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Journal articles on the topic 'Cognitive engineering'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 December 2024

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1

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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2

Wilson, Kyle M., William S. Helton, and Mark W. Wiggins. "Cognitive engineering." Wiley Interdisciplinary Reviews: Cognitive Science 4, no. 1 (October 18, 2012): 17–31. http://dx.doi.org/10.1002/wcs.1204.

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3

Larsson, Jan Eric. "Cognitive systems engineering." Automatica 33, no. 3 (March 1997): 478–79. http://dx.doi.org/10.1016/s0005-1098(97)84591-8.

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4

Noble, Douglas D. "Cockpit cognition: Education, the military and cognitive engineering." AI & Society 3, no. 4 (October 1989): 271–96. http://dx.doi.org/10.1007/bf01908619.

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5

Patterson, Robert Earl. "Cognitive engineering, cognitive augmentation, and information display." Journal of the Society for Information Display 20, no. 4 (2012): 208. http://dx.doi.org/10.1889/jsid20.4.208.

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6

MacIntyre, Hector. "A Design Model for Cognitive Engineering." International Journal of Technoethics 6, no. 1 (January 2015): 21–34. http://dx.doi.org/10.4018/ijt.2015010102.

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The author confronts some of the practical consequences of a technogenic account of cognitive agency. In the first section the author examines the commitment to a narrow locus of control shared by most extended theories of cognition, motivating a normative approach to cognitive design. The author then examines the recent appeal some normative theorists have made to responsibilist theories of knowledge to preserve their commitment. This gives the author an opportunity to explore factitious intellectual virtue as a way to defend these sorts of appeals. In the final section the author argues that factitious virtue has several benefits as a normative design model for the practice of cognitive engineering.

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7

Wiggins, Sterling. "Aligning Cognitive Engineering with Systems Engineering Practice to Address Cognition More Effectively." INSIGHT 12, no. 1 (April 2009): 23–25. http://dx.doi.org/10.1002/inst.200912123.

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8

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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9

Roth, Emilie, Ryan Kilgore, Catherine Burns, Robert Wears, John D. Lee, Greg Jamieson, and Ann Bisantz. "Cognitive Engineering Across Domains." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57, no. 1 (September 2013): 139–43. http://dx.doi.org/10.1177/1541931213571032.

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10

Cao, Shi, and Yili Liu. "An Integrated Cognitive Architecture for Cognitive Engineering Applications." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 56, no. 1 (September 2012): 323–27. http://dx.doi.org/10.1177/1071181312561075.

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11

Naweed, Anjum, and Richard Bye. "Joint cognitive systems: Patterns in cognitive systems engineering." Ergonomics 51, no. 5 (April 23, 2008): 768–70. http://dx.doi.org/10.1080/00140130701223774.

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12

Woods, David D. "GUTs or no GUTs (Grand Unified Theories): Does/Can/Should Cognitive Engineering have G.U.T.s?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 3 (September 2002): 468–71. http://dx.doi.org/10.1177/154193120204600353.

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What are the GUTs of Cognitive Systems Engineering (CSE)? G.U.T. is an abbreviation for Grand Unified Theory. As Cognitive Science matured, Allen Newell proposed a unifying model of cognition expressed as a software architecture SOAR. Similarly, John Anderson developed ACTR also claiming it represented a unified theory of cognition in the form of a computer simulation. Both of these cognitive architectures are computer programs that claim to simulate or be the basis for creating simulations of how people perform and learn cognitive tasks. Taking the development of Cognitive Science as a possible analogy for the potential development of Cognitive Systems Engineering, this panel discussion provides a platform to stimulate a vigorous exchange of ideas about the foundation of and potential futures of CSE.

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13

Vallverdú, Jordi. "Para-functional engineering: cognitive challenges." International Journal of Parallel, Emergent and Distributed Systems 37, no. 3 (March 21, 2022): 292–302. http://dx.doi.org/10.1080/17445760.2022.2047678.

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14

Bakay, Roy A. E., and P. R. Kennedy. "Cognitive Engineering: Proof of Principle." Neurosurgery 43, no. 3 (September 1998): 706. http://dx.doi.org/10.1097/00006123-199809000-00319.

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15

Lintern, Gavan. "Special issue on Cognitive Engineering." International Journal of Aviation Psychology 9, no. 3 (July 1999): 199–201. http://dx.doi.org/10.1207/s15327108ijap0903_1.

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16

Stacy, Webb, and Jean MacMillan. "Cognitive bias in software engineering." Communications of the ACM 38, no. 6 (June 1995): 57–63. http://dx.doi.org/10.1145/203241.203256.

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17

Cauwenberghs, G. "Reverse engineering the cognitive brain." Proceedings of the National Academy of Sciences 110, no. 39 (September 12, 2013): 15512–13. http://dx.doi.org/10.1073/pnas.1313114110.

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18

Bragg, Tiffani, Esa M. Rantanen, Justin M. Pelletier, and Ehsan Rashedi. "Cognitive Engineering Modeling of Phishing." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 2088–92. http://dx.doi.org/10.1177/1071181322661330.

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Using signal detection theory (SDT) and fuzzy SDT, the influence of familiarity with phishing and having a background in cybersecurity on phishing behavior was examined. The results from SDT analysis indicated that familiarity with phishing only accounted for 11% of the variance in sensitivity and 5% in bias. When examining the same using Fuzzy SDT, familiarity with phishing accounted for 6% of the variance in bias. Background in cybersecurity had a statistically significant effect on sensitivity and bias in classical SDT but only on bias in fuzzy SDT. A confusion matrix revealed that the percentage of successfully transmitted information from the stimuli to the judgements made by participants was only 26%. Participants most frequently identified requests for personal information in stimulus emails as phishing cues. Future research should continue to explore application of the different cognitive engineering models to phishing identification.

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19

Bonaceto, Craig A., and Kevin J. Burns. "A Roadmap for Cognitive Engineering in Systems Engineering." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 3 (September 2004): 576–80. http://dx.doi.org/10.1177/154193120404800364.

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20

Gualtieri, James W., Samantha Szymczak, and William C. Elm. "Cognitive System Engineering - Based Design: Alchemy or Engineering." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 3 (September 2005): 254–58. http://dx.doi.org/10.1177/154193120504900309.

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Cognitive Systems Engineering (CSE) techniques are widely used for the description and analysis of the sources of cognitive complexity and explicating the basis of expertise within a work domain. However, the results of the CSE techniques often focus on work analysis and are only weakly coupled to the design of decision support systems that are built based on those analyses. In fact, some within the CSE community have suggested that such a design epiphany occurs as if by magic. If CSE is to be treated as an engineering discipline, it cannot rely on magic to create systems. The approach described in this paper assumes that an explicit relationship between system design and supported cognitive work is fundamental to the design's effectiveness. The goal is a pragmatic, effective engineering process that explicitly designs systems according to relationships between cognitive work requirements and decision support concepts.

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21

Bonaceto, Craig, and Kevin Burns. "6.1.2 Using Cognitive Engineering to Improve Systems Engineering." INCOSE International Symposium 16, no. 1 (July 2006): 825–39. http://dx.doi.org/10.1002/j.2334-5837.2006.tb02784.x.

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22

Perlovsky, Leonid, and Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (October 2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.

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Mind is based on intelligent cognitive processes, which are not limited by language and logic only. The thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures, and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized. Semiotic approach allows for the universal representation of such diagrams. In that approach, logic is a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. The most efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the “bottom” to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the “top” of mental hierarchy. Knowledge Instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. Future research directions are discussed.

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23

Todd, P. M., and G. F. Miller. "How cognition shapes cognitive evolution." IEEE Expert 12, no. 4 (July 1997): 7–9. http://dx.doi.org/10.1109/64.608166.

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24

Elm, William C., James W. Gualtieri, Brian P. McKenna, James S. Tittle, Jay E. Peffer, Samantha S. Szymczak, and Justin B. Grossman. "Integrating Cognitive Systems Engineering Throughout the Systems Engineering Process." Journal of Cognitive Engineering and Decision Making 2, no. 3 (September 2008): 249–73. http://dx.doi.org/10.1518/155534308x377108.

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25

Gray, Wayne D., Michael J. Schoelles, and Christopher W. Myers. "Meeting Newell's other challenge: Cognitive architectures as the basis for cognitive engineering." Behavioral and Brain Sciences 26, no. 5 (October 2003): 609–10. http://dx.doi.org/10.1017/s0140525x03280134.

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We use the Newell Test as a basis for evaluating ACT-R as an effective architecture for cognitive engineering. Of the 12 functional criteria discussed by Anderson & Lebiere (A&L), we discuss the strengths and weaknesses of ACT-R on the six that we postulate are the most relevant to cognitive engineering.

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26

Justin Sagayaraj, M., Jithesh V., J. B. Singh, Dange Roshani, and K. G. Srinivasa. "A Hybrid Approach to Cognition in Radars." Defence Science Journal 68, no. 2 (March 13, 2018): 183. http://dx.doi.org/10.14429/dsj.68.12228.

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In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results.

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27

Wang, Xiao Ying. "Idealized Cognitive Model for Engineering Education." Applied Mechanics and Materials 174-177 (May 2012): 3444–47. http://dx.doi.org/10.4028/www.scientific.net/amm.174-177.3444.

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This study from the perspective of cognitive linguistics, the English ditransitive construction structural polysemy in conjunction the antisense strand of the comparative study of the constructions, reasoning semantic motivation. After the construction grammar and cognitive semantic analysis of center, semantic and extending semantic construal, pointed out in the English ditransitive construction system implementation and the first object semantic constraints. Not only the construal of English ditransitive construction implementation of the voluntary, but found the English ditransitive construction of non voluntary, so many in the ditransitive construction system is considered to be abnormal expressions to construal, formed the English ditransitive construction rational cognitive model.

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28

Reising, Dal Vernon C. "Book review of Cognitive Systems Engineering." International Journal of Aviation Psychology 9, no. 3 (July 1999): 291–302. http://dx.doi.org/10.1207/s15327108ijap0903_6.

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29

Win, Moe, Alberto Rabbachin, Jemin Lee, and Andrea Conti. "Cognitive network secrecy with interference engineering." IEEE Network 28, no. 5 (September 2014): 86–90. http://dx.doi.org/10.1109/mnet.2014.6915445.

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30

Shattuck, Lawrence G., and Jodi Heintz Obradovich. "The Cognitive Engineering of Everyday Activities." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 47, no. 3 (October 2003): 429–33. http://dx.doi.org/10.1177/154193120304700338.

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31

Patterson, Emily S., Samuel A. Bozzette, Anh D. Nguyen, José Orlando Gomes, and Steven M. Asch. "Comparing Findings from Cognitive Engineering Evaluations." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 47, no. 3 (October 2003): 483–87. http://dx.doi.org/10.1177/154193120304700349.

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32

Cooke, Nancy J., and Francis T. Durso. "Modern Technology Failures, Cognitive Engineering Successes." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 51, no. 4 (October 2007): 196–99. http://dx.doi.org/10.1177/154193120705100411.

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This panel will focus on five stories in which cognitive engineering has resulted in a significant, measurable success. Five experts will revisit problems with human-technical systems that they helped to solve. In this way, the panel will provide an intimate look at the trials, tribulations, and thought processes of dedicated scientists and engineers who have had an impact on our use of modern technologies. The five stories were motivated by serious problems, but our focus is on the solution to those problems—the repair of the human-technical system. In the question and answer period we will elicit from the panelists insights about doing cognitive engineering, wisdom about becoming a cognitive engineer, and advice about being a cognitive engineer in today's society. The lessons learned from these cognitive engineering successes will be of value to all human factors researchers.

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33

Jondral, Friedrich. "Cognitive Radio: A Communications Engineering View." IEEE Wireless Communications 14, no. 4 (August 2007): 28–33. http://dx.doi.org/10.1109/mwc.2007.4300980.

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34

Hoffman, R. R., G. Klein, and K. R. Laughery. "The state of cognitive systems engineering." IEEE Intelligent Systems 17, no. 1 (January 2002): 73–75. http://dx.doi.org/10.1109/5254.988462.

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35

Bolger, Fergus. "Cognitive expertise research and knowledge engineering." Knowledge Engineering Review 10, no. 1 (March 1995): 3–19. http://dx.doi.org/10.1017/s0269888900007232.

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AbstractThis paper is a review of research into cognitive expertise. The review is organized in terms of a simple model of the knowledge and cognitive processes that might be expected to be enhanced in experts relative to non-experts. This focus on cognitive competence underlying expert performance permits the identification of skills and knowledge that we might wish to capture and model in expert systems. The competence perspective also indicates areas of weakness in human experts. In these areas, we might wish to support or replace the expert with, for example, a normative system rather than attempting to model his or her knowledge.

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36

Mishra, Amit Kumar. "A DIKW Architecture for Cognitive Engineering." Procedia Computer Science 123 (2018): 285–89. http://dx.doi.org/10.1016/j.procs.2018.01.044.

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37

Kirlik, Alex. "Relevance versus generalization in cognitive engineering." Cognition, Technology & Work 14, no. 3 (January 12, 2012): 213–20. http://dx.doi.org/10.1007/s10111-011-0204-5.

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38

Reid, Fraser J. M., and Susan Reed. "Cognitive Entrainment in Engineering Design Teams." Small Group Research 31, no. 3 (June 2000): 354–82. http://dx.doi.org/10.1177/104649640003100305.

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39

Baxter, Gordon D., and Frank E. Ritter. "The Rising Importance of Cognitive Engineering." Contemporary Psychology 47, no. 4 (August 2002): 362–64. http://dx.doi.org/10.1037/001151.

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40

Raubal, Martin. "Cognitive Engineering for Geographic Information Science." Geography Compass 3, no. 3 (March 18, 2009): 1087–104. http://dx.doi.org/10.1111/j.1749-8198.2009.00224.x.

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41

Militello, Laura G., Gavan Lintern, Cynthia O. Dominguez, and Gary Klein. "Cognitive Systems Engineering for System Design." INSIGHT 12, no. 1 (April 2009): 11–14. http://dx.doi.org/10.1002/inst.200912111.

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42

Feldman, Yishai A., and Henry Broodney. "A Cognitive Journey for Requirements Engineering." INCOSE International Symposium 26, no. 1 (July 2016): 430–44. http://dx.doi.org/10.1002/j.2334-5837.2016.00170.x.

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43

Mayer, Marcel Ph, Barbara Odenthal, Marco Faber, Carsten Winkelholz, and Christopher M. Schlick. "Cognitive Engineering of Automated Assembly Processes." Human Factors and Ergonomics in Manufacturing & Service Industries 24, no. 3 (May 8, 2012): 348–68. http://dx.doi.org/10.1002/hfm.20390.

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44

Harmon, Paul. "Engineering cognitive performance. Varieties of knowledge." Performance + Instruction 24, no. 4 (May 1985): 30–31. http://dx.doi.org/10.1002/pfi.4150240416.

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45

Newstetter, Wendy C. "Designing Cognitive Apprenticeships for Biomedical Engineering." Journal of Engineering Education 94, no. 2 (April 2005): 207–13. http://dx.doi.org/10.1002/j.2168-9830.2005.tb00841.x.

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46

Wang, Yingxu, Bernard Widrow, Lotfi A. Zadeh, Newton Howard, Sally Wood, Virendrakumar C. Bhavsar, Gerhard Budin, et al. "Cognitive Intelligence." International Journal of Cognitive Informatics and Natural Intelligence 10, no. 4 (October 2016): 1–20. http://dx.doi.org/10.4018/ijcini.2016100101.

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The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

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47

Newton, Olivia B., Stephen M. Fiore, and Joseph J. LaViola. "An External Cognition Framework for Visualizing Uncertainty in Support of Situation Awareness." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1198–202. http://dx.doi.org/10.1177/1541931213601782.

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This paper discusses an approach for the development of visualizations intended to support cognitive processes deemed fundamental in the maintenance of Situation Awareness under conditions of uncertainty. We integrate ideas on external cognition from the cognitive sciences with methods for interactive visualization to help cognitive engineering examine how visualizations, and interacting with them, alter cognitive processing and decision-making. From this, we illustrate how designers and researchers can study principled variations in visualizations of uncertainty drawing from extended and enactive cognition theory.

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48

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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49

McNeese, Michael D. "New Frontiers in Cognitive Task Analysis: Bridging the Gap between Cognitive Analysis and Cognitive Engineering." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 3 (October 1998): 377–79. http://dx.doi.org/10.1177/154193129804200342.

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50

Durstewitz, Daniel. "Engineering the brain." Behavioral and Brain Sciences 29, no. 1 (February 2006): 76–77. http://dx.doi.org/10.1017/s0140525x06289027.

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The target article presents a stimulating account for some of the most challenging issues in cognitive neuroscience. The theory solves in neural terms cognitive problems beyond the scope of previous models. But in many aspects the neural implementation is a quite literal translation of symbolic descriptions and therefore still lacks some of the truly self-organizing properties characteristic of biological networks.

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