Recognition memory models and binary-response ROCs: A comparison by minimum description length

Kellen, David; Klauer, Karl Christoph; Bröder, Arndt

doi:10.3758/s13423-013-0407-2

Cited by 64 publications

(76 citation statements)

References 115 publications

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“…Models with higher NML values are considered to outperform their competitors. Several studies have shown the superiority of NML in comparison to other statistics and its close relation to Bayesian model-selection approaches (see Kellen et al 2013). …”

Section: Individual Classification Of Strong Risk Attitudesmentioning

confidence: 99%

Individual classification of strong risk attitudes: An application across lottery types and age groups

2017

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Empirical evaluations of risk attitudes often rely on a weak definition of risk that concerns preferences towards risky and riskless options (e.g., a lottery vs. a sure outcome). A large body of work has shown that individuals tend to be weak risk averse in choice contexts involving risky and riskless gains but weak risk seeking in contexts involving losses, a phenomenon known as the reflection effect. Recent attempts to evaluate age differences in risk attitudes have relied on this weak definition, testing whether the reflection effect increases or diminishes as we grow older. The present work argues that weak risk attitudes have limited generalizability and proposes the use of a strong definition of risk that is concerned with preferences towards options with the same expected value but different degrees of risk (i.e., outcome variance). A reanalysis of previouslypublished data and the results from a new study show that only a minority of individuals manifests the reflection effect under a strong definition of risk, and that, when facing certain lottery-pair types, older adults appear to be more risk seeking than younger adults.

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Section: Individual Classification Of Strong Risk Attitudesmentioning

confidence: 99%

Individual classification of strong risk attitudes: An application across lottery types and age groups

2017

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“…Among these are the distorting, but often ignored influences of individual differences in memory performance and response-bias settings and of analogous differences between items Rouder & Lu, 2005), an over-reliance on one method, the confidence-rating paradigm, and the absence of model-selection measures that take into account differences between models in flexibility related to functional form. The purpose of the present manuscript is to address this last problem for the important case of confidence-rating data in a similar fashion as described by Kellen et al (2013) and Klauer and Kellen (2011a) for binary OLD/NEW ROC data (see also Kellen & Klauer, 2011). It turns out that some of the solutions developed here are even more widely applicable than defined by this original purpose as elaborated on below.…”

Section: Receiver Operating Characteristicsmentioning

confidence: 92%

“…Despite decades of research, the question which of the above models provide the best description of the data in recognition memory is still under debate (e.g., Bröder & Schütz, 2009;Dube & Rotello, 2012;Kellen, Klauer, & Bröder, 2013;Kellen, Singmann, Vogt, & Klauer, 2015;Onyper, Zhang, & Howard, 2010;Province & Rouder, 2012;Wixted, 2007 andYonelinas &Parks, 2007). To our minds, several factors have prevented this very productive field from reaching a clear and non-contested decision on the most adequate model.…”

Section: Receiver Operating Characteristicsmentioning

confidence: 96%

The flexibility of models of recognition memory: The case of confidence ratings

Klauer

Kellen

2015

Journal of Mathematical Psychology

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“…Randomness and complexity also play an important role in modern approaches to selecting the "best" among a set of candidate models (i.e., model selection; e.g., Myung et al, 2006;Kellen et al, 2013), as discussed in more detail below in the section called "Relationship to complexity based model selection".…”

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confidence: 99%

Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method

et al. 2015

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Kolmogorov-Chaitin complexity has long been believed to be impossible to approximate when it comes to short sequences (e.g. of length 5-50). However, with the newly developed coding theorem method the complexity of strings of length 2-11 can now be numerically estimated. We present the theoretical basis of algorithmic complexity for short strings (ACSS) and describe an R-package providing functions based on ACSS that will cover psychologists' needs and improve upon previous methods in three ways: (1) ACSS is now available not only for binary strings, but for strings based on up to 9 different symbols, (2) ACSS no longer requires time-consuming computing, and (3) a new approach based on ACSS gives access to an estimation of the complexity of strings of any length. Finally, three illustrative examples show how these tools can be applied to psychology. Keywords Algorithmic complexity · Randomness · Subjective probability · Coding theorem method Randomness and complexity are two concepts which are intimately related and are both central to numerous recent developments in various fields, including finance (Taufemback et al., 2011, Brandouy et al., 2012, linguistics (Gruber, 2010;Naranan, 2011), neuropsychology (Machado et al., 2010;Fernández et al., 2011Fernández et al., , 2012, psychiatry (Yang and Tsai, 2012;Takahashi 2013), genetics (Yagil, 2009;Ryabko et al. 2013), sociology (Elzinga, 2010 and the behavioral sciences (Watanabe et al., 2003;Scafetta et al., 2009). In psychology, randomness and complexity have recently attracted interest, following the realization that they could shed light on a diversity of previously undeciphered behaviors and mental processes. It has been found, for instance, that the subjective difficulty of a concept is directly related to its "boolean complexity", defined as the shortest logical description of a concept (Feldman, 2000(Feldman, , 2003(Feldman, , 2006. In the same vein, visual detection of shapes has been shown to be related to contour complexity (Wilder et al., 2011).More generally, perceptual organization itself has been described as based on simplicity or, equivalently, likelihood (Chater, 1996;Chater and Vitányi, 2003), in a model reconciling the complexity approach (perception is organized to minimize complexity) and a probability approach (perception is organized to maximize likelihood), very much in line with our view in this paper. Even the perception of similarity may be viewed through the lens of (conditional) complexity (Hahn et al., 2003).Randomness and complexity also play an important role in modern approaches to selecting the "best" among a set of candidate models (i.e., model selection; e.g., Myung et Behav Res (2016) 48:314-329 315 al., 2006Kellen et al., 2013), as discussed in more detail below in the section called "Relationship to complexity based model selection".Complexity can also shed light on short term memory storage and recall, more specifically, on the process underlying chunking. It is well known that the short term memory span lies b...

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Recognition memory models and binary-response ROCs: A comparison by minimum description length

Cited by 64 publications

References 115 publications

Individual classification of strong risk attitudes: An application across lottery types and age groups

Individual classification of strong risk attitudes: An application across lottery types and age groups

The flexibility of models of recognition memory: The case of confidence ratings

Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method

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