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Journal articles on the topic 'Cognitive science'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 August 2024

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1

Alberto Greco. "Cognitive science and cognitive sciences." Journal of Cognitive Science 13, no. 4 (December 2012): 471–85. http://dx.doi.org/10.17791/jcs.2012.13.4.471.

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2

Favela, Luis H., and Jonathan Martin. "“Cognition” and Dynamical Cognitive Science." Minds and Machines 27, no. 2 (December 7, 2016): 331–55. http://dx.doi.org/10.1007/s11023-016-9411-4.

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3

Aasim, Shahzad. "Cognitive Dimensions: Where Science Meets Art." International Journal of Science and Research (IJSR) 8, no. 6 (June 5, 2019): 2422–23. http://dx.doi.org/10.21275/sr24221151213.

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4

Ross, Don. "Economics, cognitive science and social cognition." Cognitive Systems Research 9, no. 1-2 (March 2008): 125–35. http://dx.doi.org/10.1016/j.cogsys.2007.06.010.

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5

Sofronova, Lidia. "Historical Cognition and Cognitive Sciences: New in Russian Historiography." ISTORIYA 12, no. 8 (106) (2021): 0. http://dx.doi.org/10.18254/s207987840016952-6.

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The article presents an analytical review of the recent literature on cognitive history, especially the Russian collective monograph “Cognitive Sciences and Historical Cognition”, published in 2020. It traces the patterns typical for interdisciplinary research not only within the humanitarian disciplines, but also at the “borders” between the humanities and the “natural sciences”. The article highlights the paradoxical and productive nature of the “mutual interventions” of cognitive science and the humanities, which contribute to overcoming “atomism” both within the humanities and at the “frontier” between them and the natural science disciplines.
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6

Boring, Ronald Laurids. "Cognitive science." XRDS: Crossroads, The ACM Magazine for Students 10, no. 2 (December 2003): 1. http://dx.doi.org/10.1145/1027328.1027329.

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7

Wimer Brakel, Linda A. "Cognitive Science." Psychoanalytic Quarterly 64, no. 2 (April 1995): 417–28. http://dx.doi.org/10.1080/21674086.1995.11927458.

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8

Dartnall, Terry, Steve Torrance, Mark Coulson, Stephen Nunn, Brendan Kitts, R. F. Port, T. van Gelder, Donald Peterson, and Philip Gerrans. "Cognitive science." Metascience 5, no. 1 (March 1996): 95–166. http://dx.doi.org/10.1007/bf02988881.

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9

Stern, Elsbeth. "Cognitive Psychology and Cognitive Science." Contemporary Psychology: A Journal of Reviews 36, no. 6 (June 1991): 485–86. http://dx.doi.org/10.1037/029811.

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10

Leite, Diego Azevedo. "Neo-mechanistic explanatory integration for cognitive science: the problem of reduction remains." Sofia 8, no. 1 (September 5, 2019): 124–45. http://dx.doi.org/10.47456/sofia.v8i1.23198.

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One of the central aims of the neo-mechanistic framework for the neural and cognitive sciences is to construct a pluralistic integration of scientific explanations, allowing for a weak explanatory autonomy of higher-level sciences, such as cognitive science. This integration involves understanding human cognition as information processing occurring in multi-level human neuro-cognitive mechanisms, explained by multi-level neuro-cognitive models. Strong explanatory neuro-cognitive reduction, however, poses a significant challenge to this pluralist ambition and the weak autonomy of cognitive science derived therefrom. Based on research in current molecular and cellular neuroscience, the framework holds that the best strategy for integrating human neuro-cognitive theories is through direct reductive explanations based on molecular and cellular neural processes. It is my aim to investigate whether the neo-mechanistic framework can meet the challenge. I argue that leading neo-mechanists offer some significant replies; however, they are not able yet to completely remove strong explanatory reductionism from their own framework.
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11

Wang, Yingxu, Bernard Carlos Widrow, Bo Zhang, Witold Kinsner, Kenji Sugawara, Fuchun Sun, Jianhua Lu, Thomas Weise, and Du Zhang. "Perspectives on the Field of Cognitive Informatics and its Future Development." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 1 (January 2011): 1–17. http://dx.doi.org/10.4018/jcini.2011010101.

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The contemporary wonder of sciences and engineering has recently refocused on the beginning point of: how the brain processes internal and external information autonomously and cognitively rather than imperatively like conventional computers. Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of eight position statements presented in the plenary panel of IEEE ICCI’10 on Cognitive Informatics and Its Future Development contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.
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12

Bradley, Dianne. "Cognitive science and the language/ cognition distinction." Aphasiology 3, no. 8 (December 1989): 755–57. http://dx.doi.org/10.1080/02687038908249045.

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13

WANNER, E. "A Science of Cognition: Method and Tactics in Cognitive Science." Science 228, no. 4695 (April 5, 1985): 68. http://dx.doi.org/10.1126/science.228.4695.68.

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14

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

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A recent discovery in computer and software sciences is that information in general is a deterministic abstract quantity rather than a probability-based property of the nature. Information is a general form of abstract objects represented by symbolical, mathematical, communication, computing, and cognitive systems. Therefore, information science is one of the contemporary scientific disciplines collectively known as abstract sciences such as system, information, cybernetics, cognition, knowledge, and intelligence sciences. This paper presents the cognitive foundations, mathematical models, and formal properties of information towards an extended theory of information science. From this point of view, information is classified into the categories of classic, computational, and cognitive information in the contexts of communication, computation, and cognition, respectively. Based on the three generations of information theories, a coherent framework of contemporary information is introduced, which reveals the nature of information and the fundamental principles of information science and engineering.
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15

Wang, Yingxu, George Baciu, Yiyu Yao, Witold Kinsner, Keith Chan, Bo Zhang, Stuart Hameroff, et al. "Perspectives on Cognitive Informatics and Cognitive Computing." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 1 (January 2010): 1–29. http://dx.doi.org/10.4018/jcini.2010010101.

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Cognitive informatics is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. Cognitive computing is an emerging paradigm of intelligent computing methodologies and systems based on cognitive informatics that implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a set of collective perspectives on cognitive informatics and cognitive computing, as well as their applications in abstract intelligence, computational intelligence, computational linguistics, knowledge representation, symbiotic computing, granular computing, semantic computing, machine learning, and social computing.
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16

Kozhevnikova, Larisa A. "Librarianship as Knowledge and Science." Bibliotekovedenie [Russian Journal of Library Science], no. 1 (February 24, 2011): 22–27. http://dx.doi.org/10.25281/0869-608x-2011-0-1-22-27.

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The paper deals with cognitive structure of library science. A cognitive determination of various types of library knowledge is analyzed. Knowledge of institutional character and scientific knowledge are determined. The author examines essential characteristics of scientific knowledge, its objective, and stages of cognition and cognitive values of library science.
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17

Boster, James S. "Cognitive Anthropology Is a Cognitive Science." Topics in Cognitive Science 4, no. 3 (June 18, 2012): 372–78. http://dx.doi.org/10.1111/j.1756-8765.2012.01204.x.

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18

Carey, Susan. "Cognitive science and science education." American Psychologist 41, no. 10 (1986): 1123–30. http://dx.doi.org/10.1037/0003-066x.41.10.1123.

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19

Stewart, James. "Cognitive science and science education." European Journal of Science Education 7, no. 1 (January 1985): 1–17. http://dx.doi.org/10.1080/0140528850070101.

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20

Kalia, Lipika Priyadarshini. "Computer Science is Cognitive Science." Advanced Science Letters 22, no. 2 (February 1, 2016): 538–39. http://dx.doi.org/10.1166/asl.2016.6851.

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21

Gabrić, Petar. "Konferencija: Cognitive Science." Drustvena istrazivanja 28, no. 2 (June 10, 2019): 361–62. http://dx.doi.org/10.5559/di.28.2.11.

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22

Hovhannisyan, Garri. "Humanistic cognitive science." Humanistic Psychologist 46, no. 1 (March 2018): 30–52. http://dx.doi.org/10.1037/hum0000074.

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23

Carey, Susan. "Thescienceof cognitive science." Social Anthropology 23, no. 2 (May 2015): 204–7. http://dx.doi.org/10.1111/1469-8676.12119.

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24

Dean, Michael P. "Review: Cognitive Science." Perception 29, no. 6 (June 2000): 755–56. http://dx.doi.org/10.1068/p2906rvw.

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25

Rönnberg, Jerker, Mary Rudner, and Thomas Lunner. "Cognitive Hearing Science." Trends in Amplification 15, no. 3 (May 22, 2011): 140–48. http://dx.doi.org/10.1177/1084713811409762.

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26

Johansen-Berg, Heidi. "Cognitive science fiction." Trends in Cognitive Sciences 5, no. 6 (June 2001): 233. http://dx.doi.org/10.1016/s1364-6613(00)01694-6.

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27

WALTZ, D. "Cognitive science news." Cognitive Science 9, no. 4 (December 1985): ii—iv. http://dx.doi.org/10.1016/s0364-0213(85)80017-3.

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28

Goldstone, Robert L. "Becoming Cognitive Science." Topics in Cognitive Science 11, no. 4 (October 2019): 902–13. http://dx.doi.org/10.1111/tops.12463.

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29

Carr, Thomas H. "Understanding Cognitive Science?" Contemporary Psychology: A Journal of Reviews 35, no. 11 (November 1990): 1079–80. http://dx.doi.org/10.1037/030592.

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30

DANZIGER, EVE. "Cognitive Dimensions of Social Science.:Cognitive Dimensions of Social Science." Journal of Linguistic Anthropology 14, no. 2 (December 2004): 290–92. http://dx.doi.org/10.1525/jlin.2004.14.2.290.

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31

Motz, Benjamin. "Cognitive science in popular film: the Cognitive Science Movie Index." Trends in Cognitive Sciences 17, no. 10 (October 2013): 483–85. http://dx.doi.org/10.1016/j.tics.2013.08.002.

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32

Castro, Nichol, and Cynthia S. Q. Siew. "Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2238 (June 2020): 20190825. http://dx.doi.org/10.1098/rspa.2019.0825.

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Modelling the structure of cognitive systems is a central goal of the cognitive sciences—a goal that has greatly benefitted from the application of network science approaches. This paper provides an overview of how network science has been applied to the cognitive sciences, with a specific focus on the two research ‘spirals’ of cognitive sciences related to the representation and processes of the human mind. For each spiral, we first review classic papers in the psychological sciences that have drawn on graph-theoretic ideas or frameworks before the advent of modern network science approaches. We then discuss how current research in these areas has been shaped by modern network science, which provides the mathematical framework and methodological tools for psychologists to (i) represent cognitive network structure and (ii) investigate and model the psychological processes that occur in these cognitive networks. Finally, we briefly comment on the future of, and the challenges facing, cognitive network science.
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33

Asma, Stephen. "Adaptive Imagination: Toward a Mythopoetic Cognitive Science." Evolutionary Studies in Imaginative Culture 5, no. 2 (December 1, 2021): 1–32. http://dx.doi.org/10.26613/esic.5.2.236.

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Abstract A mythopoetic paradigm or perspective sees the world primarily as a dramatic story of competing personal intentions, rather than a system of objective impersonal laws. Asma (2017) argued that our contemporary imaginative cognition is evolutionarily conserved-it has structural and functional similarities to premodern Homo sapiens’s cognition. This article will (i) outline the essential features of mythopoetic cognition or adaptive imagination, (ii) delineate the adaptive sociocultural advantages of mythopoetic cognition, (iii) explain the phylogenetic and ontogenetic mechanisms that give rise to human mythopoetic mind (i.e., genetically endowed simulation and associational systems that underwrite diverse symbolic systems), (iv) show how mythopoetic cognition challeng­es contemporary trends in cognitive science and philosophy, and (v) recognize and outline empirical approaches for a new cognitive science of the imagination.
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34

Chokr, Nader N. "Mind, consciousness, and cognition: Phenomenology vs. cognitive science." Husserl Studies 9, no. 3 (1992): 179–97. http://dx.doi.org/10.1007/bf00142815.

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35

Siew, Cynthia S. Q., Dirk U. Wulff, Nicole M. Beckage, Yoed N. Kenett, and Ana Meštrović. "Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics." Complexity 2019 (June 17, 2019): 1–24. http://dx.doi.org/10.1155/2019/2108423.

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Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates how network science approaches have been applied to the study of human cognition and how network science can uniquely address and provide novel insight on important questions related to the complexity of cognitive systems and the processes that occur within those systems. Drawing on the literature in cognitive network science, with a focus on semantic and lexical networks, we argue three key points. (i) Network science provides a powerful quantitative approach to represent cognitive systems. (ii) The network science approach enables cognitive scientists to achieve a deeper understanding of human cognition by capturing how the structure, i.e., the underlying network, and processes operating on a network structure interact to produce behavioral phenomena. (iii) Network science provides a quantitative framework to model the dynamics of cognitive systems, operationalized as structural changes in cognitive systems on different timescales and resolutions. Finally, we highlight key milestones that the field of cognitive network science needs to achieve as it matures in order to provide continued insights into the nature of cognitive structures and processes.
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36

Sedlacek, Miroslav. "Models of Mind That Are Implied by Cognitive Science." JOURNAL OF INTERNATIONAL BUSINESS RESEARCH AND MARKETING 2, no. 5 (2017): 33–36. http://dx.doi.org/10.18775/jibrm.1849-8558.2015.25.3005.

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Three models of mind were proposed during the history of cognitive science: functionalist, psychological, and neural model. The functionalist model of mind is based on symbol manipulation (computation). This model has two versions: the conception of mind as an innate information processor and problem solver, and mind as innate cognitive and visual system. A psychological model of mind is the extension by “common sense”. The alternative is mind as a cognitive and visual system that is based mostly on manipulation of internal mental data. The neural model is the only model of embodied mind. It has two versions: non-pragmatic and pragmatic (autopoietic, distributed, and social mind).
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37

Powell, Janet M., Earl Hunt, and Mary Pepping. "Collaboration Between Cognitive Science and Cognitive Rehabilitation." Journal of Head Trauma Rehabilitation 19, no. 3 (2004): 266–76. http://dx.doi.org/10.1097/00001199-200405000-00007.

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38

Chater, Nick. "Can cognitive science create a cognitive economics?" Cognition 135 (February 2015): 52–55. http://dx.doi.org/10.1016/j.cognition.2014.10.015.

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39

Lopes, Jesse. "Cognitive Science, Phenomenology, and the Unity of Science." Studia Phaenomenologica 24 (2024): 81–102. http://dx.doi.org/10.5840/studphaen2024245.

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Hume once argued the basic science to be not physics but “the science of man” and the foundation of this science to be the empiricist mechanism of association governed by the law of similarity in appearance—now more popular than ever in the form of artificial neural networks. I update Hume’s picture by showing phenomenology to be centrally concerned with providing a unifying basis for all the sciences (including physics) by going beyond the psychology of associationism (passive synthesis) to reveal phenomena that are irreducibly syntactic (not associative) in structure. I therefore argue that the language of thought (LOT) is the necessary mechanism at the basis of these descriptive phenomena. I conclude by sketching a new picture of all the sciences unified by LOT based on Husserl’s opposition to Galilean “physicomathematical” science vis-à-vis the life-world (Lebenswelt).
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40

Maltseva, Kateryna. "Bridging sociology with anthropology and cognitive science perspectives to assess shared cultural knowledge." Sociology: Theory, Methods, Marketing, stmm 2020 (1) (March 16, 2020): 108–18. http://dx.doi.org/10.15407/sociology2020.01.108.

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Following the cognitive revolution of the 1960s, cultural variation in behavior and knowledge has been a long-standing subject in social sciences. The “cognitive turn” in sociology brought to light many interesting issues and complex questions. The present publication addresses both theoretical and — to some extent — methodological challenges faced by the sociologists engaged in researching shared cultural variation within the culture-and-cognition research agenda, and compares it with the status quo in cousin social sciences that share the same cognitive perspective on culture. I specifically focus on the conceptual junctures that follow from the assumptions of shared cultural knowledge and intersubjectively shared cultural worldviews to highlight the important features of culture which can be effectively used for quantitative assessment of complex cultural processes. While I discuss various aspects of the findings and failings attributable to the culture-and-cognition research direction, my principal concern centers on encouraging more enhanced and sensitized interdisciplinary communication, as well as maximized intersections between cognitively oriented studies of culture in different social sciences, to bring the sociological studies of culture and cognition to full fruition.
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41

Crippen, Matthew. "Embodied Cognition and Perception: Dewey, Science and Skepticism." Contemporary Pragmatism 14, no. 1 (May 30, 2017): 112–34. http://dx.doi.org/10.1163/18758185-01401007.

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This article examines how Modern theories of mind remain even in some materialistic and hence ontologically anti-dualistic views; and shows how Dewey, anticipating Merleau-Ponty and 4E cognitive scientists, repudiates these theories. Throughout I place Dewey’s thought in the context of scientific inquiry, both recent and historical and including the cognitive as well as traditional sciences; and I show how he incorporated sciences of his day into his thought, while also anticipating enactive cognitive science. While emphasizing Dewey’s continued relevance, my main goal is to show how his scientifically informed account of perception and cognition combats skepticism propagated by certain scientific visions, exacerbated by commonplace notions about mind, that jointly suggest that human beings lack genuine access to reality.
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42

Elin, Mirza Niaz Zaman. "Exploring the Quantum Nature of Human Cognition: Integrating Quantum Cognitive Science and Advanced Cognitive Assessment through the Verbovisual Cognitive Function Screening Tool (VCFST)." International Journal of Interdisciplinary Research Methods 10, no. 2 (February 15, 2023): 49–63. http://dx.doi.org/10.37745/ijirm.14/vol10n24963.

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The Verbovisual Cognitive Function Screening Tool (VCFST) is a cognitive screening tool that aims to evaluate cognitive abilities by drawing upon established theories such as dual-coding theory, common coding theory, propositional theory, and ideomotor theory. However, recent developments in quantum cognitive science offer promising applications to enhance the assessment model. This article explores the utilization of quantum cognition concepts within the VCFST and discusses its implications for cognitive assessment. Quantum cognitive science is an emerging interdisciplinary field that applies principles from quantum physics to the study of human cognition. It suggests that cognitive processes may not adhere strictly to classical logic and linear thinking but can exhibit characteristics of quantum phenomena such as superposition, entanglement, and contextuality. By incorporating quantum concepts into cognitive assessment, a more nuanced understanding of complex cognitive processes can be achieved.The VCFST, with its focus on verbovisual factors and comprehensive assessment of cognitive domains, can benefit from the integration of quantum cognitive science principles. Superposition, borrowed from quantum physics, implies that individuals can simultaneously perceive and attend to multiple stimuli. By considering the superposition of verbovisual stimuli within the VCFST, a more accurate representation of cognitive abilities can be obtained, capturing the simultaneous processing of different types of information. Entanglement, another key principle of quantum cognition, can be applied to evaluate the interconnectedness of cognitive domains within the VCFST. Analogous to entangled particles, changes in one cognitive domain may influence others. By assessing the impact of changes in one domain on performance in other domains, the VCFST can provide a holistic understanding of cognitive functioning. Contextuality, a fundamental concept in quantum cognition, emphasizes the role of context in shaping cognitive processes. By incorporating contextuality into the VCFST, contextual cues can be introduced, and stimulus presentation can be varied to reflect real-world cognitive demands. This approach enables a more ecologically valid measure of cognitive abilities, considering the influence of context on cognitive processing. Embracing quantum cognitive science principles in the VCFST offers several advantages, including capturing the complexity of cognitive processes, accounting for non-linear dynamics, and exploring potential quantum-like effects within human cognition. Future research can further refine the VCFST by conducting quantitative studies to assess the efficacy of incorporating quantum concepts into cognitive assessment and developing computational models inspired by quantum cognitive science.The utilization of quantum cognitive science concepts within the VCFST shows promise for advancing cognitive assessment practices. By incorporating principles such as superposition, entanglement, and contextuality, the VCFST can provide a comprehensive and nuanced evaluation of cognitive abilities. Further research in quantum cognitive science and the refinement of assessment methodologies can contribute to the development of more advanced and accurate cognitive assessment tools, facilitating personalized diagnostic and therapeutic strategies for cognitive disorders and neurodegenerative conditions. The integration of quantum cognitive science into cognitive assessment represents an exciting frontier that can revolutionize our understanding of human cognition.
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43

Adey, Philip. "Science education research and cognitive science." Research in Science Education 25, no. 1 (March 1995): 101–13. http://dx.doi.org/10.1007/bf02356463.

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44

Busemeyer, Jerome R. "Cognitive science contributions to decision science." Cognition 135 (February 2015): 43–46. http://dx.doi.org/10.1016/j.cognition.2014.11.010.

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45

Wang, Yingxu, Robert C. Berwick, Simon Haykin, Witold Pedrycz, Witold Kinsner, George Baciu, Du Zhang, and C. Bhavsar. "Cognitive Informatics and Cognitive Computing in Year 10 and Beyond." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (October 2011): 1–21. http://dx.doi.org/10.4018/jcini.2011100101.

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Cognitive Informatics (CI) is a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. The latest advances in CI leads to the establishment of cognitive computing theories and methodologies, as well as the development of Cognitive Computers (CogC) that perceive, infer, and learn. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC’11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world’s renowned researchers and scholars in the field of cognitive informatics and cognitive computing.
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46

Wilson, Patrick, and Wayne W. Reeves. "Cognition and Complexity: The Cognitive Science of Managing Complexity." Journal of Education for Library and Information Science 38, no. 3 (1997): 232. http://dx.doi.org/10.2307/40324209.

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47

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

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48

TOMONAGA, MASAKI, TAKATSUNE KUMADA, and SAKIKO YOSHIKAWA. "Visual cognition from the standpoint of comparative cognitive science." Japanese Journal of Animal Psychology 52, no. 1 (2002): 29–44. http://dx.doi.org/10.2502/janip.52.29.

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49

Chernikova, Irina V. "Cognition as a Subject of Research in Cognitive Science." Procedia - Social and Behavioral Sciences 154 (October 2014): 309–13. http://dx.doi.org/10.1016/j.sbspro.2014.10.169.

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50

Cole, Charles. "Cognition and complexity: The cognitive science of managing complexity." Information Processing & Management 33, no. 4 (July 1997): 577–78. http://dx.doi.org/10.1016/s0306-4573(97)82728-8.

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