peterson, all that is solid, bench building at the frontiers of two experimental sciences

American Sociological Review2015, Vol. 80(6) 1201 1225 American Sociological Association 2015DOI: 10.1177/0003122415607230http://asr.sagepub.com

Social psychology has recently come under unflattering scrutiny after a string of embar-rassing episodes. First, a number of social psychologists have been accused of fabricating or unethically manipulating data (Carey 2011; Ferguson 2012; Wade 2010; Yong 2012a). Second, in what is widely seen as a failure of the peer review system, a prominent journal published an article that purported to demon-strate the existence of precognition (Bem 2011; Wagenmakers et al. 2011). Third, and perhaps most threatening, a heated debate was ignited after a failed replication of one of the most well-cited priming studies in social psychol-ogy (Bargh, Chen, and Burrows 1996; Doyen et al. 2012). This has raised anxieties that the

entire branch of research that uses priming methods might have deep problems (Kahne-man 2012; Satel 2013; Yong 2012b, 2012c).

The natural sciences have had their own share of scandals. Scientific articles are fre-quently retracted due to plagiarism, fraud, or misrepresentations of data, and problems rep-licating findings are widespread (Begley

607230 ASRXXX10.1177/0003122415607230American Sociological ReviewPeterson2015

aNorthwestern University

Corresponding Author:David Peterson, Northwestern University, Department of Sociology, 1810 Chicago Avenue, Evanston, IL 60208 E-mail: [email protected]

All That Is Solid: Bench-Building at the Frontiers of Two Experimental Sciences

David Petersona

AbstractThe belief that natural sciences are more scientific than the social sciences has been well documented in the perceptions of both lay and scientific populations. Influenced by the Kuhnian concept of paradigm development and empirical studies on the closure of scientific controversies, scholars from divergent traditions associate scientific development with increased consensus and stability. However, both the macro/quantitative and micro/qualitative approaches are limited in key ways. This article is the first comparative ethnography of a natural science (molecular biology) and a social science (psychology) and it highlights important differences between the fields. Molecular biologists engage in a process of bench-building, in which they create and integrate new manipulation techniques and technologies into their practice, whereas psychologists have far less opportunity for this type of development. This suggests an alternative conception of the natural/social divide, in which the natural sciences are defined by dynamic material evolution while the social sciences remain relatively stable.

Keywordslaboratory ethnography, science and knowledge, hierarchy of the sciences, scientific frontier

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2012; Fanelli 2012; Ioannidis 2005; Owens 2011). However, while the technological accomplishments of the physical sciences has allowed such failures to be framed as isolated, exceptional incidents, problems in the social sciences are often treated as providing more evidence that these fields are not wholly legitimate.

The rising prestige of economics has been the subject of much attention (Callon 1998; Fourcade, Ollion, and Algan 2014; Knorr Cetina and Preda 2005), but social psychol-ogy is more emblematic of the trajectory and standing of most social sciences, whichdespite gaining footholds in the academy, government, and industryhave rarely received the prestige afforded the natural sci-ences. Instead, most social sciences face ongoing debates regarding their very exist-ence. Within social scientific fields, charges of crisis and fragmentation stem from fears that they lack coherence and unity (Almond 1989; Cole 2001; Davis 1994; Gard-ner 1992; Gouldner 1970; Mandler 2011; Savage and Burrows 2007; Stinchcombe 1994; Vygotsky [1927] 1997). One committee of prominent social scientists (Wallerstein et al. 1996) diagnosed social sciences problem as an inability to meet the expectations estab-lished by the natural sciences of prediction, management, and quantifiable accuracy.

The apparent lack of advancement in the social sciences has fostered a widespread belief in a hierarchy of the sciences, in which some fields are judged to be more sci-entific than others. Natural sciences like physics and chemistry sit proudly at the top, while the social sciences muddle feebly at the bottom. However, empirical research has pro-duced only mixed results trying to correlate this intuitive hierarchy with the products or social organizations of different fields. Although lay opinion strongly corresponds to this hierarchy (Lodahl and Gordon 1972; Smith et al. 2002), it is not clear what, if any-thing, this intuition actually represents. There have been some compelling developments, but no one has been able to explain why some sciences are more scientific.

This is because we still lack a basic under-standing of how the social sciences work on the level of practices. Nearly all the work comparing the natural and social sciences relies on downstream phenomena like citation patterns and professional structures as meas-ures of cognitive consensus. Yet, relatively little work focuses on what social scientists do in the process of research. Ethnographers have shed light on the practices of physicists (Knorr Cetina 1999; Traweek 1992) and biol-ogists (Gilbert and Mulkay 1984; Knorr Cet-ina 1999; Latour and Woolgar 1979; Lynch 1985), but we know almost nothing about the practices of social scientists. Our lack of knowledge surrounding social knowledge making (Camic, Gross, and Lamont 2011) has hobbled previous comparisons across the hard science/soft science divide.

This article bridges the gap between two research traditions in the social studies of sci-ence. On one side is the tradition initiated by Merton and his students, which uses mainly macro, quantitative studies to analyze differ-ences in scientific consensus across fields (Cole 1983, 1992, 1994; Hargens 1988; Lodahl and Gordon 1972; Zuckerman and Merton 1971). On the other side is a hetero-geneous set of theories and methods typically unified under the label constructivist (Cam-brosio and Keating 1988; Pinch and Bijker 1984). These studies use ethnography and historical case studies to investigate how con-sensus occurs as a matter of routine practice (Fujimura 1992; Gilbert and Mulkay 1984; Latour 1987; Latour and Woolgar 1979; Lynch 1985). Although these literatures have made important contributions, they both pre-sent incomplete understandings of how the physical and social sciences differ.

To focus on research practices and make comparisons across a social and a natural sci-ence, I conducted ethnographic studies of five laboratories in psychology and one in molec-ular biology. I demonstrate that research prac-tices in molecular biology are organized around embodied knowledge and research technologies in ways that are either absent or greatly constrained in psychology. I argue


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that the two fields have different opportuni-ties for creating and integrating new phenom-ena as part of the ongoing evolution of manipulation regimes, a process I call bench-building. Bench-building occurs when sci-entists at the unstable and ambiguous research frontier concentrate their efforts on the pro-duction of reliable effects through an iterative process whereby they incorporate new tech-niques and technologies. When successful, bench-building extends the horizons of inquiry and produces practical consensus as a byproduct, as researchers incorporate new methods to maintain their place at the cutting edge of their field. In fields in which bench-building is constrained, technological integra-tion and embodied skill remain comparatively underdeveloped and consensus is produced through other means. Although not a defini-tive statement, this article is a contribution toward understanding how research practices differ across these areas.

SCIEnTIFIC MATurITy AnD CognITIvE ConSEnSuSHow scientists achieve consensus is one of the oldest and most central questions in the sociology of scientific knowledge (Evans 2007; Shwed and Bearman 2010). It goes to the heart of what distinguishes scientific legitimacy from political or religious author-ity. Early research in the sociology of knowl-edge argued that scientific consensus was the result of an objective reading of nature and was, therefore, immune to social influence. Thus, the internal practices of science were outside the purview of sociological explana-tion (Mannheim [1936] 1985; Merton 1973).

The first sociological attempts to account for differences in the natural and social sci-ences utilized Kuhns (1970) distinction between paradigmatic and pre-paradigmatic sciences. Because low-status sciences were presumed to lack agreement on basic theoreti-cal objects and methods, sociological work in the 1970s and 1980s attempted to link scien-tific maturity with some version of cognitive consensus. Consensus was measured through

downstream phenomena like citation patterns (Cole, Cole, and Dietrich 1978; Hargens 1988; Price 1970; Zuckerman and Merton 1971, 1972). However, although natural sci-ences are widely perceived to have greater agreement than social sciences on their fields content (Lodahl and Gordon 1972; Smith et al. 2002), initial attempts to make an empirical link between cognitive consensus and the hierarchy of sciences were disappointing, leading Cozzens (1985) to suggest that social scientists engage in more qualitative work to understand the differences.

The most significant development in this tradition found that all scientific fields were characterized by low levels of consensus at the research frontier. Even high-status, natural sciences lacked the consensus to uniformly evaluate cutting-edge research. Yet, Cole (1983, 1992, 1994) argues that high- and low-status sciences can still be distinguished based on the presence or absence of a research core of widely accepted facts. Findings move from the contested frontier to the uncon-tested core through an evaluation process in which scholars consider both the qualities of the author and the cognitive content of the contribution (Cole 1992:3953).

More recently, however, scholars have dis-covered quantifiable differences between the social and natural sciences at the researcher frontier. Smith and his colleagues (Arsenault, Smith, and Beauchamp 2006; Best, Smith, and Stubbs 2001; Smith et al. 2002; Smith et al. 2000) published several studies using a novel approach to investigate the varying hardness of sciences. Expanding on Latours (1990; Latour and Woolgar 1979) argument that inscription devices (e.g., graphs, charts, and images) are a defining feature of modern science, they hypothesized that sciences per-ceived to be more scientific would allocate more journal space to inscriptions. Smith and colleagues (2000) found that the relationship between fractional graph area and the intui-tive hierarchy was nearly perfect ( r = .97, p < .01). They concluded that graphs play a cen-tral role in engendering consensus (for addi-tional evidence, see Simonton 2004).



Although recent quantitative studies have made inroads into understanding both scien-tific consensus and the differences between natural and social sciences, they have been hampered by an inability to investigate actual research practice. Quantitative studies of con-sensus focus on differences in the products of scientific work and can thus say little about the processes that undergird these differences. For instance, because Shwed and Bearmans (2010:834) network analysis of emerging sci-entific consensus explicitly brackets the con-tent of the debates they study, they concede that [a]ssessing consensus, of course, has nothing to do with the truth. Likewise, Smith and colleagues (2000:84) recognize that increased graph use in harder sciences may be an artefact or epiphenomenon of some other general characteristic that distin-guishes the hard and soft sciences and the relationship between graph use and hardness may not be specific to graph use, but rather reflect another underlying variable.

Because this sort of quantitative research remains outside the lab walls, there is little chance of uncovering what this underlying variable might be. Thus, they cannot distin-guish between consensus achieved by, for instance, domination and consensus achieved by other means. This analytic strategy renders sociologists unable to say what distinguishes consensus in scientific communities from other high-consensus groups like the military or religious cults.1

Qualitative researchers in the constructiv-ist tradition have used ethnography and his-torical case studies to breach the lab walls and explain how consensus occurs as a matter of routine practice (Fujimura 1992; Gilbert and Mulkay 1984; Latour 1987; Latour and Wool-gar 1979; Lynch 1985). In constructivist accounts, a practical (rather than cognitive) consensus occurs when controversies become closed. This typically occurs when one side marshals enough support to make challenging their facts too costly for competing camps.

Yet, the vast majority of research in this tradition has been concerned with the natural sciences, especially physics (Knorr Cetina

1999; Traweek 1992) and biology (Knorr Cetina 1999; Latour and Woolgar 1979; Lynch 1985; Owen-Smith 2001). The focus on the natural sciences has resulted in an inability to explain why some fields face challenges in achieving closure. In addition to the relative dearth of research on social science, Cole (1992) argues that the construc-tivist reliance on qualitative, micro-level studies has prevented researchers from drawing generalizable conclusions.

Although both micro/qualitative and macro/quantitative studies have produced important contributions to this discussion, their limitations indicate a need for meso-level studies that can investigate research practices while simultaneously making com-parisons across fields. The perceived success of early lab ethnographies led to a decline in the popularity of the method (Doing 2008; Lynch 1997), as sociologists of science began investigating how the laboratory was embed-ded within larger networks and social worlds (Callon 1986; Clarke and Star 2008; Fujimura 1988; Heath et al. 1999; Latour 1987, 2005). However, the lack of ethnographic research of experimental social science labs, coupled with the absence of comparative ethnography, has hampered previous attempts to under-stand differences in research practice between the social and natural sciences.

The present research contributes to the understanding of social science and its rela-tionship to natural science in three ways. First, this is an ethnography of laboratory practices in a social science (psychology). Several sci-ence studies scholars have investigated knowl-edge production in economics (Callon 1998; Fourcade 2009; Knorr Cetina and Preda 2005; MacKenzie 2006), and a handful of sociologi-cal studies are concerned with the discipline of psychology (Ashmore, Brown, and Mac-millan 2005; Ben-David and Collins 1966; Buchanan 1997; Chamak 1999; Galison 2004; Hartley, Sotto, and Pennebaker 2002; Rose 1998; Smith et al. 2000; Smyth 2001), yet lit-tle research has been done in psychology labo-ratories. Second, unlike most other lab ethnographies, which are limited to the


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observation of individual sites, I observed five psychology labs representing two major sub-fields. Third, this project is the first ethnogra-phy to compare a social and a natural science. Previous comparative ethnographic lab stud-ies have illustrated cultural differences in physics labs in the United States and Japan (Traweek 1992) or differences in high energy particle physics and molecular biology labs (Knorr Cetina 1999). To better understand how the practices of psychology contrast with those from the natural sciences, I conducted a one-year ethnography in a molecular biology lab. Of course, use of multiple research sites necessitates a tradeoff: space limitations require a thinner description than is typical in ethnography. However, the observation of multiple sites has several significant advan-tages, including greater generalizability and the ability to compare cultures.

METhoDSI conducted ethnographies in five psychology laboratories and one molecular biology lab from 2009 to 2013. The psychology labs included three developmental cognition labs and two social psychology labs located across three prestigious universities in the United States, including one at a private university on the East Coast, two at a private university in the Midwest, and two at a large public univer-sity on the West Coast. I conducted a one-year ethnography in a molecular biology laboratory at the West Coast university. Additionally, I interviewed 52 faculty members, postdoctoral researchers, and graduate students.

I chose psychology because, unlike other social scientists, most psychologists conduct their research in laboratories. This provides an important benefit to the ethnographer who finds only silent, solitary work in most social scientific fields (Camic et al. 2011). Previous research has investigated social scientific research through peer review panels (Guetz-kow, Lamont, and Mallard 2004; Lamont 2009; Lamont and Huutoniemi 2011), confer-ences (Gross and Fleming 2011), and retro-spective accounts of discovery (Koppman,

Cain, and Leahey 2015). While these studies have expanded our knowledge of how social scientific research is presented and evaluated, they cannot address questions of in situ production.

Moreover, psychology represents a good contrast because the field has modeled itself on the natural sciences far more aggressively than have other social sciences (Danziger 1990). Psychologists use experimentation as their primary method and thus cannot be accused of lacking empiricism or an experi-mental method (Collins 1994).

Although I will be mostly contrasting the two psychological subfields with molecular biology, social psychology and developmen-tal psychology differ from each other in some key respects. Specifically, labs for most social psychologists are little more than social affiliations and generic spaces where subjects can fill out paper surveys or participate in simple behavioral experiments. Labs in devel-opmental psychology, on the other hand, are more similar to natural science labs, because they are organized around relatively perma-nent pieces of experimental technology. While I address the distinction between these psychological subfields at a few points, I focus mainly on what distinguishes psychol-ogy labs from molecular biology labs.

The psychology lab is headed by a psy-chologist who primarily sits in an advisory role. Postdocs and graduate students are usually the engines of empirical work. They develop and execute experiments that further some aspect of the professors research program. Many of my observations come from lab meetings in which the professor, postdocs, graduate students, and interested undergraduates would meet for an hour or two to present and discuss research the lab was producing. These are highly concen-trated scenes of interaction focused on transi-tioning messy and ambiguous bench results into polished arguments.

In two of my three developmental psychol-ogy sites, I participated fully in lab activities, including recruiting subjects, setting up experiments, coding, and running subjects. In addition to lab meetings, I also attended a



series of one-on-one weekly meetings where a faculty member met with individual gradu-ate students and postdocs to discuss the pro-gress of ongoing projects.

For reasons that will become clear, social psychology runs fewer in-house laboratory studies. Thus, most of my ethnographic evi-dence for social psychology comes from weekly laboratory meetings. However, in addition to interviews, I toured the offices that compose the social psychology lab, watched videos of in-person experiments, and participated in dozens of online social psy-chology experiments.

Molecular biology is a good natural sci-ence to contrast with psychology because they have similar laboratory organizations. Unlike a field such as particle physics, which has a large, hierarchical social structure organized around a single piece of technology (Traweek 1992), molecular biology tends to be more horizontally structured, with several, mostly independent projects occurring side-by-side in the same lab. Like psychology, professors oversee the individualized projects of graduate students and postdocs.

The molecular biology lab I studied inves-tigated the development of neural circuits in the retina. This involved dissecting a mouse pup to extract the retina and then using a piece of machinery to either measure its elec-trical output (i.e., neural spikes) or image the retina to explore morphological and func-tional aspects of development. Typical manip-ulations included comparing mice at different stages of development, comparing normal mice to genetically altered knockout mice, using pharmacological agents to manipulate cell reaction, and using different types of stimuli (e.g., light or electrical current) to induce reaction from the retina.

The molecular biology lab was a restric-tive environment because much of their equipment was expensive, fragile, and poten-tially dangerous, so most of my observations are from two-hour lab meetings held weekly for one year. However, I was able to observe six different researchers during routine lab work to gain familiarity with their practices.

These lab visits ranged from 90 minutes to four hours.

Both classic and newer ethnographies pro-vide descriptions of biology labs that corre-spond to my experience (Knorr Cetina 1999; Latour and Woolgar 1979; Lynch 1988; Owen-Smith 2001; Rosenberger 2011). This article replicates many findings from this literature. Instead of attempting to provide a full ethno-graphic account of the lab, however, I empha-size the practices that differ most strikingly from what I observed in psychology labs.

onwArDS AnD uPwArDS or BACK To ThE BEnCh?A fundamental distinction between psycho-logical and biological research was high-lighted in a pair of lab meetings I attended on the same day in February 2013. In the first, Beth, an advanced graduate student in social psychology, was presenting data from an experiment designed to investigate the rela-tionship between the feeling of awe and humble behavior. To do this, she had pairs of subjectsone of whom either watched a video designed to elicit feelings of awe or a control videogo through three sets of tasks that took about 10 minutes in total. Each sub-ject filled out a set of surveys both before and after the experiment. In addition, Beth col-lected basic physiological data like blood pressure and heart rate. By the time she pre-sented, she had collected more than eight hours of video data, survey information, and basic physiological data from 50 dyads.

Beths goal for the meeting was stated at the beginning: What is the construct and how could I measure it? She had collected her data but needed clarification on exactly what humility was and how it could be theo-retically untangled from a concept like low self-esteem, which can look similar behavio-rally. Additionally, she was asking for sugges-tions regarding what to code in the videos. The lab spent the entire meeting discussing which aspects of the interaction should be coded and which should be ignored. Some lab members advised Beth to code nonverbal


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aspects of the interaction, including things like head nods, body posture, and facial expressions. Others suggested coding for the use of specific words like I and you that may indicate whether the subject was self- or other-focused. Still others thought Beth should simply code the entire video to get a global code for humility.

In the second lab meeting, Isobel, an under-graduate in the molecular biology lab, was presenting data from a study that compared the pupillary light reflex (PLR) in genetically engineered and non-genetically engineered mice. Although mostly a wet lab (i.e., a lab that conducts research using biological matter, chemicals, drugs, or other materials requiring specially designed rooms), undergraduates were sometimes given dry work because it was easier and would not occupy more expen-sive and popular equipment. Isobel had run the experiment several times and brought a video demonstrating a trial. She turned her laptop around toward the group and played us a grainy, 20-second close-up of a mouse react-ing to the light stimulus.

After some initial discussion to acquaint everyone with the purpose of the experiment, the lab began critiquing Isobels experimental procedures. Rose, a graduate student, noted that the mouse seemed to be moving a bit, which would make analyzing the video diffi-cult. She asked Isobel how she was restraining it. Isobel explained that she was just holding it down with her hand. Rose suggested she use a baby sock to subdue the mouse. Isobel grate-fully accepted this advice after admitting that the mice were hard to control because they jump around and nibble. Another graduate student asked how much light Isobel was using to stimulate the PLR. After she told him, he suggested that greater intensity might pro-duce a clearer response. The lab head told Isobel that she might also achieve better responses using younger mice and gave her instructions on how to force open the eye lids of mouse pups. Isobel wrote down each sug-gestion and the conversation moved to another students project.

The two meetings share some superficial similarities. On the same day in February, two

researchers presented video data from experi-ments they had run. Both had fundamental questions regarding what their videos meant and both were looking for feedback from their fellow lab members to improve their respective studies. However, the two meet-ings differed in one significant aspect. After the meeting, Beth, the social psychologist, moved forward with her study. She used the suggestions to develop a coding scheme and began training research assistants to code the 50 videos. Conversely, Isobel, the biologist, was sent back to the lab bench. She took the suggestions she received back to the site of data gathering to improve her experimental technique and collect better data.

These meetings reveal a key distinction between the two fields. The molecular biology lab was organized around the enrichment of data. Through the development of embodied knowledge and the introduction of new research technologies, molecular biologists continually changed the conditions of their research by creating and stabilizing new manipulations. This resulted in an ongoing focus on both improving embodied skill and introducing new technologies. The psychologists I observed, on the other hand, could do little to improve the conditions of their data gathering and, there-fore, benefited less from embodied technique and cutting-edge technology.

The following sections detail the differ-ences in both technique and technical devel-opment between psychology and molecular biology.

TEChnIquECollins (1974, 2001), who popularized the concept of tacit knowledge within science studies, noted recently that the term has been used to describe such diverse forms of behav-ior that there was a pressing need to clarify the concept. He developed a typology that included somatic and collective forms of tacit knowledge (Collins 2010).

Somatic tacit knowledge is the paradig-matic form described in Polanyis (1958) classic example of learning how to ride a bike. Explicit instruction is insufficient to



teach a somatic tacit skill. Simply put, no one learns to box (Waquant 2004), play an instru-ment (Sudnow 1978), or build a TEA laser (Collins 1974) without prolonged practice boxing, playing, and building. Certainly, guidance is helpful, but without physical engagement, learning will not happen. Cru-cially, this does not mean these behaviors are somehow beyond mechanization. Just because humans cannot learn some skills through explicit instruction does not mean that explication is impossible. The nature of physical actions, even highly skilled behav-iors, is essentially mechanical and, therefore, explicable.

Collective tacit knowledge, on the other hand, is akin to learning a habitus (Bourdieu 1977). If the paradigmatic example of somatic tacit knowledge is learning to ride a bicycle, collective tacit knowledge might be com-pared to learning to ride a bicycle through a city. This involves learning the subtle rules of appropriateness that govern specific contexts. Bikers may be physically able to ride full-speed through a crowd of pedestrians, but they will elicit anger if they do so.

Unlike somatic tacit knowledge, which is based on mechanical action, collective tacit knowledge is rooted in the fluid dynamics of collective judgment. This is the elusive form of tacit knowledge that allows individuals to be skillful social actors; to be graceful, taste-ful, polite, and germane. Collins argues that it is currently inconceivable to explicate or mechanize collective tacit knowledge.2

Because research on the development of scientific paradigms has been concerned with cognitive consensus and not the practices of scientists, the significantly different patterns of development of non-cognitive, tacit knowl-edge between fields has been overlooked. Collective tacit knowledgethe skills involved in designing convincing experi-ments, framing findings, and otherwise learn-ing how to operate successfully within a fieldwas a concern in both molecular biol-ogy and psychology labs. However, the role of somatic tacit knowledge was far more pronounced in molecular biology.

Technique in Molecular Biology: Good Hands

Biology graduate students spend their first two years as traveling journeymen doing rota-tions in which they move from lab to lab to pick up new skills and develop interests. One of these students, Ian, entered the lab shortly after I arrived and his experience was instructive. The lab meeting before he arrived, Dr. Owens told us that Ian was coming but warned that his background was in computational neurosci-ence. He had never conducted experiments. She told us, He hasnt been tested. After the initial meeting with Ian, she felt skeptical: I dont think he has a realistic idea about what it takes to do experiments. She was proved cor-rect. Ian struggled all semester and was unable to collect much data because he simply could not physically perform the experiments.

Later, Ian told me that when he got into the lab, he had a very brief training session with Blake, one of the older graduate students:

So, he initially trained me on how to do it. I dissected some mice earlier on another rota-tion and the very first time I did it he was like, Oh, alright. It takes a nice touch and he expected me to just destroy it and I got it out relatively nicely but it was really kind of a sick joke on me because that beginners luck just didnt persist.

After his initial success he began to have problems. Day after day, Ian continued to go through the hour-long dissection process only to discover that the retina was not providing a viable electrical signal.

Students typically present their rotation project at the end of their semester in resi-dence. Ian presented the limited data he had collected but had to admit that I had a nice response retina thanks to Blake because my touch is not so good. Ian later told me that Blake took mercy on him and did several dissections for his project because he simply could not get a usable recording. Although he might have eventually learned, Ian was not asked to join the lab permanently.


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Molecular biology students would spend weeks trying to learn tricky, fine motor meth-ods. One advanced graduate student practiced to improve her surgical technique. A younger graduate student spent months improving her ability to uncage cells (a process by which the neurotransmitter glutamate is precisely released onto cells using a laser). In addition to rotations, students would often visit other labssometimes in other states or coun-triesto learn new techniques.

Having rare or excellent somatic tacit knowledge was valuable in the biology lab. One graduate student was known for an abil-ity to capture excellent images. Her name ended up on many papers that came out of the lab. A postdoc was chosen specifically because he promised to get one of their microscopes to stimulate cells and image at the same time, which other members of the lab had tried and failed to do.

The importance of somatic tacit knowl-edge was evidenced in the way lab mem-bers referred to hands. At one point, Dr. Owens told the lab that a pair of researchers would soon be visiting. She said, Theyre both technically outstanding, like, building things. Itll be good to have another pair of hands. In another case, Dr. Owens was talking with Natalie, a graduate student, about an undergraduate who had applied to their PhD program. He had a neurological disorder that reduced his fine motor coordination. Previously, he had con-ducted some research using another student as a surrogate and thought he could con-tinue using that system in Dr. Owens lab. She was dubious.

Dr. Owens: Could you imagine doing work in physi-ology with someone else being your hands? Its bizarre.

Natalie: Does it work?Dr. Owens: Im not quite sure it would work.Natalie: You could get really mad at your hands, at

least.Dr. Owens: [laughing] I know! I already get mad at

my hands but theyre my hands. I couldnt imag-ine yelling at some student, Get that cell! I cant believe you didnt get that cell!

He was not invited into their lab.This focus on good hands supports previ-

ous research on the role of somatic tacit knowledge in both physics and biology labs and suggests that physical skill remains cru-cial in these fields despite ongoing techno-logical developments. Variously labeled good hands (Shapin 1989), lab hands (Doing 2004), and golden hands (Fujimura 1988), this line of work highlights the impor-tance of embodied knowledge in diverse sci-entific contexts. However, the necessity of technical skill did not transfer to the psycho-logical subfields.

Technique in Developmental Psychology: Warm BodiesIn the molecular biology lab, the term hands was used to denote the high levels of somatic tacit knowledge necessary to dissect animals, build equipment, and perform experiments. In contrast, hands is colloquially used as a synecdoche for generic, unskilled laborers (e.g., all hands on deck). In the develop-mental psychology labs, this is the role that many members have. For instance, because running experiments on infants requires workers to help schedule subjects, babysit siblings, and clean up the lab, one psycholo-gist frequently referred to her ongoing need for warm bodies to keep the lab running.

Because of the need for warm bodies, I was often recruited to work in the develop-mental psychology labs. Although the physi-cal skills needed to perform these tasks varied, none could be deemed challenging. For instance, when one of the coders did not show up for an experiment, I was enlisted. Dr. Parker explained my role in about 15 seconds. I was to look at the monitor and press a button on a computer keyboard (that someone had help-fully taped a happy face onto) when infants were looking at the stage and release the but-ton when they were not. That was the totality of my role.

At another lab, I got the chance to actually run an experiment. The task involved hiding



two balls in a set of buckets and seeing if the child (this time, a 17-month-old) could infer which bucket the ball was in. Learning how to do the experiment was more challenging than coding but did not involve any special skill. Although my first trial failed because I did not follow protocol perfectly, the researcher told me the second attempt was perfect. My physical skill in that domain had plateaued after less than an hour of practice.

Even the creation of the experimental equipment is not a particularly specialized skill. Dr. Parker told me that her husband, a non-psychologist, had built all of her stages during a period of unemployment. Many of the props used for stimuli were purchased from toy companies. For props that needed to be specially created, there was a bookshelf full of crafting items.

Importantly, there was marked variation between researchers in their skill in handling children. Some seemed to be uncomfortable with children and spoke primarily to their parents. Others were far more at ease and could quickly establish rapport. In some ways, this appears to be similar to the differ-ences in dissection skill between Ian and Blake. However, this is a facile comparison. While one may improve being good with kids by learning various techniques, the researchers personality was an undeniably important element. Warmth and extroversion were especially crucial. More to the point, however, even researchers who were the most skilled at dealing with children did not con-trol them or reliably manipulate them. It is significant that none of the labs in develop-mental psychology offered any explicit train-ing for working with young children.

Technique in Social Psychology: Scientists without Hands

While the role of technique is reduced in developmental psychology, it is completely irrelevant to the majority of social psychol-ogy. I asked several social psychologists if they had any technical skills. A postdoc

whose work has been discussed in the New York Times, PBS NewsHour, and other popu-lar media outlets answered no and added, I dont think I got trained in anything. One thing I got trained to do was to think like a social psychologist and learn how to write papers. Others responded by telling me they had good organizational or people skills.

Social psychologists rarely get training meant to increase their somatic tacit knowl-edge. They learn the habitus of their field, the collective tacit knowledge that will allow them to be full members of their community. They learn to think and write papers like social psychologists. They develop statistical skills and some may become excellent at designing novel experiments. But these skills do not produce reliable manipulations. Unlike molecular biology, where technical skill is vital, or even developmental psychology, where unskilled bodies are still an important part of the research apparatus, the research practice of social psychologists is largely disembodied.

Perhaps the clearest evidence that social psychology is characterized by disembodied practice is its growing use of online experi-ments (Buhrmester, Kwang, and Gosling 2011; Economist 2012; Mason and Suri 2012; Paolacci, Chandler, and Panagiotis 2010). Using platforms like Amazon.coms Mechani-cal Turk (MTurk), researchers gain access to a large, geographically diverse population. This method is attractive because it is far less expensive and easier than recruiting live sub-jects, and it provides a population that is more representative and thus not limited to the sub-jects psychologists have traditionally relied on (Henrich, Heine, and Norenzayan 2010).

However, by embracing this remote source of data-gathering, social psychologists have committed themselves to filtering all aspects of their experiment through the digital medium of the Internet. They lose all hands on access to the object of their inquiry. Greta, a graduate student in social psychol-ogy, was running a study on MTurk that involved subjects reacting to a recording of a


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laugh. She was unhappy that some subjects might be listening to the laugh on cheap lap-top speakers in a loud room while others may be listening through expensive headphones, but she acknowledged there was little she could do about it. When she presented her data to the lab four months after our exchange, she did not mention these concerns.

TEChnologyEmbodied skill is not unique to researchers in the natural sciences. Ethnographers must learn to behave normally so as not to disturb natural group behavior. Quantitative sociolo-gists learn how to navigate statistical com-puter programs. Outside of academia, many vocations require workers to learn a sophisti-cated set of physical skills.

What differentiates embodied knowledge in molecular biology from these other domains is that existing somatic tacit knowl-edge is continually given new power through technological innovation. In molecular biol-ogy, the body serves as an all-purpose tool in a larger system that regularly incorporates new research technologies. Skills like dissec-tion and microscope technique provide the necessary conditions for the introduction of new technologies.

For instance, the development of geneti-cally engineered mice occurs outside the lab but has profound implications for a labs research, because the mice can be specially designed to have biological properties that allow, for instance, for better imaging. Supe-rior imaging allows researchers to witness cellular interactions that were previously invisible, and this greater vision, in turn, gives them reason to try new manipulations they would have been unable to evaluate before. However, embodied skill is vital all along this process. Introduction of these mice would be impossible if not for the embodied skill of animal husbandry experts, and genetic manipulation would be irrelevant if it were not for the existence of skills that allow researchers to dissect retinas, maintain cells, and produce images.3

Technology in Molecular Biology: The Wild West

The material culture in the molecular biology lab was characterized by an elaborately built set of rooms containing pieces of research equipment. These included stations where animals were anesthetized and decapitated and others where their retinas could be dis-sected and stored in chambers that maintained their viability. Freezers stored dyes that allow cells to appear fluorescent and viruses that prevent specific cellular functions. There was a wall of beakers and a station with a dozen handheld pipettes that were calibrated to dis-tribute exact quantities of liquid. One station looked like it belonged in a high school shop class: on a wall next to a drill press and a band saw were hundreds of tiny, plastic drawers containing nuts, bolts, screws, nails, and other fasteners used to build and manipulate equipment.

But all of these material technologies are mere accessories to the actual sites of data collection in molecular biologythe rigs. A rig is a colloquial expression used in labs to refer to a microscope and its accoutre-ments. The lab had seven working rigs and they were in the process of building an eighth from scratch.

Although the rigs differed in their specific function, they were all designed to capture data from tissue samples. In every rig, tissue samples were the focal points for several coordinated but distinct technologies. One of the more common setups included optics, which was composed of the objectives (lenses), filters designed to allow only certain wavelengths of light to pass through, and cameras that captured still pictures and video; a stimulation system, which elicited responses from retinal neurons using light from specific wavelengths; the thin, wire electrode that touched the cell and recorded its electrical activity; and a perfusion system that circu-lated nourishing liquid around the tissue sam-ple to keep cells alive.

Each aspect of the rig is dependent on additional support technologies. For instance,



the electrode was held in place by a mechani-cal arm controlled by the manipulator, a metal box with a digital readout and three dials controlling the electrodes movement in X, Y, and Z dimensions. Because the cells are sensitive to trauma, the manipulator allows researchers to make movements far more subtle and exacting than an unaided human could accomplish. The electrode itself sends signals to an amplifier that magnifies the minuscule electrical activity the tissue pro-duces. Additional software then isolates the signal coming from the cell of interest from neighboring cells.

The material culture of the molecular biol-ogy lab was not only highly elaborated, it was constantly in development. Besides the rig that was being built while I was there, another had been built shortly before I arrived, and every week they discussed (and frequently purchased) new dyes, viruses, filters, and genetically modified mice. When I asked Sarah about the rapid development of research technology in their field, she told me, The questions that we can ask are limited by the ways that we ask them. If we have the latest and greatest technology, we can ask more sophisticated questions. However, this cre-ates a culture of volatility. As Blake explained, All the tools are advancing at the same time. He later told me that their field pushes hard even if we dont know where were going and compared the constantly receding technological frontier to the Wild West.

Technology in Social and Developmental Psychology: The Settled Lab

Knorr Cetina (1999:35) wrote that the social scientific lab does not, as a rule, involve a richly elaborated spacea place densely stacked with instruments and materials and populated by researchers. . . . The lab is a vir-tual space and, in most respects, co-extensive with the experiment. Although both social and developmental psychology labs were, in gen-eral, less elaborated than the biology lab, there were important differences between the two.

Knorr Cetinas comment can justifiably be applied to social psychology labs. As evi-denced by the increasing use of online experi-ments, much social psychological research is not confined to a specific space. Moreover, the desire for more naturalistic data often leads social psychologists out of the lab. For instance, one study attempted to elicit pride from undergraduate subjects by walking them to and pointing out important landmarks at their university. Yet, even when social psy-chological studies do take place within the lab, little is specific to the lab-space. It is merely a room where the researcher can set up some chairs, a table, and a video camera. Beths awe experiment, discussed earlier, asked its two subjects to sit next to each other and follow instructions on a clipboard while being video recorded.

Like social psychology labs, psychologists who do research on toddlers and children sometimes create a new lab space for each experiment. The necessary elements of such an experiment are (1) a subject, (2) an experi-menter, (3) a distraction-free space, (4) a toy that functions as the stimuli, and (5) a video camera to capture a record of the interaction. In one experiment, a researcher hid a toy in a box with 12 drawers to see if the child could remember the placement after some tasks. This did not require an elaborated space.

However, with younger children, experi-mental options are reduced and developmen-tal psychologists tend to use standard methods. Because infants cannot understand instructions and are highly distractible, most cognitive psychology on infants utilizes methods that rely on measuring the infants looking time. Because one of the develop-mental labs only studied children up to 12-months-old, their two experimental rooms were designed around permanent pieces of research equipmentpuppet stages with out-ward-facing cameras built into them. Differ-ent experiments altered the action on the stage, but the stage itself remained unchanged. Although they inhabited more elaborated spaces than social psychologists, develop-mental psychology labs did little evolving.


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Overall, the technologies used by psy-chologists included consumer-grade audio and visual equipment for recording subject responses, surveys, online experiments, stages, and props. These technologies are all united by the fact that they are low ceiling technologies. Getting good enough equip-ment is relatively easy to achieve and inex-pensive. In contrast to molecular biology, where an ongoing integration of cutting-edge technology provided opportunities for new types of manipulations, technological improvements yield little in many psycho-logical fields. The result of this is a much more stable material culture in the lab. For instance, psychologists want audio recording technology that is good enough to clearly hear responses, but they do not require the most advanced audio technology. Even though it is possible to purchase equipment that can record subtle contours of the sub-jects voice, that level of detail is unnecessary for their aims.

SCIEnTIFIC ProgrESS AS ThE EvoluTIon oF MAnIPulATIon rEgIMES

The argument that scientific communities are primarily organized around research technol-ogy is not new. As Shapin and Shaffer (1985:152) argue, Boyless greatest innova-tion was not his air pump but the air pumps role in reorganizing the social collective around the production of experimental find-ings, mobilized into matters of fact through collective witness. The air pump may have been an ill-designed piece of technology, but it translated a scientific controversy from an abstract, theoretical disagreement into a con-crete problem of technological function.

Later, Collins (1994) would use this argu-ment to explain why some fields, like sociol-ogy, had trouble becoming high-consensus, rapid-discovery science. By integrating self-generating genealogies of research technolo-gies (Collins 1994:162), the natural sciences were able to escape the type of intractable

oppositions that characterize the history of philosophy (Collins 1998). The ongoing introduction of new technologies led some sciences to develop more dynamic intellec-tual communities where, instead of languish-ing in stagnant debates, researchers were consistently abandoning old controversies in order to get to new ones (Collins 1994:160).

However, this answer is incomplete. The belief that technology creates scientific unity simply by being an object of collective focus leads to the same Mertonian overemphasis on social organization and consensus. For instance, it is unclear why a debate soon passes into the realm of consensus (Collins 1994:161) when a new technology is intro-duced. If researchers simply abandon old controversies to chase new technology, what distinguishes this from the desultory paths of the social sciences, fields often accused of being faddish? Conversely, if technology proves decisive in settling controversies, then how is this different from any positivist account of knowledge accumulation in the natural sciences?

Collins fails to provide a compelling rea-son why rapid-discovery sciences have been able to achieve high consensus but the social sciences have not. On one hand, he concedes that some areas of social science simply can-not be technologized, and hence cannot be turned into the rapid-discovery mode (Col-lins 1994:174). However, he suggests that conversation analysis and sociological artifi-cial intelligence are two areas that show promise in becoming rapid-discovery sci-ences. Yet, beyond the mere presence of tech-nology, he does not clarify what makes rapid-discovery science possible in these areas and not in others.

The ultimate value of embodied knowl-edge and technology is not in their capacity to focus attention but their ability to change the very conditions of data collection. For instance, Constance, a graduate student in biology, presented some early findings from a series of experiments that looked at the role of ganglion cells during retinal development. She pulled out a single piece of paper and



unfolded it on the table. The lab gathered around a scatter plot with dozens of points representing neural activity (spikes) stimu-lated by light. She was not sure what these data meant. First, she was not certain where her cells were coming from because her sam-ple was taken from a developing mouse and the retina structure changes early in the lifespan. Second, she was not sure what the data pat-tern was telling her. She made circles with her finger over two parts of the plot and said, I can kind of see two clusters, but admitted she was not sure if these (pointing to spikes earlier in time) turn into these (the rough cluster of spikes occurring later) or if the two were unrelated.

The head of the lab then began asking questions about the students microscope technique. Another graduate student asked if she could get clearer data using a fluorescent dye that enhances cell imagery. The discus-sion ended with Constance agreeing to go back to the bench to try different microscope techniques and the new dye to see if she could get more interpretable data. Like Isobel, the undergraduate doing a study of PLR, Con-stance was given suggestions for altering her embodied practice and introducing new tech-nology and was sent back to the bench to improve her data.

What separates molecular biology from psychology is that the development of manip-ulation regimes is a central goal in the former

and a peripheral interest in the latter.4 Molec-ular biologists are focused on enriching their data. They are engaged in an iterative process in which they conduct limited data gathering followed by discussion and critique that, in turn, is followed by a trip back to the bench to change some feature of the experiment (see Figure 1).5

Through repeated iterations, technique, technology, and object become interactively stabilized, creating a new surface of emer-gence (Pickering 1995). The conditions of research have literally been altered. The development of new skills and, relatedly, new technologies, changes the shape of this sur-face and promises new horizons of emer-gence. The remainder of this article looks at differences in this process between psychol-ogy and molecular biology.

Bench-Building at the Frontier

The scientific benchthe site of data col-lectionis continually transformed through the application of new embodied techniques and the introduction of new research tech-nologies. I thus label this process bench-building (see Figure 2).

Bench-building is the ongoing develop-ment and refinement of manipulation regimes that change the conditions of research. Scien-tists create new ways to make things happen and produce some predictable effect. As a

Figure 1. Iterative and Non-iterative Data Collection


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manipulation gets refined, it changes the pos-sibilities of research. Rather than being just an outcome, a stable manipulation can become integrated into the research apparatus and be used to produce new outcomes. In this way, the research frontier is altered.

In contrast to black boxing, which describes the way scientific and technical work is made invisible by its own success (Latour 1999:304), bench-building is charac-terized by hyper-visibility. Technique and technology are made into objects of scrutiny as scientists wrangle with early manipulations that are often ambiguous and unstable. Many findings are presented to the lab as mysteries rather than answers. Unstable but intriguing outcomes are made the object of scrutiny with the goal of simply finding some solid ground, some way to produce the outcome with accept-able regularity. As Cole (1992) would predict, I found the research frontier in both biology and psychology characterized by low levels of consensus. However, molecular biologists engaged with this ambiguity to a much greater degree than did the psychologists.

For instance, every six weeks or so, the molecular biology lab would hold a journal club where they would review a recent arti-cle. The first time I attended one, I was shocked that they could spend two hours dis-cussing a five-page article. However, this time investment became understandable as I came to see how even experienced members of the lab struggled to understand cutting-edge work from other labs. The following quotes are all taken from the head of the lab during my first journal club:

I dont get it.

The y-axis on [graph] J is still making my head hurt.

What the hell is this plot?

Its all going to depend upon density. Whats density?

They shock them [a specific subtype of retinal cells], which Ive never heard of.

That distribution doesnt look normal to me. Its a peaked distribution but it doesnt look normal.

I dont know enough about the colliculus. Are there really no pathways between them? No septum?

If they have more information, why show less information?

This difficulty was not considered a mark against the paper, which the lab head called awesome. The researchers in that article had been able to do something completely new and there was no expectation that the article would be fully comprehensible.

Ambiguity is an unavoidable aspect of a bench-building process that is based on techni-cal ingenuity and researchers physical skills. As Porter (1995:1516) explains, In the early life of a new technique, when it is still on the cutting edge, personal contact will most often be crucial for its spread to other laboratories. Indeed, this may be just what cutting edge means in experimental science. But experi-ments that succeed, again perhaps by defini-tion, will not long remain in the domain of intricate craft skill and personal apprentice-ship. Because cutting-edge research pushes into uncharted territories using new techniques that are not well explicated, they are sources of both inspiration and frustration to their audi-ence. However, only results that remain teth-ered to particular researchers or labs come to be seen as deeply dubious. Inexplicability is a

Figure 2. Bench-Building



defining characteristic of the research frontier, but it is only the first stage in the bench-build-ing process.

Unlike most psychologists, molecular biologists returned to the bench to engage with this ambiguity because, even in its inex-plicability, it pointed toward a new horizon of control and prediction. If skills can be physi-cally mastered or new technologies intro-duced to create or stabilize new phenomena, then there is hope that the process can be explicated, standardized, and, most ambi-tiously, mechanized.

Explication of somatic tacit knowledge is an ongoing concern in molecular biology labs. I asked Dr. Owens how molecular biolo-gists evaluate work when they struggle sim-ply to understand what was done. She explained that consensus builds through between-lab communication. She mentioned a situation in which her lab had a genetically engineered mouse that did not produce elec-trical waves in their lab but did in some oth-ers. This sparked a situation whereby the tacit knowledge embodied in the process was explicated to standardize the reactions across labs: There was a lot of back and forth between the labs saying, What were the solu-tions you used? How long is your dissec-tion? How much light were they exposed to? Through this communication, they changed a number of procedures and were finally able to replicate the waves.

This is not to say that somatic tacit knowl-edge is always on a path toward mechanization. This might actually be somewhat of a rarity.6 Roses suggestion to use a baby sock to restrain the mouse instead of a researchers hand is a simple example that shows how a task can be externalized in a piece of, albeit low-tech, tech-nology. The back and forth between Dr. Owens lab and the external labs allowed for enough explication to standardize procedures across labs. However, for many complex skills, there is little evidence of impending mechanization. For instance, although dissection procedures are mostly standardized across researchers, there has been no mechanization. Knife-work remains a vital skill.

Moreover, not all unstable frontiers can be settled. Returning to the bench does not guar-antee success. During a presentation, Julian, a graduate student, showed some data that could be explained by two hypotheses. To decide between them, a neurotransmitter would have to be completely blocked to see if it was causing the reaction in question. Yet, the technical ability had not been developed. The lab head lamented that the competing hypotheses could not be tested: The tools arent there. The drugs arent perfect and the [genetically altered mice] arent perfect. Without the proper tools, there was simply no way to improve the data.

Limits to Bench-Building in Psychological Science

Bench-building is not completely absent in psychology laboratories. I participated in sev-eral informal sessions where researchers would try out new methods or experiments on each other to make sure they elicited the desired response. One advanced graduate student in cognitive psychology had a hypoth-esis about word order affecting fluency in a reading task. He read it to the group to get their impression. It did not create the effect he wanted so he decided to develop a better stimulus. In another case, a graduate student was demonstrating a memory task for tod-dlers involving cups with toys in them taped to a Lazy Susan. However, she told her adviser that the infants were getting distracted by the tape and would not engage with the experiment. The advisor said, Let me be a baby. How does this work? They then went through the experiment together slowly and developed a number of suggestions, including putting a more desirable toy in the cups and replacing the distracting tape with velcro.

However, this sort of bench-building is severely limited in many psychological fields. Word order can only be rearranged in a few ways. There is only so much you can do to hold a toddlers attention. Repeated trips to the bench offer diminishing returns. Logical and clever experimental designs are important


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in every field, but only some fields benefit from repeated iterations of data collection. The deciding factor is whether engagement will yield greater purchase on the research object or not; that is, whether researchers can either develop their skill or introduce new technologies to produce better data.

In experimental social sciences like psy-chology, powerful manipulations are gener-ally rare. The recent debate regarding the validity of social priming methods is telling (Kahneman 2012; Young 2012b, 2012c). When Doyen and colleagues (2012) failed to replicate Barghs highly cited priming study (Bargh et al. 1996), Bargh dismissed the rep-lication and subsequent news coverage as products of pay-as-you-go publications and superficial online science journalism (Bargh 2012). This quickly descended into a squabble between Bargh, the authors of the replication, science bloggers, and online commentators. For our purposes here, it is irrelevant whether Bargh or his critics are right. An op-ed in the New York Times about the dust-up points out that the effect sizes in Barghs article were not very large to begin with, and thus failed rep-lications should not be surprising (Satel 2013).

What is significant, however, is that Bargh defended his work by arguing that the replica-tors had introduced critical changes (Bargh 2012) to the experiment, like slightly differ-ent subject instructions and priming proce-dures. This type of critique against a failed replication is a familiar strategy (Collins 1985). Naturally, experimental paradigms on the bleeding edge of science are fragile. At the research frontier, tacit knowledge is nec-essary and replications may be difficult to achieve.

However, the studies in question are not at the bleeding edge of a research frontier: they were published almost 20 years ago. Why have these methods not been refined? Why have new technologies or methods not pro-vided new vistas and united the lab with other sciences and outside institutions? What makes bench-building harder to achieve in the human sciences?

The answer has both ethical and ontologi-cal dimensions. On one hand, the ethics of experimenting on human subjects limits the types of strategies social scientists can use. The development of embodied knowledge in science involves an intimate relationship between researchers, tools, and objects of study, and anything that constrains the evolu-tion of this relationship will constrain bench-building. Certainly, there are good reasons for this oversight, but it is not surprising that some of the most famous and illuminating studies in social sciencefrom the Milgram and Stanford Prison Experiments to Hum-phreys Tearoom Tradewere also the most ethically questionable. The very methods that tell us the most about human behavior are the ones that are the most immersive, invasive, and manipulative.

The effects of these restrictions were espe-cially evident in psychology labs studying infants and toddlers. Due to legal and ethical protections, researchers were limited in their control over subjects. In many experiments, children were expected to remain seated on their parents lap looking at a stage; research-ers then directed their attention toward the stimulus so their faces could be recorded for coding. However, the children would often get antsy and begin to stand on their parents legs or lean out of frame. Because the experi-menters will was mediated through the par-ents hands, flawless adherence to protocols was sacrificed for the comfort of the child. Researchers had few options to remedy this so they simply adjusted. They would give the parent and child breaks in the middle of an experiment if the subject was getting fidgety. When the child leaned out of frame, the cod-ers could no longer see the face to measure looking-time. Yet, they continued coding based on the position of the body or some other cue. Because control of the subject was limited, environmental control was frequently compromised as well.

However, even if social scientists could attain better experimental control, they would still face an ontological challenge: they deal with objects of study that are abstract and



multivalent. As Peirce (1955:376) explained, Men who are given to defining too much inevitably run themselves into confusion in dealing with the vague concepts of common sense. For instance, power is a phenome-non that occurs between nation-states and also between children on a playground. One instan-tiation is not purer than the other. Instead, understanding power involves the capacity to draw out commonalities between these cases. In the physical sciences, on the other hand, objects of study are usually better under-stood when reduced to their components.

This results in a confounding research situ-ation for social scientists. The very methods that bring physical sciences closer to their object of study pull social scientists farther away from theirs. Researchers in the Webe-rian tradition argue that social scientific con-cepts take their meaning from specific historical and cultural circumstances (e.g., Habermas 1967; Winch 1958). Abstracting a concept of social relevance from its context degrades and deforms it.

For instance, one of the infant cognition labs was running experiments looking at infants ability to discriminate between groups of people. Laura, an advanced graduate stu-dent, was investigating whether infants based their perception of group-ness on similar appearance or collective action. Clearly, this work had implications for understanding things like the development of racial atti-tudes, an inspiration the researcher acknowl-edged. However, instead of using images of actual people (or even realistic representa-tions of people) as measures of difference, she used cartoon objects made of red circles and yellow triangles with faces on them: these cartoons were easy to standardize and maximally different to make them easier to distinguish. The initial idea was translated into a doable project through standardization and control. But it also became more abstract and farther removed from the rich concept that motivated the investigation.

Although challenging research conditions are not unique to psychology, the ethics of human subjects protection and the complex

ontological status of cultural and psychologi-cal objects constrain bench-building in fields that take human thought and behavior as their primary research object. However, it is impor-tant not to conflate these two issues. Research ethics are a product of historically situated epistemic cultures; they may become more or less constrictive under different circum-stances. Limitations to access or control in human science can, in principle, be over-come. New methods that collect biological measures or social media information push these boundaries. However, it is less obvious how ontological constraints can be overcome. Historically and culturally situated concepts may simply provide an unsound foundation for the building of stable manipulation regimes.

ConCluSIonS: SCIEnTIFIC growTh AS CrEATIvE DESTruCTIon

Scholars in the philosophy of social science have provided a justification for social sci-ence based on hermeneutics (Habermas 1967, 1968; Taylor 1985) and interpretation (Geertz 1973) in contrast to the objectification used by natural sciences. The distinction between experimental and interpretive fields has been cited publicly by social scientists to argue that their ultimate value and validity emerges from a different source than the natu-ral sciences (e.g., Gieryn 1999).

However, despite this conceptual critique of treating humans as natural objects, the experimental method remains the basis for much of the research in social science (Jack-son and Cox 2013). This raises important questions about whether treating human beingswho are laden with biography, his-tory, and cultureas natural objects affects the epistemic culture of the experimental social sciences. If humans do present particu-lar problems to the experimenter, how does this change the field at the level of practices? Ultimately, these questions can only be addressed through comparative research that


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includes both social and natural sciences. Although this study does not provide a defini-tive answer, it offers suggestive results that are supported by previous research.

Through a comparative ethnography of five psychology labs and one molecular biol-ogy lab, I highlighted profound differences in embodied knowledge and technological inte-gration. Through the development of tech-nique and the integration of new technologies, molecular biologists are able to create and stabilize new phenomena and, in doing so, change the very conditions of their research, a process I labeled bench-building. Psycholo-gists, on the other hand, are both ethically and ontologically constrained in bench-building.

This is a different picture of scientific maturity than previous theories have offered. Despite being at odds with each other on many points, macro/quantitative and con-structivist theories share a focus on consen-sus. Both argue that natural sciences achieve consensus that, in turn, produces more inte-grated social organizations. Quantitative research in the Mertonian tradition attributes this to the cohesiveness of cognitive consen-sus, whereas the constructivist tradition attributes it to the closure of controversies.

My observations paint a different picture. Rather than a picture of high consensus and cognitive integration, the molecular biology lab was a scene of constant, sometimes cha-otic, change. Where bench-building is a pos-sibility, it is a necessity. When the possibilities of research are constantly receding, labs fun-nel their resources into developing the tech-niques and technology to maintain their position along the cutting edge. Consensus and stability are sacrificed under the faith that ambiguous, local research will eventually solidify. But, by then, they have already moved to the next frontier. The value of creat-ing new manipulation regimes drives molecu-lar biologists to perpetually transfigure their working conditions. Bench-building is a type of Schumpeterian creative destruction that transforms practice from within.

On the other hand, the two psychological subfields studied here maintained relatively

unchanging methods, practices, and theories rather than a flux of competing paradigms and controversies. A social psychology post-doc told me that theres nothing that I do that couldnt have been done 60 years ago. This is not true of all social psychology, of course. The branch that utilizes implicit-association tests, for instance, has shown some techno-logical development. However, it would be hard to imagine any biologist admitting to this type of epistemic continuity. Because of the ethical and ontological challenges described earlier, social psychologists gain little by pursuing methodological changes that would only serve to make ones research less intelligible to colleagues. Where bench-building is constrained, the importation of new techniques and technologies represents a disintegrating force that may be resisted.

Although psychology is somewhat unu-sual in the social sciences for its unity around a set of methods and analytic standards (Dan-ziger 1990; Porter 1995), both economics and political science have developed high- consensus cultures despite low levels of embod-ied knowledge and technological integration (Fourcade et al. 2014; Lamont 2009; Pfeffer 1993). However, fields like anthropology and sociology remain heterogeneous. Perhaps, in the absence of evolving manipulation regimes that coax consensus out of the promise of new horizons of manipulation, consensus can occur only through political control. If so, consensus in social scientific fields represents a withdrawal from the creative destruction that defines development in the natural sci-ences. Under these conditions, consensus may occur as a product of social control rather than a byproduct of bench-building.

Future research should expand these inves-tigations beyond the fields examined in this article. By using molecular biology and two subfields of psychology as representatives of natural and social science, I am unable to definitively answer questions of how bench-building differs in different fields. I chose psychology because it has largely adopted the experimental methodology and laboratory-based social organization common in natural



sciences. However, science studies scholars have repeatedly demonstrated the diversity of epistemic and organizational forms among scientific communities. Future research should explore further variations within and between the categories of natural and social. For instance, Knorr Cetina (1999) argues that high energy particle physics resembles semiotics in some ways, and emerging fields like social neuroscience blur the boundary between the natural and the social.

One potential avenue of investigation is to look at how embodied knowledge is distrib-uted in different scientific fields. Vertesis (2012) recent work on the NASA team in charge of the Mars rover suggests that, in complex technological systems, embodiment may be distributed horizontally, with different individuals somatically attuned to different aspects of the systems function. Further-more, in hierarchically organized laboratories (e.g., particle physics), embodied knowledge tends to be associated with lower status, whereas theoretical knowledge is dominated by those with higher status (Doing 2004; Shapin 1989; Traweek 1992). That these sorts of divisions of labor have not yet occurred in molecular biology raises intriguing questions about the manifestation of bench-building in different settings.

Another potential area of elucidation is the micro-interactions involved at different stages in the bench-building process. For instance, although the importance of data visualizations in scientific research is frequently noted (Burri and Dumit 2008; Latour 1990), there is much to be learned about the specific role that data visu-alizations play at different stages in the iterative loop that characterizes bench-building.

Finally, in addition to the process of bench-building that attempts to standardize and mechanize new manipulation regimes, it is important to point out that there may be other, locally prevalent, incentives to de-stabilize and de-standardize (Jordan and Lynch 1998:795). Thus, there is a need for research that looks at how manipulation regimes are deformed and adapted to local contexts.

AcknowledgmentsI would like to thank Jeremy Freese, Chas Camic, Gary Fine, and the anonymous reviewers for their detailed and insightful comments. I also want to thank the lab heads, post docs, and graduate students who allowed me to observe and interview them. Finally, I would like to express gratitude to my informant AF who ensured the accuracy of my descriptions of molecular biology.

notes 1. Sociologists advocating theories of paradigm devel-

opment have repeatedly mistaken cognitive con-sensus for the vital prerequisite of mature science rather than a byproduct of research practice. This inability to understand what separates high-consensus social science, like economics, from physics has led to misguided suggestions for making the social sci-ences more scientific through social control and socialization (for similar claims, see Best et al. 2001; Fuchs and Turner 1986). Lamont (2009) and Pfeffer (1993) argue that political science did just this. The rest of this article highlights why these sorts of strategies are flawed.

2. In practice, the distinction between the somatic and collective forms of tacit knowledge may be less clear than Collins suggests. Even basic somatic skills may be infused with collective meaning (e.g., throwing like a girl [Young 2005]). And, although weaving a bicycle through traffic may require dif-ferent knowledge than simply learning to ride, the development of self-driving cars indicates that this sort of skill is not as insurmountably tacit as Collins suggests. However, for the purposes of this argu-ment, the basic distinction between somatic and collective tacit knowledge is a useful heuristic.

3. This entanglement between technology and embodiment has previously been shown in the fields of protein crystallography (Myers 2008) and neuroscience (Alac 2008).

4. The argument that manipulation is essential in scien-tific explanation has its roots in the philosophy of sci-ence (Hacking 1983; Menzies and Price 1993; Reed 2008; Von Wright 1971; Woodward 2005). These scholars argue that the defining characteristic of sci-ence is that researchers intervene in nature and create stable causal relationships where there were none, and science primarily progresses with the development of manipulation technology. In fact, some argue that it was the emergence of manipulation technology that enabled molecular biology to transition from a descrip-tive to an experimental science (Weinberg 1985).

5. Latours influential account suggests data visu-alizations (inscriptions) are primarily useful as rhetorically powerful devices that facilitate a swift transition from craft work to ideas (Latour and Woolgar 1979:69). After this transition, the bench space will be forgotten (1979:69; see also


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Latour 1999:63). This leads Latour (1990) to argue that inscriptions play the same role in the social and natural sciences. However, data visualizations in molecular biology are not always, or even primar-ily, rhetorical devices. They are also part of a feed-back circuit that leads researchers back to the local, material source of the data to enrich and improve their manipulations.

6. One famous example of the mechanization of embodied knowledge is the case of the Matsuchita Electric Company, which sent engineers to learn and mechanize the subtle, embodied techniques of master bakers to improve their electric bread maker (Nonaka 1991; Nonaka and Takeuchi 1995).

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