Much of scientific thinking and scientific theory building pertains to the development of causal models between variables of interest. For example, does smoking cause cancer, Prozac relieve depression, or aerosol spray deplete the ozone layer? (See also Buehner & Cheng, Chap. 7, on causality.) Scientists and nonscientists alike are constantly bombarded with statements regarding the causal relationship between such variables. How does one evaluate the status of such claims? What kinds of data are informative? How do scientists and nonscientists deal with data that are inconsistent with their theory?
One important issue in the causal reasoning literature that is directly relevant to scientific thinking is the extent to which scientists and nonscientists are governed by the search for causal mechanisms (i.e., the chain of events that lead from a cause to an effect) versus the search for statistical data (i.e., how often variables co-occur). This dichotomy can be boiled down to the search for qualitative versus quantitative information about the paradigm the scientist is investigating. Researchers from a number of cognitive psychology laboratories have found that people prefer to gather more information about an underlying mechanism than covariation between a cause and an effect (e.g., Ahn et al., 1995). That is, the predominant strategy that students in scientific thinking simulations use is to gather as much information as possible about how the objects under investigation work rather than collecting large amounts of quantitative data to determine whether the observations hold across multiple samples. These findings suggest that a central component of scientific thinking may be to formulate explicit mechanistic causal models of scientific events.
One place where causal reasoning has been observed extensively is when scientists obtain unexpected findings. Both historical and naturalistic research has revealed that reasoning causally about unexpected findings has a central role in science. Indeed, scientists themselves frequently state that a finding was attributable to chance or was unexpected. Given that claims of unexpected findings are such a frequent component of scientists' autobiographies and interviews in the media, Dunbar (1995, 1997, 1999; Dunbar & Fugelsang, 2004; Fugelsang et al., 2 004) decided to investigate the ways that scientists deal with unexpected findings. In 1 991-1 992 Dunbar spent one year in three molecular biology laboratories and one immunology laboratory at a prestigious U.S. university. He used the weekly laboratory meeting as a source of data on scientific discovery and scientific reasoning. (This type of study he has called InVivo cognition). When he examined the types of findings the scientists made, he found that more than 50%
were unexpected and that these scientists had evolved a number of important strategies for dealing with such findings. One clear strategy was to reason causally about the findings: Scientists attempted to build causal models of their unexpected findings. This causal model building resulted in the extensive use of collaborative reasoning, analogical reasoning, and problem-solving heuristics (Dunbar, 1997; 2001).
Many of the key unexpected findings that scientists reasoned about in the InVivo studies of scientific thinking were inconsistent with the scientists' pre-existing causal models. A laboratory equivalent of the biology labs therefore was to create a situation in which students obtained unexpected findings that were inconsistent with their pre-existing theories. Dunbar and Fugelsang (2005; see also Fugelsang et al., 2004) examined this issue by creating a scientific causal thinking simulation in which experimental outcomes were either expected or unexpected. (Dunbar  called this type of study of people reasoning in a cognitive laboratory InVitro cognition). They found that students spent considerably more time reasoning about unexpected findings than expected findings. Second, when assessing the overall degree to which their hypoth esis was supported or refuted, participants spent the majority of their time considering unexpected findings. An analysis of participants' verbal protocols indicates that much of this extra time is spent formulating causal models for the unexpected findings.
Scientists are not merely the victims of unexpected findings but plan for unexpected events to occur. An example of the ways that scientists plan for unexpected contingencies in their day-to-day research is shown in Figure 29.1. Figure 29.1 is an example of a diagram in which the scientist is building causal models about the ways that human immunodeficiency virus (HIV) integrates itself into the host deoxyribonucleic acid (DNA) taken from a presentation at a lab meeting. The scientist proposes two main causal mechanisms by which HIV integrates into the host DNA. The main event that must occur is that gaps in the DNA must be filled. In the left-hand branch of Diagram 2, he proposes a cellular mechanism whereby cellular polymerase fills in gaps as the two sources of DNA integrate. In the right-hand branch, he proposes that instead of cellular mechanisms filling in the gaps, viral enzymes fill in the gap and join the two pieces of DNA. He then designs an experiment to distinguish between these two causal mechanisms. Clearly, visual and diagrammatic reasoning is used here and is a useful way of representing different causal mechanisms (see also Tversky, Chap. 10 on visuospatial reasoning). In this case, the visual representations of different causal paths are used to design an experiment and predict possible results. Thus, causal reasoning is a key component of the experimental design process.
When designing experiments, scientists know that unexpected findings occur often and have developed many strategies to take advantage of them (Baker & Dunbar, 2000). Scientists build different causal models of their experiments incorporating many conditions and controls. These multiple conditions and controls allow unknown mechanisms to manifest themselves. Rather than being the victims of the unexpected, the scientists create opportunities for unexpected events to occur, and once these events do occur, they have causal models that allow them to determine exactly where in the causal chain their unexpected finding arose. The results of these InVivo and InVitro studies all point to a more complex and nuanced account of how scientists and nonscientists test and evaluate hypotheses.
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