In online networks, the polarization of opinions (e.g., regarding presidential elections or referenda) has been associated with the creation of “echo-chambers” of like- minded peers, secluded from those of contrary viewpoints. Previous work has commonly attributed such phenomena to self-regarding preferences (e.g., confirmation bias), individual differences, and the pre-dispositions of users, with clusters forming over repeated interactions. The present work provides a proof of concept Agent-Based Model that demonstrates online networks are susceptible to echo-chambers from a single opinion cascade, due to the spatiotemporal order induced by lateral transmission. This susceptibility is found to vary as a function of degree of interconnectivity and opinion strength. Critically, such effects are found despite globally proportionate levels of opinions, equally rational agents (i.e. absent conformity, confirmation bias or pre-disposition architecture), and prior to cyclical interactions. The assumptions and implications of this work, including the value of Agent-Based Modelling to cognitive psychology, are discussed.

This study identified a novel and robust reasoning error. Lay reasoners significantly deviate from the prediction of Bayesian inference by consistently underestimating the added probative value of corroborating testimonial reports. Most surprisingly, however, is that in certain contexts the sum of corroborating evidence is considered to be significantly less valuable than a single report. There is a selective devaluing of corroborating testimony when a highly reliable report is corroborated by less reliable, but credible, reports. This intuitive error is not explained by an inaccurate understanding of individual cues of reliability and number of reports, but specifically when it is required to integrate these both cues. Findings indicate the operation of alternative reasoning strategies, resulting in errors at individual and group level.

When reasoning about evidence, we must carefully consider the impact of different structures. For instance, if in the process of evaluating multiple reports, we find they rely on the same, shared evidence, then the support proffered by those reports is dependent on that evidence. Critically, normative accounts suggest that such a dependency results in redundant information across reports (reducing evidential support), relative to reports based on distinct items of evidence. In the present work we disentangle the structural and observation-based indicators of this form of dependency. In so doing, we present novel findings that lay reasoners are not only insensitive to shared evidence structures when updating their beliefs, but also that reasoners do not necessarily prefer more diverse sources of evidence. Finally, we replicate prior effects in reasoning under uncertainty, including conservative sequential updating, and difficulty in integrating contradictory reports.

When reasoning about evidence under conditions ofuncertainty, one important consideration for accurate updatingis the presence (and influence) of dependencies. For instance,if considering whether a patient has a disease, the value oftwo doctors’ diagnoses indicating the presence of the diseasemay carry more value if such diagnoses were conductedindependently, rather than if, all else being equal, one doctorhas seen the other’s diagnosis before making their own. In thepresent paper, we demonstrate that lay reasoners prefer toavoid dependencies when considering evidential support.However, we additionally illustrate two cases in whichdependencies may carry evidential advantage: namely, wheninformation is partial or contradictory. Lay reasonerserroneously remain averse to dependencies even in suchcases, reflecting the difficulties inherent to considerations ofdependence.

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.

Testimony is a fundamental feature of human life: typically, wereceive testimonial evidence from others multiple times each day.Often, we have more than one source attesting to a particularclaim. This paper examines the way people integrate testimonialevidence from multiple sources. We find evidence that participantsdeviate substantially from the normative expectation. Instead,results seem indicative of the operation of simple, non-compensatory heuristics, at least some of the time.

Recent research (Pilditch, Fenton, & Lagnado, 2019) shows that people are susceptible to zero-sum thinking in evidence evaluation, where they dismiss or underweight the probative value of evidence that is equally predicted by multiple independent hypotheses. But such an assumption is only valid when explanations are mutually exclusive and exhaustive. The present work extends these findings by looking at the context of information selection, and the decisional consequences of the zero-sum fallacy. It uses an information metric to quantify the cost of the error in terms of overlooked information.

How does one deal with the possibility of deception? Extant literature has mostly focused on identifying deception via cue detection. However, how we reason about the possibility of deception remains under-explored. We use a novel formalism to expose the complexity of this reasoning problem (e.g. separating the uncertainty of an honest mistake, from willful deception), in the process highlighting several reasoning errors regarding deception. Notably, we show reasoners to make substantial errors when reasoning about a (possibly) deceptive source in isolation (including base rate neglect errors), but find that reasoning improves when further (independently sourced) corroborative or contradicting reports are introduced.

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.

Independent pieces of corroborating evidence should provide stronger support to a hypothesis than dependent pieces of evidence. Overlooking the inferiority of dependent relative to independent items of evidence can lead to a chain reaction of double-counting evidence, over-estimating the probability that the fact under consideration is true, and making wrongful decisions. Within fictitious scenarios, we investigate people's sensitivity to the independency advantage. We assess their ability to integrate multiple items of evidence that come from (in)dependent sources who differ in reliability. We find that participants properly perceive dependencies when explicitly asked but fail to distinguish the probative value of dependent versus independent evidence in their belief updating. Still, individuals who perceive a strong dependence between sources treat the evidence as being more redundant. We find no dependency-related effects on participants' individual Bayesian network model predictions. Potential reasons why participants perceive (in)dependencies and yet (mostly) fail to discount for them are discussed.