A rational model of the Dunning–Kruger effect supports insensitivity to evidence in low performers

Jansen, Rachel; Rafferty, Anna N.; Griffiths, Thomas L.

doi:10.1038/s41562-021-01057-0

Cited by 63 publications

(61 citation statements)

References 24 publications

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“…In view of the Dunning-Kruger effect's popularity and the mixed results on its robustness, research on it continues to be important. Here, we concur with other authors (Gignac & Zajenkowski, 2020;Jansen et al, 2021) that future work should refrain from splitting data into quartiles, as this procedure does not offer the kind of resolution needed to provide sufficient answers regarding this effect. There are likely more insights to be gained from using more adequate and easily implemented statistical methods described by Gignac and Zajenkowski (2020) or the modeling approach applied by Jansen and colleagues (2021).…”

Section: Discussionsupporting

confidence: 90%

Less intelligent and unaware? Accuracy and Dunning-Kruger effects for self-estimates of different aspects of intelligence

Hofer¹,

Mraulak²,

Grinschgl³

et al. 2021

Preprint

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People’s perceptions of their intelligence correlate only moderately with objective intelligence measures. On average, people overestimate themselves. According to the popular Dunning-Kruger effect, this is particularly true for low performers: Across many domains, those in the lowest quantile overestimate their abilities the most. However, recent work using improved statistical approaches found little support for a Dunning-Kruger effect in general intelligence. We investigated the accuracy of and Dunning-Kruger effects in self-estimates of general, verbal, numerical, and spatial intelligence—domains that differed in how well they can be judged in the past. 281 participants completed self-estimates and intelligence measures online. Self-estimates showed mostly moderate correlational accuracy that was slightly higher for numerical intelligence and lower for verbal intelligence. Across domains, participants rated their intelligence as above-average. However, as their intelligence was indeed high, this was not an overestimation. While standard analyses indicated Dunning-Kruger effects in three out of four measures, improved statistical methods only yielded some support for one in verbal intelligence: People with lower verbal intelligence tended to have less self-knowledge about it. The generalizability of these findings is limited to young, highly educated populations. Nevertheless, our results contribute to a growing literature questioning the generality of the Dunning-Kruger effect.

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Section: Discussionsupporting

confidence: 90%

Less intelligent and unaware? Accuracy and Dunning-Kruger effects for self-estimates of different aspects of intelligence

Hofer¹,

Mraulak²,

Grinschgl³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Though, this type of miscalibration does not extend to all domains, these effects are typically pervasive (Jansen et al, 2017;Nelson et al, 1991;Tyszka & Zielonka, 2002;Zell et al, 2020). Additionally, though this pattern of results (often referred to as the Dunning-Kruger effect) is one of the more highly replicable and robust effects in psychology (Jansen et al, 2021;Zell et al, 2020), it is not without its critics. Indeed, there has been some debate as to whether these patterns of results reflect individual differences in metacognition (e.g., Dunning et al, 2003;Kruger & Dunning, 1999;McIntosh et al, 2019), differing memory processes (Muller et al, 2021), the "better-than-average" heuristic (Krueger & Mueller, 2002), or biases motivated toward self-enhancement (e.g., Anderson et al, 2012;Roy & Leirsch, 2013;Pronin et al, 2002).…”

Section: Criticisms Of Dunning-kruger Effectsmentioning

confidence: 99%

“…Combining both a computational modelling approach with a large-scale direct replication (N = 7068) of Kruger's and Dunning's (1999) original study, Jansen and colleagues established that so-called Dunning-Kruger effects were best explained in their data by a rational model which assumes significantly reduced sensitivity to errors among poor performers (Jansen et al, 2021;Mazor & Fleming, 2021). This account also aligns with recent work showing that people scoring higher in bullshit receptivity appear to be largely insensitive to linguistic differences in bullshit and non-bullshit leading them to misjudge the superficial profoundness of a message as roughly equivalent to its inherent profoundness (Littrell et al, 2021).…”

Section: Criticisms Of Dunning-kruger Effectsmentioning

confidence: 99%

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

Littrell¹,

Ja²

2021

Preprint

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The growing prevalence of misinformation (i.e., bullshit) in society carries with it an increased need to understand the processes underlying many people’s susceptibility to falling for it. Though several cognitive and metacognitive variables have been found to be associated with a greater propensity to falling for bullshit, little attention has been paid to people’s perceptions of and confidence in their own ability to detect it and the phenomenology of the thinking processes they employ when evaluating misleading information. Here we report two studies (N = 412) examining the associations between bullshit detection accuracy, confidence in one’s bullshit detection abilities, and the metacognitive experience of evaluating potentially misleading information. We find that people with the poorest bullshit detection performance grossly overestimate their detection abilities and significantly overplace those abilities compared to others. Additionally, highly bullshit receptive people reported using both intuitive and reflective thinking processes when evaluating misleading information. These results suggest that some people may have a “bullshit blind spot” and that traditional miserly processing explanations of receptivity to misleading information may be insufficient to fully account for these effects.

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“…Conventional statistical testing could lead to the result that the model is 'correct' because a zero or negative correlation for the two paths is unlikely. However, a risky test of the model would need to use well-founded specifications to simulate data and compare it to empirical data, only suggesting retention of the model if the two align, as this implies that the empirical data was generated by the specified relationships (Jansen et al, 2021;Robinaugh et al, 2020). Specifications of parts of the digital well-being model need to consider causality and cyclical relationships.…”

Section: Sketching An Application Of the Digital Well-being Modelmentioning

confidence: 99%

“…Researchers need to explicate the assumed mechanisms that generated the observed data-directed acyclic graphs can be employed to clearly state such a data-generating model (Elwert & Winship, 2014;Grosz et al, 2020). Ideally, formal models based on these precise assumptions are used to simulate data which is then contrasted with longitudinal empirical data (Jansen et al, 2021). Applications of advanced techniques such as random-intercept cross-lagged panel models require a minimum of three panel waves (Hamaker et al, 2015) and the testing of even simple bivariate hypotheses becomes relatively intricate (Thomas et al, 2021).…”

Section: Future Directionsmentioning

confidence: 99%

Digital Well-Being Theory and Research

Büchi¹

2020

Preprint

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How can we live a good life both thanks to and despite the constant use of digital media? The presented proto-theory of digital well-being offers guidance for theory development and theory integration to enable a cumulative science of the impacts of digital media use on well-being. The proto-theory describes the nature of and connections between three relevant phenomena—digital practices, harms/benefits, and well-being—and creates a blueprint for explanatory theories. It focuses on the mechanisms between digital media use and well-being by analyzing the often concomitant harms and benefits arising from individual’s digital practices within structural conditions; these mechanisms are theoretically plausible causal chains that lead from a specific manifestation of digital practice to a relevant individual well-being outcome with some regularity. Future digital well-being studies should prioritize descriptive validity and longitudinal designs.

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A rational model of the Dunning–Kruger effect supports insensitivity to evidence in low performers

Cited by 63 publications

References 24 publications

Less intelligent and unaware? Accuracy and Dunning-Kruger effects for self-estimates of different aspects of intelligence

Less intelligent and unaware? Accuracy and Dunning-Kruger effects for self-estimates of different aspects of intelligence

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

Digital Well-Being Theory and Research

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