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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.

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]

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.