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https://doi.org/10.1177/0963662519865687
Public Understanding of Science
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© The Author(s) 2019
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DOI: 10.1177/0963662519865687
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P U S
Jargon as a barrier to effective
science communication:
Evidence from metacognition
Olivia M. Bullock , Daniel Colón Amill,
Hillary C. Shulman and Graham N. Dixon
Ohio State University, USA
Abstract
In this experiment (N = 650), we examine the negative consequences of jargon on individuals’ perceptions
of emerging scientific technology and aim to explain these effects. We find that the presence of jargon
impairs people’s ability to process scientific information, and that this impairment leads to greater motivated
resistance to persuasion, increased risk perceptions, and lower support for technology adoption. These
findings suggest that the use of jargon undermines efforts to inform and persuade the public through the
cognitive mechanism of metacognition.
Keywords
metacognition, persuasion, processing fluency, science communication
In order for the public to support scientific endeavors, it is important that research findings are
effectively communicated to lay audiences. However, there is growing concern that scientific
communities and the public may not be successful at engaging with one another, resulting in an
uptick of practical and scholarly work aimed at clarifying science communication. From these
works, a common recommendation is to reduce jargon (e.g. Baron, 2010; Dean, 2009; Sharon
and Baram-Tsabari, 2014). Although this recommendation stems from a desire to “speak the
same language” as the target audience, little research has examined the mechanisms that under-
lie this recommendation.
Thus, the purpose of this study is to investigate and explain the ramifications of jargon use
in emerging science and technology contexts. Guided by research in metacognition (Petty
et al., 2007; Schwarz, 2015), we demonstrate that jargon impairs people’s ability to easily
process scientific information, and that this impairment leads to greater motivated resistance
to persuasion (MRP), increased risk perceptions, and lower support for technology adoption.
Taken together, these theoretically guided findings offer practical implications for science
Corresponding author:
Olivia M. Bullock, School of Communication, Ohio State University, 154 N Oval Mall, Derby Hall, Columbus, OH
43210-1132, USA.
Email: bullock.181@osu.edu
865687PUS0010.1177/0963662519865687Public Understanding of ScienceBullock et al.
research-article2019
Research note
2 Public Understanding of Science 00(0)
communicators who aim to engage, inspire, and persuade the public of their important, yet
often complicated, pursuits.
1. Jargon and processing fluency
Jargon refers to specialized, technical vocabulary terms that are associated with a situational con-
text or purpose and are rarely used outside of these particular circumstances (Sharon and Baram-
Tsabari, 2014). Jargon is often used to demonstrate expertise, convey idiosyncratic knowledge, or
reference highly particularized ideas (Grupp and Heider, 1975). In addition to being technical,
jargon is also used primarily by members of a particular group or trade, such as scientists, lawyers,
or medical professionals, and is less frequently used or understood by individuals who fall outside
of these groups (Sharon and Baram-Tsabari, 2014). Research examining the problematic impact of
jargon (Grupp and Heider, 1975; Sharon and Baram-Tsabari, 2014) theorizes that negative effects
are observed because non-experts are unable to fully comprehend jargon-laden information due to
their lack of understanding. Here, we offer and test an additional explanation guided by metacogni-
tion and the feelings associated with information processing. We argue that in addition to jargon
impairing people’s ability to comprehend information, the presence of jargon may also affect the
difficulty with which people process information. By understanding why message features produce
undesirable outcomes, future efforts can utilize this information to reduce communication barriers
to scientific engagement (e.g. Mellor, 2018).
Research from social psychology in metacognition theorizes about how one’s subjective experi-
ence with information processing can affect judgments and decision-making (Petty et al., 2007;
Schwarz, 2015). Metacognition can be defined as people’s perceptions of, or experiences with,
their own thought processes (Schwarz, 2010). The specific type of metacognitive experience stud-
ied here, called processing fluency, refers to the ease or difficulty with which new information is
processed (Schwarz, 2010). Processing fluency is associated with feelings of ease, speed, and
familiarity during information processing and is hedonically marked such that an easy processing
experience is associated with positive feelings (Schwarz, 2006), while a difficult processing expe-
rience is associated with negative feelings (Schwarz, 2010). Here, we test whether the presence or
absence of jargon produces variance in how easily people are able to process complex scientific
information and whether this variance affects perceptions of new scientific technologies.
Prior research has found that language difficulty can influence processing fluency (Shulman
and Sweitzer, 2018a, 2018b). Specifically, the use of more challenging words significantly impairs
processing fluency relative to easier language. This study extends this work by testing this notion
with jargon. Namely, we expect that scientific information that includes jargon should be more
difficult to process than ordinary terminology. Moreover, if this difference is attributable to pro-
cessing fluency, as opposed to comprehension alone, then this difference should persist even when
jargon words are defined. If this is the case, then we expect the following result:
H1: Participants in the jargon condition will report lower levels of processing fluency than par-
ticipants in the no-jargon frame condition, even when jargon definitions are provided.
2. Processing fluency and resistance to persuasion
The expected negative relationship between jargon and processing fluency suggests that support,
an outcome pertinent to scientists, may be impacted by metacognition as well. Research suggests
that processing fluency is hedonically marked such that when fluency is experienced as easy,
Bullock et al. 3
positive affective responses occur, such as feelings of knowing (Schwartz and Metcalfe, 1994),
safety (Song and Schwarz, 2009), liking (Dragojevic and Giles, 2016), interest, and efficacy
(Shulman and Sweitzer, 2018a, 2018b). These positive responses evoke the naïve theory (Schwarz,
2010) that if something feels good, it must be safe and familiar. Taken together, under conditions
of easier processing, individuals are less motivated to seek out or consider additional information
in order to be persuaded (Briñol et al., 2013). A difficult processing experience, however, is associ-
ated with unfamiliarity, which leads to negative outcomes such as uncertainty (Nelson et al., 1998),
risk (Song and Schwarz, 2009), and a lack of confidence, liking, and knowledge perceptions
(Shulman and Sweitzer, 2018a, 2018b). As such, metacognitive experiences of difficulty produce
scrutiny (Briñol and Petty, 2004) as individuals feel a greater need to seek out more information in
order to render a valid judgment (Briñol et al., 2013).
The skepticism and scrutiny associated with disfluent processing has implications for why indi-
viduals may resist scientific information. MRP refers to a person’s motivation to oppose, or resist,
perceived efforts to change existing attitudes (Nisbet et al., 2015). MRP is conceptualized as a
combination of two experiences: (1) counterarguing, which reflects the generation of thoughts that
undermine a message’s persuasiveness and credibility, and (2) reactance, which refers to an oppo-
sitional response that arises from a message that is perceived to be threatening (Moyer-Gusé and
Nabi, 2010). This experiment uniquely integrates processing fluency with MRP to consider whether
the heightened scrutiny that extends from a disfluent experience will lead people to resist the sci-
entific information presented. Guided by research in metacognition (e.g. Schwarz, 2010), we
expect that participants will misattribute negative affect from their difficult processing experience
toward the subject under investigation. If this is the case, participants should be more likely to
discredit scientific information following a disfluent processing experience evoked by jargon. This
leads to the second hypothesis:
H2: Processing fluency will mediate the relationship between exposure to jargon and motivated
resistance to persuasion.
3. Risk perceptions and support
The notion that jargon will compel a difficult processing experience and increase MRP suggests
that people’s endorsement of the scientific technologies presented should be affected by these
processes as well. When new technologies are introduced to the public, two outcomes become
important for public acceptance: (1) the risk posed by these new technologies and (2) support for
adopting these technologies. When people encounter something for the first time, a natural
response is skepticism stemming from unfamiliarity (Song and Schwarz, 2009). Thus, scientists
who need to communicate new findings must overcome a well-established cognitive obstacle—
things that are new feel unsafe (Song and Schwarz, 2009). Here, we extend this idea to test
whether communication strategies, such as the inclusion or exclusion of jargon and the process-
ing fluency evoked by this manipulation, can improve or degrade people’s responses to new
information via MRP. If this is the case, then it stands to reason that variance in MRP should
affect risk perceptions such that higher message resistance should lead to higher risk percep-
tions. This logic is reflected in the third hypothesis:
H3: Jargon will indirectly influence perceptions of risk through multiple mediators of process-
ing fluency and motivated resistance to persuasion.
4 Public Understanding of Science 00(0)
The second persuasion-related outcome, support for, or willingness to adopt, these technolo-
gies, should also be affected by participants’ response to the scientific information presented. If the
MRP scale functions as intended, then those who report higher scores on this scale should also be
less likely to support, or adopt, the technologies in question. This claim contributes to prior research
by stating that this relationship is expected based on the presence or absence of jargon and the
subsequent information processing experience induced from this manipulation. This leads to our
final hypothesis:
H4: Jargon condition will indirectly influence support through multiple mediators of processing
fluency and motivated resistance to persuasion.
4. Method
Participants
Participants were recruited from Qualtrics’ online general population panel in the United States
(N = 650).1 The sample was 62% female, and participants ranged in age from 18 to 80 (M = 44.04;
SD = 16.19) years. The racial breakdown of the sample was 74.2% White; 12.6% African American
or African; 7.1% Latino; 2.8% Asian; 1.8% American Indian or Alaska Native; 0.3% Native
Hawaiian or Pacific Islander; and 0.9% mixed.
Procedure
Participants were randomly assigned to condition in a 2 (jargon vs no-jargon) × 2 (definitions vs
no-definitions) between-subjects experimental design. All participants read three paragraphs about
three different emerging scientific technologies: self-driving cars, surgical robots, and three-
dimensional bioprinting. Three topics were chosen based on a message sampling approach, which
ensures that findings are not unique to specific messages and are therefore more generalizable to
other contexts (Jackson and Jacobs, 1983). For each of the three paragraphs, presentation order and
condition assignment were held constant. Topic paragraphs were held on-screen for at least 4 sec-
onds in an effort to ensure that individuals read the information presented. Processing fluency and
risk were assessed after each message in order to capture participants’ immediate information
processing experience and risk perceptions. This sequence was repeated for the second and third
topics. After exposure to all three paragraphs, participants responded to scales measuring MRP and
support. The survey took about 20 minutes to complete (M = 21.45, SD = 17.41), and participants
were paid through Qualtrics.
Stimuli
Before creating each experimental condition, information about the selected topics was obtained
from credible science and technology sources (for details, see Supplementary Materials). This
information was used to create three-sentence paragraphs about each scientific technology,
where the first sentence provided context, the second described how it worked, and the third
described possible risks (Supplementary Appendix A). In the jargon condition (n = 328), 10
jargon terms were included in each paragraph. In the no-jargon condition (n = 312), jargon was
replaced by short explanations using simpler synonyms. Jargon was operationalized through
terms that were technical or scientific, including descriptions of technologies, minerals, or
Bullock et al. 5
chemicals, as well as acronyms. Acronyms were replaced with their full form in the no-jargon
condition.
To control for comprehension, participants were randomly assigned to a definitions condition
(n = 323) or a no-definitions condition (n = 317). Definitions were provided using a mouseover
text feature. In this condition, participants were told they could scroll over underlined terms (jar-
gon) to receive their definition. The definition provided was identical to the language in the no-
jargon condition. Word count was held constant across topic and condition.
Measures
All items were measured using seven-point Likert-type scales wherein higher scores reflect
stronger agreement with the concept (full scales available in the Supplementary Materials).
Processing fluency. After exposure to each paragraph, participants responded to a five-item measure
assessing processing fluency (Shulman and Sweitzer, 2018a, 2018b). The scale included items such
as “A lot of the terms felt familiar to me.” To account for fluency across topics, the five items were
averaged across the three topics to form a 15-item scale, with higher scores reflective of an easier
processing experience (M = 4.92, SD = 1.07, α = .90).
MRP. MRP was measured using an eight-item scale (Nisbet et al., 2015). Items included “The sci-
entific messages tried to pressure me to think a certain way” and “The scientific messages were not
very credible” (M = 2.96, SD = 0.95, α = .84).
Risk. Risk was measured following exposure to each topic paragraph. Three-scale items were pre-
sented after each topic for a total of nine measures (M = 3.52, SD = 1.26, α = .89). An example
item includes “[self-driving cars/surgical robots/3-D bioprinting] pose a serious threat to human
safety” (Kahan et al., 2012).
Support. Support was measured using a 15-item scale that assessed support for adopting each tech-
nology. A sample item includes “Self-driving cars can solve transportation problems” (M = 4.25,
SD = 1.09, α = .91).
5. Results
Hypothesis 1 predicted that jargon condition assignment would affect reports of processing fluency
independent of definition condition. To test this hypothesis, a two-way analysis of variance
(ANOVA) was conducted. As predicted, there was a significant main effect for jargon, F(1, 636) =
76.03, p < .001, η2 = .11, such that those in the jargon condition (M = 4.57, SD = 1.11) reported
significantly lower processing fluency than those in the no-jargon condition (M = 5.27, SD =
0.90). In addition, consistent with expectations, there was not a significant main effect for defini-
tion condition, F(1, 636) = 0.37, p = .543, η2 = .0005, nor a significant interaction effect,
F(1, 636) = 0.17, p = .678, η2 = .0002. Although the manipulations of jargon use and definitions
appear to be operating independently, to isolate the effect of jargon, the definition condition was
used as a covariate for all remaining analyses.
Hypothesis 2 predicted that processing fluency would mediate the relationship between jargon
condition and MRP. This hypothesis was tested using the mediation model from Hayes’ (2013)
macro PROCESS (Model 4, 95% bias-corrected bootstrap confidence intervals (CIs) based on
10,000 resamples). As expected, significant indirect effects were obtained in the predicted
6 Public Understanding of Science 00(0)
direction, B = −.21, SE = .03, 95% CI = [−.28, −.15], such that the no-jargon condition was
associated with greater processing fluency, B = .70, SE = .08, t = 8.71, p < .001, which, in turn,
reduced MRP, B = −.29, SE = .04, t = 8.45, p < .001. In total, this model explained 10% of the
variance, indicative of a medium-to-large effect (Cohen, 1992). Thus, H2 was supported, even
when controlling for the effect of definitions on MRP, B = −.10, SE = .07, t = −1.41, p = .159.
Hypothesis 3 predicted that the presence of jargon would indirectly influence perceptions of risk
through the multiple mediators of processing fluency and MRP. This hypothesis was tested using
Hayes’ (2013) serial mediation model with two mediators, Model 6, 95% bias-corrected bootstrap
CIs based on 10,000 resamples. Figure 1 represents this model and includes labels that correspond
with each of the paths estimated in Table 1. In support of H3, the indirect effect was significant,
B = −.11, SE = .02, 95% CI = [−.15, −.07], and explained 20% of the variance in risk, which is a
large effect (Cohen, 1992). Once again, the covariate of definition condition never reached statisti-
cal significance (−1.34 < t’s < −0.86).
Finally, H4 predicted that the presence of jargon would indirectly influence support for emerg-
ing science technologies through the multiple mediators of processing fluency and MRP. The same
serial mediation model from H3 (Figure 1) was used to test H4, with support as the outcome meas-
ure (see Table 1). As expected, the indirect effect of jargon on support through processing fluency
and MRP was significant, B = .08, SE = .02, 95% CI = [.06, .12]. Unlike other models, definition
condition was found to be a significant predictor of support, B = .22, SE = .08, t = 2.79, p < .05.
Nevertheless, despite this finding, all relationships consistent with H4 were supported and
explained 21% of the variance in technology support, which is a large effect (Cohen, 1992).
6. Discussion
This study examined the effect of jargon and processing fluency on individuals’ resistance to per-
suasion, perceptions of risk, and willingness to support three different science technologies.
Understanding how jargon impacts audiences has become particularly important amid concerns
about a growing communication gap between scientific communities and the public. Here, we find
support for the extant practical and scholarly recommendation that scientists reduce their jargon
use but build on what is already known in several ways. First, we extend existing literature that
recognizes how easy language can evoke engagement with science information (Scharrer et al.,
2017) by offering processing fluency as another mechanism that explains these effects. Second, we
believe that these findings generalize to other contexts where language difficulty has been found to
alter judgments and decision-making, including politics and policy preferences (Carpenter and
Boster, 2013; Goldberg and Carmichael, 2017; Sweitzer and Shulman, 2018).
We find that using jargon significantly disrupts processing fluency, in addition to and separate
of comprehension. Furthermore, this reduction in processing fluency increases MRP,
Figure 1. Model 6 from Hayes’ (2013) PROCESS along with path labels that correspond with Table 1,
wherein indirect effects are calculated as the product of paths 1, 2, and 3.
Bullock et al. 7
risk perceptions, and reduces overall support. Because science communication often serves to
introduce scientific advancements to non-scientific audiences, these results suggest that initial
messaging should strive to facilitate an easy processing experience and eliminate jargon where
possible. In addition to this recommendation, the insight offered here extends to other commu-
nication techniques that also might impair processing fluency. This could include complex
graphs, branding that includes acronyms, the offering of unintuitive data, or highly technical
evidence, to provide just a few examples (see Shulman and Bullock, 2019). More broadly, we
recommend that scholars not only consider information and comprehension in their communica-
tion to the public but also think about how message presentation may inadvertently impair infor-
mation processing.
Despite these findings, there were methodological limitations of this study. First, we used an
online experiment with a non-representative sample, thus limiting the generalizability of our
findings. Second, these messages were free of any images, source cues, or context. The absence
of these features hampers the ecological validity of our results, even though the information
presented was obtained from real science communication sources. Finally, we asked participants
to view three messages, rather than one. We chose to do this to increase the generalizability of
our findings beyond any one science topic but recognize that these messages may have been dif-
ferentially effective.
Theoretically, it is important to acknowledge that we did not directly measure comprehen-
sion. Our goal was to hold comprehension constant by including the same information across
conditions. Furthermore, the manner in which we held information constant—through the use
of mouseover text—introduced the behavior of searching for definitions if desired. Additional
research should consider alternative strategies for capturing changes in comprehension without
altering information presentation or adding a behavioral, and possibly affective, component.
Finally, we presented a serial mediation model despite using cross-sectional data. Because
the measure of our dependent variables lacked a temporal element, we cannot be sure that we
find causal effects between processing fluency, MRP, risk, and support. Nonetheless, we believe
that the model presented has strong theoretical support and practical implications for science
communicators.
In sum, this experiment provides evidence for the negative effects of jargon use on lay audi-
ences. Our results imply that minimizing jargon within science communication should reduce
resistance to persuasion and risk perceptions, and ultimately increase support. Future research
Table 1. Results from the serial mediation analyses for hypotheses 3 and 4.
Outcomes Path 1
B (SE)
Path 2
B (SE)
Path 3
B (SE)
R2Indirect effect
B (SE)
95% CI
[LL, UL]
H3
Risk perceptions .73 (.08)*** −.30 (.04)*** .51 (.05)*** .20 –.11 (.02) [–.15, –.07]
H4
Support .70 (.08)*** –.31 (.04)*** –.39 (.04)*** .21 .08 (.02) [.06, .12]
CI: confidence interval; LL: lower limit; UL: upper limit.
Path 1 denotes the path coefficient between the jargon condition (0: jargon, 1: no-jargon) and processing fluency. Path
2 denotes the relationship between processing fluency (higher scores = easier experience) and motivated resistance to
persuasion. Path 3 indicates the relationship between motivated resistance to persuasion and outcomes (Figure 1). All
models were run using Model 6 (Hayes’ (2013) 95% bias-corrected bootstrap CIs based on 10,000 resamples), with defi-
nition condition as a covariate. Non-zero indirect effects indicate support for the serial mediation model hypothesized.
*p < .05; **p < .01; ***p < .001.
8 Public Understanding of Science 00(0)
should explore the effects of jargon, or other forms of language that may affect processing fluency,
with the hopes of ultimately enabling communicators to craft more effective appeals.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iD
Olivia M. Bullock https://orcid.org/0000-0001-5403-7149
Supplemental material
Supplemental material for this article is available online.
Note
1. This dataset is used in another paper that also considers the effects of jargon on metacognition. However,
that paper examines this topic with a different theoretical framework and outcome measures that are not
reported here.
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Author biographies
Olivia M. Bullock (BA, American University) is a PhD student in the School of Communication at the Ohio
State University. Her research focuses on message design strategies that can reduce ideologically motivated
processing of science, health, and political information.
Daniel Colón Amill (BA, University of Puerto Rico) is a Master’s student in the School of Communication at
the Ohio State University. His research interests include understanding how people process political informa-
tion, particularly in online spaces.
Hillary C. Shulman (PhD, Michigan State University) is an assistant professor in the School of Communication
at the Ohio State University. Her research interests include understanding how message design can improve
information processing in the areas of politics, health, and science.
Graham Dixon (PhD, Cornell University) is an assistant professor in the School of Communication at the
Ohio State University. His research interests include science and risk communication.