Evidence map›Paper›PMID 35165435›Full record

ArticleNature human behaviour2022

Scaling up interactive argumentation by providing counterarguments with a chatbot.

Sacha Altay, Marlène Schwartz, Anne-Sophie Hacquin, Aurélien Allard, Stefaan Blancke, Hugo Mercier

Abstract read
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In one paragraph

Article in Nature human behaviour, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sacha AltayInstitut Jean Nicod, Département d'études cognitives, ENS, EHESS, PSL University, CNRS, Paris, France. sacha.altay@gmail.com.ORCID http://orcid.org/0000-0002-2839-7375
Marlène SchwartzInstitut Jean Nicod, Département d'études cognitives, ENS, EHESS, PSL University, CNRS, Paris, France.
Anne-Sophie HacquinInstitut Jean Nicod, Département d'études cognitives, ENS, EHESS, PSL University, CNRS, Paris, France.
Aurélien AllardUniversity of Geneva, Geneva, Switzerland.ORCID http://orcid.org/0000-0001-9655-3715
Stefaan BlanckeDepartment of Philosophy, Tilburg University, Tilburg, the Netherlands.ORCID http://orcid.org/0000-0002-7807-5606
Hugo MercierInstitut Jean Nicod, Département d'études cognitives, ENS, EHESS, PSL University, CNRS, Paris, France. hugo.mercier@gmail.com.ORCID http://orcid.org/0000-0002-0575-7913

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Research DesignSoftwareAttitudeDissent and DisputesHumansPersuasive Communication

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.