The credibility of politicians is crucial to their persuasivenessas election candidates. The paper applies a parameter-freeBaysian source credibility model (integrating trustworthinessand epistemic authority) in a real-life test predictingparticipants’ posterior belief in the goodness of an unnamedpolicy after a named candidate has publically supported orattacked it.Two studies test model predictions against policy supportand attack of five presidential candidates from the USA.Model predictions were measured against observed posteriorbelief in the goodness of the policy.The results strongly suggest the model captures essentialtraits of how participants update beliefs in policies givenappeals to a candidates’ support of attack. Further, individualdifferences suggest that people consider other factors thanthe ones elicited for the study. More studies into appeals tospecific candidates are warranted to construct more accuratemodels of the influence of source credibility on politicalreasoning.

A study in political psychology identifies four item-based factors of political trustworthiness in the USA: capability, consistency and closeness, egotism and opportunism, and communal commitment. Additionally, a list of items describe epistemic expertise. Together, these elements make up a description of political source credibility in the USA. The current study examines the power of these elements to predict source credibility. Eliciting estimations of likelihood and importance of each item on a Likert-type scale as well as overall estimations of trustworthiness and expertise, the paper presents weighted as well as non-weighted models that predict the likelihood that election candidates are trustworthy, have expertise, and are credible sources for individual respondents. Multiple regression analyses show that non-weighted scales have slightly better predictive power than weighted scales. The findings further provide an example of a datadriven method for applying a general cognitive models of source credibility to specific domains.

Much of our knowledge comes from other people. In considering how argument quality and source reliability influences message persuasiveness, we conduct a comparison of the Elaboration Likelihood Model of Persuasion and the Bayesian Model of Argumentation, which are based on different assumptions. Participants were asked to judge a fictitious character’s degree of belief in a claim given evidence. To test competing predictions, we manipulate the character’s elaboration level, the argument’s quality, and the source’s reliability. The elaboration did not moderate the main effects of argument quality and source reliability, as they both were integral to the overall message strength in both high and low elaboration conditions. Bayesian predictions have better fit with the observed data, whilst ELM predictions did not align well. Overall, the BM is supported, but we discuss how this model could be further improved while the ELM is contested.

This research explores the effects of the zero-sum fallacy and the interaction of source credibility in three experimental parts (Pilditch, Fenton & Lagnado, 2019). The zero-sum fallacy is a reasoning error wherein individuals presented with two equally plausible competing causal debates erroneously assume that neither can be true. Experiment 1 (N=16) was an unsuccessful replication of Pilditch and colleagues (2019) experiment 1 which previously significantly demonstrated the effects of the zero-sum fallacy. Experiment 2 (N=53), found significant results favouring the existence and robustness of the zero-sum fallacy using logically identical but contextually different experimental stimuli. Experiment 3 (N=101), found the zero-sum fallacy persisted when source credibility statements were incorporated, but that source credibility had a significant impact on participants’ reasoning. As part of explanatory analysis, data from all 3 experiments was subjected to Bayesian analysis.

Sequential testimonies where more or less reliable sourcesargue about an issue are central to public debates. Often, themajority of sources may argue that a hypothesis is true whilea minority dissenter may claim the opposite (e.g. scientistsand lobbyists in the climate change debate).In this paper, we show that people are sensitive to sourcereliability as well as the structural relationship between thesources. Participants follow Bayesian predictions for revisingbelief in the hypothesis and the reliability of the competingsources given majority consent, minority dissent, and sharedreliability between sources. Shared reliability and dissent is akey issue for public debate and belief revision. The paperprovides novel insight into the workings of these aspects.

The dilution effect occurs when the introduction of non-diagnostic information lessens the impact on reasoning ofdiagnostic information despite having no relevance to thehypothesis in question. While the effect has been reproducedin several studies, the psychological basis of the effectremains unclear. Some believe it to be conversational whileothers believe it to be cognitive and social.The paper tests the conversational basis of the effect byminimising pragmatic, conversational influence. To this end,it makes use of a legal setting with witness testimonies. Thestudies replicate the dilution effect, which suggests that thebasis of the results in the original studies is notconversational. However, the credibility of the sourcestrongly influences whether or not the effect occurs. Ifreliable sources provide the non-diagnostic information, theeffect lessens. Conversely, if unreliable sources provide thenon-diagnostic information, we observe a stronger dilutioneffect.

Existing research suggests that White individuals are morelikely to categorise biracial faces as Black in conditions ofresource scarcity. It has been theorised that this effect is dueto in-group boundaries becoming more exclusive in scarceconditions. An alternative explanation refers to implicit socio-economic association between Black individuals and lowerlevel of resources. These two approaches entail differentpredictions for Black participants performing thecategorisation task. If scarcity prompts greater in-groupexclusivity, Black participants should, ceteris paribus,categorise more biracial faces as White. If, however, scarcityinvokes socio-economic status associations, Blackparticipants should categories biracial faces in the same wayas White participants. Experiment 1, explored the effects ofpriming on White and Black groups. It provided support forthe implicit socio-economic association theory. Furthermore,experiment 2 on Asian sample, provided additional support asAsian participants showed the same pattern of response. Thepaper discusses implications of these findings.

Predicting the effect of persuasion campaigns is difficult, asbelief changes may cascade through a network. In recentyears, political campaigns have adopted micro-targetingstrategies that segment voters into fine-grained clusters formore specific targetting. At present, there is little evidencethat explores the efficiency of this method. Through anAgent-Based Model, the current paper provides a novelmethod for exploring predicted effects of strategic persuasioncampaigns.The voters in the model are rational and revise their beliefsin the propositions expounded by the politicians inaccordance with Bayesian belief updating through a sourcecredibility model.The model provides a proof of concept and shows strategicadvantages of micro-targeted campaigning. Despite havingonly little voter data allowing crude segmentation, the micro-targeted campaign consistently beat stochastic campaignswith the same reach. However, given substantially greaterreach, a positively perceived stochastic candidate can nullifyor beat a strategic persuasion campaigns.

This paper sought to understand the impact of labelling an argument as AI-generated compared to human-authored, and how factors such as portrayals of expertise and the nature of arguments presented (narrative versus statistical) may affect the persuasiveness of the arguments. Three domains were explored: health, finance, and politics. We show that arguments with AI source labels, both non-expert and expert, were rated by participants as less persuasive than when they had their counterpart human-authored source labels attached. Moreover, although the statistical arguments were found to be more persuasive than the narrative arguments, this did not affect the impact of an AI source label, with a significant interaction effect only being seen for the domain of politics for the expert AI source. The study explored the role of attitude towards AI on the impact of source labels as an exploratory analysis and found no significant interaction effect across the three domains.

Vaccines are among the best tools to limit the spread of preventable diseases. yet, recent years have seen a rise in anti-vaccination sentiments for vaccines against COVID-19, the MMR, and more. It is critical to understand the factors that influence whether a person will accept or reject a vaccine for a given disease. This paper tests a Bayesian model to predict attitudes toward vaccines. with five factors (subjective beliefs concerning danger of illness, safety of the vaccine, prevalence of the disease, perceived social norm, and governmental recommendation). To parameterize the model, Study 1 elicits the full conditional probability table while Study 2 tests model predictions by eliciting people’s priors for COVID-19 and the common flu. We find a good fit between predictions and observations, accounting for 53% and 44% of the variance. This suggests the usefulness of a formal model to capture people’s beliefs about vaccination.