Learner-Centered Design: A Cognitive View of Managing Complexity in Product, Information, and Environmental Design


Wayne Reeves

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  • Part I: The Nature of Complexity

    Part II: Managing Complexity

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    To Doug Engelbart, for encouraging an unrelenting exploration of the future and our ability to augment the human capacity for understanding in the face of overwhelming complexity.

    Preface: Consumers and Complexity

    In the long run we can master change not through force or fear, but only through the free work of an understanding mind.

    —Robert F. Kennedy

    Recent polls have shown that about 75% of the people in the United States feel that life is too complicated and that technology contributes to that complexity in a significant manner. In keeping with this sense of being overwhelmed, we have also recently seen the trend in the definition of success move from the “ability to travel” to simply “being in control of one's life.” Both of these growing trends are being addressed by advertisers pitching the simplicity or simplifying nature of their products. This has been the response of advertisers. What will be the response of the design world? This book is about an approach to design that can substantially lessen the cognitive complexity of a wide range of consumer products from ecommerce to workspaces. It is, of course, this actual new design of products, and not advertising slogans, that will ultimately ensure future competitiveness and true customer satisfaction.

    Complexity and Our Age

    What is the correct moniker for this age? Some people have called it the Space Age or the Electronics Age or the postmodern era. Bell (1973) has often referred to it as the Postindustrial Information Age, and our society has been referred to as a postindustrial society. Just as Gergen (1991) bemoaned the use of the word postmodern because the term fails to specify any essence (defining itself merely as post) I believe that there is an essence to our era: complexity. We are living in the Age of Complexity. The fundamental task of this era is to address the scientific, technological, political, economic, psychological, and sociological complexity that threatens to overwhelm us. This book addresses a fundamental part of that overall task, the role of design, for it is inadequate design that contributes so much complexity to our daily lives.

    This study of complexity and the design of products, information, and environments focuses on managing cognitive complexity: those elements and forces in our environment and society that add both a necessary and an unnecessary neural load to most of the aspects of our postindustrial lives. The basic weapon in our arsenal will be the practical aspects of what we have learned from 30 years of cognitive research about learning and understanding. To that powerful, insightful weapon I will add a fuller conceptualization of the nature of complexity and the design evaluation techniques pioneered by Nielsen (1995), Cooper (1999), and others.

    This book is concerned with how to manage complexity and how we can use the design of products, information, and workspaces to aid in this management. In a sense, we are in a race with complexity. Will it overwhelm our capacity to deal with it, as is increasingly the case today, or will we use what we are learning in the cognitive and information sciences to stave off the chaos? Actually, chaos theory, or the science of the complex itself, may give us some insight into the future of learning and thinking and how scaffolding, or supporting understanding as a primary goal of design, could be the most effective strategy for the management of complexity in the long run. From these insights, and associated ones from distributed cognition, perhaps we will see a significant change in our paradigm of learning, instruction, and what constitutes understanding. Perhaps it will support the growing application of cognitive science research in education (Bruer, 1993). I say this because chaos theory, like the trajectory of cognitive science research, supports the concept of a complex world not reducible to artificial simplicities and because successful theories about our world often end up influencing our educational philosophies.

    When learning theory was developed under the Newtonian paradigm of the world, learning was considered mechanical, behavioral, and individual. Learning systems were geared to stimulus-response activity, rote rehearsal, and simple association. To an extent, the paradigm shift to quantum mechanics and relativity lagged in its effect on learning theory. The quantum world was seen as more complex, more probabilistic and less directly observable, and this gained only partial translation into educational styles. But we have eventually moved to a point where learning is considered in terms of the internal functions of an active, constructive, motivated mind. At the same time, the idea of what constitutes learning has grown broader and more flexible than the all-too-brittle mechanistic perspective. We are currently engaged in an eclectic synthesis of learning by scaffolding, collaboration, narrative, discovery, critical engagement, and heuristic example, as well as the more algorithmic, observational, and didactic informing methods. And all of these efforts have an impact on the methods of modern design. It is debatable whether people set out to consciously create learning theories that reflect the received scientific view of the time, but we are already experiencing a new shift in learning based on chaos theory's simpler, even more demonstrative view of the underlying order of our world. If we choose to take conscious strides to evolve our learning theory, informed by our understanding of how the world operates, we may have a chance of getting ahead of the evolutionary learning curve in our design processes as well because design practice flows from our educational models.

    Long before mathematicians discovered that the world sits on the edge of chaos, the average person already saw that life could be overwhelmingly complex. We could see that many of the more difficult problems would remain untouched and left to future generations that would be just as inadequately prepared to solve them. We see the exacerbated effect of this evolution of complexity in our postindustrial information societies, but the effects are not isolated there. Technology has linked problems on a global basis, making them increasingly harder to visualize, let alone resolve.

    We need an ongoing collaborative research effort for the development of newly informed learning systems and fully distributed information, product, and workspace design principles that will be capable of coping with the new set of problem-solving and decision-making complexities that, as the science of complexity itself contends, will arise irresistibly and irreversibly in the future (Cohen & Stewart, 1994; Goerner, 1990; Nicolis & Prigogine, 1989).

    We cannot afford to ignore the rising cognitive complexity within our society and to pass it on unmanaged to future generations. This would be a growing deficit in our world's problem-solving capacity that we cannot afford to bequeath to our children. Three points seem clear:

    • Complexity will follow an evolutionary projection (Laszlo, 1987; Prigogine & Stengers, 1984).
    • The key to future information, product, and workspace design is a focus on the management of complexity at the cognitive level (individual and distributed) (Cohen & Stewart, 1994; Reeves, 1996).
    • Understanding and design must be taken as a systemic distributed event comprising the quality of the transfer of information between object or workspace and user; the consumer's or learner's information-seeking and knowledge-building capacity; and and the presence of learner-centered interfaces and other knowledge management tools aimed at scaffolding the construction of useful mental models (Banathy, 1991; Collins & Mangieri, 1992; Kuhlthau, 1991; Marchionini, 1995; Norman, 1988; Norman and Spohrer, 1996; Soloway, Guzdial, & Hay 1994; Soloway & Pryor, 1996).

    In developing a general-purpose learner-centered design (LCD) scheme, this book is organized around the following activities:

    • Building a model of the origins and characteristics of cognitive complexity, including complexity-simplicity word-pair scales
    • Proposing an LCD evaluation tool kit
    • Reviewing matched pairs of cognitive and information science topics in a cognitive hierarchy
    • Extracting and presenting from this review the content of the LCD evaluation tools and design principles

    My particular approach is transdisciplinary in an attempt to bridge the already interdisciplinary fields of information science and cognitive science. Cognitive science supplies much of the philosophical and psychological foundations for the LCD scheme, and information science supplies many of the implications of application and implementation. And, as our discussions will exemplify, there are basic insights from each science that should be fruitful for the other, for both are ultimately engaged in an endeavor to better understand and manage human information and knowledge processing.

    Thus, my task in Learner-Centered Design is to create an awareness of “managing complexity” as a fundamental design issue, to use cognitive insights to view all consumers as learners, and to expose the unique opportunity that design has in creating and sustaining human performance and organizational competitiveness.

    Wayne W.ReevesSunnyvale, CA


    Many people have contributed to making this book possible and guiding me in the management of its complexity. I would like to thank Professors Blanche Woolls, Stuart Sutton, Bill Fisher, David Loertscher, and Ruth Hafter of the School of Library and Information Science (SLIS) at San Jose State University (SJSU) for their ongoing encouragement and an opportunity to give the first course based on this material; Professor Christine Borgman of the University of California, Los Angeles, for the right word at the right time; Dr. Albert Lowe and David Wick of Sun Microsystems for their recognition of the value of learner-centered design in the corporate world; Drs. Doug Engelbart from the Bootstrap Institute; Donald Norman, Richard Wurman, Jakob Nielsen, and Robert Sternberg for their prodigious work in design, cognition, and learning that continues to inspire the development of my own creative processes; Sarah Rice for her outstanding research assistance from beginning to end; Anise Kirkpatrick for her special research efforts; and Paul Perrotta of Sequoia Document Design for his immeasurable editorial and graphic assistance. I would especially like to thank my editor at Sage, Peter Labella, my reviewers for their very helpful suggestions, and the students at SJSU SLIS, for their courage and encouragement in participating in the various stages of this eclectic and complex proposal for the future of product, workspace, and information design.

  • Glossary of Key LCD Terms

    The new circumstances under which we are placed call for new words, new phrases, and for the transfer of old words to new objects.

    —Thomas Jefferson
    • Artifact

      Anything that is made and designed by humans. In our context, this includes products for home or office, information designs such as maps and guides, electronic or computer-based information systems, or corporate workspaces/office designs. These are the designed objects of the person-object interaction I have termed an interactive-information-field.

    • Chaos Theory

      In this context, a theory that explains the growth of complex entities from the interaction of very simple components and processes. It is a set of principles that explains and predicts a broad scope of phenomena that have been thought to be random or chaotic but that, in reality, are governed by laws. Chaos theory may be better termed complexity science, for it reveals order out of what seems to be chaos.

    • Cognitive Complexity

      These are factors in our environment that make it difficult to understand how to use our artifacts effectively or efficiently. Complexity elements include societal forces such as rapid change and numerous social and informational interactions; information overload; difficult problems, such as poverty; complex systems, such as our environment; and incoherent design, design characterized by its useless complexity.

    • Complex

      Difficult to understand. Intricate; so full of parts and the interrelationships between those parts that it is difficult to separate one from the other.

    • Constructivist

      An approach to learning and instruction based on the work of Piaget and others. Its basic tenet in instruction is to consider students to be active co-constructors of their own understanding. This is opposed to the idea that students are empty vessels waiting to be filled up. Thus, under this scheme, learners are given a chance to discover understanding through inquiry rather than developing knowledge through the simple memorization of facts and figures.

    • Distributed Cognition

      Refers to the idea that people do not operate as cognitive silos (separate cognitive units), totally independent from others, designed artifacts, and the culture that surrounds them. In LCD, this is the recognition that a user's interaction with an artifact forms a systemic (whole) unit. This unit carries the responsibility for the total cognitive capacity of making the person-object interaction understandable, effective, and engaging. In LCD, the person-object unit is called an interactive-information-field.

    • Environmentals

      This object of design includes workspaces and corporate office layout along with furniture and other equipment, plus in a broader conception of design, such things as the Vietnam War Memorial, the Reagan Library, a corporate information center, or a museum like the Getty in Los Angeles.

    • I-F

      See Interactive-Information-Field.

    • Information Design/Architect

      After the work of Wurman and Tufte, this is the design process and presentation of information geared toward understanding and usefulness. This includes artifacts from the textual Smart Yellow Pages, to the graphic explanations of David Macaulay, and the design of whole environments.

    • Interactive-Information-Field

      The frame of reference used in LCD; the communicative interaction between the designed object and the human. This frame of reference, borrowed from distributed cognition, focuses on distributing the neural load (the responsibility for understanding) between the user and the object being used. Information-fields are composed of human and designed artifacts. In this context, the “designed artifacts” are of three types: consumer and industrial products, information design and information systems, and workspaces and environments.

    • Heuristic

      Reintroduced by Polya in his modern heuristic approach to solving math problems, a heuristic is a rule of thumb or a set of guidelines that supports problem solving and learning. It is any general guide to thinking that helps to direct one toward solutions without yielding a specific answer.

    • LCD

      See Learner-Centered Design.

    • Learnability

      In this context, learnability refers to the elements of design that help to get a user started using an artifact.

    • Learner-Centered Design (LCD)

      This is an attitude or approach to design that views users of artifacts as active, participative learners seeking to understand the “what” and “how” of their environment. It is aimed at scaffolding or supporting users in gaining that understanding. It includes a set of design principles based on learnability, usability, and understandability, where understandability means not only a deep grasp of how to use something, the ability to interact with the design beyond a surface level, but also an understanding of the content or meaning conveyed by the system. LCD should be used on those objects of sufficient complexity as warranted. LCD extends understandability to the design of actual content (text, text flow, and graphics used within the artifact). It focuses on managing cognitive complexity and thus focuses on the design of artifacts that are of sufficient complexity to warrant the extra effort. The challenge of today is that a large percentage of our products do possess significant intricacy to be considered complex. It is important to note that LCD, in the context of this book, addresses the design of things other than computer-based learning systems (see Interactive-information-field).

    • Mental Model

      A version of a knowledge structure, found in long-term memory, that holds the understanding of a topic or procedure. In learner- and user-centered design one of the key design and performance goals is to facilitate the development of a mental model of how an application works.

    • Metacognitive

      This refers to the awareness one has of his or her own thinking processes. Metacognition comprises the monitoring or evaluative aspects of thinking. It is metacognition that selects a problem-solving method, monitors how successful it is, and changes to another method as appropriate. It is considered a key factor in learning success, along with domain knowledge, and higher-level thinking strategies. LCD should support metacognitive awareness.

    • Neural Load

      In LCD, this refers to the amount of effort it takes to understand something. A basic tenet of LCD is that proper design makes the designed object responsible for carrying more and more of the neural load of the artifact-human interaction.

    • Scaffolding

      Any of a wide variety of techniques to extend a learner to perform beyond his or her current knowledge and skill levels. These could include a just-in-time help system, mentoring, tutoring, training wheels on a bicycle, heuristics, or flying a plane with an instructor. In our LCD context, it broadly refers to any designed aids that support and facilitate the learning process.

    • Schema, Script, Frame

      Like mental models these are versions of knowledge structures or how knowledge of different types are represented in long-term memory.

    • System-Content Competency

      Understanding of the information provided within the system in terms of help screens, messages, displays, audio, video, hyperlinked text, and so on.

    • UCD

      See User-Centered Design.

    • Understandability

      This concept has two parts. First, it is an extension of usability principles to include understanding the product or system one is working with to such a level that the user can easily do all the things the design of the system might require. For example, a television set of the 1960s or 1970s requires no understandability of design. One can run it easily from the obvious surface cues, knobs, and so on. However, a television of the year 2000 will require understandability because its use in connecting with the Internet and a hundred other appliances will be too complicated to be left to regular design principles. Thus, understandability is especially applicable to the design of complex products from pagers to workspaces. Second, understandability is concerned with the adequacy of the actual content (text, graphics, examples, audio clips, and so on) in delivering understanding, in being instructional. This becomes very important as we increasingly add information into our products, systems, and spaces. It is the difference in importance between understanding how to navigate the nodes and links of the Internet and understanding the information that the Internet provides. The Internet's ultimate usefulness will reside with the quality and understandability of its content.

    • Usability

      Design elements associated with ease of learning, efficiency of use, protection against errors, and comfort of use.

    • User-Centered Design

      Developed in the 1980s by Donald Norman and others, this design philosophy was based on the tenet of moving design from being technology centered to being human centered. Its major tools are the application of cognitive-based design principles, with a special emphasis on facilitating mental models.

    • Workspace

      The workplace is undergoing profound changes as we move to self-directed teams of independent knowledge workers. Collaborative problem solving is increasing. All this change is supported by a steady flow of information circulating through the workplace in terms of technology and increased human-to-human interaction. This whole new working environment is the “workspace.” Those engaged in creating the workspaces of the future have a great opportunity to use integrated ergonomic and environmental designs to scaffold the cognitive capacities of teams that are often not ready to carry the increased load of decision making and information processing that is being demanded of them.


    Adams, J. (1974). Conceptual blockbusting. Stanford, CA: Stanford Alumni Association.
    Anderson, J. (1985). Cognitive psychology. New York: W. H. Freeman.
    Andriole, S., & Adelman, L. (1995). Cognitive systems engineering for user-computer interface design, prototyping, and evaluation. Hillsdale, NJ: Lawrence Erlbaum.
    Anzai, Y. (1991). Learning and use of representations for physics expertise. In K.Ericsson & JSmith (Eds.), Toward a general theory of expertise (pp. 64–92). Cambridge, UK: Cambridge University Press.
    Baldonado, M., & Winograd, T. (1996). SenseMaker: An information-exploration interface supporting the contextual evolution of the user's interest. Stanford University Digital Library Initiative working paper. <http://www-diglib.stanford.edu/cgi-bin/wp/get/SIDL-WP-1996-0048>.
    Banathy, B. H. (1991). Systems design of education. Englewood Cliffs, NJ: Educational Technologies Publications.
    Barsalou, L. (1983). Ad hoc categories. Memory and Cognition, 11, 221–227.
    Barsalou, L. (1992). Cognitive psychology: An overview for cognitive scientists. Hillsdale, NJ: Lawrence Erlbaum.
    Bell, D. (1973). The coming post industrial society. New York: Basic Books.
    Bigge, M. L. (1982). Learning theories for teachers. New York: HarperCollins.
    Borgman, C. (1986). The user's mental model of an information retrieval system: An experiment on a prototype online catalog. International Journal Man-Machine Studies, 34, 47–64. http://dx.doi.org/10.1016/S0020-7373%2886%2980039-6
    Bruer, J. T. (1993). Schools for thought. Cambridge: MIT Press.
    Buckland, M. (1991). Information and information systems. New York: Praeger.
    Card, S., Moran, J., & Newell, A. (1983). The psychology of human-computer interaction. Hillsdale, NJ: Lawrence Erlbaum.
    Card, S. & Moran, T. (1995). User technology: From pointing to pondering. In R.Baecker, J.Grudin, W.Buxton, & S.Greenberg (Eds.), Readings in human-computer interaction: Toward the year 2000 (
    ). San Francisco: Morgan Kaufmann.
    Ceci, S., & Ruiz, A. (1992). The role of general ability in cognitive complexity: A case study of expertise. In R.Hoffman (Ed.); The psychology of expertise: Cognitive research and empirical AI (pp. 218–230). New York: Springer-Verlag. http://dx.doi.org/10.1007/978-1-4613-9733-5_13
    Checkland, P. (1981). Systems thinking, systems practice. New York: John Wiley.
    Chen, H., & Dhar, V. (1991). Cognitive process as a basis for intelligent retrieval systems design. Information Processing and Management, 27, 405–422. http://dx.doi.org/10.1016/0306-4573%2891%2990060-Y
    Cohen, J., & Stewart, I. (1994). The collapse of chaos. New York: Viking.
    Collins, C., & Mangieri, J. (Eds.). (1992). Teaching thinking. Hillsdale, NJ: Lawrence Erlbaum.
    Cooper, A. (1995). About face. Foster City: Programmer's Press.
    Cooper, A. (1999). The inmates are running the asylum. Indianapolis: Sams Publishing.
    Cousins, S. (1996). A task oriented interface to a digital library. Stanford University Digital Library Initiative working paper. <http://www-diglib.stanford.edu/diglib/WP/PUBLIC/DOC44.html>.
    Cowan, N. (1995). Attention and memory: An integrated framework. Oxford, UK: Oxford University Press.
    Crane, H., & Rtischev, D. (1993). Pen and voice unite. Byte, 10, 98–102.
    Cremmins, E. (1982). The art of abstracting. Philadelphia: ISI.
    Csikszentmihalyi, M. (1993). The evolving self. New York: HarperCollins.
    D'Andrade, R. (1995). The development of cognitive anthropology. Cambridge, UK: Cambridge University Press. http://dx.doi.org/10.1017/CBO9781139166645
    Daniels, P. (1986). Cognitive models in information retrieval: An evaluative review. Journal of Documentation, 42, 272–304. http://dx.doi.org/10.1108/eb026797
    de Bono, E. (1970). Lateral thinking. New York: Harper & Row.
    Dillon, A. (1996). Myths, misconceptions, and an alternative perspective on information usage and the electronic medium. In J.Rouet, J.Levonen, A.Dillon, & R.Spiro (Eds.), Hypertext and cognition, (pp. 25–42). Mahwah, NJ: Lawrence Erlbaum.
    Dörner, D. (1983). Heuristics and cognition in complex systems. In R.Groner, M.Groner, & W.Bischof (Eds.), Methods of heuristics (pp. 89–107). Hillsdale, NJ: Lawrence Erlbaum.
    Ehrlich, K. (1996). Applied mental models in human-computer interaction. In J.Oakhill & A.Garnham (Eds.), Mental models in cognitive science (pp. 223–246). Hove, UK: Psychology Press.
    Esperet, E. (1996). Notes on hypertext, cognition, and language. In J.Rouet, J.Levonen, A.Dillon, & R.Spiro (Eds.), Hypertext and cognition, (pp. 149–156). Mahwah, NJ: Lawrence Erlbaum.
    Estes, W. (1994). Classification and cognition. Oxford, UK: Oxford University Press. http://dx.doi.org/10.1093/acprof:oso/9780195073355.001.0001
    Farooq, M. & Dominick, W. (1988). A survey of formal tools and models for developing user interfaces. International Journal Man-Machine Studies, 29, 479–496. http://dx.doi.org/10.1016/S0020-7373%2888%2980007-5
    Flood, R., & Carson, E. (1990). Dealing with complexity. New York: Plenum.
    Foley, M., & Hart, A. (1991). Expert-novice differences and knowledge elicitation. In R.Hoffman (Ed.), The psychology of expertise: Cognitive research and empirical AI (pp. 233–244). New York: Springer-Verlag.
    Foltz, P. (1996). Comprehension, coherence, and strategies in hypertext and linear text. In J.Rouet, J.Levonen, A.Dillon, R.Spiro (Eds.), Hypertext and cognition (pp. 109–136). Mahwah, NJ: Lawrence Erlbaum.
    Friedman, M., & Carterette, E. (Eds.). (1996). Cognitive ecology. San Diego: Academic Press.
    Funke, J. (1991). Solving complex problems: Exploration and control of complex social problems. In R.Sternberg & P.Frensch (Eds.), Complex problem solving (pp. 185–222). Hillsdale, NJ: Lawrence Erlbaum.
    Furnas, G. W. (1986). Generalized fisheye views. In Human Factors in Computing Systems CHI '86 conference proceedings, Boston, ACM, 16–23.
    Furnas, G. W. (1991). New graphical reasoning models for understanding graphical interfaces. In Human Factors in Computing Systems chi '91 conference proceedings, New Orleans, ACM, 71–78.
    Furnas, G. W., & Bederson, B. (1995). Space-scale diagrams: Understanding multiscale interfaces. In Human Factors in Computing Systems CHI '95 conference proceedings, Denver, ACM, 234–241.
    Furnas, G. W., & Zacks, J. (1994). Multitrees: Enriching and reusing hierarchical structure. In Human Factors in Computing Systems CHI '94 conference proceedings, Boston, ACM, 330–336.
    Gardner, H. (1985). The mind's new science: A history of the cognitive revolution. New York: Basic Books.
    Gergen, K. (1991). The saturated self. New York: Basic Books.
    Goerner, S. (1990). Chaos and the evolving ecology world hypothesis: Implications for just systems. in Proceedings of the 34th International Society of Systems Science, Portland, ISSS, 433–446.
    Gove, P. (Ed.). (1986). Webster's third international dictionary. Springfield, MA: Merriam-Webster.
    Guillemette, R. (1989). Development and validation of a reader-based documentation measure. International Journal Man-Machine Studies, 30, 551–574. http://dx.doi.org/10.1016/S0020-7373%2889%2980034-3
    Halfhill, T. (1997). Good-bye, GUI, hello, NUI. Byte, 22, (7), 60–72.
    Halpern, D. (1996). Thinking critically about critical thinking. Mahwah, NJ: Lawrence Erlbaum.
    Holland, J., Holyoak, K., Nisbett, R., & Thagard, P. (1986). Induction: Processes of inference, learning, and discovery. Cambridge: MIT Press.
    Hurd, M. (1991). Dialectical reasoning in the adult years. Unpublished doctoral dissertation, Saybrook Institute, San Francisco.
    Hutchins, E. (1995). Cognition in the wild. Cambridge; MIT Press.
    Hymes, C. (1997). Classification in Object-Oriented Programming. Unpublished dissertation proposal, University of Michigan, Ann Arbor.
    Jacob, E. (1991). Classification and categorization: Drawing the line. In Proceedings of the 2nd ASIS SIG/CR Classification Research Workshop, 67–81.
    Jefferies, R., Miller, J., Wharton, C., & Uyeda, K. (1991). User interface evaluation in the real world: A comparison of four techniques. In Human Factors in Computing Systems CHI '91 conference proceedings, New Orleans, ACM, 119–124.
    Johnson-Laird, P. (1990). Mental models. In M.Posner (Ed.), Foundations of cognitive science, (pp. 469–500). Cambridge: MIT Press.
    Kepner, C., & Tregoe, B. (1981). The new rational manager. Princeton, NJ: Princeton Research Press.
    Kieras, D., & Bovair, S. (1984ate a device. Cognitive Science, 8, 255–273. http://dx.doi.org/10.1207/s15516709cog0803_3
    Kieras, D., & Polson, P. (1985). An approach to the formal analysis of user complexity. International Journal of Man-Machine Studies, 22, 365–394. http://dx.doi.org/10.1016/S0020-7373%2885%2980045-6
    Kirkpatrick, A. (1997). Personal communication.
    Klapp, O. (1986). Overload and boredom. Westport, CT: Greenwood.
    Kosslyn, S. (1996). Image and brain. Cambridge: MIT Press.
    Kuhlthau, C. (1991). Inside the search process: Information seeking from the user's perspective. Journal of the American Society of Information Science, 42, 361–371. http://dx.doi.org/10.1002/%28SICI%291097-4571%28199106%2942:5%3C361::AID-ASI6%3E3.0.CO;2-#
    Lakoff, G., & Johnson, M. (1980). Metaphors to live by. Chicago: University of Chicago Press.
    Landsdale, M., & Ormerod, T. (1994). Understanding interfaces: A handbook of human-computer dialogue. London: Academic Press.
    Laszlo, E. (1987). Evolution: The grand synthesis. Boston: Shambhala.
    Legrenzi, P., & Girotto, V. (1996). Mental models in reasoning and decision making processes. In J.Oakhill & A.Garnham (Eds.), Mental models in cognitive science (pp. 95–118). Hove, UK: Psychology Press.
    Lesgold, A., & Lajoie, S. (1991). Complex problem solving in electronics. In R.Sternberg & P.Frensch (Eds.), Complex problem solving (pp. 287–316). Hillsdale, NJ: Lawrence Erlbaum.
    Madsen, K. (1994). A guide to metaphorical design. Communications of the ACM, 37 (12) 57–61. http://dx.doi.org/10.1145/198366.198381
    Marchionini, G. (1995). Information seeking in electronic environments. Cambridge, UK: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511626388
    Marcus, A. (1993). Human communications issues in advanced UI. Communications of the ACM, 36 (4) 101–108. http://dx.doi.org/10.1145/255950.153670
    Marshall, S. (1995). Schemas in problem solving. Cambridge, UK: Cambridge University Press. http://dx.doi.org/10.1017/CBO9780511527890
    Mayer, R. (1983). Thinking, problem solving, cognition. New York: W. H. Freeman.
    Moran, T. (1981). An applied psychology of the user. ACM Computing Surveys, 13, 1–11. http://dx.doi.org/10.1145/356835.356836
    Mullet, K., & Sano, D. (1995). Designing visual interfaces. Mountain View, CA: SunSoft.
    Newell, A. & Card, S. (1985). The prospects for psychological science in human-computer interaction. Human-Computer Interaction, 1, 209–242. http://dx.doi.org/10.1207/s15327051hci0103_1
    Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice Hall.
    Nicolis, G., & Prigogine, I. (1989). Exploring complexity. New York: W. H. Freeman.
    Nielsen, J. (1992). Finding usability problems through heuristic evaluation. In Human Factors in Computing Systems CHI '92 conference proceedings, Monterey, ACM, 373–392.
    Nielsen, J. (1993). Hypertext and hypermedia. Boston: Academic Press.
    Nielsen, J. (1995). Multimedia and hypertext. Boston: Academic Press.
    Nielsen, J., & Molich, R. (1990). Heuristic evaluation of user interfaces. In Human Factors in Computing Systems CHI '90 conference proceedings, Seattle, ACM, 249–256.
    Norman, D. (1986). Cognitive engineering. In D.Norman & S.Draper (Eds.), User centered system design (pp. 31–61). Hillsdale, NJ: Lawrence Erlbaum.
    Norman, D. (1988). The psychology of everyday things. New York: Basic Books.
    Norman, D. (1993). Things that make us smart. New York: Addison-Wesley.
    Norman, D., & Draper, S. (Eds.). (1986). User centered system design. Hillsdale, NJ: Lawrence Erlbaum.
    Norman, D., & Spohrer, J. (1996). Learner-centered education. Communications of the ACM, 39 (4), 24–27. http://dx.doi.org/10.1145/227210.227215
    Olson, J., & Biolsi, K. (1991). Techniques for representing expert knowledge. In K.Ericsson & J.Smith (Eds.), Toward a general theory of expertise (pp. 240–285). Cambridge, UK: Cambridge University Press.
    Olson, J., & Nilsen, E. (1987). Analysis of the cognition involved in spreadsheet software interaction. Human-Computer Interaction, 3, 309–349. http://dx.doi.org/10.1207/s15327051hci0304_1
    Paepcke, A. (1996, May). Digital libraries: Searching is not enough. What we learned on site. D-LIB magazine, Stanford University. <http://www.dlib.org/dlib/may96/stanford/05paepcke/html.>.
    Patel, V., & Groen, G. (1991). The general and specific nature of medical expertise: A critical look. In K.Ericsson & J.Smith (Eds.), Toward a general theory of expertise (pp. 93–125). Cambridge, UK: Cambridge University Press.
    Paul, R. (1990). Critical thinking. Rohnert Park, CA: Center for Critical Thinking and Moral Critique.
    Pea, R. (1993). Distributed intelligence and designs for education. In G.Salomon (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 47–87). Cambridge, UK: Cambridge University Press.
    Peat, D. (1985). Artificial intelligence: How machines think. New York: Baen Enterprises.
    Perry, W. (1970). Forms of intellectual and ethical development in the college years. New York: Holt, Rinehart & Winston.
    Piaget, J. (1954). The construction of reality in the child. New York: Basic Books. http://dx.doi.org/10.1037/11168-000
    Polson, P. (1988). The consequences of consistent and inconsistent user interfaces. In R.Guindon (Ed.), Cognitive science and its applications for human-computer interaction (pp. 59–108). Hillsdale, NJ: Lawrence Erlbaum.
    Polya, G. (1985). How to solve it. Princeton, NJ: Princeton University Press.
    Posner, M. (1990). Foundations of cognitive science. Cambridge: MIT Press.
    Prigogine, I., & Stengers, I. (1984). Order out of chaos. New York: Bantam.
    Reeves, W. (1996). Cognition and complexity. Lanham, MD: Scarecrow.
    Rosch, E. (1978). Principles of categorization. In E.Rosch & B.Lloyd (Eds.), Cognition and categorization (pp. 27–48). Hillsdale, NJ: Lawrence Erlbaum.
    Rouet, J., Levonen, J., Dillon, A. and Spiro, R. (Eds.). (1996). Hypertext and cognition. Mahwah, NJ: Lawrence Erlbaum.
    Rubinstein, M. (1986). Tools for thinking and problem solving. Englewood Cliffs, NJ: Prentice Hall.
    Rudnicky, A. (1993). Matching the input mode to the task. Byte, 10, 100.
    Salomon, G. (Ed.). (1993). Distributed cognitions: Psychological and educational considerations. Cambridge, UK: Cambridge University Press.
    Scardamalia, M. & Bereiter, C. (1991). Literate expertise. In K.Ericsson & J.Smith (Eds.), Toward a general theory of expertise (pp. 172–194). Cambridge, UK: Cambridge University Press.
    Schank, R. (1997). Virtual learning. New York: McGraw-Hill.
    Schumacher, R., & Czerwinski, M. (1991). Mental models and the acquisition of expert knowledge. In R.Hoffman (Ed.), The psychology of expertise: Cognitive research and empirical AI (pp. 61–79). New York: Springer-Verlag.
    Senge, P. M. (1990). The fifth discipline. Garden City, NY: Doubleday.
    Shenk, D. (1997). Data smog. San Francisco: HarperEdge.
    Shneiderman, B. (1992). Designing the user interface: Strategies for effective human-computer interaction. Reading, MA: Addison-Wesley.
    Sokal, R. (1974). Classification: purposes, principles, progress, prospects. Science, 185, 115–123. http://dx.doi.org/10.1126/science.185.4157.1115
    Soloway, E., Guzdial, M., & Hay, K. (1994, April). Learner-centered design. Interactions, 1 (2), 37–48. http://dx.doi.org/10.1145/174809.174813
    Soloway, E. & Pryor, A. (1996). The next generation in human-computer interaction. Communications of the ACM, 39 (4), 16–17. http://dx.doi.org/10.1145/227210.227213
    Solso, R. (1994). Cognition and the visual arts. Cambridge: MIT Press.
    Stein, J. (Ed.). (1982) Random House college dictionary. New York: Random House.
    Sternberg, R. (1990a). Metaphors of mind. Cambridge, UK: Cambridge University Press.
    Sternberg, R. (Ed.). (1990b). Wisdom: its nature, origins and development. Cambridge, UK: Cambridge University Press. http://dx.doi.org/10.1017/CBO9781139173704
    Strunk, W., Jr., & White, E. B. (1979). The elements of style. (
    3rd ed.
    ). New York: Macmillan.
    Tanin, E., Beigel, R., & Shneiderman, B. (1996). Incremental data structures and algorithms for dynamic query interfaces. SIGMOD Record, 25 (4), 21–24. http://dx.doi.org/10.1145/245882.245891
    Toffler, A. (1970). Future shock. New York: Bantam Books.
    Treu, S. (1994). User interface evaluation. New York: Plenum. http://dx.doi.org/10.1007/978-1-4615-2536-3
    Trikha, M. (1997, January 14). Browsers hailed as new wave. San Jose Mercury News, p. I—C.
    Tufte, E. (1990). Envisioning information. Cheshire, CT: Graphics Press.
    Tufte, E. (1997). Visual explanations. Cheshire, CT: Graphics Press.
    von Bertalanffy, L. (1968). General systems theory. New York: George Braziller.
    Voss, J., Wolfe, C., Lawrence, J., & Engle, R. (1991). From representation to decision: An analysis of problem solving in international relations. In R.Sternberg & P.Frensch (Eds.), Complex problem solving (pp. 119–158). Hillsdale, NJ: Lawrence Erlbaum.
    Wurman, R. S. (1989). Information anxiety. New York: Random House.
    Wurman, R. S. (1997). Information architects. New York: Graphis.
    Yates, F. (1978). Complexity and the limits to knowledge. American Journal of Physiology, 4, R201–R204.

    About the Author

    Wayne W. Reeves has served as the project manager and content architect for a variety of intranets at Sun Microsystems including SunWeb, Sun's first corporate intranet. He was also the founding Manager of the eLibrary and Information Services for Sun and the Manager of Engineering Information Development, Engineering Training, and Library Services for Siemens, IBM, and ROLM. He holds a bachelor of arts degree in philosophy from the University of California, Santa Cruz, a master's in psychology from Lone Mountain College, and a doctorate in human science/cognitive systems from the Saybrook Graduate Institute. He is currently an adjunct professor at San Jose State University's School of Library and Information Science, a creative thinking advocate, and a consulting cognitive architect, focusing on information design in corporate training, design, and knowledge management solutions.

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