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Relevant bibliographies by topics / Cognitive science

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Academic literature on the topic 'Cognitive science'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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Journal articles on the topic "Cognitive science"

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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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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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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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N N, Bindhu, and A. Jahitha Begum. "Educational Cognitive Science and Teacher Education." International Journal of Science and Research (IJSR) 10, no. 9 (September 27, 2021): 167–71. https://doi.org/10.21275/sr21904121615.

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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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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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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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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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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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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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Dissertations / Theses on the topic "Cognitive science"

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Schmitt, Wolfram. "The Cognitive View in Cognitive Science." Diss., lmu, 2006. http://nbn-resolving.de/urn:nbn:de:bvb:19-76547.

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Greenlee, Christopher Alan. "Situated Cognition, Dynamicism, and Explanation in Cognitive Science." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/46501.

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The majority of cognitive scientists today view the mind as a computer, instantiating some function mapping the inputs it gets from the environment to the gross behaviors of the organism. As a result, the emphasis in most ongoing research programmes is on finding that function, or some part of that function. Moreover, the types of functions considered are limited somewhat by the preconception that the mind must be instantiating a function that can be expressed as a computer program. I argue that research done in the last two decades suggests that we should approach cognition with as much consideration to the environment as to the inner workings of the mind. Our cognition is often shaped by the constraints the environment places on us, not just by the "inputs" we receive from it. I argue also that there is a new approach to cognitive science, viewing the mind not as a computer but as a dynamical system, which captures the shift in perspective while eliminating the requirement that cognitive functions be expressable as computer programs. Unfortunately, some advocates of this dynamical perspective have argued that we should replace all of traditional psychology and neuroscience with their new approach. In response to these advocates, I argue that we cannot develop an adequate dynamical picture of the mind without engaging in precisely those sorts of research and hypothesizing that traditional neuroscience and psychology engage in. In short, I argue that we require certain types of explanations in order to get our dynamical (or computational) theories off the ground, and we cannot get those from other dynamical (or computational) theories.<br>Master of Arts

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Shaik, Kadir Munirah. "Enlightening Science: Addressing the Cognitive and Non-Cognitive Aspects of Science Learning." Thesis, Australian Catholic University, 2018. https://acuresearchbank.acu.edu.au/download/62b15f077f8030a2d790b4b72bc33a91600baaf09ed4f6985e88b65dd6c99d62/6070465/SHAIK_KADIR_2018_Enlightening_science_addressing_the_cognitive_and.pdf.

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Physical science (or physics) is known to be one of the least popular school curriculum domains, mainly because of its complexity. When students encounter seemingly insurmountable difficulties when learning something, they lose the motivation to continue. It has been suggested that both the cognitive (e.g., students’ conceptual understanding and achievement) and non-cognitive (e.g., psychological aspects such as academic self-concept and motivation) factors of learning are essential for helping students achieve their optimal best in a curriculum domain. However, there has not been much research, if any, which uses a dual approach to investigate both aspects of science learning. Most research focused on either the cognitive or non-cognitive aspect. Research on cognitive aspects of learning suggests that element interactivity is a useful construct with which to examine students’ cognitive processes and the complexity of learning materials. However, there has been no illustration on how an analysis of interacting elements in science learning tasks may improve learning. Studies on the effects of reducing element interactivity on students’ achievement and motivation are also scarce. Research on non-cognitive aspects of learning suggests that motivation is necessary to sustain students’ engagement in learning. However, if the complexity of learning tasks is so high that students experience repeated failures, their motivation is not sustained. Therefore, both cognitive and non-cognitive factors play a crucial role in students’ learning and both must be present to ensure an optimal learning environment. The overarching aim of this thesis is to investigate the cognitive (i.e., students’ achievement and cognitive processes in terms of element interactivity) and non-cognitive aspects (i.e., self-concept and other motivational factors) of students’ learning of science. The thesis includes five studies. The first study showed that the five main findings from past self-concept research were applicable to the Grade 7 students from Singapore selected for the study. Students’ sense of competence in a curriculum domain enhanced their future achievement in that domain only, except for physics and math, which showed interrelatedness (i.e., the enhancement was transferable from one domain to the other). The findings showed a strong interplay between academic self-concept and achievement and highlighted the important role that academic self-concept plays in determining students’ learning outcomes. Therefore, strategies to enhance students’ self-concept should be implemented in schools. The results of the second study showed strong positive correlations between students’ achievement and their motivation within a school year. Students’ Grade 6 (final primary school year) achievement did not strongly contribute to their motivation in Grade 7, indicating the importance of providing an optimal learning environment in Grade 7 for a positive start to their secondary school education. The third study showed how the interactions between the elements (i.e., element interactivity) in problem solving tasks reflect their level of complexity and how the number of operational lines that students used to solve problems could indicate their level of expertise in problem solving in that domain. This study highlighted the role of element interactivity as a planning tool for learning tasks and how teachers may use it to gain insights into students’ cognitive processes. The fourth study involved an intervention, which reduced element interactivity during science instruction, and the results revealed that students’ achievement improved, and their science self-concept was maintained. The results and implications of the first four studies were used to design a dual-approach instruction to facilitate both cognitive and non-cognitive aspects of students’ learning in the fifth and final study. The results of the final intervention study indicated that the dual-approach instruction was beneficial. The experimental group of students outperformed the comparison group in both cognitive and non-cognitive factors. Results from multiple regression analyses revealed that those who experienced the intervention not only had higher achievement than those in the comparison group in the complex problem tasks, but also had higher motivation (i.e., self-regulation, task goal, inquiry, and educational and career aspirations) and higher academic self-concept (i.e., sense of competence). This thesis demonstrates that there are strong associations and a significant interplay between students’ achievement and motivation levels (i.e., cognitive and non-cognitive aspects of learning). The analysis of learning tasks and instruction in terms of element interactivity enables the scaffolding of complex learning tasks to suit students’ cognitive levels, leading to higher achievement. Higher achievement contributes to higher motivation levels, including students’ academic self-concept. When learning environments attend to basic psychological needs (i.e., a sense of competence, autonomy, and relatedness), students’ motivation is enhanced and when motivated students experience learning that is within their ability and cognitive load capacities, their self-beliefs and motivation in the learning domain are sustained. Attention to both cognitive and non-cognitive factors in learning situations maximizes students’ learning potential and should therefore be strongly considered by educators and curriculum planners.

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Downes, Stephen Matthew. "Prospects for a cognitive science of science." Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-08252008-162811/.

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OÌ, Maoldomhnaigh MicheaÌl. "Cognitive stage, cognitive style, attitude and physical science option." Thesis, King's College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406231.

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Akagi, Mikio Shaun Mikuriya. "Cognition in practice| Conceptual development and disagreement in cognitive science." Thesis, University of Pittsburgh, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10183682.

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<p> Cognitive science has been beset for thirty years by foundational disputes about the nature and extension of cognition—e.g. whether cognition is necessarily representational, whether cognitive processes extend outside the brain or body, and whether plants or microbes have them. Whereas previous philosophical work aimed to settle these disputes, I aim to understand what conception of cognition scientists could share given that they disagree so fundamentally. To this end, I develop a number of variations on traditional conceptual explication, and defend a novel explication of cognition called the sensitive management hypothesis.</p><p> Since expert judgments about the extension of “cognition” vary so much, I argue that there is value in explication that accurately models the variance in judgments rather than taking sides or treating that variance as noise. I say of explications that accomplish this that they are <i> ecumenically extensionally adequate</i>. Thus, rather than adjudicating whether, say, plants can have cognitive processes like humans, an ecumenically adequate explication should classify these cases differently: human cognitive processes as paradigmatically cognitive, and plant processes as controversially cognitive.</p><p> I achieve ecumenical adequacy by articulating conceptual explications with <i>parameters</i>, or terms that can be assigned a number of distinct interpretations based on the background commitments of participants in a discourse. For example, an explication might require that cognition cause “behavior,” and imply that plant processes are cognitive or not depending on whether anything plants do can be considered “behavior.” Parameterization provides a unified treatment of embattled concepts by isolating topics of disagreement in a small number of parameters.</p><p> I incorporate these innovations into an account on which cognition is the “sensitive management of organismal behavior.” The sensitive management hypothesis is ecumenically extensionally adequate, accurately classifying a broad variety of cases as paradigmatically or controversially cognitive phenomena. I also describe an extremely permissive version of the sensitive management hypothesis, arguing that it has the potential to explain several features of cognitive scientific discourse, including various facts about the way cognitive scientists ascribe representations to cognitive systems. </p>

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Svensson, Henrik. "Notions of Embodiment in Cognitive Science." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-588.

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<p>Cognitive science has traditionally viewed the mind as essentially disembodied, that is, the nature of mind and cognition is neither affected by the ¡Èsystem¡É it is implemented in nor affected by the environment that the system is situated in. But since the mid-1980s a new approach emerged in artificial intelligence that emphasized the importance of embodiment and situatedness and since then terms like embodied cognition, embodied intelligence have become more and more apparent in discussions of cognition. As embodied cognition has increased in interest so have the notions of embodiment and situatedness and they are not always compatible. This report has found that there are, at least, four notions of embodiment in the discussions of embodied cognition: software embodiment, physical embodiment, biological embodiment and human(oid) embodiment.</p>

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Chada, Daniel de Magalhães. "From cognitive science to management science: two computational contributions." reponame:Repositório Institucional do FGV, 2011. http://hdl.handle.net/10438/17053.

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Submitted by Kelly Ayala (kelly.ayala@fgv.br) on 2016-09-12T12:57:06Z No. of bitstreams: 1 Chada 2011 FINAL ENTREGUE.pdf: 579283 bytes, checksum: f463590c20f51b84ba0f9357ab1a6e08 (MD5)<br>Approved for entry into archive by Kelly Ayala (kelly.ayala@fgv.br) on 2016-09-12T12:58:17Z (GMT) No. of bitstreams: 1 Chada 2011 FINAL ENTREGUE.pdf: 579283 bytes, checksum: f463590c20f51b84ba0f9357ab1a6e08 (MD5)<br>Approved for entry into archive by Kelly Ayala (kelly.ayala@fgv.br) on 2016-09-12T13:00:07Z (GMT) No. of bitstreams: 1 Chada 2011 FINAL ENTREGUE.pdf: 579283 bytes, checksum: f463590c20f51b84ba0f9357ab1a6e08 (MD5)<br>Made available in DSpace on 2016-09-12T13:03:31Z (GMT). No. of bitstreams: 1 Chada 2011 FINAL ENTREGUE.pdf: 579283 bytes, checksum: f463590c20f51b84ba0f9357ab1a6e08 (MD5) Previous issue date: 2011<br>This work is composed of two contributions. One borrows from the work of Charles Kemp and Joshua Tenenbaum, concerning the discovery of structural form: their model is used to study the Business Week Rankings of U.S. Business Schools, and to investigate how other structural forms (structured visualizations) of the same information used to generate the rankings can bring insights into the space of business schools in the U.S., and into rankings in general. The other essay is purely theoretical in nature. It is a study to develop a model of human memory that does not exceed our (human) psychological short-term memory limitations. This study is based on Pentti Kanerva’s Sparse Distributed Memory, in which human memories are registered into a vast (but virtual) memory space, and this registration occurs in massively parallel and distributed fashion, in ideal neurons.<br>Este trabalho é composto de duas contribuições. Uma se usa do trabalhode Charles Kemp e Joshua Tenenbaum sobre a descoberta da forma estrutural: o seu modelo é usado para estudar os rankings da revista Business Week sobre escolas de administração, e para investigar como outras formas estruturais (visualizações estruturadas) da mesma informação usada para gerar os rankings pode trazer discernimento no espaço de escolas de negócios nos Estados Unidos e em rankings em geral. O outro ensaio é de natureza puramente teórica. Ele é um estudo no desenvolvimento de um modelo de memória que não excede os nossos (humanos) limites de memória de curto-prazo. Este estudo se baseia na Sparse Distributed Memory (Memória Esparsa e Distribuida) de Pentti Kanerva, na qual memórias humanas são registradas em um vasto (mas virtual) espaço, e este registro ocorre de forma maciçamente paralela e distribuida, em neurons ideais.

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Vervaeke, John Alexander. "The naturalistic imperative in cognitive science." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ28308.pdf.

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Ladbury, Martin Samuel Durham. "The idea of a cognitive science." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342914.

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Books on the topic "Cognitive science"

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N, Osherson Daniel, and Gleitman Lila R, eds. An invitation to cognitive science. 2nd ed. Cambridge, Mass: MIT Press, 1995.

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R, Gleitman Lila, and NetLibrary Inc, eds. An invitation to cognitive science: Thinking. 2nd ed. Cambridge, Mass: MIT Press, 1995.

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I, Posner Michael, ed. Foundations of cognitive science. Cambridge, Mass: MIT Press, 1989.

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A, Stillings Neil, ed. Cognitive science: An introduction. 2nd ed. Cambridge, Mass: MIT Press, 1995.

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Barsalou, Lawrence W. Cognitive psychology: An overview for cognitive scientists. Hillsdale, New Jersey: Lawrence Erlbaum Associates, 1992.

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1954-, Lambalgen Michiel van, ed. Human reasoning and cognitive science. Cambridge, MA: MIT Press, 2008.

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Emmanuel, Dupoux, and Mehler Jacques, eds. Language, brain, and cognitive development: Essays in honor of Jacques Mehler. Cambridge, Mass: MIT Press, 2001.

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Lamberts, Koen. Cognitive Science. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2008. http://dx.doi.org/10.4135/9781446261040.

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Noel, Sheehy, and Chapman Antony J, eds. Cognitive science. Aldershot: Edward Elgar, 1995.

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1955-, Sheehy Noel, and Chapman Antony J, eds. Cognitive science. New York: New York University Press, 1995.

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Book chapters on the topic "Cognitive science"

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Wiggins, Osborne P., and Manfred Spitzer. "Cognitive Science." In Contributions to Phenomenology, 101–4. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-5344-9_22.

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Ream, Derek, and Isaac Tourgeman. "Cognitive Science." In Encyclopedia of Evolutionary Psychological Science, 1–7. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-16999-6_477-1.

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Mazzola, Guerino, René Guitart, Jocelyn Ho, Alex Lubet, Maria Mannone, Matt Rahaim, and Florian Thalmann. "Cognitive Science." In The Topos of Music III: Gestures, 867–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64481-3_4.

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Grush, Rick. "Cognitive Science." In The Blackwell Guide to the Philosophy of Science, 272–89. Oxford, UK: Blackwell Publishers Ltd, 2008. http://dx.doi.org/10.1002/9780470756614.ch13.

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Coulson, Seana, and Teenie Matlock. "Cognitive science." In Handbook of Pragmatics, 1–30. Amsterdam: John Benjamins Publishing Company, 2005. http://dx.doi.org/10.1075/hop.9.cog5.

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Coulson, Seana. "Cognitive science." In Handbook of Pragmatics, 123–40. Amsterdam: John Benjamins Publishing Company, 1995. http://dx.doi.org/10.1075/hop.m.cog5.

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Coulson, Seana, and Teenie Matlock. "Cognitive science." In Cognition and Pragmatics, 86–109. Amsterdam: John Benjamins Publishing Company, 2009. http://dx.doi.org/10.1075/hoph.3.06cou.

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Coulson, Seana, and Teenie Matlock. "Cognitive science." In Handbook of Pragmatics, 195–216. Amsterdam: John Benjamins Publishing Company, 2022. http://dx.doi.org/10.1075/hop.m2.cog5.

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Peterson, Gregory. "Cognitive Science." In Encyclopedia of Sciences and Religions, 408. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_200174.

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Yanhong, Wu. "Cognitive Science." In The ECPH Encyclopedia of Psychology, 246–48. Singapore: Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7874-4_905.

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Conference papers on the topic "Cognitive science"

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Lane, Peter C. R., and Fernand Gobet. "Computational Scientific Discovery in Cognitive Science." In 2024 IEEE 6th International Conference on Cognitive Machine Intelligence (CogMI), 275–77. IEEE, 2024. https://doi.org/10.1109/cogmi62246.2024.00043.

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Murphy, Dominic. "Cognitive Science Without Cognitive Psychology." In 9th Conference of the Australasian Society for Cognitive Science. Sydney: Macquarie Centre for Cognitive Science, 2010. http://dx.doi.org/10.5096/ascs200938.

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Adams, Ray. "Cognitive science meets computing science: The future of cognitive systems and cognitive engineering." In Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces (ITI). IEEE, 2009. http://dx.doi.org/10.1109/iti.2009.5196041.

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"Open Science and Goodhart's Law." In Cognitive Science 2024. Jožef Stefan Instutute, 2024. http://dx.doi.org/10.70314/is.2024.cog.4.

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"Exploring Cognitive Science under Analytical Idealism." In Cognitive Science 2024. Jožef Stefan Instutute, 2024. http://dx.doi.org/10.70314/is.2024.cog.15.

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Flanagan, Brian J. "Supersymmetry For Cognitive Science." In 1988 Robotics Conferences, edited by David P. Casasent. SPIE, 1989. http://dx.doi.org/10.1117/12.960287.

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Mukherjee, Sohini, Ashish Kumar Gupta, Sarif Aziz, Tariq Aziz, Pratik Jaiswal, and Souvik Chatterjee. "Cognitive science: A review." In 2017 4th International Conference on Opto-Electronics and Applied Optics (Optronix). IEEE, 2017. http://dx.doi.org/10.1109/optronix.2017.8349979.

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Wang, Yingxu. "From information revolution to intelligence revolution: Big data science vs. intelligence science." In 2014 IEEE 13th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2014. http://dx.doi.org/10.1109/icci-cc.2014.6921432.

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Suwara, Marek, and Jan Werszowiec Plazowski. "Is cognitive science a science at all?" In 2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom). IEEE, 2012. http://dx.doi.org/10.1109/coginfocom.2012.6421948.

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Cēdere, Dagnija, Inese Jurgena, Ineta Helmane, Inta Tiltiņa, and Gunita Praulīte. "COGNITIVE INTEREST: PROBLEMS AND SOLUTIONS IN THE ACQUISITION OF SCIENCE AND MATHEMATICS IN SCHOOLS OF LATVIA." In 1st International Baltic Symposium on Science and Technology Education. Scientia Socialis Ltd., 2015. http://dx.doi.org/10.33225/balticste/2015.33.

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The low level of pupils’ knowledge and skills in science and mathematics is a serious problem in the economic development of the country. Cognitive interest is a crucial learning motive; no successful learning process is possible without inciting interest. Grade 9 pupils were surveyed to find out the respondents’ cognitive interest in the field of exact sciences. On average the interest in subjects of exact sciences is poorly pronounced. Respondents have good understanding about the causal relations while the cognitive activity, the skill to overcome difficulties in learning is low. Cognitive interest in learning is promoted by diverse methodological approaches that are oriented towards pupils’ self-actualization and purposefulness. Key words: cognitive interest, learning process, science and mathematics.

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Reports on the topic "Cognitive science"

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Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, March 2025. https://doi.org/10.62311/nesx/rrvi225.

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Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts for uncertainty, ambiguity, and adaptive decision-making in complex environments. This research explores the foundations of quantum cognition, its empirical validation in behavioral experiments and neuroscience, and its applications in artificial intelligence (AI), behavioral economics, and decision sciences. Additionally, it examines how quantum-inspired AI models enhance predictive analytics, machine learning algorithms, and human-computer interaction. The study also addresses challenges related to mathematical complexity, cognitive interpretation, and the potential link between quantum mechanics and brain function, providing a comprehensive framework for the integration of quantum cognition into decision science and AI-driven cognitive computing. Keywords Quantum cognition, quantum probability, decision-making models, cognitive science, superposition in cognition, interference effects, entanglement in decision-making, probabilistic reasoning, preference reversals, cognitive biases, order effects, quantum-inspired AI, behavioral economics, neural quantum theory, artificial intelligence, cognitive neuroscience, human-computer interaction, quantum probability in psychology, quantum decision theory, uncertainty modeling, predictive analytics, quantum computing in cognition.

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Forbus, Kenneth D., Kate Lockwood, Emmett Tomai, Morteza Dehghani, and Jakub Czyz. Machine Reading as a Cognitive Science Research Instrument. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada470412.

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McGee, Steven, Amanda Durik, and Jess Zimmerman. The Impact of Text Genre on Science Learning in an Authentic Science Learning Environment. The Learning Partnership, April 2015. http://dx.doi.org/10.51420/conf.2015.2.

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A gap exists between research on learning and research on interest. Cognitive researchers rarely consider motivational processes, and interest researchers rarely consider cognitive process. However, it is essential to consider both since achievement and interest are in fact intertwined. In this paper we (1) discuss a theoretical model that intertwines cognitive and interest development, (2) describe how that model informed the development of educational materials, and (3) report on the results of the cognitive components of a randomized research study examining the impact of text genre on learning and interest. In our prior analyses, we examined the effects of text characteristics (i.e., narrative or expository genre) on situational interest. We found that students with higher levels of prior individual interest preferred the narrative versions of text whereas students with lower levels of prior individual interest preferred the expository versions of text. In this paper, we examine the impact of text characteristics on student learning. The results of this research showed that contrary to prior research, there was no significant difference in comprehension based on text characteristics. These results provide evidence that is possible to differentiate instruction based students' prior interest without sacrificing learning outcomes.

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Sanchez, Christopher A. Support for the Annual Meeting (30th) of the Cognitive Science Society. Fort Belvoir, VA: Defense Technical Information Center, October 2008. http://dx.doi.org/10.21236/ada488149.

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

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6

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

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López Bóo, Florencia, and Nicolás Ajzenman. 10 Lessons About Behavioral Economics for Policy Making in the Social Sector. Inter-American Development Bank, September 2018. http://dx.doi.org/10.18235/0008045.

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Abstract:

Behavioral Economics is the science of evaluating psychological, cognitive, emotional, cultural and social factors and their impact on economic decisions. Enhancing our knowledge on Behavioral Sciences and their impact on public policies is a priority. The present document explores this intersection and offers 10 lessons for policy making in the social sector.

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Gray, Wayne. Request for AFOSR Support of the 24th Annual Conference of the Cognitive Science Society (CogSci2002). Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada420734.

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Pinchuk, O. P., V. A. Tkachenko, and O. Yu Burov. AV and VR as Gamification of Cognitive Tasks. CEUR Workshop Proceedings, 2019. http://dx.doi.org/10.33407/lib.naes.718697.

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Abstract:

The paper presents a comparative analysis of the functionality of mobile applications of the augmented reality Da Vinci Machines AR, Electricity AR, Bridges AR, Geometry, the collection of VR models VictoryVR Science Curriculum and the digital collection Mozaik. The possibility of using these tools for educational purposes is explored, in particular, to construct cognitive tasks for students during the study of subjects in the natural and mathematical cycle. The indicated shortcomings are stated, didactic requirements for such educational activities are formulated. Among others, attention is focused on the following indicators: hardware, usability, variability of model parameters, interactivity, interdisciplinary use, and the ability to activate certain cognitive actions of students, degree/form of gamification. The educational potential of using interactive models and video is analyzed for both group and individual work with students. Examples of methodical developments are given.

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LI, Na, Xia AI, Xinrong Guo, Juan Liu, Rongchao Zhang, and Ruihui Wang. Effect of acupuncture treatment on cognitive impairment after traumatic brain injury in adults: A systematic review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0113.

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Review question / Objective: Are acupuncture more effective than control interventions (i.e. treatment as sham acupuncture or placebo) in the treatment of motor and cognitive impairment after traumatic brain injury in adults? Information sources: search database:The following electronic databases will be searched for relevant literature: the Cochrane Library, MEDLINE, EMBASE, Web of Science, Springer, the Chinese Science Citation Database (CSCD), China National Knowledge Infrastructure (CNKI), the Chinese Biomedical Literature Database (CBM),Wanfang, and. the Chinese Scientific Journal Database (VIP). Time limit: the searches will be conducted from the inception of each database to November 30, 2021. Protocol of Systematic review and Meta analysis of acupuncture in the treatment of cognitive impairment after traumatic brain injury and the included literatures were all RCTS with English and Chinese on language.

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