What role does deliberation play in susceptibility to political misinformation and "fake news"? The Motivated System 2 Reasoning (MS2R) account posits that deliberation causes people to fall for fake ...news, because reasoning facilitates identity-protective cognition and is therefore used to rationalize content that is consistent with one's political ideology. The classical account of reasoning instead posits that people ineffectively discern between true and false news headlines when they fail to deliberate (and instead rely on intuition). To distinguish between these competing accounts, we investigated the causal effect of reasoning on media truth discernment using a 2-response paradigm. Participants (N = 1,635 Mechanical Turkers) were presented with a series of headlines. For each, they were first asked to give an initial, intuitive response under time pressure and concurrent working memory load. They were then given an opportunity to rethink their response with no constraints, thereby permitting more deliberation. We also compared these responses to a (deliberative) 1-response baseline condition where participants made a single choice with no constraints. Consistent with the classical account, we found that deliberation corrected intuitive mistakes: Participants believed false headlines (but not true headlines) more in initial responses than in either final responses or the unconstrained 1-response baseline. In contrast-and inconsistent with the Motivated System 2 Reasoning account-we found that political polarization was equivalent across responses. Our data suggest that, in the context of fake news, deliberation facilitates accurate belief formation and not partisan bias.
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Objective
Fake news represents a particularly egregious and direct avenue by which inaccurate beliefs have been propagated via social media. We investigate the psychological profile of individuals ...who fall prey to fake news.
Method
We recruited 1,606 participants from Amazon’s Mechanical Turk for three online surveys.
Results
The tendency to ascribe profundity to randomly generated sentences—pseudo‐profound bullshit receptivity—correlates positively with perceptions of fake news accuracy, and negatively with the ability to differentiate between fake and real news (media truth discernment). Relatedly, individuals who overclaim their level of knowledge also judge fake news to be more accurate. We also extend previous research indicating that analytic thinking correlates negatively with perceived accuracy by showing that this relationship is not moderated by the presence/absence of the headline’s source (which has no effect on accuracy), or by familiarity with the headlines (which correlates positively with perceived accuracy of fake and real news).
Conclusion
Our results suggest that belief in fake news may be driven, to some extent, by a general tendency to be overly accepting of weak claims. This tendency, which we refer to as reflexive open‐mindedness, may be partly responsible for the prevalence of epistemically suspect beliefs writ large.
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The Psychology of Fake News Pennycook, Gordon; Rand, David G.
Trends in cognitive sciences,
20/May , Volume:
25, Issue:
5
Journal Article
Peer reviewed
Open access
We synthesize a burgeoning literature investigating why people believe and share false or highly misleading news online. Contrary to a common narrative whereby politics drives susceptibility to fake ...news, people are ‘better’ at discerning truth from falsehood (despite greater overall belief) when evaluating politically concordant news. Instead, poor truth discernment is associated with lack of careful reasoning and relevant knowledge, and the use of heuristics such as familiarity. Furthermore, there is a substantial disconnect between what people believe and what they share on social media. This dissociation is largely driven by inattention, more so than by purposeful sharing of misinformation. Thus, interventions can successfully nudge social media users to focus more on accuracy. Crowdsourced veracity ratings can also be leveraged to improve social media ranking algorithms.
Recent evidence contradicts the common narrative that partisanship and politically motivated reasoning explain why people fall for 'fake news'.Poor truth discernment is linked to a lack of careful reasoning and relevant knowledge, as well as to the use of familiarity and source heuristics.There is also a large disconnect between what people believe and what they will share on social media, and this is largely driven by inattention rather than by purposeful sharing of misinformation.Effective interventions can nudge social media users to think about accuracy, and can leverage crowdsourced veracity ratings to improve social media ranking algorithms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Does cooperating require the inhibition of selfish urges? Or does "rational" self-interest constrain cooperative impulses? I investigated the role of intuition and deliberation in cooperation by ...meta-analyzing 67 studies in which cognitive-processing manipulations were applied to economic cooperation games (total N = 17,647; no indication of publication bias using Egger's test, Begg's test, or p-curve). My meta-analysis was guided by the social heuristics hypothesis, which proposes that intuition favors behavior that typically maximizes payoffs, whereas deliberation favors behavior that maximizes one's payoff in the current situation. Therefore, this theory predicts that deliberation will undermine pure cooperation (i.e., cooperation in settings where there are few future consequences for one's actions, such that cooperating is not in one's self-interest) but not strategic cooperation (i.e., cooperation in settings where cooperating can maximize one's payoff). As predicted, the meta-analysis revealed 17.3% more pure cooperation when intuition was promoted over deliberation, but no significant difference in strategic cooperation between more intuitive and more deliberative conditions.
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Combining evolutionary models with behavioral experiments can generate powerful insights into the evolution of human behavior. The emergence of online labor markets such as Amazon Mechanical Turk ...(AMT) allows theorists to conduct behavioral experiments very quickly and cheaply. The process occurs entirely over the computer, and the experience is quite similar to performing a set of computer simulations. Thus AMT opens the world of experimentation to evolutionary theorists. In this paper, I review previous work combining theory and experiments, and I introduce online labor markets as a tool for behavioral experimentation. I review numerous replication studies indicating that AMT data is reliable. I also present two new experiments on the reliability of self-reported demographics. In the first, I use IP address logging to verify AMT subjects' self-reported country of residence, and find that 97% of responses are accurate. In the second, I compare the consistency of a range of demographic variables reported by the same subjects across two different studies, and find between 81% and 98% agreement, depending on the variable. Finally, I discuss limitations of AMT and point out potential pitfalls. I hope this paper will encourage evolutionary modelers to enter the world of experimentation, and help to strengthen the bond between theoretical and empirical analyses of the evolution of human behavior.
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Reducing the spread of misinformation, especially on social media, is a major challenge. We investigate one potential approach: having social media platformalgorithms preferentially display content ...from news sources that users rate as trustworthy. To do so, we ask whether crowdsourced trust ratings can effectively differentiate more versus less reliable sources. We ran two preregistered experiments (n = 1,010 from Mechanical Turk and n = 970 from Lucid) where individuals rated familiarity with, and trust in, 60 news sources from three categories: (i) mainstream media outlets, (ii) hyperpartisan websites, and (iii) websites that produce blatantly false content (“fake news”). Despite substantial partisan differences, we find that laypeople across the political spectrum rated mainstream sources as far more trustworthy than either hyperpartisan or fake news sources. Although this difference was larger for Democrats than Republicans—mostly due to distrust of mainstream sources by Republicans—every mainstream source (with one exception) was rated as more trustworthy than every hyperpartisan or fake news source across both studies when equally weighting ratings of Democrats and Republicans. Furthermore, politically balanced layperson ratings were strongly correlated (r = 0.90) with ratings provided by professional fact-checkers. We also found that, particularly among liberals, individuals higher in cognitive reflection were better able to discern between low- and high-quality sources. Finally, we found that excluding ratings from participants who were not familiar with a given news source dramatically reduced the effectiveness of the crowd. Our findings indicate that having algorithms up-rank content from trusted media outlets may be a promising approach for fighting the spread of misinformation on social media.
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There is an increasing imperative for psychologists and other behavioral scientists to understand how people behave on social media. However, it is often very difficult to execute experimental ...research on actual social media platforms, or to link survey responses to online behavior in order to perform correlational analyses. Thus, there is a natural desire to use self-reported behavioral intentions in standard survey studies to gain insight into online behavior. But are such hypothetical responses hopelessly disconnected from actual sharing decisions? Or are online survey samples via sources such as Amazon Mechanical Turk (MTurk) so different from the average social media user that the survey responses of one group give little insight into the on-platform behavior of the other? Here we investigate these issues by examining 67 pieces of political news content. We evaluate whether there is a meaningful relationship between (i) the level of sharing (tweets and retweets) of a given piece of content on Twitter, and (ii) the extent to which individuals (total N = 993) in online surveys on MTurk reported being willing to share that same piece of content. We found that the same news headlines that were more likely to be hypothetically shared on MTurk were also shared more frequently by Twitter users, r = .44. For example, across the observed range of MTurk sharing fractions, a 20 percentage point increase in the fraction of MTurk participants who reported being willing to share a news headline on social media was associated with 10x as many actual shares on Twitter. We also found that the correlation between sharing and various features of the headline was similar using both MTurk and Twitter data. These findings suggest that self-reported sharing intentions collected in online surveys are likely to provide some meaningful insight into what content would actually be shared on social media.
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Humans often cooperate with strangers, despite the costs involved. A long tradition of theoretical modeling has sought ultimate evolutionary explanations for this seemingly altruistic behavior. More ...recently, an entirely separate body of experimental work has begun to investigate cooperation’s proximate cognitive underpinnings using a dual-process framework: Is deliberative self-control necessary to reign in selfish impulses, or does self-interested deliberation restrain an intuitive desire to cooperate? Integrating these ultimate and proximate approaches, we introduce dual-process cognition into a formal game-theoretic model of the evolution of cooperation. Agents play prisoner’s dilemma games, some of which are one-shot and others of which involve reciprocity. They can either respond by using a generalized intuition, which is not sensitive to whether the game is one-shot or reciprocal, or pay a (stochastically varying) cost to deliberate and tailor their strategy to the type of game they are facing. We find that, depending on the level of reciprocity and assortment, selection favors one of two strategies: intuitive defectors who never deliberate, or dual-process agents who intuitively cooperate but sometimes use deliberation to defect in one-shot games. Critically, selection never favors agents who use deliberation to override selfish impulses: Deliberation only serves to undermine cooperation with strangers. Thus, by introducing a formal theoretical framework for exploring cooperation through a dual-process lens, we provide a clear answer regarding the role of deliberation in cooperation based on evolutionary modeling, help to organize a growing body of sometimes-conflicting empirical results, and shed light on the nature of human cognition and social decision making.
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What explains variability in norms of cooperation across organizations and cultures? One answer comes from the tendency of individuals to internalize typically successful behaviors as norms. ...Different institutional structures can cause different behavioral norms to be internalized. These norms are then carried over into atypical situations beyond the reach of the institution. Here, we experimentally demonstrate such spillovers. First, we immerse subjects in environments that do or do not support cooperation using repeated prisoner’s dilemmas. Afterwards, we measure their intrinsic prosociality in one-shot games. Subjects from environments that support cooperation are more prosocial, more likely to punish selfishness, and more trusting in general. Furthermore, these effects are most pronounced among subjects who use heuristics, suggesting that intuitive processes play a key role in the spillovers we observe. Our findings help to explain variation in one-shot anonymous cooperation, linking this intrinsically motivated prosociality to the externally imposed institutional rules experienced in other settings.
Data, as supplemental material, are available at
http://dx.doi.org/10.1287/mnsc.2015.2168
.
This paper was accepted by Uri Gneezy, behavioral economics.
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Online labor markets such as Amazon Mechanical Turk (MTurk) offer an unprecedented opportunity to run economic game experiments quickly and inexpensively. Using Mturk, we recruited 756 subjects and ...examined their behavior in four canonical economic games, with two payoff conditions each: a stakes condition, in which subjects' earnings were based on the outcome of the game (maximum earnings of $1); and a no-stakes condition, in which subjects' earnings are unaffected by the outcome of the game. Our results demonstrate that economic game experiments run on MTurk are comparable to those run in laboratory settings, even when using very low stakes.
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