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Journal articles on the topic 'Computer science. Cognitive psychology'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Kumar, David D., Stanley L. Helgeson, and Arthur L. White. "Computer technology-cognitive psychology interface and science performance assessment." Educational Technology Research and Development 42, no. 4 (December 1994): 6–16. http://dx.doi.org/10.1007/bf02298052.

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2

Oaksford, Mike, Nick Chater, and Keith Stenning. "Connectionism, classical cognitive science and experimental psychology." AI & Society 4, no. 1 (January 1990): 73–90. http://dx.doi.org/10.1007/bf01889765.

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3

Robertson, Scott P. "Applications of Cognitive Science to Human-Computer Interaction." Contemporary Psychology: A Journal of Reviews 35, no. 12 (December 1990): 1132–33. http://dx.doi.org/10.1037/029203.

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Gentner, Dedre. "Psychology in Cognitive Science: 1978-2038." Topics in Cognitive Science 2, no. 3 (July 9, 2010): 328–44. http://dx.doi.org/10.1111/j.1756-8765.2010.01103.x.

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5

Agarwal, Pooja K., and Henry L. Roediger. "Lessons for learning: How cognitive psychology informs classroom practice." Phi Delta Kappan 100, no. 4 (November 26, 2018): 8–12. http://dx.doi.org/10.1177/0031721718815666.

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The learning sciences span multiple fields, including psychology, computer science, and neuroscience. In particular, cognitive psychology focuses on complex operations that occur inside the brain. Attention to these hidden processes can reveal ways to promote lasting learning. Pooja Agarwal and Henry Roediger draw on research in both the laboratory and the classroom to identify four important strategies — retrieval practice, feedback, spaced practice, and interleaving — that have been shown to help students learn. The effects of these practices apply across multiple grade levels and subject areas, and teachers can often incorporate them by making minor tweaks to their current practices.

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Gierl, Mark J., and Jiawen Zhou. "Computer Adaptive-Attribute Testing." Zeitschrift für Psychologie / Journal of Psychology 216, no. 1 (January 2008): 29–39. http://dx.doi.org/10.1027/0044-3409.216.1.29.

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The influence of interdisciplinary forces stemming from developments in cognitive science, mathematical statistics, educational psychology, and computing science are beginning to appear in educational and psychological assessment. Computer adaptive-attribute testing (CA-AT) is one example. The concepts and procedures in CA-AT can be found at the intersection between computer adaptive testing and cognitive diagnostic assessment. CA-AT allows us to fuse the administrative benefits of computer adaptive testing with the psychological benefits of cognitive diagnostic assessment to produce an innovative psychologically-based adaptive testing approach. We describe the concepts behind CA-AT as well as illustrate how it can be used to promote formative, computer-based, classroom assessment.

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Halford, Graeme S. "Applications of Cognitive Developmental Theory and Data to Educational Psychology." Australian Educational and Developmental Psychologist 8, no. 1 (May 1991): 7–8. http://dx.doi.org/10.1017/s0816512200026304.

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Recent developments in Cognitive Psychology and in the new discipline of Cognitive Science (an integration of Cognitive Psychology, Computer Science, Linguistics, Philosophy of Mind, and Cognitive Neuroscience) have made it appropriate to consider new ways in which Cognitive Development and Educational Psychology can benefit each other. Cognitive Development can contribute to Educational Psychology by specifying cognitive processes entailed in educationally relevant tasks, by analysing processing loads, and by indicating more efficient ways of using available capacity. Cognitive Psychology and Cognitive Science have now produced some penetrating theories of the cognitive processes that underlie a wide variety of intellectual activities. Although there is still much work remaining to be done, these developments can be used to analyse the strategies children and adults use in solving problems in areas such as mathematics and science. This can result in benefits in both learning and remediation. Educational Psychology can benefit Cognitive Development by offering alternativeconcepts, by providing realistic problems for analysis, and by providing a testing ground for its theories. I will illustrate these ideas in the area of mathematics.

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8

Ellis, Nick. "COGNITIVE APPROACHES TO SLA." Annual Review of Applied Linguistics 19 (January 1999): 22–42. http://dx.doi.org/10.1017/s0267190599190020.

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Getting to know a second language is an act of cognition par excellence. Yet 'Cognitive Approaches to SLA' implies something more than the general research enterprise of SLA. It highlights the goals of cognitive psychologists who search for explanations of second language cognition in terms of mental representations and information processing. It places SLA within the broader remit of cognitive scientists, who—influenced by Marr (1982) to seek understanding at all three levels of function, algorithm, and hardware—work in collaborations involving cognitive psychology, linguistics, epistemology, computer science, artificial intelligence, connectionism, and the neurosciences. It implies the empiricism of cognitive psychology, searching for truths about the world through observation and experimentation and, at times, the rationalism of cognitive scientists who theorize through the construction of formal systems such as those in mathematics, logic, or computational simulation. Much of the research is purely theoretical, but, as in applied cognitive psychology, pure theory can often spin off into important applications, and applied research using longitudinal or training designs in field situations can often advance theory.

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Tempel, Tobias, Claudia Niederée, Christian Jilek, Andrea Ceroni, Heiko Maus, Yannick Runge, and Christian Frings. "Temporarily Unavailable: Memory Inhibition in Cognitive and Computer Science." Interacting with Computers 31, no. 3 (May 1, 2019): 231–49. http://dx.doi.org/10.1093/iwc/iwz013.

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Abstract Inhibition is one of the core concepts in Cognitive Psychology. The idea of inhibitory mechanisms actively weakening representations in the human mind has inspired a great number of studies in various research domains. In contrast, Computer Science only recently has begun to consider concepts such as digital forgetting or suppression of irrelevant information to complement activation and highlighting of relevant information. Here, we review psychological research on inhibition in memory and link the gained insights with the current efforts and opportunities in Computer Science of incorporating inhibitory principles for reducing information overload and improving information retrieval in Personal Information Management. Four common aspects guide this review in both domains: (i) the purpose of inhibition to increase processing efficiency; (ii) its relation to activation; (iii) its links to contexts; (iv) its temporariness. In summary, the principle of suppressing information has been used by Computer Science for enhancing software in some ways already. Yet, we consider how novel methods for reducing information overload can be inspired by a more systematic involvement of the inhibition concept.

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Ismafairus Abd Hamid, Aini, Jafri Malin Abdullah, and Norsiah Fauzan. "The Future of Cognitive Neuroscience." International Journal of Engineering & Technology 7, no. 3.22 (August 8, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i3.22.17111.

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Cognitive neuroscience is an interdisciplinary area focusing on the application of neuroscience knowledge in areas such as neuroimaging studies, computer science, psychology, marketing, business, general and special education, social sciences, engineering, biology, learning science, health, etcetra. It is a new emerging field that may help Malaysia in the move towards 2050 for the development of economic, improve levels of knowledge and education, intensify healthcare, enhance people’s well-being and expand network collaboration. Academicians, scientists, industry and educators must concentrate on the application cognitive neuroscience in their field of studies. There is a lack of neuroscientists in these fields, and concentrated efforts must come from the top down as well as the bottom up. We need to bring brain and mind sciences and neuroscience to a reputable level that will improve physical and mental health and increase creativity and innovation in Malaysia: A national institute to amalgamate the creative and innovative mind, behaviour, and brain sciences and neuroscience must be established.

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11

Halcomb, Charles G. "Cognitive Science: A Tool for Designing the User-Computer Interface." Contemporary Psychology: A Journal of Reviews 41, no. 11 (November 1996): 1114–15. http://dx.doi.org/10.1037/003209.

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12

Gullickson, Terri. "Review of Human-Computer Interaction: Research Directions in Cognitive Science." Contemporary Psychology: A Journal of Reviews 39, no. 9 (September 1994): 917. http://dx.doi.org/10.1037/034676.

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13

Proudfoot, D. "On Wittgenstein on Cognitive Science." Philosophy 72, no. 280 (April 1997): 189–217. http://dx.doi.org/10.1017/s0031819100056849.

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Cognitive science is held, not only by its practitioners, to offer something distinctively new in the philosophy of mind. This novelty is seen as the product of two factors. First, philosophy of mind takes itself to have well and truly jettisoned the ‘old paradigm’, the theory of the mind as embodied soul, easily and completely known through introspection but not amenable to scientific inquiry. This is replaced by the ‘new paradigm’, the theory of mind as neurally-instantiated computational mechanism, relatively opaque to introspection and the proper subject of detailed empirical investigation. Second, in the constitutive disciplines of cognitive science (cognitive psychology, neuroscience, computer science etc.) we have for the first time the theoretical, experimental and technological resources to begin this investigation. My concern here is to show that, despite its scientific and philosophical sophistication, the new (computational) paradigm is in certain striking ways very similar to the old paradigm and that Wittgenstein's criticisms of the former apply to much of the latter.

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Flannery, Kathleen A., and Mihaela Malita. "An Interdisciplinary Team Project: Psychology and Computer Science Students Create Online Cognitive Tasks." College Teaching 62, no. 4 (October 2014): 143–49. http://dx.doi.org/10.1080/87567555.2014.956040.

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15

Stephan, Achim. "Was zeichnet eine moderne Auffassung von Geist aus?" History of Philosophy and Logical Analysis 18, no. 1 (April 5, 2015): 114–28. http://dx.doi.org/10.30965/26664275-01801009.

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‘Having a mind’ is construed as having a variety of mental capacities such as perceiving, memorizing, learning, or reasoning. In cognitive science, these capacities are studied from an integrative trans-disciplinary perspective that combines anthropology, artificial intelligence, computational linguistics, neuroscience, philosophy and psychology. To approach mental phenomena by combining philosophical insights with those from the natural sciences is part of the Aristotelian tradition. Accordingly, the paper also portrays the most salient models of mental processing – the computer model, connectionism and situated cognition. Eventually, an example of an artificial agent – Affective AutoTutor – is introduced that exhibits striking cognitive capacities, but still seems to lack what is expected from someone who ‘has a mind’.

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Woods, David D., Jennifer C. Watts, John M. Graham, Daniel L. Kidwell, and Philip J. Smith. "Teaching Cognitive Systems Engineering." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 4 (October 1996): 259–63. http://dx.doi.org/10.1177/154193129604000425.

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Our motivation for this paper is to stimulate discussions within the human factors community about teaching Cognitive Engineering at the undergraduate level. For the last fourteen years, the Cognitive Systems Engineering Laboratory at the Ohio State University has offered an undergraduate course in Cognitive Engineering (multiple offerings per year to Industrial Engineering, Industrial Design, Computer Science and Psychology students). In this paper, we will draw from our teaching experiences and describe our framework for teaching Cognitive Engineering.

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17

BUDELMANN, FELIX, and PAT EASTERLING. "Reading Minds in Greek Tragedy." Greece and Rome 57, no. 2 (September 21, 2010): 289–303. http://dx.doi.org/10.1017/s0017383510000033.

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A notable intellectual development of the past decade or two has been the ever-growing interest in human consciousness and the workings of the mind. Sometimes grouped under the umbrella term ‘cognitive sciences’, diverse disciplines such as neuroscience, psychology, philosophy, computer science, and linguistics have all made major contributions to our understanding of the human mind and brain; and the large number of popular science books published in this area show that this can be an engrossing topic for the layperson as much as for experts. In this article we want to explore, at a rather general and non-technical level, how this focus on matters of cognition can help us think about an aspect of Greek tragedy.

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18

Cummins, Denise, Robert Cummins, and Pierre Poirier. "Cognitive evolutionary psychology without representational nativism." Journal of Experimental & Theoretical Artificial Intelligence 15, no. 2 (January 2003): 143–59. http://dx.doi.org/10.1080/0952813021000055162.

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19

Crowston, Kevin, Thomas W. Malone, and Felix Lin. "Cognitive Science and Organizational Design: A Case Study of Computer Conferencing." Human–Computer Interaction 3, no. 1 (March 1987): 59–85. http://dx.doi.org/10.1207/s15327051hci0301_4.

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20

Searle, John R. "Consciousness, explanatory inversion, and cognitive science." Behavioral and Brain Sciences 13, no. 4 (December 1990): 585–96. http://dx.doi.org/10.1017/s0140525x00080304.

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AbstractCognitive science typically postulates unconscious mental phenomena, computational or otherwise, to explain cognitive capacities. The mental phenomena in question are supposed to be inaccessible in principle to consciousness. I try to show that this is a mistake, because all unconscious intentionality must be accessible in principle to consciousness; we have no notion of intrinsic intentionality except in terms of its accessibility to consciousness. I call this claim the “Connection Principle.” The argument for it proceeds in six steps. The essential point is that intrinsic intentionality has aspectual shape: Our mental representations represent the world under specific aspects, and these aspectual features are essential to a mental state's being the state that it is.Once we recognize the Connection Principle, we see that it is necessary to perform an inversion on the explanatory models of cognitive science, an inversion analogous to the one evolutionary biology imposes on preDarwinian animistic modes of explanation. In place of the original intentionalistic explanations we have a combination of hardware and functional explanations. This radically alters the structure of explanation, because instead of a mental representation (such as a rule) causing the pattern of behavior it represents (such as rule-governed behavior), there is a neurophysiological cause of a pattern (such as a pattern of behavior), and the pattern plays a functional role in the life of the organism. What we mistakenly thought were descriptions of underlying mental principles in, for example, theories of vision and language were in fact descriptions of functional aspects of systems, which will have to be explained by underlying neurophysiological mechanisms. In such cases, what looks like mentalistic psychology is sometimes better construed as speculative neurophysiology. The moral is that the big mistake in cognitive science is not the overestimation of the computer metaphor (though that is indeed a mistake) but the neglect of consciousness.

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Yang, Xing, Tingjun Yong, Meihua Li, Wenying Wang, Huichun Xie, and Jinping Du. "Relationship between Cognitive Learning Psychological Classification and Neural Network Design Elements." Complexity 2021 (February 27, 2021): 1–10. http://dx.doi.org/10.1155/2021/6625922.

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This article first analyzes the research background of the design elements of cognitive psychology and neural networks at home and abroad, roughly understands the research status and research background of these two courses at home and abroad, and discusses the application of cognitive psychology to neural networks. The design method has not yet formed a systematic theoretical system. Then, a systematic theoretical analysis of the research in this article is carried out to analyze the relationship between the various characteristics of cognitive psychology and the design elements of the neural network, and it uses these relationships to guide the design practice. Second, it analyzes the relationship between the influence and interaction of cognitive psychology on neural network design and connects cognitive psychology with neural network design. Finally, according to the theoretical analysis and research of the system, the application of cognitive psychology in neural network design, design practice, and the relationship between the two are systematically reviewed. Through the exploratory research on cognitive psychology in neural network design, we can see that the combination of neural network design and psychology, art aesthetics, and other cross-disciplinary and multidisciplinary research is necessary, which can promote the scientific and technological progress of neural network design in the context of the information age and the improvement of public mental health. Under the background of the era in which the neural network design becomes the link between people's emotions and culture, we must fully understand the essential role of each element in neural network design and build a design concept based on cognitive psychology and emotional experience. It is hoped that the content of this topic can provide a certain reference value for the future development of neural network design and cognitive psychology and clarify the new development direction.

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22

Deiglmayr, Anne, Elisabeth Paus, Cade McCall, Dejana Mullins, Kirsten Berthold, Jörg Wittwer, Nicole Krämer, and Nikol Rummel. "Toward an Integration of the Learning and Communication Perspectives in Computer-Supported Instructional Communication." Journal of Media Psychology 25, no. 4 (January 1, 2013): 180–89. http://dx.doi.org/10.1027/1864-1105/a000101.

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Research on computer-supported instructional communication (CSIC) involves the study of interactions between instructors, learners, and system components in computer-based learning environments. At least two strands of research can be identified that are crucial for the understanding of CSIC: From the learning perspective, rooted in cognitive and educational psychology, CSIC is analyzed with regard to its potential for promoting specific cognitive processes, and thus ultimately for improving learning. From the communication perspective, rooted in social psychology and communication science, CSIC is analyzed with regard to conditions that affect its effectiveness and efficiency. CSIC researchers face the challenge of integrating the two traditionally separate research strands and their distinct methodological frameworks. In turn, new methods and findings emerging from an integrative application of research methods are leading to new conceptual challenges regarding the causal mechanisms mediating between the interindividual and the intraindividual levels in CSIC. We provide examples of CSIC research that demonstrate successful methodological integration, and introduce open conceptual challenges.

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Seitz, Michael J., Anne Templeton, John Drury, Gerta Köster, and Andrew Philippides. "Parsimony versus Reductionism: How Can Crowd Psychology be Introduced into Computer Simulation?" Review of General Psychology 21, no. 1 (March 2017): 95–102. http://dx.doi.org/10.1037/gpr0000092.

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Computer simulations are increasingly being used to monitor and predict the movement behavior of crowds. This can enhance crowd safety at large events and transport hubs, and increase efficiency such as capacity utilization in public transport systems. However, the models used are mainly based on video observations, not an understanding of human decision making. Theories of crowd psychology can elucidate the factors underpinning collective behavior in human crowds. Yet, in contrast to psychology, computer science must rely upon mathematical formulations in order to implement algorithms and keep models manageable. Here, we address the problems and possible solutions encountered when incorporating social psychological theories of collective behavior in computer modeling. We identify that one primary issue is retaining parsimony in a model while avoiding reductionism by excluding necessary aspects of crowd psychology, such as the behavior of groups. We propose cognitive heuristics as a potential avenue to create a parsimonious model that incorporates core concepts of collective behavior derived from empirical research in crowd psychology.

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Chiriacescu, Vlad, Leen-Kiat Soh, and Duane F. Shell. "Understanding Human Learning Using a Multi-agent Simulation of the Unified Learning Model." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (October 2013): 1–25. http://dx.doi.org/10.4018/ijcini.2013100101.

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Within cognitive science and cognitive informatics, computational modeling based on cognitive architectures has been an important approach to addressing questions of human cognition and learning. This paper reports on a multi-agent computational model based on the principles of the Unified Learning Model (ULM). Derived from a synthesis of neuroscience, cognitive science, psychology, and education, the ULM merges a statistical learning mechanism with a general learning architecture. Description of the single agent model and the multi-agent environment which translate the principles of the ULM into an integrated computational model is provided. Validation results from simulations with respect to human learning are presented. Simulation suitability for cognitive learning investigations is discussed. Multi-agent system performance results are presented. Findings support the ULM theory by documenting a viable computational simulation of the core ULM components of long-term memory, motivation, and working memory and the processes taking place among them. Implications for research into human learning, cognitive informatics, intelligent agent, and cognitive computing are presented.

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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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Jonassen, David H., and Sherwood Wang. "Using Expert Systems to Build Cognitive Simulations." Journal of Educational Computing Research 28, no. 1 (January 2003): 1–13. http://dx.doi.org/10.2190/ttgm-5twg-3p1f-gg9d.

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Cognitive simulations are runnable computer programs for modeling human cognitive activities. Traditionally used to develop expert and learner models for intelligent tutoring systems, building simulations are also effective learning activities in psychology-related courses. Using inexpensive and easy-to-use expert system shells, students can develop simulations of cognitive processes. This article reports a case study where expert systems were used as a formalism for modeling metacognitive processes in a seminar. Building cognitive simulations engages intensive introspection, ownership, and meaning making in learners who build them.

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Khabiri, E., Y. Li, P. Mazzoleni, and D. Vadgama. "Cognitive color palette creation using client message and color psychology." IBM Journal of Research and Development 63, no. 1 (January 2019): 4:1–4:10. http://dx.doi.org/10.1147/jrd.2019.2893904.

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Bond, Gail E., Vivian Wolf-Wilets, Fred E. Fiedler, and Robert L. Burr. "Computer-Aided Cognitive Training of the Aged." Clinical Gerontologist 22, no. 2 (March 13, 2001): 19–42. http://dx.doi.org/10.1300/j018v22n02_03.

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Pylyshyn, Zenon. "Is vision continuous with cognition?: The case for cognitive impenetrability of visual perception." Behavioral and Brain Sciences 22, no. 3 (June 1999): 341–65. http://dx.doi.org/10.1017/s0140525x99002022.

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Although the study of visual perception has made more progress in the past 40 years than any other area of cognitive science, there remain major disagreements as to how closely vision is tied to cognition. This target article sets out some of the arguments for both sides (arguments from computer vision, neuroscience, psychophysics, perceptual learning, and other areas of vision science) and defends the position that an important part of visual perception, corresponding to what some people have called early vision, is prohibited from accessing relevant expectations, knowledge, and utilities in determining the function it computes – in other words, it is cognitively impenetrable. That part of vision is complex and involves top-down interactions that are internal to the early vision system. Its function is to provide a structured representation of the 3-D surfaces of objects sufficient to serve as an index into memory, with somewhat different outputs being made available to other systems such as those dealing with motor control. The paper also addresses certain conceptual and methodological issues raised by this claim, such as whether signal detection theory and event-related potentials can be used to assess cognitive penetration of vision.A distinction is made among several stages in visual processing, including, in addition to the inflexible early-vision stage, a pre-perceptual attention-allocation stage and a post-perceptual evaluation, selection, and inference stage, which accesses long-term memory. These two stages provide the primary ways in which cognition can affect the outcome of visual perception. The paper discusses arguments from computer vision and psychology showing that vision is “intelligent” and involves elements of “problem solving.” The cases of apparently intelligent interpretation sometimes cited in support of this claim do not show cognitive penetration; rather, they show that certain natural constraints on interpretation, concerned primarily with optical and geometrical properties of the world, have been compiled into the visual system. The paper also examines a number of examples where instructions and “hints” are alleged to affect what is seen. In each case it is concluded that the evidence is more readily assimilated to the view that when cognitive effects are found, they have a locus outside early vision, in such processes as the allocation of focal attention and the identification of the stimulus.

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McClelland, James L. "Emergence in Cognitive Science." Topics in Cognitive Science 2, no. 4 (September 14, 2010): 751–70. http://dx.doi.org/10.1111/j.1756-8765.2010.01116.x.

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Gorman, Michael E. "Trading Zones, Interactional Expertise, and Future Research in Cognitive Psychology of Science." Topics in Cognitive Science 2, no. 1 (January 2010): 96–100. http://dx.doi.org/10.1111/j.1756-8765.2009.01071.x.

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32

Pin-Ying, Gu, Chen Ting, Gu Bin-Fei, and Liu Guo-Lian. "Research on Dividing Modeling of Female Suits Based on Cognitive Psychology." Information Technology Journal 12, no. 15 (July 15, 2013): 3430–35. http://dx.doi.org/10.3923/itj.2013.3430.3435.

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Bonnefon, Jean-François. "Formal Models of Reasoning in Cognitive Psychology." Argument & Computation 4, no. 1 (March 2013): 1–3. http://dx.doi.org/10.1080/19462166.2013.767559.

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GUO, Y., and G. STYLIOS. "An intelligent summarization system based on cognitive psychology." Information Sciences 174, no. 1-2 (June 28, 2005): 1–36. http://dx.doi.org/10.1016/j.ins.2004.08.004.

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35

Adelson, Beth. "Comparing Natural and Abstract Categories: A Case Study from Computer Science." Cognitive Science 9, no. 4 (October 1985): 417–30. http://dx.doi.org/10.1207/s15516709cog0904_3.

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Tchoshanov, M. A. "Learning Sciences Perspective on Engineering of Distance Learning. Part 1." Vysshee Obrazovanie v Rossii = Higher Education in Russia 30, no. 2 (February 22, 2021): 33–49. http://dx.doi.org/10.31992/0869-3617-2021-30-2-33-49.

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There is an on-going debate in the literature on theoretical underpinnings of distance learning. Scholars consider different theoretical perspectives including but not limited to theory of independence and autonomy, theory of industrialization, and theory of interaction and communication through the lens of a traditional Learning Theory approach. There is a lack of discussion on a potential role of a newly emerging field of Learning Sciences in framing the theory of distance learning. Thus, in this paper we provide a theoretical analysis of the Learning Sciences as a new approach to understand distance learning in the era of Information and Communication Technology (ICT). Learning sciences is an interdisciplinary field that studies teaching and learning. This emerging innovative field includes but is not limited to multiple disciplines such as cognitive science, educational psychology, anthropology, computer science, to name a few. The Learning Sciences’ major objective is to understand and design effective learning environments, including distance learning, based on the latest findings about the processes involved in human learning.

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Tchoshanov, M. A. "Learning Sciences Perspective on Engineering of Distance Learning. Part 2." Vysshee Obrazovanie v Rossii = Higher Education in Russia 30, no. 3 (April 1, 2021): 43–58. http://dx.doi.org/10.31992/0869-3617-2021-30-3-43-58.

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There is an on-going debate in the literature on theoretical underpinnings of distance learning. Scholars consider different theoretical perspectives including but not limited to theory of independence and autonomy, theory of industrialization, and theory of interaction and communication through the lens of a traditional Learning Theory approach. There is a lack of discussion on a potential role of a newly emerging field of Learning Sciences in framing the theory of distance learning. Thus, in this paper we provide a theoretical analysis of the Learning Sciences as a new approach to understand distance learning in the era of Information and Communication Technology (ICT). Learning Sciences is an interdisciplinary field that studies teaching and learning. This emerging innovative field includes but is not limited to multiple disciplines such as cognitive science, educational psychology, anthropology, computer science, to name a few. The Learning Sciences’ major objective is to understand and design effective learning environments, including distance learning, based on the latest findings about the processes involved in human learning.

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Null, Cynthia H. "Science, politics, and computers." Behavior Research Methods, Instruments, & Computers 20, no. 2 (March 1988): 73–80. http://dx.doi.org/10.3758/bf03203803.

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Nebel, Steve, Sascha Schneider, Janine Schledjewski, and Günter Daniel Rey. "Goal-Setting in Educational Video Games." Simulation & Gaming 48, no. 1 (November 30, 2016): 98–130. http://dx.doi.org/10.1177/1046878116680869.

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Background. The increased usage of educational video games with their strong graphical and technical potential raises the question of how to optimize the instructional elements of gameplay. In this article, the instructional goal was analyzed with the theoretical background of both motivational psychology (goal-setting theory) and cognitive psychology (goal-free effect). Aim. We compared different goal types within an educational video game specifically produced for this context, using MINECRAFT as a content creation tool. Method. Within a quantitative, empirical experiment, 87 students divided into three goal groups (specific learning goal, specific performance goal, goal-free condition) played the video game for nearly three hours per test session to gain knowledge about basic elements of computer science and basic electric engineering. Results. The results show effects of goal-setting on cognitive load and affective measures. Having specific learning goals lowered extraneous and intrinsic cognitive load. Additionally, players following a specific learning goal reported having significantly more fun, indicating the affective impacts of goal-setting. Conclusions. The outcomes of this study have practical implications for the creation of educational video games and theoretical implications for further work within the field of instructional psychology.

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Upal, M. Afzal. "Towards a Cognitive Science of New Religious Movements." Journal of Cognition and Culture 5, no. 1-2 (2005): 214–39. http://dx.doi.org/10.1163/1568537054068598.

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AbstractTraditionally cognitive scientists have had little to say about religion (and even less to say about new religious movements (NRMs)) partly because religion is arguably a social phenomenon (Bainbridge et al. 1994) and partly because of the pervasive scientific bias of relegating religion to the heap of the irrational, the illogical, and 'a fading vestige of the prescientific times' (Lewis 2003). While sociologists of religion have been studying NRMs, their analysis has been limited to the macro-level. Recent trends in sociology and economics focused on the so called rational choice theories (Iannaccone 1998) and in cognitive sciences of religion (Barrett 2000; Boyer 2001) offer the hope of moving in the direction of a more complete explanation of emergence of new religious movements by providing both macro and micro level analyses. I believe that a closer interaction between these two developments is possible, indeed necessary, for the development of a scientific theory of NRMs that weds the macro and the micro levels. I illustrate the benefits of this approach by focusing on a hitherto ignored phenomenon, the emergence of charismatic leaders who inspire new religious movements. The primary aim of this paper is to identify the contextual factors (both social and cognitive) that cause self-interested utility maximizing agents to take actions that result in new religious movements (NRM). Our secondary aim is to identify factors that cause other self interested utility maximizing agents to align themselves with NRM founders and become the believers. I illustrate how various tools of cognitive science including computer modeling can help in this regard.

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Dougherty, Tom, Stephen McAdams, and Emmanuel Bigand. "Thinking in Sound: The Cognitive Psychology of Human Audition." Computer Music Journal 19, no. 4 (1995): 101. http://dx.doi.org/10.2307/3680998.

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Nicaise, Molly. "Cognitive Research, Learning Theory, & Software Design: The Virtual Library." Journal of Educational Computing Research 18, no. 2 (March 1998): 105–21. http://dx.doi.org/10.2190/qnxx-xce2-u0hu-emq1.

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The idea that theory and research on learning and cognition could influence the way we teach and structure learning environments has established a new paradigm for software designers [1]. According to a social-constructivist perspective, discourse, authentic tasks, exploratory and student-centered learning are integral to learning and understanding. The purpose of this article is to describe design considerations that were made for a software prototype called the Virtual Library—considerations that were framed by cognitive research and learning theory. When completed, the software will assist preservice teachers in learning about educational psychology and instructional design. The software resembles a library where students browse through a series of 3-D, virtual buildings to understand research, theory, and classroom applications of educational psychology and instructional design. Students have access to movies, text, images, and World Wide Web sites. They also have opportunities to participate in interactive lessons and e-mail conversations with other students, classroom teachers, educational psychologists, and instructional designers.

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Smith, Brooke, and Peter Caputi. "Cognitive interference in computer anxiety." Behaviour & Information Technology 20, no. 4 (January 2001): 265–73. http://dx.doi.org/10.1080/01449290110069392.

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Kim, Sunha, Mido Chang, Namok Choi, Jeehyun Park, and Heejung Kim. "The Direct and Indirect Effects of Computer Uses on Student Success in Math." International Journal of Cyber Behavior, Psychology and Learning 6, no. 3 (July 2016): 48–64. http://dx.doi.org/10.4018/ijcbpl.2016070104.

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To promote the academic success of middle school students, the authors examined the effects of computer uses on mathematics self-efficacy and mathematics performance of students, paying focused attention to immigrant students. They analysed the effects of computer use for schoolwork and gaming of middle school students applying a Structural Equation Modeling (SEM) to Trends in International Mathematics and Science Study (TIMSS) USA. The results showed that when students frequently used computer for schoolwork, they revealed high mathematics self-efficacy, which in turn led to high mathematics performance. On the other hand, the students that used computer for gaming frequently indicated low mathematics self-efficacy and mathematics performance. The authors' study results highlight the importance of guiding students to use computers properly, which is directly and indirectly associated with students' self-efficacy and performance in mathematics.

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Rhonda N., McEwen, and Adam K. Dubé. "Intuitive or Idiomatic? An information-cognitive psychology study of child-tablet computer interaction." Proceedings of the American Society for Information Science and Technology 51, no. 1 (2014): 1–10. http://dx.doi.org/10.1002/meet.2014.14505101046.

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Keen, Ian. "Does Cognitive Science Need Anthropology?" Topics in Cognitive Science 6, no. 1 (November 8, 2013): 150–51. http://dx.doi.org/10.1111/tops.12053.

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Rosenbloom, Paul S., and Kenneth D. Forbus. "Expanding and Repositioning Cognitive Science." Topics in Cognitive Science 11, no. 4 (October 2019): 918–27. http://dx.doi.org/10.1111/tops.12468.

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Milkevich, Yelena, and Yelena Severina. "The course of cognitive linguistics in the curriculum of the master’s degree programme on digital humanities." E3S Web of Conferences 273 (2021): 12151. http://dx.doi.org/10.1051/e3sconf/202127312151.

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The Master’s Degree Programme “Digital technologies in philology. Computer linguistics” at Southern Federal University, Russia, Rostov-on-Don, offers innovative multi-disciplinary courses based on the ideology of Digital Humanities. It combines knowledge from various sciences which complement each other in digital research. Cognitive linguistics is interdisciplinary in its essence as it makes use of different sciences, such as philosophy, linguistics, psychology, neuroscience, anthropology, mathematical statistics and others in order to explain the workings of our mind. This humanitarian science widely implements in its linguistic inquiry technologies, methods and tools from other related spheres. All these make it important to incorporate the academic course of cognitive linguistics into the Master’s Degree Programme. To better benefit the Programme the course is divided into two parts, theoretical and practical. During the course the students get knowledge of basic assumptions in cognitive linguistics, which help to set their minds in ‘cognitive linguistics’ thinking. Further course-based experience enables students to carry out linguistic research applying modern methods of corpus analysis, cognitive analysis, introspection and other big data tools. The practical part is based on a definite algorithm of steps, which help to conduct multidisciplinary analysis and easy to follow in the students’ independent research.

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Blomberg, Olle. "Conceptions of Cognition for Cognitive Engineering." International Journal of Aviation Psychology 21, no. 1 (January 6, 2011): 85–104. http://dx.doi.org/10.1080/10508414.2011.537561.

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Bechtel, William. "Constructing a Philosophy of Science of Cognitive Science." Topics in Cognitive Science 1, no. 3 (July 2009): 548–69. http://dx.doi.org/10.1111/j.1756-8765.2009.01039.x.

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