Bullshit blind spots: the roles of miscalibration and information processing in bullshit detection

Shane Littrell & Jonathan A. Fugelsang

(2023) Thinking & Reasoning

DOI: 10.1080/13546783.2023.2189163

Abstract

The growing prevalence of misleading information (i.e., bullshit) in society carries with it an increased need to understand the processes underlying many people’s susceptibility to falling for it. Here we report two studies (N = 412) examining the associations between one’s ability to detect pseudo-profound bullshit, confidence in one’s bullshit detection abilities, and the metacognitive experience of evaluating potentially misleading information. We find that people with the lowest (highest) bullshit detection performance overestimate (underestimate) their detection abilities and overplace (underplace) those abilities when compared to others. Additionally, people reported using both intuitive and reflective thinking processes when evaluating misleading information. Taken together, these results show that both highly bullshit-receptive and highly bullshit-resistant people are largely unaware of the extent to which they can detect bullshit and that traditional miserly processing explanations of receptivity to misleading information may be insufficient to fully account for these effects.

The research is here.

Here's my summary:

The authors of the article argue that people have two main blind spots when it comes to detecting bullshit: miscalibration and information processing. Miscalibration is the tendency to overestimate our ability to detect bullshit. We think we're better at detecting bullshit than we actually are.

Information processing is the way that we process information in order to make judgments. The authors argue that we are more likely to be fooled by bullshit when we are not paying close attention or when we are processing information quickly.

The authors also discuss some strategies for overcoming these blind spots. One strategy is to be aware of our own biases and limitations. We should also be critical of the information that we consume and take the time to evaluate evidence carefully.

Overall, the article provides a helpful framework for understanding the challenges of bullshit detection. It also offers some practical advice for overcoming these challenges.

Here are some additional tips for detecting bullshit:

• Be skeptical of claims that seem too good to be true.
• Look for evidence to support the claims that are being made.
• Be aware of the speaker or writer's motives.
• Ask yourself if the claims are making sense and whether they are consistent with what you already know.
• If you're not sure whether something is bullshit, it's better to err on the side of caution and be skeptical.

'You can't bullshit a bullshitter' (or can you?): Bullshitting frequency predicts receptivity to various types of misleading information

Littrell, S., Risko, E. F., & Fugelsang, J. A. (2021).

The British journal of social psychology, 60(4), 

1484–1505.

https://doi.org/10.1111/bjso.12447

Abstract

Research into both receptivity to falling for bullshit and the propensity to produce it have recently emerged as active, independent areas of inquiry into the spread of misleading information. However, it remains unclear whether those who frequently produce bullshit are inoculated from its influence. For example, both bullshit receptivity and bullshitting frequency are negatively related to cognitive ability and aspects of analytic thinking style, suggesting that those who frequently engage in bullshitting may be more likely to fall for bullshit. However, separate research suggests that individuals who frequently engage in deception are better at detecting it, thus leading to the possibility that frequent bullshitters may be less likely to fall for bullshit. Here, we present three studies (N = 826) attempting to distinguish between these competing hypotheses, finding that frequency of persuasive bullshitting (i.e., bullshitting intended to impress or persuade others) positively predicts susceptibility to various types of misleading information and that this association is robust to individual differences in cognitive ability and analytic cognitive style.

Conclusion

Gaining a better understanding of the differing ways in which various types of misleading information are transmitted and received is becoming increasingly important in the information age (Kristansen & Kaussler, 2018). Indeed, an oft-repeated maxim in popular culture is, “you can’t bullshit a bullshitter.” While folk wisdom may assert that this is true, the present investigation suggests that the reality is a bit more complicated. Our primary aim was to examine the extent to which bullshitting frequency is associated with susceptibility to falling for bullshit. Overall, we found that persuasive bullshitters (but not evasive bullshitters) were more receptive to various types of bullshit and, in the case of pseudo-profound statements, even when controlling for factors related to intelligence and analytic thinking. These results enrich our understanding of the transmission and detection of certain types of misleading information, specifically the associations between the propensity to produce and the tendency to fall for bullshit and will help to inform future research in this growing area of scholarship.

A pdf is here.

A framework for understanding reasoning errors: From fake news to climate change and beyond

Pennycook, G. (2022, August 31).

https://doi.org/10.31234/osf.io/j3w7d

Abstract

Humans have the capacity, but perhaps not always the willingness, for great intelligence. From global warming to the spread of misinformation and beyond, our species is facing several major challenges that are the result of the limits of our own reasoning and decision-making. So, why are we so prone to errors during reasoning? In this chapter, I will outline a framework for understanding reasoning errors that is based on a three-stage dual-process model of analytic engagement (intuition, metacognition, and reason). The model has two key implications: 1) That a mere lack of deliberation and analytic thinking is a primary source of errors and 2) That when deliberation is activated, it generally reduces errors (via questioning intuitions and integrating new information) than increasing errors (via rationalization and motivated reasoning). In support of these claims, I review research showing the extensive predictive validity of measures that index individual differences in analytic cognitive style – even beyond explicit errors per se. In particular, analytic thinking is not only predictive of skepticism about a wide range of epistemically suspect beliefs (paranormal, conspiratorial, COVID-19 misperceptions, pseudoscience and alternative medicines) as well as decreased susceptibility to bullshit, fake news, and misinformation, but also important differences in people’s moral judgments and values as well as their religious beliefs (and disbeliefs). Furthermore, in some (but not all cases), there is evidence from experimental paradigms that support a causal role of analytic thinking in determining judgments, beliefs, and behaviors. The findings reviewed here provide some reason for optimism for the future: It may be possible to foster analytic thinking and therefore improve the quality of our decisions.

Evaluating the evidence: Does reason matter?

Thus far, I have prioritized explaining the various alternative frameworks. I will now turn to an in-depth review of some of the key relevant evidence that helps mediate between these accounts. I will organize this review around two key implications that emerge from the framework that I have proposed.

First, the primary difference between the three-stage model (and related dual-process models) and the social-intuitionist models (and related intuitionist models) is that the former argues that people should be able to overcome intuitive errors using deliberation whereas the latter argues that reason is generally infirm and therefore that intuitive errors will simply dominate. Thus, the reviewed research will investigate the apparent role of deliberation in driving people’s choices, beliefs, and behaviors.

Second, the primary difference between the three-stage model (and related dual-process models) and the identity-protective cognition model is that the latter argues that deliberation facilitates biased information processing whereas the former argues that deliberation generally facilitates accuracy. Thus, the reviewed research will also focus on whether deliberation is linked with inaccuracy in politically-charged or identity-relevant contexts.

The research is here.