Discussion is more convincing than standard, unidirectional messaging, but its interactive nature makes it difficult to scale up. We created a chatbot to emulate the most important traits of discussion. A simple argument pointing out the existence of a scientific consensus on the safety of genetically modified organisms (GMOs) already led to more positive attitudes towards GMOs, compared with a control message. Providing participants with good arguments rebutting the most common counterarguments against GMOs led to much more positive attitudes towards GMOs, whether the participants could immediately see all the arguments or could select the most relevant arguments in a chatbot. Participants holding the most negative attitudes displayed more attitude change in favour of GMOs. Participants updated their beliefs when presented with good arguments, but we found no evidence that an interactive chatbot proves more persuasive than a list of arguments and counterarguments.
In many domains—from the safety of vaccination to the reality of anthropogenic climate change—there is a gap between the scientific consensus and public opinion1. The persistence of this gap in spite of numerous information campaigns shows how hard it is to bridge. It has even been suggested that information campaigns backfire, either by addressing audiences with strong pre-existing views2,3 or by attempting to present too many arguments4,5.
Fortunately, it appears that, in most cases, good arguments do change people’s mind in the expected direction6,7. Still, the effects of short arguments aimed at large and diverse audiences, even if they are positive, are typically small8,9,10. By contrast, when people can exchange arguments face to face, more ample changes of mind regularly occur. Compare, for example, how people react to simple logical arguments. On the one hand, when participants are provided with a good argument for the correct answer to a logical problem, a substantial minority fails to change their mind11,12. On the other hand, when participants tackle the same problems in groups, nearly everyone discussing with a participant defending the correct answer changes their mind11,12,13. More generally, argumentation has been shown, in a variety of domains, to allow people to change their minds and adopt the best answers available in the group13,14,15,16,17. Even on contested issues, discussions with politicians18, canvassers19 or scientists20,21 can lead to changes of mind that are significant, durable19 and larger than those observed with standard messages18,21.
Mercier and Sperber17 have suggested that the power of interactive argumentation, in contrast to the presentation of a simple argument, to change minds stems largely from the opportunity that discussion affords to address the discussants’ counterarguments. In the course of a conversation, people can raise counterarguments as they wish, the counterarguments can be rebutted, the rebuttals contested and so forth22. When people are presented with challenging arguments in a one-sided manner, as in typical messaging campaigns, they also generate counterarguments.23,24,25 However, these counterarguments remain unaddressed. Arguably, the production of counterarguments that remain unaddressed is not only why standard information campaigns are not very effective but also why they sometimes backfire26.
In a discussion, not only can all counterarguments be potentially addressed but only the relevant counterarguments are addressed. Different people have different reasons to disagree with any given argument. Attempting to address all the existing counterarguments should lead to the production of many irrelevant rebuttals, potentially diluting the efficacy of the relevant rebuttals. This might be why, in a normal conversation, we do not attempt to lay out all the arguments for our side immediately, waiting instead for our interlocutor’s feedback to select the most relevant counterarguments to address27.
Unfortunately, discussion does not scale up well—indeed, it is most natural in groups of at most five people28,29. Here, we developed and tested two ways of scaling up discussion. The first consisted in gathering the most common counterarguments for a given issue and a given population and creating a message that rebuts the most common counterarguments as well as the responses to the rebuttals, as would happen in a conversation. The issue, then, is that many—potentially most—of these counterarguments are likely to be irrelevant for most of the audience. As a result, we developed a second way of scaling up discussion by using a chatbot in which participants could select which counterarguments they endorse and only see the rebuttals to these counterarguments. Studies on argumentation using chatbots or similar automated computer-based conversational agents suggest that they can be useful to change people’s mind30,31 and that asking users what they are concerned about increases chatbots’ efficacy by providing users with more relevant counterarguments32.
In the remaining of the introduction, we present the topic we have chosen to test our methods for scaling up discussion, as well as the design of the experiment, and how the different conditions were constructed. Finally, specific hypotheses are introduced.
We choose genetically modified organisms (GMOs) and genetically modified (GM) food as a topic for our experiment because, despite the broad scientific consensus on GM food safety for human health33,34,35,36,37,38,39, public opinion remains, in many countries, staunchly opposed to GM food and GMOs more generally40,41,42,43. In the United States, it is the topic on which discrepancy between scientists and laypeople’s opinion is highest1. In France, where the pilot study was conducted (see Pilot data section and Pilot study section in Supplementary information), rejection of GMO is pervasive.40 Indeed, 84% of the public thinks that GM food is highly or moderately dangerous44, and 79% of the population is worried that some GMO may be present in their daily diet45. In the United Kingdom, where the pre-registered study was conducted, rejection of GMO is common. There, 45% of the public thinks that GM food is dangerous (while only 25% think that it is not)40, and 58% of the public does not want to eat this type of food (while only 24% wants to)40. On the whole, British people appear to be largely un-persuaded by the benefits of GMOs46,47,48. The gap between the scientific consensus and public opinion on GMOs is all the more problematic since GM food and GMOs more generally can not only improve health and food security but also help fight climate change37,49,50.
Our goal was thus to test whether rebutting participants’ counterarguments against GMOs will lead them to change their mind on this topic. To properly evaluate the efficiency of this intervention, we used the following four conditions.
First, as a control condition, we provided participants with a sentence describing what GMOs are. Given that no persuasion should take place in this condition, any attitude change (measured as the difference between the pre- and post-intervention attitudes) would reflect task demands and can thus be used as a baseline against which to compare attitude change in the other conditions.
Second, we compared our interventions with one of the most common techniques used to bridge the gap between scientific consensus and public opinion, that is, informing the public of the existence and strength of the scientific consensus (the so-called gateway belief model). Some studies using this gateway belief model have proven it to be effective at reducing the gap between public opinion and the scientific consensus on a variety of topics51,52,53,54,55,56 (although see refs. 8,9). This consensus condition allowed us to tell whether our interventions could improve attitude change by comparison with a popular messaging strategy.
Third, in the counterarguments condition, participants were provided with a series of counterarguments against GMOs, rebuttals against these counterarguments, counterarguments of these rebuttals and so forth (for at most four steps; how these arguments were created is described in the Design section). One of these counterarguments mentions the existence and strength of the scientific consensus, as in the consensus condition. Comparing the attitude change obtained in the consensus and counterarguments conditions allowed us to test whether countering participants’ arguments, instead of only presenting a forceful argument, was more effective at changing people’s mind.
Fourth, in the chatbot condition, participants could read exactly the same materials as in the counterarguments condition, but through a chatbot, enabling them to easily access the most relevant, and only the most relevant, rebuttals to their counterarguments (the workings of the chatbot are detailed in the Design section; see Fig. 1 for a visualization of the chatbot’s interface). Comparing the changes of minds obtained in the chatbot and the counterarguments condition allowed us to test whether presenting participants only with the rebuttals that are most relevant for them leads to more ample changes of mind.
Comparison of these four conditions allowed us to tell whether (i) any of these interventions resulted in attitude change, (ii) whether the attitude change was larger when arguments were provided (that is in the consensus, counterarguments and chatbot conditions), (iii) whether any argument-driven attitude change was larger when rebuttals to counterarguments were provided (counterarguments and chatbot conditions) and (iv) whether any rebuttal-driven attitude change was larger when only relevant rebuttals were provided (chatbot condition).
On the basis of the literature reviewed above, we derived the following hypotheses. First, the literature on the gateway belief model, on the importance of addressing counterarguments and on the importance of only addressing relevant counterarguments, led to the following hypotheses:
H1: Participants will hold more positive attitudes towards GMOs after the experimental task in the consensus condition than in the control condition, controlling for their initial attitudes towards GMOs.
H2: Participants will hold more positive attitudes towards GMOs after the experimental task in the counterarguments condition than in the control condition, controlling for their initial attitudes towards GMOs.
H3: Participants will hold more positive attitudes towards GMOs after the experimental task in the chatbot condition than in the control condition, controlling for their initial attitudes towards GMOs.
H4: Participants will hold more positive attitudes towards GMOs after the experimental task in the counterarguments condition than in the consensus condition, controlling for their initial attitudes towards GMOs.
H5: Participants will hold more positive attitudes towards GMOs after the experimental task in the chatbot condition than in the counterarguments condition, controlling for their initial attitudes towards GMOs.
H6: In the chatbot condition, the number of arguments explored by participants will predict holding more positive attitudes towards GMOs after the experimental task, controlling for their initial attitudes towards GMOs (that is, exploring more arguments should lead to more positive attitude change).
H7: In the chatbot condition, time spent on the task should lead to more positive attitudes towards GMOs after the experimental task than time spent on the counterarguments condition, controlling for initial attitudes towards GMOs.
Participants were given the opportunity to read many more arguments in the counterarguments condition and in the chatbot condition than in the consensus condition. Models of attitude change—such as the elaboration likelihood model57—suggest that participants might use the number of arguments as a low-level cue that they should change their minds, at least when they are not motivated to process the arguments in any depth58. However, it has also been argued that presenting people with too many arguments—even good ones—might make a message less persuasive if the misinformation that the arguments aim to correct is simpler and more appealing5, so that more is not necessarily best when it comes to the number of arguments provided. Still, if H6 and H7 proved true, it could be argued that participants use a low-level heuristic in which they are convinced by the sheer number of arguments instead of the content of the arguments. If people use the number of arguments in this manner, it should affect their overall attitudes towards GMOs. By contrast, if people pay attention to the content of the arguments, the arguments should only change the participants’ mind on the specific topic they bear upon, leading us to the following hypothesis:
H8: In the chatbot condition, participants will hold more positive attitudes after the experimental task on issues for which they have explored more of the rebuttals related to the issue, controlling for their initial attitudes on the issues, the type of issue and the total (that is, related to the issue or not) number of arguments explored.
Finally, given that the backfire effect has been observed in several experiments2,4,59,60,61 but has rarely, or not at all, been observed in several large-scale studies6,7,62,63,64, we formulated the following hypothesis:
H9: H1, H2 and H3 also hold true among the third of the participants initially holding the most negative attitudes about GMOs. (Note that this criterion is more stringent than an absence of backfire effect, as it claims that there will be a positive effect even among participants with the most negative initial attitudes.)
Although it is methodologically impossible to completely disentangle the effects of the mode of presentation (for example, degree of interactivity) and of the specific information presented, the present experiment provides a test of whether addressing people’s counterarguments, in particular by using an interactive chatbot, results in attitude changes that are larger than those obtained with a common messaging technique. From a theoretical point of view, these results help us better understand the process of attitude change, potentially highlighting its rationality. If people are sensitive to the rebuttals of their counterarguments, it suggests that their rejection of the initial argument was not driven by sheer pigheadedness but by having unanswered counterarguments. From an applied point of view, positive results would provide an efficient and easy-to-use tool to help science communicators bridge the gap between scientific consensus and public opinion.
In the control condition, participants read a sentence describing what GMOs are. In the consensus condition, they read a paragraph on the scientific consensus on the safety of GMOs. In the counterarguments condition, they were exposed to the most common counterarguments against GMOs, together with their rebuttal. In the chatbot condition, they were exposed to the same arguments as in the counterarguments condition but through a chatbot (that is, instead of scrolling, they had to click to make the arguments appear). The effect of the treatments on participants’ attitudes towards GMOs is depicted in Figs. 2 and 3.
In line with H1, participants held more positive attitudes towards GMOs after the treatment in the consensus condition than in the control condition (b = 0.37 [0.23, 0.51], t(1,144) = 5.36, P < 0.001, Bayes factor (BF10) = 104). In other words, being exposed to the scientific consensus led to a 0.37 point (on a 7-point scale) move towards a pro-GMO opinion.
In line with H2, participants held more positive attitudes towards GMOs after the treatment in the counterarguments condition than in the control condition (b = 0.99 [0.86, 1.12], t(1,144) = 14.65, P < 0.001, BF10 = 1041). That is, scrolling through the chatbot’s arguments led to a 0.99-point move towards pro-GMO opinion.
In line with H3, participants held more positive attitudes towards GMOs after the treatment in the chatbot condition than in the control condition (b = 0.77 [0.63, 0.90], t(1,144) = 11.38, P < 0.001, BF10 = 1025). In sum, interacting with the chatbot by clicking on the arguments led to 0.77-point move towards pro-GMO opinion.
In line with H4, participants held more positive attitudes towards GMOs after the treatment in the counterarguments condition than in the consensus condition (b = 0.62 [0.49, 0.75], t(1,144) = 9.15, P < 0.001, BF10 = 1016). In other words, scrolling through the chatbot’s arguments led to a 0.62-point move towards pro-GMO opinion compared with exposure to the scientific consensus.
Contrary to H5, participants held more positive attitudes towards GMOs after the treatment in the counterarguments condition than in the chatbot condition (b = 0.22 [0.09, 0.35], t(1,144) = 3.37, P < 0.001, BF10 = 15.32). That is, scrolling through the chatbot’s arguments led to a 0.22-point move towards pro-GMO opinion compared with interacting with the chatbot by clicking on the arguments.
In line with H6, the number of arguments explored by participants in the chatbot condition predicted holding more positive attitudes towards GMOs after the treatment (b = 0.04 [0.02, 0.06], t(299) = 4.09, P < 0.001, BF10 = 14.41). That is, each additional argument that participants explored led to a 0.04-point move towards pro-GMO opinion.
Contrary to H7, time spent in the chatbot condition did not lead to significantly more positive attitudes towards GMOs after the treatment than time spent on the counterarguments condition (b = 0.004 [−0.05, 0.04], t(596) = 0.20, P = 0.84, BF10 = 0.08). This effect is negligible as the 90% CI [−0.08, 0.06] falls inside the pre-registered [−0.1, 0.1] interval corresponding to an effect smaller than β = 0.1. Figure 4 offers a visual representation of the interaction. In both conditions, time spent on the task led to more positive attitudes towards GMOs (b = 0.07 [0.05, 0.09], t(596) = 7.54, P < 0.001, BF10 = 1012; chatbot condition: b = 0.07 [0.03, 0.10], t(299) = 3.36, P < 0.001, BF10 = 19; counterarguments condition: b = 0.07 [0.05, 0.09], t(296) = 7.35, P < 0.001, BF10 = 109). In other words, the longer participants spent on the chatbot’s interface, the more they developed pro-GMO opinions.
Contrary to H8, participants did not hold significantly more positive attitudes after the treatment on issues for which they had explored more of the related rebuttals (b = 0.006 [−0.02, 0.04], t(898.9) = 0.09, P = 0.77, BF10 = 0.09). This effect is negligible as the 90% CI [−0.04, 0.07] falls inside the pre-registered [−0.1, 0.1] interval corresponding to an effect smaller than β = 0.1. For instance, participants exploring more arguments related to GM food safety did not develop more pro-GMO opinions on GM food safety compared with other issues.
In line with H9, H1–3 held true among the third of the participants initially holding the most negative attitudes towards GMOs (chatbot condition: b = 0.93 [0.67, 1.20], t(376) = 6.92, P < 0.001, BF10 = 108; counterarguments condition: b = 1.35 [1.09, 1.62], t(376) = 9.96, P < 0.001, BF10 = 1017; consensus condition: b = 0.47 [0.29, 0.75], t(376) = 3.33, P < 0.001, BF10 = 24). That is, being exposed to the consensus, and scrolling or clicking through the chatbot’s arguments, led to more pro-GMO opinions even among the third of participants initially holding the most anti-GMO opinions.
To assess whether time spent on the task might explain the greater impact of the counterarguments condition compared with the chatbot condition, we tested whether participants had spent more time in the former than the latter, revealing that they had (counterarguments condition, M = 5.74 min, s.d. 5.63 min; chatbot condition, M = 3.70 min, s.d. 2.56 min; t(415.48) = 5.73, P < 0.001). In a regression model without time as predictor, condition (chatbot versus counterarguments) was a significant predictor of attitude change (b = 0.23 [0.07, 0.38], t(599) = 2.83, P = 0.008), but when adding time spent in the model, the effect of condition was not significant anymore (b = 0.08 [−0.07, 0.23], t(598) = 1.01, P = 0.37), whereas the effect of time was (b = 0.07 [0.05, 0.09], t(598) = 8.29, P < 0.001). Then, a mediation analysis (non-parametric bootstrap confidence intervals with the percentile method65) suggested that 65% of the effect of condition was mediated by time ([0.37, 1.84], P = 0.004), with an indirect effect via the time mediator estimated to be b = 0.15 [0.10, 0.21] (P < 0.001). However, this should not be taken as proof of causality because non-observed variables could create (or inflate) the correlation observed between time and attitude change66. Nevertheless, time remains a credible mediator since it plausibly plays a role in attitude change, and more time spent reading the arguments might translate into greater attitude change.
To investigate H9 further, we examined the relationship between participants’ initial attitudes, and attitude change. More precisely we tested the interaction between participants’ initial attitudes and the experimental condition (with the control condition as baseline) on attitude change. By contrast with H9, here all the participants are included in the analysis. We found that, compared with the control condition, participants initially holding more negative attitudes displayed more attitude change in favour of GMOs in the counterarguments condition (b = 0.30 [0.17, 0.44], t(1,144) = 4.47, P < 0.001) and in the chatbot condition (b = 0.19 [0.06, 0.33], t(1,144) = 2.81, P = 0.008), but only marginally in the consensus condition (b = 0.14 [0.006, 0.28], t(1,144) = 2.05, P = 0.06).
We also found that H1–3 held true for each question of the GMOs attitudes scale: participants deemed GM food to be safer to eat, and less bad for the environment, reported being less worried about the socio-economic impacts of GMOs and perceived GMOs as more useful after the treatment in the consensus condition, counterarguments condition and chatbot condition compared with the control condition (see Exploratory analyses section in Supplementary information).
Finally, for three out of the four main arguments on GMOs, the best predictor of whether a participant clicked on a given argument in the chatbot was how negative their initial attitudes were regarding that argument (see Exploratory analyses section in Supplementary information). This suggests that participants selected arguments that addressed their concerns, instead of arguments that might have reinforced their priors.
We investigated whether addressing many of participants’ arguments against GMOs would result in significant changes of mind on that issue. First, despite previous failures to apply the gateway belief model to GMOs8,9, we found that a simple argument pointing out the existence of a scientific consensus on the safety of GMOs led to more positive attitudes towards GMOs (β = 0.32). Second, we found that addressing many of the participants’ arguments against GMOs led to much more positive attitudes towards GMOs (counterarguments condition, β = 0.85; chatbot condition, β = 0.66). These effect sizes compare very favourably with those observed in other interventions, such as refs. 8,9,10,62,67,68,69. After reading the rebuttals against criticisms of GMOs, a large number of participants adopted strongly pro-GMOs views: the number of participants with an average score of at least 5 (on the 1–7 attitude scale) went from 104 to 299 (out of 601), and the number of participants with an average score of at least 6 went from 15 to 107 (out of 601). A subsequent study found similar results in another domain: a chatbot aimed at alleviating concerns about coronavirus disease 2019 (COVID-19) vaccines generated more positive attitudes towards these vaccines, and higher intentions to vaccinate69.
Our results reveal that participants changed their mind more as they spent more time reading counterarguments, and tended to spend more time when all the counterarguments were available (counterarguments condition) than when they were offered the possibility of only selecting the most relevant counterarguments (chatbot condition). Moreover, being exposed only to the counterarguments that participants had selected, by contrast with all the counterarguments, did not make the counterarguments more efficient. It is possible that participants used the sheer number of arguments presented as a cue to change their mind. It is also plausible that, in the case at hand, all the counterarguments presented to the participants were sufficiently relevant that none detracted from the persuasiveness of the whole set, or that participants selected the most relevant via scrolling, and that this selection was more efficient than via clicking. If this is the case, then the main reason for the increased efficiency of the chatbot (controlling for time spent), that is, that people avoid reading irrelevant arguments, disappears.
This finding has practical consequences: given the available evidence, it is probably best to give chatbot users the option to scroll through the arguments instead of clicking on them, as in our counterarguments condition. In the chatbot that we used to inform French people about COVID-19 vaccines, we gave users the possibility of scrolling through the arguments instead of clicking on them69. In that study, users selecting the non-interactive chatbot did so as a complement of the interactive chatbot.
In line with a growing body of literature7,70, we found no evidence that participants initially holding more negative attitudes towards GMOs held even more negative attitudes towards GMOs after having been exposed to arguments in favour of GMOs. Instead, we found the opposite pattern: participants initially holding more negative attitudes displayed more attitude change in favour of GMOs. Similar evidence suggests that corrections work best on people who are the most misinformed71,72,73 and that, in general, those whose attitudes were initially furthest from the facts changed their minds the most towards the facts20,69.
These results are good news for science communicators, showing that participants can be convinced by good, well-supported arguments. Moreover, the initially very negative attitudes of some participants did not prove an obstacle to changing their mind. This should encourage science communicators to discuss heated topics with the public, even with those furthest away from the scientific consensus (see also refs. 20,62).
Our study has some important limitations. First, we do not know the impact our chatbot would have in more naturalistic settings. In the present experiments, participants were paid to interact with the chatbot. How many people would spontaneously want to spend five minutes learning about GMOs without being paid? And would people opposed to GMOs be less likely to interact with it? Second, all our dependent variables are declarative, and attitudes do not always translate into behaviours. For instance, we do not know whether the chatbot will increase people’s willingness to buy and consume GM products. Third, our sample of participants is not representative of the general population. The chatbot might work better on some segments of the population than others, such as young and educated people (although the experimental manipulations should be robust across samples, see ref. 74). All these metrics are key to estimate the chatbot’s effects outside of an experimental setting.
Exploratory hypotheses point to two interesting patterns in our data. First, participants’ behaviour in the chatbot was in line with their attitudes, as they selected the issues on which they had the most negative attitudes, thereby exposing them to the most relevant counterarguments. Second, the counterarguments—including simply providing information about the scientific consensus—had effects beyond the specific issue they addressed. While this might suggest that participants were falling prey to a kind of halo effect, it is also possible that participants drew judicious inferences from one set of arguments to others. For example, participants who come to accept that GMOs are safe to eat might also see them as more useful.
At first glance, our results might seem to suggest that presenting counterarguments in a chatbot, by contrast with a more standard text, offers little advantage, or might even prove less persuasive. However, it should be noted that, even when not presented in a chatbot, the counterarguments were organized according to a clear dialogic structure (the exact same one as the chatbot), which might have facilitated their understanding and the identification of the most relevant counterarguments. Moreover, it is possible that participants not expressly paid to take part in an experiment might find the chatbot’s interactivity more alluring than a standard text. Future experiments should investigate whether that is the case. Another promising avenue for future research is whether the very large effects observed here persist in time (see, for example, ref. 19).
The present research received approval from an ethics committee (CER-Paris Descartes, no. 2019-03-MERCIER). Participants were presented with a consent form and had to give their informed consent to participate in the study. They were paid £1.38.
Among 147 French participants who pre-tested the chatbot, we found that:
Participants’ attitudes toward GMOs became more positive after having interacted with the chatbot (t(69) = 3.68, P < 0.001, d = 0.28, 95% CI [0.13, 0.44]).
The number of arguments explored by participants significantly predicted a larger shift towards positive attitudes towards GMOs (β = 0.23, 95% CI [0.09, 0.37], t(67) = 3.32, P = 0.001).
Participants who only provided their attitudes toward GMOs after having interacted with the chatbot did not have significantly different attitudes towards GMOs compared with participants who provided their attitudes toward GMOs both before and after having interacted with the chatbot (t(138.57) = 0.71, P = 0.48, d = 0.14, 95% CI [−0.21, 0.44]).
Participants judged the bot as quite enjoyable (M = 60.13, s.d. 25.3), intuitive (M = 65.94, s.d. 26.45) and not very frustrating (M = 35.39, s.d. 31.22) (all scales from 0 to 100).
More details about the pilot can be found in Pilot study section in Supplementary information.
To create the counterarguments condition, we systematically gathered the most common counterarguments to the acceptance of GMOs, relying on a variety of methods. First, we drew on popular anti-GMOs websites (such as nongmoproject.org), and on the scientific literature on public opinion towards GMOs40,77,78,79,80. Second, we relied on the expertise of two of the co-authors, who have both participated in public events about GMOs20,81. Third, we conducted a preliminary study in which we asked participants to rate how convincing and how accurate they found our rebuttals to the most common counterarguments against GMOs. When the rebuttals were found to be unconvincing, participants were asked to explain what made the rebuttals unconvincing and write any counterarguments that came to their mind that could weaken the rebuttals (participants who found the rebuttals convincing were also asked to explain why they found them convincing). At the end of the preliminary study, participants were asked to write whether they had any counterarguments against GMOs that had not been raised during the experiment. This ensured that we covered most of the arguments people hold against GMOs and that the rebuttals to these counterarguments were taken seriously.
To develop the rebuttals to the most common counterarguments, we relied on personal communication with an expert on GMOs, the website gmoanswers.com, the scientific literature on attitudes towards GMOs40,77,78,79,80, the scientific literature on GMOs36,82,83,84,85 and Wikipedia as well as the publications of scientific agencies33,34,35.
The counterarguments and rebuttals were used to build the counterarguments condition. In this condition, participants are presented on the chatbot interface with the counterarguments and rebuttals available on the chatbot. However, participants cannot select the arguments. Instead, they have to scroll to read the counterarguments and rebuttals. The only difference between the counterarguments condition and the chatbot condition is the interactivity of the chatbot (that is, having to click on the counterarguments, seeing the rebuttals appear progressively instead of instantly and having the option of not displaying at all some rebuttals). We estimated the reading time for the counterarguments and rebuttals (~3,000 words) to be approximately 11 min (for a reading time of four words per second86).
In the counterarguments condition, participants were exposed to the most common counterarguments that we gathered against GMOs, as well as the rebuttals of these counterarguments. However, many participants might not share the concerns expressed in some counterarguments and thus find the rebuttals largely irrelevant. To address this problem, we created a chatbot whose content was identical to the content of the counterarguments condition but in which participants had to select (by clicking on them) the counterarguments against GMOs (or against the rebuttals to their previous counterarguments) that they were most concerned about, and they were provided with rebuttals addressing the selected counterargument.
The chatbot was organized as follows. After a brief technical description of GMOs (used in part in the control condition), participants were asked whether they had any concerns about GMOs and were given a choice of four counterarguments to select from: ‘GMOs might not be safe to eat’, ‘GMOs could hurt the planet’, ‘The way GMOs are commercialized is problematic’, and ‘We don’t really need GMOs’. Participants were also be able to select, at any stage, an option ‘Why should I trust you?’, which informed them about who we were, who funded us and what our goals were (all the materials are available on the Open Science Framework (OSF) at https://osf.io/cb7wf/).
Each time participants selected a counterargument, the chatbot offered a rebuttal. Participants could select between several counterarguments to these rebuttals, which were addressed by the chatbot as they were selected. In total, the chatbot offered 35 counterarguments against GMOs, together with their 35 rebuttals. Participants were not able to write open-ended counterarguments addressed to the chatbot. They were only able to select among the counterarguments offered, to which the chatbot answered with a predefined rebuttal. If the rebuttal exceeded five lines, it was displayed in separate discussion bubbles appearing progressively to give participants the impression that the bot was typing. As an example, here is the text participants saw after selecting the first counterargument that the chatbot presented at each step (sections in brackets did not appear to the participants):
Participant [first counterargument]: GMOs might not be safe to eat.
Chatbot [first rebuttal]: Did you know that the scientific consensus today is that genetically modified products on the market are as safe as non-genetically modified products? Each GMO is heavily tested before being introduced on the market. The testing process takes on average 13 years. Humans have been eating GMOs for more than 20 years and no ill effects have ever been reported. In 2016, an authoritative (and independent) report including more than 900 studies, from The National Academies of Science, Engineering, and Medicine concluded that there is “no substantial evidence of a difference in risks to human health between current commercially available genetically engineered crops and conventionally bred crops.”
Participant [follow-up counterargument]: We don’t know about the long-term effects.
Chatbot [follow-up rebuttal]: After over 40 years of research we have a good idea of the long-term effects of genetically modified food. On genetically modified corn alone more than 6000 studies have been published in scientific journals. A recent independent review of the scientific literature on long-term effects of genetically modified food concluded that: “genetically modified plants are nutritionally equivalent to their non-genetically modified counterparts and can be safely used in food and feed.”
Arguments in favour of GMOs contained hyperlinks to scientific articles, reports from scientific agencies, and Wikipedia pages (which were identical in the counterarguments condition). At any time, users had the possibility of coming back to the first four basic counterarguments of the main menu, or of exiting the chatbot.
Participants were asked to either read a simple explanation of what a GMO is (control condition), read a short paragraph on the scientific consensus on the safety of GM food (consensus condition), read counterarguments to GMOs accompanied by rebuttals of these arguments (counterarguments condition) or explore the same counterarguments and rebuttals by interacting with a chatbot (chatbot condition) (see Table 1).
Participants first had to complete a consent form and answer a few questions to measure their attitudes towards GMOs. Participants had to express the extent to which they agreed with the following four statements on a seven-point Likert scale:
Genetically modified food is safe to eat.
Genetically modified organisms (GMO) are bad for the environment.
GMOs are useless.
I’m worried about the socio-economic impacts of GMOs (on farmers in poor countries, wealth distribution, lack of competition, etc.).
In all analyses (except for H8), these four variables were treated as a single composite variable that we refer to as ‘GMOs attitude’. Next, participants were presented with one of the four following conditions:
Participants were randomly assigned to one of the four conditions by a pseudo-randomizer on the survey platform Qualtrics (that is, a randomizer that ensures that an equal number of participants is attributed to each condition). In all the conditions, participants were told to spend as much or as little time as they wanted interacting with the chatbot and exploring the text. By doing so, we improved the ecological validity of the task as participants were explicitly given leeway to engage with the arguments to the extent they wished—as they would if they had encountered the arguments in any other setting. Once they finished reading the arguments, participants answered the same questions regarding their GMOs attitudes as before the experimental task. Finally, participants provided basic demographic information (age, gender and education). Since data collection was automatized on Qualtrics and Prolific Academic, all our statistical analyses were pre-registered and there was no subjective coding of the data, the experimenters were not blind to the conditions of the experiments. Participants were not blinded to the study hypotheses. However, since the experiment had a between-participants design and most of our hypotheses (except H6 and H8) bear on comparisons across conditions, participants should not have been able to infer our hypotheses and act accordingly. See Table 1 for an overview of our design.
The neutral GMOs description used in the control condition reads as follows:
Genetically modified organisms are plants and animals whose DNA has been modified in a laboratory.
In the consensus condition, participants were provided with an account of the scientific consensus accompanied by sources. This account was more detailed than the ones used by most studies highlighting the scientific consensus on GMOs, such as ‘Did you know? A recent survey shows that 90% of scientists believe genetically modified foods are safe to eat.’9 The text used in the present experiment was:
There is a scientific consensus on the fact that genetically modified products on the market are as safe as non-genetically modified products. In 2016, an authoritative (and independent) report including more than 900 studies, from The National Academies of Science, Engineering, and Medicine concluded that there is “no substantial evidence of a difference in risks to human health between current commercially available genetically engineered crops and conventionally bred crops.” 88% of scientists of the American Association for the Advancement of Science think that GM crops are safe to eat.
All the materials can be found on OSF at https://osf.io/cb7wf/ (in French and in English). The control and the consensus condition were displayed on the survey platform Qualtrics. The chatbot and the counterarguments condition (composed of all the counterarguments and rebuttals available on the chatbot) were displayed on the same, custom-made website. The only difference between the two conditions were that, in the chatbot condition, participants selected counterarguments and thus only saw the rebuttals that address these counterarguments, whereas in the counterarguments condition, participants scrolled through all the counterarguments and rebuttals. Figure 1 offers a visualization of the chatbot’s interface.
Based on the literature and on our pilot study (see below), we expected the effect of the chatbot on the evolution of attitudes towards GMOs to be large. Previous studies have shown that learning about the science behind genetic modification technology leads to more positive attitudes towards GMOs (ANOVA, P < 0.001, η² = 0.09)67, as does discussion of the scientific evidence on GMOs safety in small groups (ANOVA, P < 0.001, η² = 0.45)20. In our pilot study, we found a large effect of the chatbot on attitude change (ANOVA, η² = 0.15). However, because the current pre-registered study compared the chatbot with controls, where some attitude change occurred, we expected the effect to be smaller (small to medium instead of large).
We performed an a priori power analysis with G*Power375. To compute the necessary number of participants, we decided that the minimal effect size of interest would correspond to a Cohen’s d of 0.2 between two different experimental conditions, since this corresponds to what is generally seen as a small effect76. Based on a correlation of 0.75 between the initial and final GMOs attitudes (estimated from the pilot), we needed at least 275 participants per condition to detect this effect, at an α level of 5% and a power of 95%, and based on a two-tailed test (see Power analysis section in Supplementary information for more details). We expected that approximatively 15% of participants would encounter problems accessing the chatbot’s interface (a percentage estimated while pre-testing the chatbot). We planned to exclude these participants. To anticipate the losses in participants unable to access the chatbot, we planned on recruiting 324 participants (275/0.85) instead of 275 in the chatbot condition and in the counterarguments condition. We planned to recruit a total of 1,198 UK participants on the crowd-sourcing platform Prolific Academic. Data collection stopped when each condition reached the minimum number of participants required by the power analysis after exclusions (due to inability to access the chatbot).
During the stage 2 peer-review process, reviewers noticed that our a priori power analysis at stage 1 had not taken into account our correction for multiple testing and that it was based on a comparison of two conditions instead of four. To enable readers to evaluate the impact of this on our power calculations, we repeated our simulations with fixed sample sizes and a fixed effect size (the a priori defined minimum effect size of interest, that is, d = 0.2). These simulations showed that the actual power to detect the smallest effect size of theoretical interest was higher than 91% in all conditions (see Supplementary information for further details).
Between 15 October 2020 and 26 October 2020, we recruited 1,306 participants (paid £1.38) from the United Kingdom on Prolific Academic. We excluded 156 participants who could not, or did not, access the chatbot, leaving 1,150 participants in total (776 women, Mage = 34.74, s.d. 12.87) with 302 participants in the chatbot condition, 299 participants in the counterarguments condition, 273 participants in the consensus condition and 275 participants in the control condition.
All analyses were conducted with R (version 3.6.1)87, using R Studio (version 1.2.5019)88. All statistical tests are two-sided. We refer to ‘statistically significant’ as P value being lower than an α of 0.05. We controlled for multiple comparisons by applying the Benjamini–Hochberg method to H1–8 (which controls for the false discovery rate and has a less negative impact on statistical power than alternative methods), but not to H9, for two reasons. First, we had planned on testing H9 only if one of the first three hypotheses were supported. As a consequence, H9 does not increase the familywise error rate (this is a special case of the closure principle in multiple comparisons89). Second, since H9 was conducted only on a third of the participants, controlling for multiple comparisons would have reduced our statistical power even more. Due to this reduced power and the lack of correction, we were especially cautious in interpreting the results of H9.
All the P values reported in the exploratory analyses have been corrected for multiple comparisons by applying the Benjamini–Hochberg method. This correction included the P values of the confirmatory analyses (whereas the correction applied to the P values of the confirmatory analyses did not include the P values of the exploratory analyses). We used this method to maximize power for the confirmatory analyses (and conform to the pre-registered plan) while limiting the risk of false positives for the exploratory analyses.
Given that we used null hypothesis statistical testing, null results were interpreted as the impossibility to reject H0, and as an absence of support for the hypothesis tested, but not as support for H0. Data from previous studies suggested that our experimental design would allow us to test our hypotheses. First, survey data on attitudes about GMOs in the UK, or other European countries, suggested that participants would be far from the ceiling (that is, being maximally in favour of GMOs)40,77, so that we would be able to observe attitude change towards attitudes more favourable to GMOs. Second, previous studies using consensus messaging9,10 suggested that some attitude change should be observed in our consensus condition, which could thus be used as a positive control.
We compared participants’ attitudes before and after being exposed to one of the four conditions by using a composite measure composed of the mean ratings of the four GMOs attitudes questions. To make our measures more intuitive, we reverse-coded all but one of the questions (the first) such that higher numbers denote a more positive attitude towards GMOs. Time was measured by our custom-made website that provides a precise and reliable measure of the time spent by participants interacting with the chatbot in the chatbot condition or reading the arguments in the counterarguments condition. To estimate whether an effect was small enough to be considered negligible, we conducted equivalence tests using the two one-sided tests (TOST) method90,91, which we implemented by computing the 90% CI around the estimate of the regression coefficient. To further assist with interpretation, we provided Bayes factors at stage 2 for all confirmatory analyses (BFs were not registered at stage 1). To calculate the Bayes factors, we used the ‘lmBF’ function from the BayesFactor R package (package ‘bayesfactor’92). The choice of priors and Markov chain Monte Carlo settings were the default settings of the package (as of 2020). The R script used to analyse the data, together with the mock dataset on which the script was tested, are available at https://osf.io/cb7wf/.
According to H1, participants will hold more positive attitudes towards GMOs after the experimental task in the consensus condition than in the control condition, when controlling for their initial attitudes towards GMOs.
According to H2, participants will hold more positive attitudes towards GMOs after the experimental task in the counterarguments condition than in the control condition, when controlling for their initial attitudes towards GMOs.
According to H3, participants will hold more positive attitudes towards GMOs after the experimental task in the chatbot condition than in the control condition, when controlling for their initial attitudes towards GMOs.
H1–3 were tested on the full dataset with one multivariate regression. Attitudes after the experimental task were set as the dependent variable, while attitudes before the experimental task and condition were set as predictors. The control condition was set as the baseline for the variable condition. In other words, consensus condition, counterarguments condition and chatbot condition were each compared with the control condition. Attitudes before the experimental task was mean-centred to facilitate the interpretation of the intercept, which corresponds to the mean post-attitude for the control condition.
According to H4, participants will hold more positive attitudes towards GMOs after the experimental task in the counterarguments condition than in the consensus condition, controlling for their initial attitudes towards GMOs.
H4 was based on the same regression model as in H1–3. We conducted a linear contrast analysis between the counterarguments condition and the consensus condition. H4 led us to expect that the consensus condition would predict less attitude change in the direction of more positive attitudes towards GMOs than the counterarguments condition.
According to H5, participants will hold more positive attitudes towards GMOs after the experimental task in the chatbot condition than in the counterarguments condition, controlling for their initial attitudes towards GMOs
H5 was based on the same regression model as in H1–3. We conducted a linear contrast analysis between the chatbot condition and the counterarguments condition. H5 led us to expect that the counterarguments condition would predict less attitude change in the direction of more positive attitudes towards GMOs than the chatbot condition.
According to H6, in the chatbot condition, the number of arguments explored by the participants will predict holding positive attitudes towards GMOs after the experimental task when controlling for their initial attitudes towards GMOs (that is, exploring more arguments should lead to more positive attitude change).
H6 was tested with one multivariate regression among participants in the chatbot condition, with attitudes after the experimental task as the dependent variable, and attitudes before the experimental task together with the total number of arguments explored by participants as predictors.
According to H7, in the chatbot condition, time spent on the task should lead to more positive attitude towards GMOs after the experimental task than time spent on the counterarguments condition, when controlling for their initial attitudes towards GMOs.
H7 was tested with one multivariate regression among participants in the chatbot condition and the counterarguments condition, with attitudes after the experimental task as the dependent variable, and using the time variable, the condition variable and an interaction between the time variable and the condition variable as predictors. The time variable was mean-centred to facilitate the interpretation of the regression coefficients.
The four questions measuring attitudes towards GMOs correspond to concerns about GM foods safety, GMOs’ ecological impact, GMO’s usefulness and the socio-economic dimension of GMOs. The internal consistency of the scale was higher after the treatment (α = 0.79) than before the treatment (α = 0.68), but this effect was mostly driven by the chatbot (pre, 0.67; post, 0.79) and counterarguments condition (pre, 0.66; post, 0.81) rather than the control (pre, 0.70; post, 0.71) and consensus condition (pre, 0.68; post, 0.71). The chatbot menu is also composed of four main counterarguments against GMOs: ‘GMOs might not be safe to eat’, which targets health concerns, ‘GMOs could hurt the planet’, which targets ecological concerns, ‘The way GMOs are commercialized is problematic’, which targets economic concerns, and ‘We don’t really need GMOs’, which targets the usefulness of GMOs. Each of these main counterarguments is answered by a rebuttal, which can then be answered by several counterarguments, which have their own rebuttals, and so forth. According to H8, on the chatbot condition, participants will hold more positive attitudes after the experimental task on issues for which they have explored more of the relevant rebuttals targeted at the issue, when controlling for their initial attitudes on the issues and the total number of arguments explored (not necessarily related to the issue). To test H8, we counted the number of arguments explored in each of the four branches (between zero and nine).
To investigate the relation between the type of arguments that participants explored on the chatbot and attitude change on these particular aspects of GMOs (health, ecology, economy and usefulness), we conducted a linear mixed-effects model with participants as random effect (varying intercepts), attitudes after the experimental task on a specific issue as the dependent variable and number of arguments explored on the same specific issue, together with attitudes before the experimental task on the same issue, the total number of arguments explored and the type of issue as predictors.
According to H8, we expect that the number of arguments explored by participants on the topic related to the subscale will be a predictor of more favourable attitudes towards GMOs after having interacted with the chatbot, when controlling for their initial attitudes towards GMOs.
According to H9, the hypotheses H1, H2 and H3 should hold true among the third of the participants most opposed to GMOs.
H9 was tested by conducting the same analysis used to test H1, H2 and H3 (that is, a multivariate regression with attitudes after the experimental as dependent variable, and attitudes before the experimental task together with condition as predictors, with the control condition as the baseline for the variable condition) among the one-third of participants initially holding the most negative attitudes toward GMOs.
We made no predictions regarding gender, education or other socio-demographic variables. We did not add these variables in the models since their influence should mostly be taken into account when controlling for initial attitudes.
The stage 1 protocol for this Registered Report was accepted in principle on 8 October 2020. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.13122527.v1.
The sentence ‘Although it is methodologically impossible to completely disentangle the effects of the mode of presentation (for example degree of interactivity) and of the specific information presented, the present experiment provides the first test of whether addressing people’s counterarguments, in particular by using an interactive chatbot, results in attitude changes that are larger than those obtained with a common messaging technique.’ was modified in the stage 1 portion of the manuscript before acceptance at stage 2 on editorial request, in order to comply with journal policies regarding priority claims.
The following paragraph ‘However these studies remain limited in particular as they did not include control groups comparable to the present control conditions. Instead the chatbots were compared either (i) to argumentation between participants31 (ii) to a chatbot that could not address all counterarguments30 or (iii) to a chatbot that did not take into account users’ counterarguments32. Moreover the robustness of these results is questionable since their design had poor sensitivity to detect even large effect sizes of dz = 0.5 (the study with the greatest sensitivity32, which recruited 25 participants per condition on average, had no more than 0.71 power to detect large effects (dz = 0.5) with an alpha of 0.05; in the other studies power was even lower: 0.4931 and 0.5230).’ was removed from the stage 2 submission because, since in principle acceptance of the stage 1 protocol, studies that are not subject to these limitations have been published69.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
The data associated with this research, together with the code of the chatbot and the materials, are available on OSF at https://osf.io/cb7wf/.
The R scripts associated with this research are available on OSF at https://osf.io/cb7wf/.
Public and Scientists’ Views on Science and Society (Pew Research Center, 2015).
Nyhan, B. & Reifler, J. When corrections fail: the persistence of political misperceptions. Polit. Behav. 32, 303–330 (2010).
Nyhan, B., Reifler, J., Richey, S. & Freed, G. L. Effective messages in vaccine promotion: a randomized trial. Pediatrics 133, e835–e842 (2014).
Cook, J. & Lewandowsky, S. The Debunking Handbook (Sevloid Art, 2011).
Lewandowsky, S., Ecker, U. K., Seifert, C. M., Schwarz, N. & Cook, J. Misinformation and its correction: continued influence and successful debiasing. Psychol. Sci. Public Interest 13, 106–131 (2012).
Guess, A. & Coppock, A. Does counter-attitudinal information cause backlash? Results from three large survey experiments. Br. J. Polit. Sci. https://doi.org/10.1017/S0007123418000327 (2018).
Wood, T. & Porter, E. The elusive backfire effect: mass attitudes’ steadfast factual adherence. Polit. Behav. 41, 135–163 (2019).
Landrum, A. R., Hallman, W. K. & Jamieson, K. H. Examining the impact of expert voices: communicating the scientific consensus on genetically-modified organisms. Environ. Commun. https://doi.org/10.1080/17524032.2018.1502201 (2018).
Dixon. Applying the gateway belief model to genetically modified food perceptions: new insights and additional questions. J. Commun. https://doi.org/10.1111/jcom.12260 (2016).
Kerr, J. R. & Wilson, M. S. Changes in perceived scientific consensus shift beliefs about climate change and GM food safety. PLoS ONE 13, e0200295 (2018).
Claidière, N., Trouche, E. & Mercier, H. Argumentation and the diffusion of counter-intuitive beliefs. J. Exp. Psychol. Gen. 146, 1052–1066 (2017).
Trouche, E., Sander, E. & Mercier, H. Arguments, more than confidence, explain the good performance of reasoning groups. J. Exp. Psychol. Gen. 143, 1958–1971 (2014).
Laughlin, P. R. Group Problem Solving (Princeton Univ. Press, 2011).
Minson, J. A., Liberman, V. & Ross, L. Two to tango. Personal. Soc. Psychol. Bull. 37, 1325–1338 (2011).
Smith, M. K. et al. Why peer discussion improves student performance on in-class concept questions. Science 323, 122–124 (2009).
Mercier, H. The argumentative theory: predictions and empirical evidence. Trends Cogn. Sci. 20, 689–700 (2016).
Mercier, H. & Sperber, D. The Enigma of Reason (Harvard Univ. Press, 2017).
Minozzi, W., Neblo, M. A., Esterling, K. M. & Lazer, D. M. Field experiment evidence of substantive, attributional, and behavioral persuasion by members of Congress in online town halls. Proc. Natl Acad. Sci. USA 112, 3937–3942 (2015).
Broockman, D. & Kalla, J. Durably reducing transphobia: a field experiment on door-to-door canvassing. Science 352, 220–224 (2016).
Altay, S. & Lakhlifi, C. Are science festivals a good place to discuss heated topics? J. Sci. Commun. 19, A07 (2020).
Chanel, O., Luchini, S., Massoni, S. & Vergnaud, J.-C. Impact of information on intentions to vaccinate in a potential epidemic: swine-origin influenza A (H1N1). Soc. Sci. Med. 72, 142–148 (2011).
Resnick, L. B., Salmon, M., Zeitz, C. M., Wathen, S. H. & Holowchak, M. Reasoning in conversation. Cogn. Instr. 11, 347–364 (1993).
Edwards, K. & Smith, E. E. A disconfirmation bias in the evaluation of arguments. J. Personal. Soc. Psychol. 71, 5–24 (1996).
Greenwald, A. G. in Psychological Foundations of Attitudes (eds. Greenwald, A. G., Brock, T. C. & Ostrom, T. M.) 147–170 (Academic Press, 1968).
Taber, C. S. & Lodge, M. Motivated skepticism in the evaluation of political beliefs. Am. J. Polit. Sci. 50, 755–769 (2006).
Trouche, E., Shao, J. & Mercier, H. Objective evaluation of demonstrative arguments. Argumentation 33, 23–43 (2019).
Mercier, H., Bonnier, P. & Trouche, E. in Cognitive Unconscious and Human Rationality (eds. Macchi, L., Bagassi, M. & Viale, R.) 205–218 (MIT Press, 2016).
Fay, N., Garrod, S. & Carletta, J. Group discussion as interactive dialogue or as serial monologue: the influence of group size. Psychol. Sci. 11, 481–486 (2000).
Krems, J. A. & Wilkes, J. Why are conversations limited to about four people? A theoretical exploration of the conversation size constraint. Evol. Hum. Behav. 40, 140–147 (2019).
Andrews, P., Manandhar, S. & De Boni, M. in Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue 138–147 (2008). https://doi.org/10.3115/1622064.1622093
Rosenfeld, A. & Kraus, S. in Proceedings of the Twenty-Second European Conference on Artificial Intelligence 320–328 (IOS Press, 2016). https://doi.org/10.3233/978-1-61499-672-9-320
Chalaguine, L. A., Hunter, A., Hamilton, F. L. & Potts, H. W. Impact of argument type and concerns in argumentation with a chatbot. in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) https://doi.org/10.1109/ICTAI.2019.00224 (2019).
Baulcombe, D., Dunwell, J., Jones, J., Pickett, J. & Puigdomenech, P. GM Science Update: a Report to the Council for Science and Technology (2014). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/292174/cst-14-634a-gm-science-update.pdf
A Decade of EU-Funded GMO Research (European Commission, 2010).
National Academies of Sciences and Medicine. Genetically Engineered Crops: Experiences and Prospects (National Academies Press, 2016).
Nicolia, A., Manzo, A., Veronesi, F. & Rosellini, D. An overview of the last 10 years of genetically engineered crop safety research. Crit. Rev. Biotechnol. 34, 77–88 (2014).
Ronald, P. Plant genetics, sustainable agriculture and global food security. Genetics 188, 11–20 (2011).
Statement by the AAAS Board of Directors on Labeling of Genetically Modified Foods (American Assoication for the Advancement of Science, 2012).
Yang, Y. T. & Chen, B. Governing GMOs in the USA: science, law and public health. J. Sci. Food Agric. 96, 1851–1855 (2016).
Bonny, S. Why are most Europeans opposed to GMOs?: factors explaining rejection in France and Europe. Electron. J. Biotechnol. 6, 7–8 (2003).
Cui, K. & Shoemaker, S. P. Public perception of genetically-modified (GM) food: a nationwide Chinese consumer study. npj Sci. Food 2, 10 (2018).
Gaskell, G., Bauer, M. W., Durant, J. & Allum, N. C. Worlds apart? The reception of genetically modified foods in Europe and the US. Science 285, 384–387 (1999).
Scott, S. E., Inbar, Y. & Rozin, P. Evidence for absolute moral opposition to genetically modified food in the United States. Perspect. Psychol. Sci. 11, 315–324 (2016).
Baromètre sur la Perception des Risques et de la Sécurité par les Français (IRSN, 2017).
Les Français et les OGM (Ifop, 2012).
Burke, D. GM food and crops: what went wrong in the UK? EMBO Rep. 5, 432–436 (2004).
Poortinga, W. & Pidgeon, N. Public Perceptions of Genetically Modified Food and Crops, and the GM Nation? Public Debate on the Commercialisation of Agricultural Biotechnology in the UK: Main Findings of a British Survey (Centre for Environmental Risk, 2004).
Cordon, G. GM crops opposition may have been ‘over-estimated’. The Scotsman (19 February 2004).
Bonny, S. Will Biotechnology Lead to More Sustainable Agriculture? in Proc. of NE-165 Conference (2000).
Hielscher, S., Pies, I., Valentinov, V. & Chatalova, L. Rationalizing the GMO debate: the ordonomic approach to addressing agricultural myths. Int. J. Environ. Res. Public Health 13, 476 (2016).
Ding, D., Maibach, E. W., Zhao, X., Roser-Renouf, C. & Leiserowitz, A. Support for climate policy and societal action are linked to perceptions about scientific agreement. Nat. Clim. Change 1, 462 (2011).
Dunwoody, S. & Kohl, P. A. Using weight-of-experts messaging to communicate accurately about contested science. Sci. Commun. 39, 338–357 (2017).
Kohl, P. A. et al. The influence of weight-of-evidence strategies on audience perceptions of (un)certainty when media cover contested science. Public Understand. Sci. 25, 976–991 (2016).
Lewandowsky, S., Gignac, G. E. & Vaughan, S. The pivotal role of perceived scientific consensus in acceptance of science. Nat. Clim. Change 3, 399–404 (2013).
van der Linden, S. L., Leiserowitz, A. A., Feinberg, G. D. & Maibach, E. W. The scientific consensus on climate change as a gateway belief: experimental evidence. PLoS ONE 10, e0118489 (2015).
van der Linden, S. L., Leiserowitz, A. & Maibach, E. Gateway illusion or cultural cognition confusion? J. Sci. Commun. https://doi.org/10.22323/2.16050204 (2017).
Petty, R. E. & Cacioppo, J. T. in Advances in Experimental Social Psychology (ed. Berkowitz, L.) 123–205 (Academic Press., 1986).
Petty, R. E. & Cacioppo, J. T. The effects of involvement on responses to argument quantity and quality: central and peripheral routes to persuasion. J. Personal. Soc. Psychol. 46, 69 (1984).
Ecker, U. K. H. & Ang, L. C. Political attitudes and the processing of misinformation corrections. Polit. Psychol. 40, 241–260 (2019).
Kahan, D. Ideology, motivated reasoning, and cognitive reflection. Judgm. Decis. Mak. 8, 407–424 (2013).
Kahan, D., Jenkins-Smith, H. & Braman, D. Cultural cognition of scientific consensus. J. Risk Res. 14, 147–174 (2011).
Schmid, P. & Betsch, C. Effective strategies for rebutting science denialism in public discussions. Nat. Hum. Behav. https://doi.org/10.1038/s41562-019-0632-40 (2019).
van der Linden, S., Leiserowitz, A. & Maibach, E. The gateway belief model: a large-scale replication. J. Environ. Psychol. 62, 49–58 (2019).
van der Linden, S., Maibach, E. & Leiserowitz, A. Exposure to scientific consensus does not cause psychological reactance. Environ. Commun. https://doi.org/10.1080/17524032.2019.1617763 (2019).
Tingley, D., Yamamoto, T., Hirose, K., Keele, L. & Imai, K. Mediation: R package for causal mediation analysis. J. Stat. Softw. 59, 5 (2014).
Bullock, J. G., Green, D. P. & Ha, S. E. Yes, but what’s the mechanism? (don’t expect an easy answer). J. Personal. Soc. Psychol. 98, 550–558 (2010).
McPhetres, J., Rutjens, B. T., Weinstein, N. & Brisson, J. A. Modifying attitudes about modified foods: increased knowledge leads to more positive attitudes. J. Environ. Psychol https://doi.org/10.1016/j.jenvp.2019.04.012 (2019).
Hasell, A., Lyons, B. A., Tallapragada, M. & Jamieson, K. H. Improving GM consensus acceptance through reduced reactance and climate change-based message targeting. Environ. Commun. https://doi.org/10.1080/17524032.2020.1746377 (2020).
Altay, S., Hacquin, A.-S., Chevallier, C. & Mercier, H. Information delivered by a chatbot has a positive impact on COVID-19 vaccines attitudes and intentions. J. Exp. Psychol. Appl. https://doi.org/10.1037/xap0000400 (2021).
Swire-Thompson, B., DeGutis, J. & Lazer, D. Searching for the backfire effect: measurement and design considerations. https://doi.org/10.1016/j.jarmac.2020.06.006 (2020).
Bode, L., Vraga, E. K. & Tully, M. Correcting misperceptions about genetically modified food on social media: examining the impact of experts, social media heuristics, and the gateway belief model. Sci. Commun. 43, 225–251 (2021).
Bode, L. & Vraga, E. K. In related news, that was wrong: the correction of misinformation through related stories functionality in social media. J. Commun. 65, 619–638 (2015).
Vraga, E. K. & Bode, L. Using expert sources to correct health misinformation in social media. Sci. Commun. 39, 621–645 (2017).
Coppock, A., Leeper, T. J. & Mullinix, K. J. Generalizability of heterogeneous treatment effect estimates across samples. Proc. Natl Acad. Sci. USA 115, 12441–12446 (2018).
Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res Methods 39, 175–191 (2007).
Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd edn (Lawrence Erlbaum Assoicates, 1988).
Bonny, S. Factors explaining opposition to GMOs in France and the rest of Europe. Consumer Acceptance of Genetically Modified Foods 169 (2004).
Evenson, R. E. & Santaniello, V. Consumer Acceptance of Genetically Modified Foods (CABI, 2004).
McHughen, A. & Wager, R. Popular misconceptions: agricultural biotechnology. N. Biotechnol. 27, 724–728 (2010).
Parrott, W. Genetically modified myths and realities. N. Biotechnol. 27, 545–551 (2010).
Blancke, S., Van Breusegem, F., De Jaeger, G., Braeckman, J. & Van Montagu, M. Fatal attraction: the intuitive appeal of GMO opposition. Trends Plant Sci. https://doi.org/10.1016/j.tplants.2015.03.011 (2015).
Key, S., Ma, J. K. & Drake, P. M. Genetically modified plants and human health. J. R. Soc. Med. 101, 290–298 (2008).
Klümper, W. & Qaim, M. A meta-analysis of the impacts of genetically modified crops. PLoS ONE 9, e111629 (2014).
Pellegrino, E., Bedini, S., Nuti, M. & Ercoli, L. Impact of genetically engineered maize on agronomic, environmental and toxicological traits: a meta-analysis of 21 years of field data. Sci. Rep. 8, 3113 (2018).
Snell, C. et al. Assessment of the health impact of GM plant diets in long-term and multigenerational animal feeding trials: a literature review. Food Chem. Toxicol. 50, 1134–1148 (2012).
Brysbaert, M. How many words do we read per minute? A review and meta-analysis of reading rate. J. Mem. Lang. https://doi.org/10.1016/j.jml.2019.104047 (2019).
R: a Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
RStudio: Integrated Development for R (RStudio, 2015).
Bretz, F., Hothorn, T. & Westfall, P. Multiple Comparisons Using R (CRC Press, 2016).
Lakens, D. Equivalence tests: a practical primer for t tests, correlations, and meta-analyses. Soc. Psychol. Personal. Sci. 8, 355–362 (2017).
Campbell, H. Equivalence testing for standardized effect sizes in linear regression. arXiv https://arxiv.org/abs/2004.01757 (2020).
Morey, R. D., Rouder, J. N., Jamil, T. & Morey, M. R. D. Package ‘bayesfactor’. http://www.cran/r-projectorg/web/packages/BayesFactor/BayesFactorpdfi (2015).
This research was supported by the CONFIRMA grant from the Direction Générale de L’armement, together with grants ANR-17-EURE-0017 to FrontCog and ANR-10-IDEX-0001-02 to PSL. S.A.’s PhD thesis is funded by the Direction Générale de l’Armement (DGA). The funders have had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank C. Williams for statistical advice.
The authors declare no competing interests.
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Altay, S., Schwartz, M., Hacquin, AS. et al. Scaling up interactive argumentation by providing counterarguments with a chatbot. Nat Hum Behav 6, 579–592 (2022). https://doi.org/10.1038/s41562-021-01271-w
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