatial abilities provides clarity to conflicting results in the few studies that have examined the role of working memory in higher level math achievement.
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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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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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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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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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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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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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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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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"

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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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Анотація:
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 aspects in styling, such as “beauty” and “similarity.” We report the guideline with regard to circumstance and cognition experiments to mathematically formulate the macroscopic feature “complexity.”
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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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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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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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