Mind The Model

Mental models are personal, internal representations of external reality that people use to interact with the world around them. They are constructed by individuals based on their unique life experiences, perceptions, and understandings of the world.  Mental models are used to reason and make decisions and can be the basis of individual behaviors. They provide the mechanism through which new information is filtered and stored. Recognizing and dealing with the plurality of stakeholder’s perceptions, values, and goals is currently considered a key aspect of effective natural cognitive experience design.

Simply stated, a mental model is a cognitive representation. That representation can be constructed and simulated within a conscious mind.

The notion of a mental model was originally postulated by the psychologist Kenneth Craik (1943) who proposed that people carry in their minds a small-scale model of how the world works. These models are used to anticipate events, reason, and form explanations. Decades later, psychologist Johnson-Laird (1983) further developed Craik’s idea of a mental model in his research on human reasoning. For Johnson-Laird, a mental model is a reasoning mechanism that exists in a person’s working memory. His research, carried out within the domain of experimental psychology, supports Craik’s claim that people reason by way of thought experiments using internal models.

Gaining a better understanding of how mental models represent complex, dynamic systems and how these representations change over time will allow us to develop hardware, software and wetware to enhance effective management and use of natural resources. Realizing this potential, however, relies on developing and testing adequate tools and techniques to elicit these internal representations of the world effectively. The human mind has an extraordinary ability to simulate mental models of our immediate physical reality. Things get harder when we start thinking about abstract systems.

A market is a good example of an abstract system. In a market system, price acts as a signal of aggregate demand for a commodity. You can’t “see” a market like you can “see” a tree in front of you. A market does not exist in a particular physical location. A market is an abstract concept that exists in the collective minds of all who participate in it. Even though markets do not exist physically, they have an enormous impact on our lives nonetheless.


When the global economic crisis hit in late 2008, retailers began to struggle financially because consumer purchases declined rapidly. People were worried about the economy and started saving money instead of spending it. This started happening just before the holiday shopping season—a make-or break period for many retailers. So, in an effort to boost demand, retailers began dropping prices (see “Price Deflation Flywheel”).

This process, or flywheel, led to price deflation, because consumers saw prices dropping rapidly and began delaying purchases as a result. The outcome of simulating their mental models of the market informed their decision making: “I should wait to buy this because the price keeps dropping.”

This mental model paints a pretty picture for consumers over the short term: low prices in a down economy. As the deflationary dynamics play out over the long term, however, the picture becomes bleak. As prices spin downward, profits decline, and businesses are forced to lay off workers or close up shop entirely. As unemployment increases, consumers’ perception of the stability of the economy decreases, and they spend even less (see “Economic Stability Loop”).

Economists and policy makers use sophisticated computer models to help them understand markets. Consumers, on the other hand, use simple mental models when making purchasing decisions. The more sophisticated models inform policy makers of the long-term consequences of consumers’ reduction in spending, so they react by trying to jump-start spending with stimulus programs. In the U. S., we’ve seen a few of these programs during 2009: the “Cash for Clunkers” rebate program, the first-time home buyer tax credit, and the social security payroll tax cut.


Often, it is hard for us to define the optimal boundaries for a mental model. We tend to have a narrow focus and act on short-term dynamics within our mental models. For example, in the model above, our understanding changes when we expand the boundaries to include profits and layoffs.

However, we are generally not very good at mentally simulating complex systems with interdependencies, lots of variables, and delays. This is where software steps in. Using systems thinking software, we can transform our mental models into operational models that we can simulate more reliably using a computer. Doing so not only helps us create new knowledge and understanding, but also helps us construct better mental models in the future.


A dynamic representation

There is widespread agreement in the literature that mental models are ‘working models’ (Craik 1943, Johnson-Laird 1983) and are therefore dynamic. The dynamic character of a mental model is discussed in the literature in three ways, in relation to reasoning, causal dynamics, and learning.


A defining feature of a mental model from a psychology perspective is that it is a computational structure (Rutherford and Wilson 2004). A mental model is constructed in working memory and can then be run like a computer simulation allowing an individual to explore and test different possibilities mentally before acting. Working memory is the system responsible for selecting and manipulating information for the purpose of reasoning and learning. Changes made to a mental model in the simulation process represent what would happen if such changes took place in reality.

Causal dynamics

The second dynamic attribute of a mental model discussed at length in the literature refers to ‘causal knowledge’. The capacity of a mental model to represent (perceived) cause-and-effect dynamics of a phenomenon is studied from a systems dynamics and naive theory perspective. Researchers interested in systems dynamics use the mental model construct in a pragmatic sense: as a tool to better understand complex, dynamic systems to ultimately improve their design and usability (Doyle and Ford 1998, Moray 2004). A widely cited definition of a mental model in this context is that of Rouse and Morris (1986) who consider a mental model in terms of its functionality and conceive it as a cognitive structure that enables a person to describe, explain, and predict a system’s purpose, form, function, and state. Given the focus on dynamic phenomena, a mental model in this field has been conceived of as a model that is built of “causal knowledge about how a system works” (Moray 1998:295).


The capacity of mental models to change over time through experience and learning is another dynamic quality often referred to in the literature. Researchers, mainly from the fields of human-computer interaction (HCI), education, and organizational studies, take interest in the difference between lay (or student) and expert mental models in terms of knowledge content and organization. Research shows that lay understanding is characteristically concrete while expert understanding is more abstract (DiSessa 1983, Greeno 1983, Larkin 1983). This highlights the idea that the formation of a mental model in a person’s mind is the result of both biology, i.e., an ability inherent to the human mind, and ‘learning’ (Nersessian 2002). Nersessian (2002:140) states that, “the nature and richness of models one can construct and one’s ability to reason develops with learning domain-specific content and techniques”.

Systems dynamics researchers focus on the role of mental models in information feedback loops. They are particularly interested in the problems which hinder information feedback in a system and therefore hinder learning (Doerner 1980, Brehmer 1992).

An inaccurate and incomplete representation

Mental models tend to be functional rather than complete or accurate representations of reality. A mental model is a simplified representation of reality that allows people to interact with the world. Because of cognitive limitations, it is neither possible nor desirable to represent every detail that may be found in reality. Aspects that are represented are influenced by a person’s goals and motives for constructing the mental model as well as their background knowledge or existing knowledge structures, which, as noted above, may be conceptualized as ‘mental models existing in long term memory’. Mental models thus play a role in filtering incoming information. The theory of ‘confirmation bias’ (Klayman and Ha 1989) suggests that people seek information that fits their current understanding of the world. Incoming information may reinforce existing mental models or may be rejected outright.

Different fields of study are interested in, and therefore view, the inaccurate and incomplete quality of mental models differently. Those applying the mental model construct to complex systems regard the mapping process involved in constructing a mental model as a many-to-one ‘homo-morphic’ mapping. This involves decomposing a complex system into a number of smaller models representing subcomponents of the system. Conceiving the construction of mental models in this way suggests that the model is an “imperfect representation” and acknowledges that people make errors (Moray 2004). Similarly, systems dynamics researchers draw attention to peoples’ cognitive limitations in terms of processing information feedback, particularly when there are long time delays between action and response (Sterman 1994). Controlled experiments, mainly computer-based, show that people’s mental models demonstrate a limited capacity to take account of feedback delays and the side effects of decisions made (Doerner 1980, Brehmer 1992). In his study of mental models, Sterman (1994:305) concluded that “people generally adopt an event-based, open-loop view of causality, ignore feedback processes, fail to appreciate time delays between action and response and in the reporting of information, and are insensitive to non-linearities that may alter the strengths of different feedback loops as a system evolves”. This literature treats these limitations in people’s mental models as presenting an impediment to learning; it assumes that addressing the limitations and critical flaws in mental models can improve system functionality.

Despite potential limitations, individuals’ mental models are not necessarily amenable to alteration. As the psychology literature recognizes, people tend to filter new information according to its congruence or otherwise with their existing understandings, beliefs, and values. They may reject discrepant evidence, or compartmentalize it within a subsystem of larger systems of understanding. Acceptance of new information is also related to personal orientations toward learning. Some mental models research therefore focuses on communication toward outcomes such as behavior change, seeking to provide information in forms compatible with current understandings (Morgan et al. 2002).

Using the mental model construct to gain insight into how people conceive, and therefore are inclined to act toward, the world around them is, theoretically speaking, an attractive proposition for natural resource management practitioners. A mental model approach to cognition goes beyond stakeholders’ preferences, goals, and values associated with a given resource, to provide a rich picture of how stakeholders perceive natural resource systems to function. This picture can tell us not only what concepts stakeholders’ consider important to a given issue, but also how these concepts are organized cognitively and the dynamic interactions between them. It offers some insight into how people comprehend a system, how they believe the system might respond to interventions, and how they might intervene themselves. Similarities and differences in understanding can be compared across time and space to improve overall understanding of a given system and to support collective action.

Theoretical evidence continues to mount within the fields of psychology and cognitive science that people do indeed use mental models to reason and make predictions about the world around them.

CognitiveExperience.design explores the role of hardware, software and wetware in constructing, simulating, and communicating mental models in part two of this series.

Sources & Literature Cited

Abel, N., H. Ross, and P. Walker. 1998. Mental models in rangeland research, communication and management. Rangeland Journal 20:77-91.

Argyris, C., and D. Schon. 1974. Theory in practice: increasing professional effectiveness. Jossey-Bass, Oxford, UK.

Austin, D. E. 1994. Incorporating cognitive theory into environmental policymaking. The Environmental Professional 16:262-274.

Bainbridge, L. 1991. Mental models and cognitive skill: the example of industrial process operation. Pages 119-144 in A. Rutherford and Y. Rogers, editors. Models in the mind. Academic Press, New York, New York, USA.

Bartlett, F. C. 1932. Remembering: a study in experimental and social psychology. Cambridge University Press, Cambridge, UK.

Biggs, H., D. Du Toit, M. Etienne, N. Jones, A. Leitch, T. Lynam, S. Pollard, and S. Stone-Jovicich. 2008. Preliminary exploration of two approaches to documenting elements of the mental models of stakeholders in the Crocodile Catchment, South Africa. Water Research Commission, Report KV 216/08, South Africa.

Breakwell, G. M. 2001. Mental models and social representations of hazards: the significance of identity processes. Journal of Risk Research 4:341-351.

Brehmer, B. 1992. Dynamic decision making: human control of complex systems. Acta Psychologica 81:211-241.

Brewer, W. F. 1987. Schemas versus mental models in human memory. Pages 187-197 in P. Morris, editor. Modelling cognition. John Wiley & Sons, Chichester, UK.

Carley, K., and M. Palmquist. 1992. Extracting, representing and analyzing mental models. Social Forces 70:601-635.

Collins, A., and D. Gentner. 1987. How people construct mental models. Pages 243-268 in D. Holland and N. Quinn, editors. Cultural models in language and thought. Cambridge University Press, Cambridge, UK.

Cooke, N. 1999. Knowledge elicitation. Pages 479-509 in F. T. Durso, R. S. Nickerson, S. T. Dumais, S. Lewandowsky, and T. J. Perfect, editors. Handbook of applied cognition. John Wiley & Sons, Chichester, UK.

Cooke, N., E. Salas, J. A. Cannon-Bowers, and R. Stout. 2000. Measuring team knowledge. Human Factors 42:151-173.

Craik, K. J. W. 1943. The nature of explanation. Cambridge University Press, Cambridge, UK.

D’Andrade, R. 1995. The development of cognitive anthropology. Cambridge University Press, Cambridge, UK.

DiSessa, A. A. 1983. Phenomenology and the evolution of intuition. Pages 15-34 in D. Gentner and A. Stevens, editors. Mental models. Lawrence Erlbaum Associates, Hillsdale, New Jersey, USA.

Doerner, D. 1980. On the difficulties people have in dealing with complexity. Simulation and Gaming 11:87-106.

Dove, J. E., L. A. Everett, and P. F. Preece. 1999. Exploring a hydrological concept through children’s drawings. International Journal of Science Education 21:485-497.

Downs, R. M. 1976. Cognitive mapping and information processing: a commentary. Pages 67-70 in G. Moore and R. G. Golledge, editors. Environmental knowing: theories, research and methods. Dowden, Hutchinson and Ross, Stroudsburg, Pennsylvania, USA.

Doyle, J. K., and D. N. Ford. 1998. Mental models concepts for system dynamics research. System Dynamics Review 14:3-29.

Dray, A., P. Perez, N. Jones, C. Le Page, P. D’Aquino, I. White, and T. Auatabu. 2006. The AtollGame experience: from knowledge engineering to a computer-assisted role playing game. Journal of Artificial Societies and Social Simulation 9:6.

Dray, A., P. Perez, C. Le Page, P. D’Aquino, and I. White. 2007. Who wants to terminate the game? The role of vested interests and meta-players in the AtollGame experience. Simulation and Gaming 38:494-511.

Etienne, M., D. R. Du Toit, and S. Pollard. 2011. ARDI: a co-construction method for participatory modeling in natural resources management. Ecology and Society 16(1):44 [online] URL: http://www.ecologyandsociety.org/vol16/iss1/art44/.

Gentner, D., and D. R. Gentner. 1983. Flowing waters or teeming crowds: mental models of electricity. Pages 99-130 in D. Gentner and A. Stevens, editors. Mental models. Lawrence Erlbaum, Hillsdale, New Jersey, USA.

Greeno, G. J. 1983. Conceptual entities. Pages 227-252 in D. Gentner and A. Stevens, editors. Mental models. Lawrence Erlbaum, Hillsdale, New Jersey, USA.

Hall, R. I., P. W. Aitchison, and W. L. Kocay. 1994. Causal policy maps of managers: formal methods for elicitation and analysis. Systems Dynamics Review 10:337-360.

Hodgkinson, G. P., A. J. Maule, and N. Brown, J. 2004. Causal cognitive mapping in the organizational strategy field: a comparison of alternative elicitation procedures. Organizational Research Methods 7:3-26.

Holland, J. H., K. J. Holyoak, R. E. Nisbett, and P. R. Thagard. 1986. Induction: processes of inference, learning, and discovery. MIT Press, Cambridge, Massachusetts, USA.

Johnson-Laird, P. N. 1983. Mental models. Cambridge University Press, Cambridge, UK.

Johnson-Laird, P. N. 1989. Mental models. Pages 467-499 in M. I. Posner, editor. Foundations of cognitive science. MIT Press, Cambridge, Massachusetts, USA.

Kearney, A. R., G. Bradley, R. Kaplan, and S. Kaplan. 1999. Stakeholder perspectives on appropriate forest management in the Pacific Northwest. Forest Science 45:62-73.

Kearney, A. R., and S. Kaplan. 1997. Toward a methodology for the measurement of knowledge structures of ordinary people: the conceptual content cognitive map (3CM). Environment and Behavior 29:579-617.

Klayman, J., and Y.-W. Ha. 1989. Hypothesis testing in rule discovery: strategy, structure and content. Journal of Experimental Psychology 5:596-604.

Klimoski, R., and S. Mohammed. 1994. Team mental model: construct or metaphor. Journal of Management 20:403-437.

Kolkman, M. J., M. Kok, and A. van der Veen. 2005. Mental model mapping as a new tool to analyse the use of information in decision-making in integrated water management. Physics and Chemistry of the Earth 30:317-332.

Langan-Fox, J., S. Code, and K. Langfield-Smith. 2000. Team mental models: techniques, methods and analytic approaches. Human Factors 42:242-271.

Langan-Fox, J., A. Wirth, S. Code, K. Langfield-Smith, and A. Wirth. 2001. Analyzing shared and team mental models. International Journal of Industrial Ergonomics 28:99-112.

Larkin, J. H. 1983. The role of problem representation in physics. Pages 75-97 in D. Gentner and A. Stevens, editors. Mental models. Lawrence Erlbaum Associates, Hillsdale, New Jersey, USA.

Lowe, T. D., and I. Lorenzoni. 2007. Danger is all around: eliciting expert perceptions for managing climate change through a mental models approach. Global Environmental Change 17:131-146.

Lynam, T., F. Bousquet, C. Le Page, P. d’Aquino, O. Barreteau, F. Chinembiri, and B. Mombeshora. 2002. Adapting science to adaptive managers: spidergrams, belief models, and multi-agent systems modeling. Conservation Ecology 5(2):24. [online] URL: http://www.ecologyandsociety.org/vol5/iss2/art24/.

Mathevet, R., M. Etienne, T. Lynam, and C. Calvet. 2011. Water management in the Camargue Biosphere Reserve: insights from comparative mental models analysis. Ecology and Society 16(1):43. [online] URL: http://www.ecologyandsociety.org/vol16/iss1/art43/

Moore, G. T., and R. G. Golledge. 1976. Environmental knowing: concepts and theories. Pages 3-24 in G. T. Moore and R. G. Golledge, editors. Environmental knowing: theories, research and methods. Dowden Hutchinson and Ross Inc, Stroudsburg, Pennsylvania, USA.

Moray, N. 1998. Identifying mental models of complex human-machine systems. International Journal of Industrial Ergonomics 22:293-297.

Moray, N. 2004. Models of models of…mental models. Pages 506-526 in N. Moray, editor. Ergonomics: major writings. Taylor and Francis, London, UK.

Morgan, M. G., B. Fischhoff, A. Bostrom, and C. Atman, J. 2002. Risk communication: a mental models approach. Cambridge University Press, New York, New York, USA.

Nersessian, N. J. 2002. The cognitive basis of model-based reasoning in science. Pages 133-153 in P. Carruthers, S. Stich, and M. Siegal, editors. The cognitive basis of science. Cambridge University Press, Cambridge, UK.

Osborne, R. J., and M. M. Cosgrove. 1983. Children’s conceptions of the changes of the state of water. Journal of Research in Science Teaching 20:825-838.

Ozesmi, U., and S. L. Ozesmi. 2004. Ecological models based on people’s knowledge: a multi-step fuzzy cognition mapping approach. Ecological Modelling 176:43-64.

Pahl-Wostl, C., and M. Hare. 2004. Processes of social learning in integrated water management. Journal of Community and Applied Social Psychology 14:193-206.

Quinn, N. 2005. How to reconstruct schemas people share. Pages 33-81 in N. Quinn, editor. Finding culture in talk: a collection of methods. Palgrave Miller, New York, New York, USA.

Rickheit, G., and L. Sichelschmidt. 1999. Mental models: some answers, some questions, some suggestions. Pages 9-40 in G. Rickheit and C. Habel, editors. Mental models in discourse processing and reasoning. Elsevier, Amsterdam, The Netherlands.

Rouse, W. B., and N. M. Morris. 1986. On looking into the black box: prospects and limits in the search for mental models. Psychological Bulletin 100:349-363.

Rutherford, A., and J. R. Wilson. 2004. Models of mental models: an ergonomist-psychologist dialogue. Pages 309-323 in N. Moray, editor. Ergonomics major writings: psychological mechanisms and models in ergonomics. Taylor and Francis, London, UK.

Samarapungavan, A., S. Vosniaudou, and W. F. Brewer. 1996. Mental models of the earth, sun and moon. Cognitive Development 11:491-521.

Sterman, J. D. 1994. Learning in and about complex systems. System Dynamics Review 10:291-330.

Sterman, J. D. 2000. Business dynamics: systems thinking and modeling for a complex world. Irwin McGraw-Hill, Boston, Massachusetts, USA.

Stone-Jovicich, S. S., T. Lynam, A. Leitch, and N. A. Jones. 2011. Using Consensus Analysis to Assess Mental Models about Water Use and Management in the Crocodile River Catchment, South Africa. Ecology and Society 16(1):45. [online] URL: http://www.ecologyandsociety.org/vol16/iss1/art45/

Strauss, C., and N. Quinn. 1997. A cognitive theory of cultural meaning. Cambridge University Press, Cambridge, UK.

Swan, J., and S. Newell. 1998. Making sense of the technological innovation: the political and social dynamics of cognition. Pages 108-129 in C. Eden and J.-C. Spencer, editors. Managerial and organisational cognition. Sage Publications, London, UK.

Tikkanen, J., T. Isokaanta, J. Pykalaninen, and P. Leskinen. 2006. Applying cognitive mapping approach to explore the objective-structure of forest owners in a northern Finnish case area. Forest Policy and Economics 9:139-152.

Vosniaudou, S. 1994. Universal and culture-specific properties of children’s mental models of the Earth. Pages 412-430 in L. A. Hirschfeld and S. A. Gelman, editors. Mapping the mind: domain specificity in cognition and culture. Cambridge University Press, Cambridge, UK.

Vosniaudou, S., and W. F. Brewer. 1992. Mental models of the earth: a study of conceptual change in childhood. Cognitive Psychology 24:535-585.

Walker, P. A. 1997. Resolving problems with INFLUENCE. Australian Local Government Yearbook 1997:167-168.

Wilson, J. R., and A. Rutherford. 1989. Mental models: theory and application in human factors. Human Factors 31:617-634.

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