Science beliefs, political ideology, and cognitive sophistication

Some theoretical models assume that a primary source of contention surrounding science belief is political and that partisan disagreement drives beliefs; other models focus on basic science knowledge and cognitive sophistication, arguing that they facilitate proscientific beliefs. To test these competing models, we identified a range of controversial issues subject to potential ideological disagreement and examined the roles of political ideology, science knowledge, and cognitive sophistication on science beliefs. Our results indicate that there was surprisingly little partisan disagreement on a wide range of contentious scientific issues. We also found weak evidence for identity-protective cognition (where cognitive sophistication exacerbates partisan disagreement); instead, cognitive sophistication (i.e., reasoning ability) was generally associated with proscience beliefs. In two studies focusing on anthropogenic climate change, we found that increased political motivations did not increase polarization among individuals who are higher in cognitive sophistication, which indicates that increased political motivations might not have as straightforward an impact on science beliefs as has been assumed in the literature. Finally, our findings indicate that basic science knowledge is the most consistent predictor of people’s beliefs about science across a wide range of issues. These results suggest that educators and policymakers should focus on increasing basic science literacy and critical thinking rather than on the ideologies that purportedly divide people

What Makes us Think? A Three-Stage Dual-Process Model of Analytic Engagement

The distinction between intuitive and analytic thinking is common in psychology. However, while often being quite clear on the characteristics of the two processes (‘Type 1’ processes are fast, autonomous, intuitive, etc. and ‘Type 2’ processes are slow, deliberative, analytic, etc.), dual-process theorists have been heavily criticized for being unclear on the factors that determine when an individual will think analytically or rely on their intuition. I address this issue by introducing a three-stage model that elucidates the bottom-up factors that cause individuals to engage Type 2 processing. According to the model, multiple Type 1 processes may be cued by a stimulus (Stage 1), leading to the potential for conflict detection (Stage 2). If successful, conflict detection leads to Type 2 processing (Stage 3), which may take the form of rationalization (i.e., the Type 1 output is verified post hoc) or decoupling (i.e., the Type 1 output is falsified). I tested key aspects of the model using a novel base-rate task where stereotypes and base-rate probabilities cued the same (non-conflict problems) or different (conflict problems) responses about group membership. My results support two key predictions derived from the model: 1) conflict detection and decoupling are dissociable sources of Type 2 processing and 2) conflict detection sometimes fails. I argue that considering the potential stages of reasoning allows us to distinguish early (conflict detection) and late (decoupling) sources of analytic thought. Errors may occur at both stages and, as a consequence, bias arises from both conflict monitoring and decoupling failures

Conflict detection in dual-process theory: Are we good at detecting when we are biased at decision making?

In the domain of reasoning and decision making, some dual-process theorists have suggested that people are highly efficient at detecting conflicting outputs engendered by competing intuitive and analytic processes (De Neys & Glumicic, 2008; De Neys, Vartanian & Goel, 2008). For example, De Neys and Glumicic (2008) demonstrated that participants’ reason longer about problems that are characterized by a conflict between a stereotypical personality description and a base-rate probability of group membership. Crucially, this increase occurred even when participants gave the nominally erroneous stereotypical response (i.e., “neglecting” the base-rate probability), indicating that their participants detected that there was a conflict and, as a result, engaged in slow, analytic processing to resolve it. However, this finding, and much of the additional support for the efficient conflict detection hypothesis, has come from base-rate neglect problems constructed with probabilities (e.g., 995 doctors and 5 nurses) that were much more extreme than typically used in studies of base-rate neglect. I varied the base-rate probabilities over five experiments and compared participants’ response time for conflict problems with non-conflict problems. It was demonstrated that the integral increase in response time for stereotypical responses to conflict problems was fully mediated by extreme probabilities. I conclude that humans are not as efficient at detecting when they are engaging in biased reasoning as De Neys and colleagues have claimed

The role of answer fluency and perceptual fluency in the monitoring and control of reasoning: Reply to Alter, Oppenheimer, and Epley

In this reply, we provide an analysis of Alter et al. (2013) response to our earlier paper (Thompson et al., 2013). In that paper, we reported difficulty in replicating Alter, Oppenheimer, Epley, and Eyre’s (2007) main finding, namely that a sense of disfluency produced by making stimuli difficult to perceive, increased accuracy on a variety of reasoning tasks. Alter, Oppenheimer, and Epley (2013) argue that we misunderstood the meaning of accuracy on these tasks, a claim that we reject. We argue and provide evidence that the tasks were not too difficult for our populations (such that no amount of “metacognitive unease” would promote correct responding) and point out that in many cases performance on our tasks was well above chance or on a par with Alter et al.’s (2007) participants. Finally, we reiterate our claim that the distinction between answer fluency (the ease with which an answer comes to mind) and perceptual fluency (the ease with which a problem can be read) is genuine, and argue that Thompson et al. (2013) provided evidence that these are distinct factors that have different downstream effects on cognitive processe

How many others have shared this? Experimentally investigating the effects of social cues on engagement, misinformation, and unpredictability on social media

Unlike traditional media, social media typically provides quantified metrics of how many users have engaged with each piece of content. Some have argued that the presence of these cues promotes the spread of misinformation. Here we investigate the causal effect of social cues on users' engagement with social media posts. We conducted an experiment with N=628 Americans on a custom-built newsfeed interface where we systematically varied the presence and strength of social cues. We find that when cues are shown, indicating that a larger number of others have engaged with a post, users were more likely to share and like that post. Furthermore, relative to a control without social cues, the presence of social cues increased the sharing of true relative to false news. The presence of social cues also makes it more difficult to precisely predict how popular any given post would be. Together, our results suggest that -- instead of distracting users or causing them to share low-quality news -- social cues may, in certain circumstances, actually boost truth discernment and reduce the sharing of misinformation. Our work suggests that social cues play important roles in shaping users' attention and engagement on social media, and platforms should understand the effects of different cues before making changes to what cues are displayed and how

Quantifying attention via dwell time and engagement in a social media browsing environment

Modern computational systems have an unprecedented ability to detect, leverage and influence human attention. Prior work identified user engagement and dwell time as two key metrics of attention in digital environments, but these metrics have yet to be integrated into a unified model that can advance the theory andpractice of digital attention. We draw on work from cognitive science, digital advertising, and AI to propose a two-stage model of attention for social media environments that disentangles engagement and dwell. In an online experiment, we show that attention operates differently in these two stages and find clear evidence of dissociation: when dwelling on posts (Stage 1), users attend more to sensational than credible content, but when deciding whether to engage with content (Stage 2), users attend more to credible than sensational content. These findings have implications for the design and development of computational systems that measure and model human attention, such as newsfeed algorithms on social media.Comment: All Things Attention NeurIPS Worksho

Displaying News Source Trustworthiness Ratings Reduces Sharing Intentions for False News Posts

Professional fact-checking of individual news headlines is an effective way to fight misinformation, but it is not easily scalable, because it cannot keep pace with the massive speed at which news content gets posted on social media. Here we provide evidence for the effectiveness of ratings of news sources, instead of individual news articles. In a large pre-registered experiment with quota-sampled Americans, we find that participants are less likely to share false headlines (and more discerning of true versus false headlines) when 1-to-5 star trustworthiness ratings were applied to news headlines. This is true both when the ratings are generated by fact-checkers and by laypeople (although the effect is stronger using fact-checker ratings). We also observe a positive spillover effect: sharing discernment also increases for headlines whose source was not rated, likely because the presence of ratings on some headlines prompts users to reflect on source quality more generally. This study suggests that displaying information regarding the trustworthiness of news sources provides a scalable approach for reducing the spread of low-quality information

Beliefs About COVID-19 in Canada, the United Kingdom, and the United States: A Novel Test of Political Polarization and Motivated Reasoning

What are the psychological consequences of the increasingly politicized nature of the COVID-19 pandemic in the United States relative to similar Western countries? In a two-wave study completed early (March) and later (December) in the pandemic, we found that polarization was greater in the United States (N = 1,339) than in Canada (N = 644) and the United Kingdom. (N = 1,283). Political conservatism in the United States was strongly associated with engaging in weaker mitigation behaviors, lower COVID-19 risk perceptions, greater misperceptions, and stronger vaccination hesitancy. Although there was some evidence that cognitive sophistication was associated with increased polarization in the United States in December (but not March), cognitive sophistication was nonetheless consistently negatively correlated with misperceptions and vaccination hesitancy across time, countries, and party lines. Furthermore, COVID-19 skepticism in the United States was strongly correlated with distrust in liberal-leaning mainstream news outlets and trust in conservative-leaning news outlets, suggesting that polarization may be driven by differences in information environments

Belief bias during reasoning among religious believers and skeptics

We provide evidence that religious skeptics, as compared to believers, are both more reflective and effective in logical reasoning tasks. While recent studies have reported a negative association between an analytic cognitive style and religiosity, they focused exclusively on accuracy, making it difficult to specify potential underlying cognitive mechanisms. The present study extends the previous research by assessing both performance and response times on quintessential logical reasoning problems (syllogisms). Those reporting more religious skepticism made fewer reasoning errors than did believers. This finding remained significant after controlling for general cognitive ability, time spent on the problems, and various demographic variables. Crucial for the purpose of exploring underlying mechanisms, response times indicated that skeptics also spent more time reasoning than did believers. This novel finding suggests a possible role of response slowing during analytic problem solving as a component of cognitive style that promotes overriding intuitive first impressions. Implications for using additional processing measures, such as response time, to investigate individual differences in cognitive style are discussed