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After learning that one stimulus predicts an outcome (e.g., an aqua-colored rectangle leads to shock) and a very similar stimulus predicts no outcome (e.g., a slightly greener rectangle leads to no shock), some participants generalize the predictive relationship on the basis of physical similarity to the predictive stimulus, while others generalize on the basis of the relational difference between the two stimuli (e.g., “higher likelihood of shock for bluer stimuli”). To date, these individual differences in generalization rules have remained unexplored in associative learning. Here, we present evidence that a given individual simultaneously entertains belief in both “similarity” and “relational” rules, and generalizes using a mixture of these strategies. Using a “mixture of experts” modelling framework constrained by participants self-reported rule beliefs, we show that considering multiple rules predicts generalization gradients better than a single rule, and that generalization behavior is better described as switching between, rather than averaging over, different rules.

We report the results of a preregistered study that tested the effectiveness of inoculating participants against Islamophobic and radical-Islamist disinformation. Participants in the experimental (inoculation) condition watched a video that explained common rhetorical markers of radical-Islamist and Islamophobic disinformation that had been identified in an analysis of YouTube content. The information was presented in a neutral context not involving Islam and focused on analysis of the misleading argumentation. The control group watched a video about an unrelated topic. Participants were then exposed to target videos with "gateway" content that constituted an entry point to potential Islamist or Islamophobic radicalization. Both videos contained numerous items of disinformation. Participants then answered a variety of questions such as how likely they were to share the video, their level of agreement, and their perceived accuracy of the video. Participants who had received the inoculation displayed less agreement with the video content, perceived the video as less reliable, and were less likely to share it in comparison with participants in the control group. The study provides support for the use of argument-based inoculation in combatting extremist messages.

In recent years, many kinds of interventions have been developed that seek to reduce susceptibility to misinformation. In two preregistered longitudinal studies (N1 = 503, N2 = 673), we leverage two previously validated "inoculation" interventions (a video and a game) to address two important questions in misinformation interventions research: (1) whether displaying additional stimuli (such as videos unrelated to misinformation) alongside an intervention interferes with its effectiveness, and (2) whether administering an immediate posttest (in the form of a social media post evaluation task after the intervention) plays a role in the longevity of the intervention. We find no evidence that other stimuli interfere with intervention efficacy, but strong evidence that immediate posttests strengthen the learnings from the intervention. In study 1, we find that 48 h after watching a video, participants who received an immediate posttest continued to be significantly better at discerning untrustworthy social media posts from neutral ones than the control group (d = 0.416, p =.007), whereas participants who only received a posttest 48 h later showed no differences with a control (d = 0.010, p =.854). In study 2, we observe highly similar results for a gamified intervention, and provide evidence for a causal mechanism: immediate posttests help strengthen people's memory of the lessons learned in the intervention. We argue that the active rehearsal and application of relevant information are therefore requirements for the longevity of learning‐based misinformation interventions, which has substantial implications for their scalability. [ABSTRACT FROM AUTHOR]

Health communicators are faced with the challenge that people can hesitate vaccines for different reasons. Our aim was to identify and describe the qualities of distinct COVID-19 and influenza vaccine-hesitancy subgroups to facilitate the development of tailored vaccine-hesitancy communication. In two studies, we used agglomerative hierarchical cluster analysis to identify COVID-19 (N = 554) and influenza (N = 539) vaccine-hesitancy subgroups in the general population based on nine vaccine hesitancy-related variables (intent to get vaccinated, perceived vaccine safety, perceived vaccine efficacy, perceived disease threat, perceived vaccination responsibility, perceived vaccination convenience, distrust in authorities, conspiracy mentality, and reliance on anecdotal testimonies). We identified and described six distinct COVID-19 vaccine-hesitancy subgroups (the Vaccination Positive, the Ambivalent, the Fearing Skeptic, the Unconvinced, the Constrained Skeptic, and the Vaccination Opponent), and three influenza vaccine-hesitancy subgroups (the Vaccination Positive, the Complacent, and the Vaccination Opponent), with different levels of hesitancy. We discuss the implications of the results for health communicators. Our results shed light on the (dis)similarities between people who hesitate COVID-19 and influenza vaccines and suggest that there is greater variety in hesitancy concerning COVID-19 vaccinations than influenza vaccinations. These findings can be used to design and test tailored vaccination messages. [ABSTRACT FROM AUTHOR]

Background: Healthcare professionals (HCPs) can play an important role in encouraging patients and their caregivers to be vaccinated. The objective of this qualitative study was to investigate HCPs' perspectives on challenges in vaccine communication and unmet training needs in this domain. Methods: Semi-structured interviews were conducted with 41 HCPs (mainly nurses and physicians) with vaccination roles (23 in England; 18 in France), gathering information on: (1) HCPs' approach to vaccine conversations with patients; (2) Challenges of communicating about vaccines; (3) Vaccine-related training and learning resources available to HCPs, and; (4) HCPs' training needs around vaccine communication. Results: HCPs described a range of communication experiences that indicated insufficient time, information, and skills to confidently navigate difficult conversations with vaccine-hesitant patients. Communication skills were especially important to avoid conflict that could potentially damage the patient-provider relationship. Some HCPs interviewed had received communication training, but for most, this training was not specific to vaccination. Although general communication skills were transferable to vaccine conversations, most HCPs welcomed specific training and informational resources to support countering patients' misconceptions or misinformation about vaccines. Conclusions: HCPs would benefit from training tailored to address vaccine communication with patients, and this should be part of a systemic approach that also provides time and space to have effective vaccine conversations. [ABSTRACT FROM AUTHOR]

People's negative attitudes to vaccines can be motivated by psychological factors—such as fears, ideological beliefs, and cognitive patterns—known as 'attitude roots'. This study had two primary objectives: (1) to identify which of 11 known attitude roots are featured in individuals' self-expressed reasons for negative vaccine attitudes (i.e., a linguistic analysis); (2) to explore how attitude roots present in self-expressed texts are linked to specific psychological measures. To achieve Objective 1, our study collected data from December 2022 to January 2023 from 556 participants from the US, who wrote texts to explain the reasons for their negative vaccine attitudes. The texts encompassed 2327 conceptually independent units of anti-vaccination argumentation, that were each coded for its attitude root(s) by at least two psychological experts. By allowing participants to spontaneously express their attitudes in their own words, we were able to observe how this differed from what participants reported to endorse when presented with a list of arguments. We found that there were four groups of attitude roots based on linguistic similarity in self-expression. In addition, latent class analysis of participants' coded texts identified three distinct groups of participants that were characterised by their tendency to express combinations of arguments related to (1) fears, (2) anti-scientific conceptions, and (3) politicised perspectives. To achieve Objective 2, we collected participants' responses to 11 validated measures of psychological constructs expected to underlie the respective 11 attitude roots, and used a correlational design to investigate how participants' self-expressed attitude roots were linked to these measures. Logistic regressions showed that an expected psychological construct was the strongest, and significant, predictor for expression of three out of the four attitude root groups. We discuss the implications of these findings for health communicators and practitioners. [ABSTRACT FROM AUTHOR]

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or "particles," available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies ("knowledge restructuring"). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles--increasing the number of particles leads to both faster category learning and improved strategy-switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best-fit number of particles for strategy switching. However, no association between WMC and best-fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.

[This corrects the article DOI: 10.1093/biosci/bix133.].

Increasing surface temperatures, Arctic sea-ice loss, and other evidence of anthropogenic global warming (AGW) are acknowledged by every major scientific organization in the world. However, there is a wide gap between this broad scientific consensus and public opinion. Internet blogs have strongly contributed to this consensus gap by fomenting misunderstandings of AGW causes and consequences. Polar bears (Ursus maritimus) have become a "poster species" for AGW, making them a target of those denying AGW evidence. Here, focusing on Arctic sea ice and polar bears, we show that blogs that deny or downplay AGW disregard the overwhelming scientific evidence of Arctic sea-ice loss and polar bear vulnerability. By denying the impacts of AGW on polar bears, bloggers aim to cast doubt on other established ecological consequences of AGW, aggravating the consensus gap. To counter misinformation and reduce this gap, scientists should directly engage the public in the media and blogosphere.

SOB-CS is an interference-based computational model ofworking memory that explains findings from simple and com-plex span experiments. According to the model’s mechanismof interference by superposition, high similarity between mem-ory items and subsequently processed distractors is beneficialbecause the more a distractor is similar to an item, the morethey share similar units, leading to less distortion of the mem-ory item. When time allows, SOB-CS removes interfering dis-tractors from memory by unbinding them from their context.The combination of these two mechanisms leads to the predic-tion that when free time is long enough to remove the distrac-tors entirely, similarity between items and distractors shouldno longer be beneficial to memory performance. The aim ofthe present study was to test this prediction. Adult participantsperformed a complex-span task in which the free time follow-ing each distractor and the similarity between items and dis-tractors were varied. As predicted by the model, we observeda positive effect of the similarity between items and distrac-tors, and a negative effect of pace on the mean working mem-ory performance. However, we did not observe the predictedinteraction. An analysis of the errors produced during recallshowed that longer free time reduced the tendency of distrac-tors to intrude in recall much less than the model predicted.The SOB-CS model accounted well for the data after a sub-stantial reduction of the removal-rate parameter.

Algorithms for approximate Bayesian inference, such asMonte Carlo methods, provide one source of models of howpeople may deal with uncertainty in spite of limited cognitiveresources. Here, we model learning as a process of sequentialsampling, or ‘particle filtering’, and suggest that an individ-ual’s working memory capacity (WMC) may be usefully mod-elled in terms of the number of samples, or ‘particles’, that areavailable for inference. The model qualitatively captures twodistinct effects reported recently, namely that individuals withhigher WMC are better able to (i) learn novel categories, and(ii) flexibly switch between different categorization strategies.

Anti‐science attitudes can be resilient to scientific evidence if they are rooted in psychological motives. One such motive is trait reactance, which refers to the need to react with opposition when one's freedom of choice has been threatened. In three studies, we investigated trait reactance as a psychological motivation to reject vaccination. In the longitudinal studies (n = 199; 293), we examined if trait reactance measured before the COVID‐19 pandemic was related to people's willingness to get vaccinated against COVID‐19 up to 2 years later during the pandemic. In the experimental study (n = 398), we tested whether trait reactance makes anti‐vaccination attitudes more resistant to information and whether this resistance can be mitigated by framing the information to minimize the risk of triggering state reactance. The longitudinal studies showed that higher trait reactance before the COVID‐19 pandemic was related to lower willingness to get vaccinated against COVID‐19. Our experimental study indicated that highly reactant individuals' willingness to vaccinate was unaffected by the amount and framing of the information provided. Trait reactance has a strong and durable impact on vaccination willingness. This highlights the importance of considering the role of trait reactance in people's vaccination‐related decision‐making. [ABSTRACT FROM AUTHOR]