What is Cognitive Ergonomics? By Peter Budnick and Rachel Michael Editor’s Note: This is a revised version of Cognitive Ergonomics and Engineering Psychology which appeared in Ergonomics Today(TM)on June 11, 2001. Ergonomics is sometimes described as “fitting the system to the human,” meaning that through informed decisions; equipment, tools, environments and tasks can be selected and designed to fit unique human abilities and limitations. Typical examples in the “physical ergonomics” arena include designing a lifting job to occur at or near waist height, selecting a tool shape that reduces awkward postures, and reducing unnecessary tasks and movements to increase production or reduce errors and waste. “Cognitive ergonomics,” on the other hand, focuses on the fit between human cognitive abilities and limitations and the machine, task, environment, etc. Example cognitive ergonomics applications include designing a software interface to be “easy to use,” designing a sign so that the majority of people will understand and act in the intended manner, designing an airplane cockpit or nuclear power plant control system so that the operators will not make catastrophic errors. Cognitive ergonomics is especially important in the design of complex, high-tech, or automated systems. A poorly designed cellular phone user-interface may not cause an accident, but it may well cause great frustration on the part of the consumer and result in a marketplace driven business failure. A poor interface design on industrial automated equipment, though, may result in decreased production and quality, or even a life threatening accident. Complex automated systems create interesting design challenges, and research and post accident analysis indicate that the human role in automated systems must be closely considered. Automation can result in increased operator monitoring and vigilance requirements, complex decision-making requirements, and other issues that can increase the likelihood of errors and accidents. Another interesting effect in automation is that humans will sometimes over-trust or mistrust an automated system. The Three Mile Island nuclear power plant accident is in part an example of the effect of people over-trusting a system. During that event, the control panel indicated that an important valve had operated as instructed, and the control room operators trusted the system was reporting accurately. Actually, the valve had not operated as instructed, and it became a key point in the failure that resulted in a serious mishap. (Interestingly, some will blame the operators, when in fact, under the mental load created by the evolving accident, they performed as an ergonomist would expect. The actual cause of the accident is a control system design error that provided incorrect information to the operators). An example of mistrusting a system occurred at a medium security women’s prison in Oregon, USA, when a new surveillance system was installed. The alarm was triggered whenever it sensed motion in particular areas of the facility. During the first few weeks, the alarm was repeatedly triggered by everything from birds to leaves blowing in the wind. The guards became conditioned to the fact that it often triggered in error, and began to ignore it. Using this to her advantage, a prisoner climbed over the fences knowing that the alarm would go off, but that the guards would most likely ignore it long enough for her to escape. It worked. When this same mistrust effect occurs with something as important as a fire alarm, the results can be deadly. Physical ergonomics issues, primarily in the workplace, dominate the public view and understanding of ergonomics. Fortunately, ergonomists are busy behind the scenes working to improve all human-machine interfaces, including the cognitive aspects. Unfortunately, many companies, engineers, regulators, and other decision makers fail to recognize the human factor in design, and many unnecessary errors, accidents, product failures and other business costs are the predictable result. https://ergoweb.com/what-is-cognitive-ergonomics/
Cognitive technical systems are equipped with artificial sensors and actuators, integrated and embedded into physical systems, and act in a physical world. They differ from other technical systems in that they perform cognitive control and have cognitive capabilities. Cognitive control orchestrates reflexive and habitual behavior in accord with longterm intentions. Cognitive capabilities such as perception, reasoning, learning, and planning turn technical systems into ones that "know what they are doing". More specifically, a cognitive technical system becomes a technical system "that can reason using substantial amounts of appropriately represented knowledge, learn from its experience so that it performs better tomorrow than it did today, explain itself and be told what to do, be aware of its own capabilities and reflect on its own behavior, and respond robustly to surprise". Technical systems that are cognitive in this sense will be much easier to interact and cooperate with, be robust, flexible, and efficient. Three demonstrative scenarios are pursued (Research Area F): cognitive aerial and terrestrial vehicles, cognitive humanoid robots, and cognitive factories. CoTeSys organizes and structures the intended interplay of disciplines, institutes, research foci, and demonstration test beds in the following way.
Our graduate program foundations and issues courses are taught at a level that assumes a background similar to that provided by our undergraduate program. This list is a compilation of the textbooks used in undergraduate courses as well as a general reading list to round out the background. Several of the undergraduate courses also use reading packets of numerous journal articles from the field. Methodology Behavior Brain Computation The list is a guide; there are several other texts and journal articles that could provide the preparation needed for graduate studies. We don't expect you to go out and read all of these texts. The textbooks are listed just to indicate the material covered in the corresponding courses. Some books listed below are relatively unique to the UCSD way of doing Cognitive Science, and you should probably read them if you haven't. A few of the books are more advanced, and their material overlaps quite a bit with graduate courses in this department. If this is all very new to you, then we recommend one book on cognitive psychology, one on language, one on experimental design, one on the brain, on programming and/modeling, one on Human-computer interaction. Methodology Design and Analysis of Experiments (Cog Sci 14) If you have taken any undergraduate courses in statistics and logic, you probably have this covered. Statistics textbooks: (14)Introduction to probability theory, statistics and experimental design. Hinkle, D., Wiersma, W., Jurs, S. Applied Statistics for the Behavioral Sciences (5th edition), Boston: Houghton Mufflin Behavior Fundamental Cognitive Phenomena (Cog Sci 101A-B) Course textbooks: Baddeley, A. (199X). Human Memory: Theory and Practice , 2nd ed. Allyn Bacon Hunt Ellis, Fundamentals of Cognitive Psychology. Posner, M. I. (1989). Foundations of Cognitive Science. Cambridge: MIT Press. (This text has good readings in all areas of cognitive science.) Linguistics and Semantics (Cog Sci 101C) Introductory linguistic textbooks: Barsalou, L. Cognitive Psychology: An Overview for Cognitive Scientists: Lawrence Erlbaum. Bollinger, D., Sears, D. (1981). Aspects of language (3rd ed.) . New York: Harcourt, Brace, Jovanovich. Finegan, E., Besnier, N. (1989). Language, its structure and use. San Diego, CA: Harcourt, Brace, Jovanovich. Computational linguistics: Allen, J. (1987). Natural language understanding. Menlo Park, CA: Benjamin/Cummings. Readings in semantics: The New Psychology of Language: Cognitive and Functional Approaches to Language Structure . Ed. M. Tomasello, Lawerence Erlbaum. 1998. Coulson, Seana. Semantic Leaps . Cambridge, New York: Cambridge University Press. 2001. Eco, M., Santambrogio, M., Violi, P. (1988). Meaning and mental representations. Bloomington: Indiana University Press. Lakoff, G. (1987). Women, Fire, and Dangerous Things. University of Chicago Press. Pragmatics, speech act theory, and conversation analysis: Levinson, S. (1983). Pragmatics . Cambridge: Cambridge University Press. Recommended: Bates, E., Goodman, J. On the inseparability of grammar and the lexicon: Evidence from acquisition, aphasia, and real-time processing. Language and Cognitive Processes, 1997, 12(5/6), 507-586. Cognitive Development (Cog Sci 113) Recommended: Elman, J., Bates, E., Johnson, M., Karmiloff-Smith, A., Parisi, D. Plunkett, K. Rethinking Innateness: a connectionist perspective on development . Cambridge, Mass.: MIT Press/Bradford Books, 1998. Haith, M., Benson (1998). Infant cognition. In D. Kuhn R. Siegler (Volume Eds.), W. Damon (Series Ed.), Handbook of child psychology (5th Ed.): Volume 2: Cognition, perception, and language (pp. xxx-xxx). New York: Wiley. Maratsos, M. P. (1998). The acquisition of grammar. In D. Kuhn R. Siegler (Volume Eds.), W. Damon (Series Ed.), Handbook of child psychology (5th Ed.): Volume 2: Cognition, perception, and language (pp. 421-466). New York: Wiley. Siegler, R.S. (1996). Emerging Minds . Oxford. Thelen, E. Smith, L.B. (1998). Dynamic systems theories. In R. Lerner (Volume Ed.), W. Damon (Series Ed.), Handbook of child psychology (5th Ed.): Volume 1: Theoretical models of human development (pp. 563-634). New York: Wiley. Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA: Harverd University Press. Vygotsky, L. (1978). Mind in Society . Harvard. Distributed Cognition, Everyday Cognition, Cognitive Engineering (Cog Sci 102A, 102B, 102C) You should be somewhat familiar with these books, especially the first, obviously. Happily, they are all interesting reading. Norman, D. A. (1988). The design of everyday things. New York: Doubleday. Baecker, Grudin, Buxton, Greenberg. Human-Computer Interaction: Toward the year 2000. Morgan Kaufman. Fleck, L. (1979). Genesis and Development of a Scientific Fact. University of Chicago. Hutchins, E. (1995). Cognition in the wild. MIT Press. Holland, D., Quinn, N. (1987). Cultural models in language and thought. Cambridge: Cambridge University Press. Rogoff, B., Lave, J. (1984). Everyday cognition: Its development in social context. Cambridge: Harvard University Press. Perrow, C., (1984). Normal accidents: Living with high-risk technologies. New York: Basic Books. Foundations Churchland, P. S. (1986). Neurophilosophy . Cambridge: MIT Press. Clark, Andy. (1997). Being There: Putting Brain, Body, and World Together Again. Cambridge, MA: MIT Press. Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin. Haugeland, J. (1981). Mind design. Cambridge: MIT Press. Lakoff, G., Johnson, M. (1980). Metaphors we live by. Chicago: University of Chicago Press. Lakoff, G. Nunez, R. (2000). Where Mathematics Comes From: How the Embodied Mind Brings Mathematics into Being. New York: Basic Books. Maturana, H. Varela, F. (1987). The Tree of Knowledge: The Biololgical Roots of Human Understanding. Boston: Shambhala. Newell, A. (1990). Unified theories of cognition. Cambridge: Harvard University Press. Varela, F., Thompson, E. Rosch, E. (1991). The Embodied Mind: Cognitive Science and Human Experience. Cambridge: MIT Press. Recommended: Nunez, R. Freeman, W. (Eds.) (1999). Reclaiming Cognition: The Primacy of Action, Intention, and Emotion. Throverton, UK: Imprint Academic. Brain Neurobiology (Cog Sci 17) This requirement is covered by any reasonable undergraduate course in neurobiology. Course textbook: Carlson. Physiology of Behavior Allyn Bacon Another good text: Shepherd, G. M. (1988). Neurobiology (2nd ed). New York: Oxford University Press. Cognitive Neuroscience (Cog Sci 107A-B-C) Course textbooks: Rosenzweig, Breedlove L. (2001). Biological Psychology. Sinauer Associates, Inc. Gazzaniga, Ivry, Mangun (2008). Cognitive Neuroscience: The biology of the mind . W.W. Norton. Computation Programming for Cognitive Science (Cog Sci 18) Students entering our Ph.D. program should be able to program in a higher language, e.g. Java. Programming languages frequently used in reach and teaching include but are not limited to, Java, Matlab, C++. Computational Modeling and Artificial Intelligence (Cog Sci 108DEF) An important early book: McClelland, J., Rumelhart, D. (1988). Parallel distributed processing (Vols. 1 2). Cambridge: MIT Press. Modern Books: Ballard, D.H. (1997). An Introduction to Natural Computation. Cambridge, MA : MIT Press. Dayan, P. and Abbot, L.F.(2001). Theoretical Neuroscience. Cambridge, MA: MIT Press. Duda, Hart Stork (2001). Pattern Classification. (2nd Ed.). Wiley. Forsyth, David A. and Ponce, Jean (2003). Computer Vision: A Modern Approach. Prentice Hall. Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Prentice Hall. MacKay, David J.C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press. Recommended for students not specializing in computation: O'Reilly, Randall C. and Munakata, Yuko (2000). Computational Explorations in Cognitive Neuroscience. A Bradford Book, The MIT Press.
Mark Sprevak Home Publications Research Seminars CV Contact I'm a Research Fellow in Philosophy at King's College, Cambridge. My primary research interests are in philosophy of mind, philosophy of science, philosophy of language, and metaphysics. My email address is mds26 @ cam.ac.uk I teach for the History and Philosophy of Science (HPS) Department, and Philosophy Faculty in Cambridge. I am Directing Studies in Philosophy at King's College in Lent term. I run the Philosophy Workshop at the HPS department. I have wide research interests in philosophy. The projects on which I'm currently working can be seen on my research page. Completed projects, and drafts of papers near completion, can be seen on my publications page. I am the organiser of the Computation and Cognitive Science conference that took place 7-8th July 2008 in King's College, Cambridge.
标签: Cognitive
