Our Model of What “learning for understanding” of Psychology Entails

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
those goals.
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,
2000).

Potential Benefits of Engaging Students in Explanatory Modeling Activities

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.

References

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,
1-25.

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
Press.

Nersessian, N. (2008). Creating scientific concepts. MIT Press.

Interdisciplinary research on conceptual innovation and change in science

The selected studies of expert problem solving by scientists focus on (1) examining the problem solving activities that advance scientific understanding and (2) aligning these activities with those that nonscientists use to solve problems and understand the world.   These studies use methods and analytic techniques from history, philosophy, and cognitive science to establish a cognitive basis for creative scientific reasoning that is continuous with everyday reasoning activities.  Historical records of the practices of preeminent scientists and ethnographic observations of current scientific practices provide information about the kinds of problem solving activities that promote theoretical understanding within the scientific community.  Philosophical analysis of these problem solving practices describe them as forms of nonformal reasoning like analogical modeling and simulative model-based reasoning.  Cognitive analyses of these “model-based reasoning” practices help to align them with the cognitive resources’ and limitations that scientists share with other humans.
Taken together, the results of these interdisciplinary studies indicate that advances in theoretical understanding can be produced through a dynamic, incremental process of model construction, manipulation, evaluation, and revision.  Traditional notions of scientific reasoning as the application of symbolic and mathematical logic are too narrowly constrained to account for advances in theoretical understanding.  To account for conceptual innovation and change, these philosophical notions of reasoning must be extended to include analogical reasoning activities that involve model construction, evaluation, and adaptation.  Models are representations of phenomenon, systems, or situations that highlight epistemicaly relevant features of these targets.  The function of a model is to afford epistemic access to problem relevant features of the target and display the significance of those features to potential problem solutions.  Theory development and change in science typically involves the construction, manipulation, evaluation, and revision of dynamic models that serve as structural or functional analogs of real world systems rather than axiomatic systems or propositional networks (Darden, 1991; Giere, 1988; Morgan & Morrison, 1999).  The scientific practices that were found to contribute to advances in theoretical understanding share several key features that are invariant across scientific disciplines.  Cognitive analyses of these key features suggest that they reflect the extension and refinement of the human capacity for simulative thinking through modeling (e.g. Dunbar, 2001, 2002; Nersessian, 2005, 2008).

Research on Creative Scientific Problem Solving by Students

Several studies have shown that simulative model-based reasoning plays a key role in the thinking of both scientists and students (e.g. Clement, 2009; Dunbar, 2001; Nersessian, 1995).  The studies selected for analysis examined the extent to which nonformal reasoning processes used by scientists to advance theoretical understanding occur naturally in students and how students’ ability to use these processes can be utilized in instruction.  The primary focus of these selected studies was on students’ use of the kinds of representational and inferences processes employed in scientific practice as opposed to the more formal expressions that appear in scientific articles.  The results of these studies indicate that students have the natural ability to perform most of the nonformal reasoning activities that contribute to conceptual change in science and learning to perform these activities can help them to achieve greater depth of understanding of a scientific discipline.  These results also indicate that the kinds of model-based reasoning activities that contribute most to the achievement of theoretical understanding in students are those that involve the generation, manipulation, evaluation, and adaptation of models that represent explanatory mechanisms.

Philosophical Analysis of the Role of Explanations in Science

Philosophers of science have long debated the nature of explanation, but most contemporary ones agree that explanations enhance understanding of phenomena by situating them within the organizational structure (logical or causal) of a target domain (Salmon, 1998; Strevens, 2008).  The studies selected for analysis are those that focus on the construction and use of explanations by psychologists (e.g. Bechtel, 2008; Cummins, 1883) and neuroscientists (e.g. Craver, 2001, 2007; Machamer, Darden, & Craver, 2000).

References

Bechtel, W. (2008). Mental mechanisms.  Lawrence Erlbaum  Associates.
Clement, J. (2009). Creative model construction in scientists and students.  Springer-Verlag.
Craver, C. (2001).  Role functions, mechanisms, and hierarchy.  Philosophy of Science, 68, 53-74.
Craver, C. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford
University Press.
Cummins, R. (1983). The nature of psychological explanation.  MIT Press.
Darden, L. (1991).  Theory change in science: Strategies from Mendelian genetics.  Oxford University
Press.
Dunbar, K. (2001).  What scientific thinking reveals about the nature of cognition.  In K. Crowley, C.
Scheen, & T. Okada (Eds.), Designing for science: Implications from everyday and professional
Settings (pp. 115-140).  Lawrence Erlbaum Associates.
Giere, R. (1988).  Explaining science: A cognitive approach.  University of Chicago Press.
Machamer, P. Darden, L., & Craver, C. (2000).  Thinking about mechanisms.  Philosophy of Science, 67,
1-25.
Morgan, M. & Morrison, M. (Eds.), Models as Mediators. Cambridge University Press.
Nersessian, N. (1995). Should physicists preach what they practice?: Constructive modeling in doing and learning physics.  Science and Education, 4, 203-226.
Nersessian, N. (2005). Interpreting scientific and engineering practices: Integrating the cognitive, social,
and cultural dimensions.  In M. Gorman, R. Tweney, D. Gooling, & A. Kincannon (Eds.),
Scientific and technical thinking.  Lawrence Erlbaum Associates.
Nersessian, N. (2008). Creating scientific concepts. MIT Press.
Salmon, W. (1998). Causality and explanation. MIT Press.
Strevens, M. (2008).  Depth: An account of scientific explanation. Harvard University Press.

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