Topics in Cognitive Science 1 (2009) 675–685 Copyright 2009 Cognitive Science Society, Inc. All rights reserved. ISSN: 1756-8757 print / 1756-8765 online DOI: 10.1111/j.1756-8765.2009.01034.x
Introduction to Cognition in Science and Technology Michael E. Gorman Department of Science, Technology and Society, University of Virginia Received 13 March 2009; received in revised form 16 April 2009; accepted 11 May 2009
Abstract Cognitive studies of science and technology have had a long history of largely independent research projects that have appeared in multiple outlets, but rarely together. The emergence of a new International Society for Psychology of Science and Technology suggests that this is a good time to put some of the latest work in this area into topiCS in a way that will both acquaint readers with the cutting edge in this domain and also give them a hint of its history. One core theme includes how scientists, inventors, and engineers represent and solve problems; another, related theme is the extent to which they distribute and share cognition. Methodologies include fine-grained studies of historical records, protocols of working scientists, observations and comparisons of engineering science laboratories, and computational simulations designed both to serve as research tools and also to improve scientific problem-solving. The series of articles will conclude with the Associate Editor’s suggestions for future research. Keywords: Distributed cognition; Shared cognition; Science; Technology; Engineering; Computational simulations
There are rovers on the surface of Mars that are gathering data about the presence of water. What kind of thinking led to the development of these devices and the data they collect? A biomedical laboratory is trying to develop replacement, living blood vessels for treating arteriosclerosis. How is cognition shared and distributed among people and devices in this laboratory? Papers in this issue of topiCS1 will look at these and other issues on the cutting edge of cognition and science. Science and technology are the means by which the human species has investigated our own evolution and the origins of the universe, and also created both the means to cover the planet with our species and provide weapons that could destroy most Correspondence should be sent to Michael E. Gorman, STS, SEAS, Department of Science, Technology and Society, 351 MC Cormick Road, p.o. Box 400744, Thornton Hall, Charlottesville, VA 22904-4744
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(if not all) of us. Scientific and technological thinking should therefore be a topic of great interest to all human beings—especially cognitive scientists. The history of attempts to apply cognitive science to scientific and technological thinking includes a classic volume of selections edited by Tweney (Tweney, Doherty, & Mynatt, 1981) and volumes of papers edited by Giere (1992) and Shrager (Shrager & Langley, 1990). The work in this issue certainly has roots in these classic studies, but it represents substantial new material, methodologically and conceptually. The volume grew out of a paper session at the Nashville meeting of the Cognitive Science Society in August 2007 on ‘‘When Social and Cognitive Perspectives Blur: The Case of Developing Expertise in Science and Engineering’’; hence, the papers are weighted toward the participants in that session, although the editor of this issue has made an effort to include others—some of whom, regrettably, were unable to meet the deadline for this and the following issue. In this introduction, I will attempt to put this issue in context with other important work in cognition and science by using a methodological framework developed by Kevin Dunbar (Dunbar & Fugelsang, 2005) and amplified in a recent volume of papers on scientific and technological thinking (Gorman et al., 2005). Dunbar’s framework is based on an analogy with biological research. Where the analogy does not fit the domain of scientific and technological thinking, I have added additional categories.
1. In vitro In vitro studies in cognitive science correspond to laboratory tasks performed with nonscientists (who can be children or adults) as participants. A classic example is Wason’s 2,4,6 task and analogous problems (Gorman, 1992; Wason, 1960). Participants in Wason’s original experiment were given the numbers 2, 4, 6 as an example of a rule the experimenter had in mind and were asked to propose additional number triples to determine the rule. Participants tended to exhibit a confirmation bias—they would propose triples that fit their initial notion of the rule, for example, 6, 8, 10 and 10, 12, 14. They did not falsify rules like ‘‘numbers go up by twos’’ by trying additional triples like ‘‘1, 2, 3.’’ Wason’s original rule was ‘‘ascending numbers.’’ Wason was not trying to study cognition in science. He was interested in reasoning. But the kind of reasoning he was studying is relevant to science. Each triple is an experiment that allows participants to develop and test hypotheses. In vitro problems allow control of a wide range of variables, including the type of task, the rule, and the instructions given. Consider instructions, for example. Participants can be told specifically to try not only to propose experiments that fit their hypothesis but also ones that do not, which makes it easy for most to guess and test rules like Wason’s (Gorman, 1992). But the 2, 4, 6 task has low ecological validity. The participants are not scientists, the task is simple and unambiguous, there are no grants, careers, or awards hinging on the outcome, etc.
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1.1. Ecological validity of in vivo tasks can be increased Experimental simulations of science can be made more realistic. For example, Mynatt, Doherty, and Tweney (1978) developed an artificial universe in which participants had to conduct experiments to determine the laws governing the motions of particles; they found that confirmation was actually a very useful heuristic in this complex environment. Scientific reasoning tasks can be modified to incorporate the possibility of error (Gorman, 1989); confirmation is also a useful heuristic for detecting these errors. Shrager, Klahr, and others asked children and adults to program a device called the Big Trak and observed that the most effective participants searched and coordinated hypothesis and experiment spaces (Klahr, 2000; Shrager & Klahr, 1986). One way to make these tasks even more realistic is to use actual scientists as participants.
2. Ex vivo This approach involves taking scientists out of their (in vivo) working environments and testing them on in vitro problems. Typically, these problems are not the artificial tasks used by Wason, Gorman, Tweney, Shrager, and others—although Michael J. Mahoney compared scientists with Protestant ministers on the 2,4,6 task and found the former more likely to show confirmation bias than the latter (Mahoney, 1976)!2 The typical ex vivo problems involve a certain amount of domain knowledge, and they resemble the sorts of word problems encountered by students. Indeed, the early ex vivo studies compared novices, usually students, with experts in areas like physics (Chi, 2006). One of the general conclusions was that novices tend to rely on a variety of general heuristics for solving textbook-style problems, including working backward from the goal. Experts, by contrast, quickly classify a problem in a way that suggests what procedures and information are necessary to solve it. The article by Clement in this issue is a particularly sophisticated example of the ex vivo approach. He picked professors and advanced graduate students from physics, mathematics, and computer science. His problem sounds simple enough: Would doubling the diameter of the coils in a spring cause it to stretch more or less, given a constant weight? Clement asked his participants to think aloud as they worked. He found that they relied heavily on thought experiments, which he defines as using a mental model to make a prediction about an untested aspect of a system. Clement compares his participants’ methods to analyses of historical thought experiments by scholars like Nersessian and Gooding (who have papers in this volume). Clement’s research therefore focuses on a comparison between an ex vivo study of current scientists and mathematicians and inferences made from studies of historical figures like Galileo. Clement’s paper illustrates several of the themes that characterize the research reported in this issue. There is a heavy focus on visualization in science and technology and also on the kinesthetic element: Clement discusses perceptual-motor schema. The goal is a solid qualitative understanding of the problem-solving processes couched in language that can be
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used for comparison with other types of studies. In the past, there was an almost dogmatic insistence that any such analyses had to be translated into a computational simulation (Gorman, 1992), but Clement is not alone among the authors here in putting off formal modeling until the phenomenon is better understood.
3. In vivo The next obvious step toward ecological validity is to observe scientists and ⁄ or engineers in vivo, working on genuine problems that arose in the course of their work. Several of the authors in this volume have taken this approach, which is a significant change from the heyday of purely experimental methods. Here one exchanges control for validity. In vivo studies of individual scientists can be done by observation combined with thinkaloud protocols. An example is the study by Trickett, Trafton, and Schunn in this volume, who compared the way theoretical and applied scientists dealt with anomalous results. Their sample included two astronomers and one expert in computational fluid dynamics, which they considered theoretical scientists, and five meteorologists, which they considered applied scientists. All were videotaped as they worked, and were asked to think aloud. These protocol statements are coded, so that there is inter-rater agreement on categories like what constitutes an anomalous result. In addition to statistical comparisons of results from the coding categories, in vivo studies should include examples of the cognitive processes used, and in some cases, graphical representations of the thinking processes of an individual scientist worked over time (we will discuss these graphical representations later in the Subspecies Historiae section). Trickett et al.’s conclusion that theoretical scientists used more conceptual simulations than applied after an anomaly has a high ecological validity, but in the uncontrolled in vivo setting, it is possible that the perceived differences are due to some other factor, for example, the nature of the very different tasks. That is why in vivo studies have to describe the problem-solving context in detail, and why it is important to find ways of making the data available to other researchers who might want to try another coding scheme. 3.1. In vivo studies of laboratories Science and engineering are social activities, with much of the important problemsolving and strategizing occurring in teams. One advantage of studying laboratories and other team activities is that participants talk aloud to each other, without prompting from an experimenter. However, they do not always provide the same level of details on their problem-solving process as they do in a think-aloud protocol when working alone. In a laboratory setting, there is a great deal of tacit knowledge that does not need to be articulated among team members. So the ideal method would include both individual protocols and analyses of group processes, as investigators go between individual and team situations.
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Kevin Dunbar did a now-classic study of three molecular biology laboratories in the United States (Dunbar & Fugelsang, 2005). He interviewed scientists, attended talks, read relevant materials, and taped and coded laboratory meetings. One of the issues he looked at was similar to the focus of Trickett et al.’s studies: how scientists deal with anomalous results. About half of the results obtained by Dunbar’s molecular biology laboratories were unexpected. The first strategy the scientists used was to classify most of the anomalies as methodological errors. When the anomalies persisted, scientists shifted to offering theoretical explanations. Members of the laboratory often proposed different models or hypotheses to account for the anomalies. Often, they searched for common features of anomalies and tried to propose a general model that would account for all of them. Dunbar checked his in vivo results with an in vitro study, in which individual participants were protocoled on a scientific reasoning simulation. Like the scientists, the experimental participants spent more time reasoning about unexpected data and tried to look for common mechanisms of actions that would explain them. One take-away from Dunbar’s research is the importance of trying to triangulate multiple methodological approaches to issues like the role of anomalies in scientific reasoning. Nersessian’s article in this issue is an in vivo study of two biomedical research laboratories, one working on tissue engineering and the other on neural engineering. While Dunbar focused on laboratory meetings, Nersessian and her colleagues adopted the methods of cognitive anthropology and became participant-observers of the ongoing research, developing a coding scheme gradually and then implementing it as they made further observations. They also studied the texts generated by the researchers. One major phenomenon they investigated parallels a theme of this introduction: how researchers combined in vitro and in vivo approaches. In biology and psychology, experiments are limited by ethics: There are a wide range of medical and psychological procedures that are completely off limits for human beings; the constraints on animal research are less, but still very stringent, and in all cases the research has to pass through an extremely stringent review. All of these restrictions are commendable, but this means that many questions that could be answered by in vitro research involve studies that are off limits. The biomedical engineering scientists in Nersessian’s research solved this problem by constructing model systems, often a hybrid of computational models and physical devices. These models created spaces where the lines between science and engineering blur; as Galison and many others have pointed out, instrumentation has always played a critical role in scientific thinking, and the scientists themselves have been involved in creating the technologies they use to observe and manipulate nature (Galison, 1999).
4. Subspecies historiae This approach involves cognitive analysis of historical casestudies. In terms of the in vitro ⁄ in vivo analogy, this research corresponds to paleontology. Episodic memory is
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reconstructive, so the cognitive scientist has to beware of scientists’ and inventors’ recollections later in life—it is best to have records akin to those that would have been obtained from a protocol or real-time transcript. Therefore, cognitive historical analysis requires detailed notebooks, drafts of publications, letters and, where possible, tape recordings and interviews. Like the paleontologist, the cognitive historian is often confronted with an incomplete and ⁄ or distorted ‘‘fossil’’ record from which to make inferences. 4.1. The invention of the telephone Let me take the liberty of using my comparative study of three telephone inventors as an example of the advantages and difficulties of this method. I collaborated with a historian of technology, W. Bernard Carlson, who knew where the best possible archival information was located and how to access it, and also who the relevant experts were (Gorman & Carlson, 1990). He and I not only read letters, notebooks, patent applications, and secondary sources, we also went to museums where artifacts were stored and inspected them. Our goal was to compare three inventors: Alexander Graham Bell, usually considered the inventor of the telephone; Bell’s closest rival Elisha Gray; and Thomas Edison, who was brought in by Western Union to design around Bell’s telephone patents.3 Here the problem of records became paramount. Bell did the best job of documenting his invention process—he wrote letters to his mother and father detailing significant accomplishments and kept a notebook from just before he obtained his first telephone patent through his successful transmission of speech. In court, Bell was an eloquent witness who told a convincing invention story. Elisha Gray, by contrast, did not keep a notebook—we had to rely on his patent applications, his memory for events in court, and his later reconstructions. In addition to patent applications, Edison’s invention records consisted of hundreds of sketches, often several scribbled on the same piece of paper, without accompanying explanations. To figure out how to do the cognitive analysis, I took the case with the best records. It turned out also to be the simplest case. Bell admitted early on that he would have to be a theoretical inventor because his hands-on skills and resources were limited. Bell followed a VOTAT strategy in his experiments—vary one thing at a time. Edison, by contrast, was running the first independent R&D laboratory at Menlo Park and could have assistants to help him try multiple variations. To analyze Bell’s notebook, I followed in the footsteps of Ryan Tweney, who constructed problem–behavior graphs of Michael Faraday’s discovery processes (Tweney, 1989). Faraday kept the most detailed notebooks of any scientist or inventor. Even so, David Gooding felt he had to rebuild some of Faraday’s devices in order to better understand gaps in the notebook (Gooding, 1990). The problem behavior graphs helped me make two discoveries regarding Bell’s processes (Gorman, 1995). The distinguished historian Robert Bruce had called a sequence of experiments Bell conducted on March 8, 1876 ‘‘random’’ (Bruce, 1973). By March 10, Bell and Watson had transmitted speech, using an apparatus that looked very different from the one Bell used on March 8, so it is easy to see why the experiments seemed almost disconnected. But in fact what Bell did on March 8 was to replicate a
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design he had used frequently for multiple telegraphy, to make certain it worked before he introduced significant variations. While the March 8 problem behavior graph improved on Bruce’s account, my graphs for March 9 and 10 confirmed one of his major contentions. Bell’s successful speaking telegraph looked a lot like one Gray had sketched in a preliminary patent application, leading to accusations that Bell must have stolen the design from Gray (Gorman, Mehalik, Carlson, & Oblon, 1993). But Bruce noted that the two devices in fact were viewed by the inventors themselves as functioning differently, a fact which was confirmed by the experiments Bell conducted on March 9 and 10. To make a long story short, both Bell and Gray stuck needles on a diaphragm and dipped them into water that had another contact at the bottom to complete a circuit. When someone spoke against the diaphragm, the needle would vibrate in the water, varying the resistance in the circuit—perhaps enough to transmit speech. Gray’s design depended on the depth of the needle in the water. Bell’s depended on the relationship between the relative sizes of the contact hanging from the membrane and the one at the bottom of the water. Bell compared sticking a bell in the water to having the point of a needle barely touching the surface, and he concluded that the latter arrangement produced a better effect. Therefore, he drew the opposite conclusion from Gray about the benefits of immersing the contact attached to the diaphragm deeply in the water. Bell and Gray produced devices that appeared similar but operated from fundamentally different mental models. This was made clear to me by a sketch I ‘‘discovered’’ in the Library of Congress, from Bell’s notebook. I say ‘‘discovered’’ because it had been seen by researchers like Bruce before, but no one had grasped its significance. It shows the bones of the ear attached to a diaphragm and a speaking tube, vibrating close to two different arrangements of electromagnets. Over this crude sketch, Bell announced his approach: that he would follow the analogy of nature by using the ear as a model for the telephone, which meant that he would need an armature that would function like the bones of the ear, translating the vibrations of the diaphragm into a current that precisely mimicked the form of the sound wave (see http://www2.iath.virginia.edu/albell/emm.2.html for Bell’s original sketch). Gray’s mental model for a speaking telegraph was a device called the ‘‘lover’s telegraph,’’ the old two tin cans connected by a string most of us build in childhood. Gray at one point briefly considered that the ear might be a useful analogy for the speaking telegraph, but he did not have Bell’s detailed knowledge of the workings of the ear. Bell in fact had built a device that used the bones of the middle ear to trace sound waves, so his mental model was based on hands-on expertise. The strength and weakness of Bell’s mental model are immediately apparent. The ear is a great receiver for sound waves, but not a transmitter. Other inventors like Edison realized that Bell had solved the problem of receiving sound waves, but that his transmitter was far from optimal. Edison, in particular, developed a greatly improved transmitter. My analysis of Bell highlighted the utility of cognitive methods like problem–behavior graphs and concepts like mental models for analyzing and comparing historical records of inventors. But the comparison part was hindered by the fact that the Edison and Gray
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records did not include the kind of detail that was available for Bell. Edison’s records were detailed, but mainly visual, and hard to interpret without a real-time protocol. Gray’s were enough to infer his mental model, but not sufficient to construct more than a partial graph of his invention process. Tweney’s contribution to this issue picks up on the comparative historical theme. He contrasts Faraday and Maxwell, the latter ‘‘mathematized’’ the former’s field theory. Maxwell referred to Faraday as an ‘‘intuitive mathematician.’’ Tweney’s main point is that Faraday and Maxwell relied on similar mental models, but whereas Faraday represented them visually and tactually, Maxwell represented them mathematically—using a mathematical style that was peculiar to Cambridge ‘‘Wranglers.’’ Tweney, in this paper, is developing theory from subspecies historiae research done on Faraday and Maxwell by himself and a long list of other scholars. His conclusions arise out of intimate knowledge of the notebooks and treatises of Faraday and Maxwell. Gooding synthesizes data from multiple historical analyses to create an iterative model of how scientists go from two-dimensional images to marked sketches or plots, then structural models, and finally process models. For example, Gooding describes how Bragg took a photograph of an X-ray diffraction pattern, marked it to indicate magnitude of each impact, drew the probable paths of electrons that would create such a pattern, then built an apparatus that verified diffraction patterns could be created in accordance with his process model. Gooding provides another example from paleontology, involving the reconstruction of a three-dimensional model of an organism from the photograph of a fossil imprint. Gooding emphasizes the hybrid multimodal, plastic nature of scientific images; therefore, his model is itself the kind of process inference he is studying, using multiple historical cases as data. Much of the inference process is tacit, in part because it is based on human neurological capabilities, but in part because it also reflects the tacit communication norms of a community—norms that can be made explicit over time. Gooding uses these insights to make the case that visual inference is distributed cognition: There is no distinction between subjective and collective.
5. In silico Computational simulations, which Dunbar refers to as ‘‘in silico,’’ are another important method for exploring scientific thinking. The late great Herbert Simon was a leading advocate of programs that he felt could discover, and he facilitated an active research program in this area (Langley, Simon, Bradshaw, & Zykow, 1987). Paul Thagard developed a connectionist program (ECHO) that demonstrated explanatory coherence could account for the gradual acceptance of a scientific theory over its rivals, for example, how the asteroid theory of the dinosaurs triumphed (Thagard, 1988, 1989). The problem with these early programs was a lack of ecological validity. The most interesting programs sought to get around this problem by emulating closely the thinking processes that led to a particular discovery, for example, Krebs’ of the ornithine cycle
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(Kulkarni & Simon, 1988). But even the programs that tried to follow a scientist’s path were not embodied, so could not emulate the hands-on and perceptual aspects of science, and they were not embedded in the kinds of social networks that decide what counts as a major discovery deserving of awards like the Nobel (Shrager & Langley, 1990). Instead of modeling a complete discovery, computational simulations can model processes that are often (but not always) part of discovery. The article by Langley and Bridewell adopts this course of action by improving computational simulations of inductive processes. An inductive process simulation is given the sorts of information a scientist might have, like observations of continuous variables over time and generic processes; from this and other information, the program generates candidate models, most plausible but some implausible. Langley and Bridewell describe an improved program that reduces the number of candidate models and eliminates implausible ones without sacrificing model accuracy. They include results from several application areas. This kind of program highlights the importance of theoretical constraints on model building. It could also potentially serve as an aid for scientists constructing models. Shrager et al.’s article describes a different computational aid to discovery. Cache is designed to scaffold collaboration by providing web-based decision support. The primary example is genetics; the goal of Cache is to continually update discoveries and broadcast that information to the research community, saving everyone the trouble of digging through the literature to retrace the path of discoveries of new gene functions, re-analyses of these discoveries, and further iterations. The end result is a community that is immediately informed about any new knowledge concerning the function of genes—and the sources of the knowledge. Each practitioner in the community who adds information is identified, and the path of practitioner discoveries and modifications is made available to the community. Cache is therefore not a computational simulation; it is a way of tracking, organizing, and publishing a research community’s own inference processes. Shrager et al.’s article raises interesting questions for future research. Would Cache combat confirmation bias? What kind of collaborations would be facilitated by CACHE—and what kind might actually be hindered? Perhaps CACHE could have heuristic value, as a complement to existing systems—and as a way to encourage and monitor the formation of trading zones (Collins, Evans, & Gorman, 2007) among researchers from different disciplinary communities. (See concluding section entitled ‘‘Trading zones, interactional expertise, and future research in cognition in science and technology.’’) Cache allows precise tracing of the web of inferences and people that lead to the current state of knowledge. Therefore, Cache could potentially be of great value to psychologists of science, providing a trace of the community research process that could suggest directions for further research.
6. Shared cognition in teams The issue includes one theory paper as well, by Paletz and Schunn, that provides an extensive review of the literature on shared cognition in teams, referring to concepts like
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shared mental models and transactive memory. They create a framework for future research that focuses on convergent and divergent processes, focusing on how team structures and processes affect outcomes. A convergent process will include formal roles for team members and shared mental models, leading to a consensus on the outcome. A divergent process will take advantage of diversity in team members’ expertise and result in multiple scenarios for action. The two processes can be used by the same team at different phases. The Mars exploration teams serve as an example throughout; the authors are currently conducting research on the extensive transcript data produced by the Mars Rover teams. One strength of this approach is the way the cognitive and social are combined, as they are in Nersessian’s research. The authors conclude by recommending a research program that would compare multidisciplinary and nonmultidisciplinary groups in science and engineering, using both experimental and in vivo measures and keeping track of multiple variables. Such research could only be conducted by a multidisciplinary team, because the point would be to triangulate different methods and measures.
Notes 1. Papers on this theme may be shared among more than one issue, depending on the publisher’s page limits and other constraints. 2. There is an extensive developmental-cognitive literature comparing conceptual change in children to scientists, using a variety of tasks. Piaget famously proposed that the child’s development of scientific concepts might parallel the historical evolution of science (Piaget & Garcia, 1989), but there is no consensus that he was right (Kuhn, 1989). Child-scientist comparisons may be of value in understanding the kinds of conceptual shifts that Kuhn characterized as paradigms (Kuhn, 1962; Vosniadou & Brewer, 1992), but there are enough differences between children and scientists to justify focusing on the latter rather than the former, given the limited space in this special issue (Gorman, 2006). 3. Actually, Bell’s two most significant patents were for improvements in telegraphy that included transmission of speech as one of the improvements; they did not contain the word telephone, which came into our modern usage later.
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