Elementary Signal Detection and Threshold Theory

Kellen, David; Klauer, Karl Christoph

doi:10.1002/9781119170174.epcn505

Cited by 29 publications

(50 citation statements)

References 124 publications

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“…This is problematic in a statistical sense, because psychometrically the accuracy index should be unaffected by the response bias to be interpretable. Yet, d' is only unaffected by c if the distributions of familiarity rating of signal CLAIMS ABOUT OVERCLAIMING 6 trials (which translates to the hit rate) and familiarity ratings of noise trials (which corresponds to the false alarm rate) are normally distributed, have equal variances (Kellen & Klauer, 2018;Stanislaw & Todorov, 1999), and are uncorrelated. However, these assumptions are usually not met (e.g., DeCarlo, 2010;MacMillan & Creelman, 2005;Mickes et al, 2007;Selker et al, 2019;Stanislaw & Todorov, 1999;Starns & Ratcliff, 2014;Swets, 1986).…”

Section: Scoring Methods Of Overclaiming: Raw Responses Vs Signal Dementioning

confidence: 99%

Testing competing claims about overclaiming

et al. 2020

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Overclaiming has been described as people's tendency to overestimate their cognitive abilities in general and their knowledge in particular. We discuss four different perspectives on the phenomenon of overclaiming that have been proposed in the research literature: Overclaiming as a result of a) self-enhancement tendencies, b) as a cognitive bias (e.g., hindsight bias, memory bias), c) as proxy for cognitive abilities, and d) as sign of creative engagement. Moreover, we discuss two different scoring methods for an OCQ (signal detection theory vs. familiarity ratings). To distinguish between the different viewpoints of what overclaiming is, we juxtaposed overclaiming, as indicated by claiming familiarity with non-existent terms, with fluid and crystallized intelligence, self-reported knowledge, creativity, faking ability, and personality. Overclaiming was measured with a newly comprised overclaiming questionnaire. Results of several latent variable analyses based upon a multivariate study with 298 participants were: First, overclaiming is neither predicted by honesty-humility nor faking ability and therefore reflects something different than mere self-enhancement tendencies. Second, overclaiming is not predicted by crystallized intelligence, but is highly predictive of self-reported knowledge and, thus, not suitable as an index or a proxy for cognitive abilities. Finally, overclaiming is neither related to divergent thinking and originality, and only moderately predicted by self-reported openness creativity from the HEXACO which means that overclaiming does not reflect creative ability. In sum, our results favor an interpretation of overclaiming as a phenomenon that requires more than self-enhancement motivation, in contrast to the claim that was initially proposed in the literature.

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Section: Scoring Methods Of Overclaiming: Raw Responses Vs Signal Dementioning

confidence: 99%

Testing competing claims about overclaiming

et al. 2020

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“…Under this selective-influence assumption, we can derive clear predictions from both models about the way that the probability of "same" responses in Same and Change trials covaries across values of g or τ . These predictions are commonly referred to as the models' Receiver Operating Characteristic (ROC) functions (Green & Swets, 1966;Kellen & Klauer, 2018). We illustrate these ROCs in Figure 3 .…”

Section: Response-bias Manipulations and Rocsmentioning

confidence: 99%

Bias in confidence: A critical test for discrete-state models of change detection.

Winiger¹,

Singmann²,

Kellen³

2021

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…We use a signal detection framework (e.g. Kellen & Klauer, 2018;Macmillan, 2002) to relate hits and false alarms in a principled manner to obtain independent measures of memory performance and response bias. We assume that the presentation of positive and negative probes evoke memory signals whose distributions can be described by a normal (i.e., Gaussian) distribution with variance 1, and mean μ for the negative probes, and mean μ P for the positive probes.…”

Section: Signal Detection Frameworkmentioning

confidence: 99%