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Abstract This paper describes model construction practices used by scientifically trained experts. Our work on science experts has involved analyzing data from videotaped protocols of experts thinking aloud about unfamiliar explanation problems. These studies document the value of nonformal heuristic reasoning processes such as analogies, identification of new variables, Gedanken experiments, and the construction and running of visualizable explanatory models. Although theses processes are less formal than formal deduction or induction or statistical inference procedures, the case study analyzed here shows that they can lead to real insights and conceptual change. At a larger time scale, the subject went through model evolution cycles of model generation, evaluation, and modification that utilized the heuristic reasoning processes above. In addition, the prevalence of imagistic simulation as an underlying foundation in these episodes suggests that it may be important to pay greater attention to an imagistic level of processing in the analysis of expert thinking. Larger time scale modes of model evolution and model competition were also evidenced. The analysis leads to four levels of processes or practices: IV. An overarching set of Model Construction Modes, primarily alternating between Model Evolution, in which a model is improved, and Model Competition, in which two or more models compete. III. Modeling (GEM) Cycle process of Model Generation, Evaluation, and Modification at a Macro level, as shown in Figure 10. II. Nonformal Reasoning Processes at a Micro level: e.g. analogy, running a model, identifying a new variable, and conducting a Gedanken experiment. I. Underlying Imagistic process including Imagistic Simulation that may have been occurring within all of the above processes. To our knowledge these four levels of processes have not been analyzed together in the past. They complement empirical processes of discovery, experimentation, and evaluative argumentation documented by others. Diagrams of how the above processes interact may give us some new ways to picture the roles of nonformal reasoning and imagistic processes during qualitative model construction. We call the set of processes at all four levels a 'Modeling Practices Framework'. Processes at a lower level serve as subprocesses for the level above it in this framework. Each level has multiple "things to try" to achieve tasks at the level above it. Thus the framework is an organized but flexible structure of heuristic processes. This lies between and contrasts with those who would describe theory making in science as either 'anarchistic', with no method structure, or 'algorithmic', with fairly standardized procedures.
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This content will become publicly available on August 31, 2026
Identifying Multiple Levels of Heuristic Reasoning Used in Scientific Model Construction: A Framework Grounded in Imagistic Processing
This chapter first consolidates a set of important heuristic strategies used for constructing innovative scientific models from three books, including studies in the history of genetics and electromagnetism, and an expert think-aloud study in mechanics. Twenty-four strategies are identified, most of which are field-general. Patterns in their use suggest a partially organized hierarchy of interconnected strategies and substrategies, contrary to the view that heuristics are simply tried in random order. Strategies at four different size and time scale levels are described, including larger Modeling Cycle Phases of model generation, evaluation, and modification, each of which can utilize many smaller Tactical Heuristics as substrategies, e.g., analogy, or testing predictions from the model. These in turn can utilize Grounded Imagistic Processes, such as imagistic mental simulation, an important alternative to deduction for evaluating a model by running it. The framework links higher level, serially organized processes with lower level, imagery-based processes. Its intermediate degree of organization is neither anarchistic, nor fully algorithmic. Possible benefits of organization are narrowing the search space involved and balancing sources of model construction and criticism for productive creativity. Unorganized, spontaneous processes are also discussed, along with their possible benefits.
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- Award ID(s):
- 1503456
- PAR ID:
- 10637691
- Editor(s):
- Ippoliti, E; Sterpetti, F
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- ISBN:
- 978-3-031-94708-7
- Page Range / eLocation ID:
- 133 to 169
- Subject(s) / Keyword(s):
- scientific reasoning, imagery, mental simulation, creativity, heuristics, grounded cognition, embodiment, spatial reasoning
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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