The explanatory modeling activities recommended by GLN Consulting are designed to promote
students’ use of higher-order thinking skills to achieve a deeper understanding of the subject matter of
psychology. There is an important difference between learning that leads to rote knowledge and
learning that leads to depth of understanding. Deeper understanding of a scientific domain can be very
difficult to teach, but students can often acquire this kind of understanding by engaging in the kinds of
activities that have been shown to promote conceptual change in science. These kinds of activities
involve the coordination of existing scientific theories with empirical evidence to describe, predict,
explain, and control phenomena in the target domain. Cognitive analyses of the practices of scientists
(contemporary and past) (e.g. Dunbar, 1999, 2001; Machamer, Darden, & Craver, 200; Nersessian, 1999,
2002, 2008) have identified several key features of the kinds of cognitive processing strategies that
contribute to conceptual innovation across the sciences. Studies that compare performance of these
kinds of activities by expert scientists to that of college students indicate that students have the natural
ability to execute many of these cognitive processing strategies and can learn to execute others with
scaffolding from teachers (Clement, 2009; Nersessian, 1995). The service packages offered by GLN
Consulting provide advice and guidance on how to construct effective explanatory modeling tasks,
provide students with appropriate scaffolding, and integrate these activities with teaching methods (e.g.
lectures, discussions) and instructional resources (e.g. texts, assigned readings and other activities).
Like other scientific disciplines, psychology involves the use of empirical methods, such as
correlational and experimental investigations, that are systematically structured to provide information
that is relevant to the goals of describing, predicting, controlling, and explaining target phenomena.
Phenomena are relatively stable, observable events that can be produced, manipulated, or detected in a
variety of ways. Their relevant characteristics include their manifestations and potential precipitating,
inhibiting, and modulating conditions. The development and use of systematically structured empirical
methods is guided by constraints provided by the characteristics of target phenomena, the goals of the
reasoning process, and currently accepted scientific theories. Depth of scientific understanding involves
knowing how to coordinate scientific theories with these empirical methods of investigation to achieve
Depth of scientific understanding also involves knowledge of how scientific theories guide the
use of empirical methods of investigation by organizing a wide range of phenomena into a coherent
conceptual system that links both observable and unobservable real world events. The structure of the
natural world1 consists of clusters of dynamically coupled systems of interactions among events with
their own distinctive levels and kinds of patterning’s (spatial, temporal, and causal). This organizational
structure places constraints on the occurrence of and interaction among particular kinds of real world
events, so scientific theories are specifically tailored to the structures within clusters that are relevant to
the target domain. This tailoring involves the construction of theoretical systems of interrelated
concepts at multiple levels of abstraction. As a result, the content and structure of scientific theories
will vary as a function of the kinds of phenomena that are the focus of the target domain (Wilson & Keil,
By engaging in explanatory modeling activities, students can learn to use those constraints
embedded in the content and structure of scientific theories and those provided by the characteristics
of target phenomena to construct models that limit the range of plausible hypotheses by satisfying
these constraints. By learning to perform these activities, students can achieve a deeper understanding
of the broad range of psychological phenomena, the meaning and use of the concepts by which
psychologists categorize events and relate classes of events to each other in ways that achieve scientific
goals, and how the meaning and use of these concepts changes through scientific activity. Psychological
phenomena may consist of various combinations of overt behavior, physiological processes, covert
conscious mental processes, and covert unconscious processes and the complexity of these phenomena
is reflected in psychological concepts and their relations to other concepts within psychological theories.
The recommended strategies for performing explanatory modeling activities involve the use of
mental simulations to evaluate how well their candidate model complies with the same constraints as
the target phenomenon. Engaging in these kinds of activities can enhance students’ understanding in at
least two ways: It can help them achieve a better understanding of how new constraints emerge and
existing ones change and/or interact as a function of the real time characteristics of the target system’s
operation (Nersessian, 1995). Engaging in these kinds of activities can also help students to recognize
and correct misconceptions about psychological phenomena by generating evaluative relations of
dissonance and/or activating prior experiential knowledge for the first time (Clement, 2009).
Once a model is achieved that satisfies all known relevant constraints, students use the
constraints embedded in that model to limit the space of plausible hypotheses, compare each
hypothesis to empirical evidence, and use the results of those comparisons to confirm, extend, and/or
revise specific aspects of the model. Engagement in these kinds of activities can help students achieve a
deeper understanding of how hypotheses can be used to coordinate theory and empirical evidence.
1Psychological phenomena are considered to be part of the natural world.
Clement, J. (2009). Creative model construction in scientists and students. Springer Verlag.
Dunbar, K. (1999). Scientific thinking and its development. In R. Wilson and F. Keil (Eds.), The MIT
Encyclopedia of Cognitive Science (pp.730-733). MIT Press.
Dunbar, K. (2001). What scientific thinking reveals about the nature of cognition. In K. Crowley, C.
Scheen, and T. Okada (Eds.), Designing for science: Implications from everyday classroom and
Professional settings. (pp. 115-140) Erlbaum.
Machamer, P., Darden, L., and Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67,
Nersessian, N. (1995). Should physicists preach what they practice?: Constructive modeling in doing and
Learning physics. Science and Education, 4, 203-226.
Nersessian, N. (1999). Model-based reasoning in conceptual change. In L. Magnani, N. Nersessian, and
P. Thagard (Eds.), Model-based reasoning in scientific discovery (pp.2-22). Plenum Publishers.
Nersessian, N. (2002). The cognitive basis of model-based reasoning in science. In P. Carruthers, S.
Stich, and M. Segal (Eds.) The cognitive basis of science (pp.133-153). Cambridge University
Nersessian, N. (2008). Creating scientific concepts. MIT Press.