Elementary Catastrophe Theory

Wagenmakers, Eric‐Jan; Grasman, Raoul P. P. P.; Maas, Han L. J. van der; Molenaar, Peter

doi:10.1002/9781118445112.stat06427.pub2

Cited by 8 publications

(15 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We removed 141 trials from the IAT (0.4% of the trials) in Data Set 1, 4,850 trials from the IAT (3.2% of the trials) in Data Set 2, 377 trials from the IAT (0.8% of the trials) in Data Set 3,626 trials from the IAT (1.1% of the trials) in Data Set 4, 4,054 trials from the IAT (2.7% of the trials) in Data Set 5, 1,017 trials from the IAT (2.1% of the trials) in Data Set 6, and 136 trials from the IAT (0.2% of the trials) in Data Set 7. As suggested by Wagenmakers et al (2007), we corrected the percentage of correct responses that equaled exactly 1.0 by subtracting half an error from the percentage of correct responses before running further analyses. We also corrected the percentage of correct responses that equaled exactly 0 and 0.5 by adding half an error, respectively.…”

Section: Iatsmentioning

confidence: 99%

Lying on the Dissection Table: Anatomizing Faked Responses

2022

View full text Add to dashboard Cite

Research has shown that even experts cannot detect faking above chance, but recent studies have suggested that machine learning may help in this endeavor. However, faking differs between faking conditions, previous efforts have not taken these differences into account, and faking indices have yet to be integrated into such approaches. We reanalyzed seven data sets (N = 1,039) with various faking conditions (high and low scores, different constructs, naïve and informed faking, faking with and without practice, different measures [self-reports vs. implicit association tests; IATs]). We investigated the extent to which and how machine learning classifiers could detect faking under these conditions and compared different input data (response patterns, scores, faking indices) and different classifiers (logistic regression, random forest, XGBoost). We also explored the features that classifiers used for detection. Our results show that machine learning has the potential to detect faking, but detection success varies between conditions from chance levels to 100%. There were differences in detection (e.g., detecting low-score faking was better than detecting high-score faking). For self-reports, response patterns and scores were comparable with regard to faking detection, whereas for IATs, faking indices and response patterns were superior to scores. Logistic regression and random forest worked about equally well and outperformed XGBoost. In most cases, classifiers used more than one feature (faking occurred over different pathways), and the features varied in their relevance. Our research supports the assumption of different faking processes and explains why detecting faking is a complex endeavor.

show abstract

Section: Iatsmentioning

confidence: 99%

Lying on the Dissection Table: Anatomizing Faked Responses

2022

View full text Add to dashboard Cite

show abstract

“…For non-decision time T er Wabersich and Vandekerckhove (2014). EZ and EZ2 refer to estimation methods for the simple DDM developed by Wagenmakers et al (2007) and Grasman et al (2009), respectively. and across-trial variability in non-decision time s T er , the selected model was a zero-bounded truncated t distribution (wAIC = 1 for both T er and s T er ).…”

Section: Resultsmentioning

confidence: 99%

“…When the DDM was applied to the same data across different articles, we extracted the parameter estimates from the first application; if the first application did not report parameter estimates, we used the most recent application that reported parameter estimates. Finally, articles that obtained estimates using the EZ (Wagenmakers et al, 2007) or EZ2 (Grasman et al, 2009) methods, or the RWiener R package (Wabersich and Vandekerckhove, 2014), which all fit the simple diffusion model estimating only the four main DDM parameters (Stone, 1960), were excluded due to concerns about potential distortions caused by ignoring across-trial parameter variability (Ratcliff, 2008). Note that we did not automatically exclude all articles without across-trial variability parameters.…”

Section: Data Extractionmentioning

confidence: 99%

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

et al. 2021

View full text Add to dashboard Cite

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient design of experiments. Importantly, parameter reviews provide crucial—if not indispensable—information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters (Lee, 2018). In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model (DDM; Ratcliff and McKoon, 2008), the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process.

show abstract

“…Using simpler process models for empirical research is supportive of previous literature, with the notion that "less is more" when it comes to selecting a psychometric model for accurately estimating predictor and participant effects from experimental data. For example, van Ravenzwaaij, Donkin, and Vandekerckhove (2016) show that simpler SSMs with fewer parameters (e.g., the EZ-diffusion model, Wagenmakers, Van Der Maas, & Grasman, 2007) recovered the significant predictor effects in experiments better than their more complex counterparts, the Diffusion Decision Model (DDM, Ratcliff, 1978;Ratcliff & Smith, 2004;Ratcliff & McKoon, 2008). Such findings also highlight the growing differences between simple SSMs as apt measurement (quantitative, data-driven, psychometric) models, and others which are more suited for theoretical exploration (simulation testing, data-producing) of specific neural dynamics.…”

Section: Discussionmentioning

confidence: 99%

Improved information pooling for hierarchical cognitive models through multiple and covaried regression

2017

View full text Add to dashboard Cite

Cognitive process models are fit to observed data to infer how experimental manipulations modify the assumed underlying cognitive process. They are alternatives to descriptive models, which only capture differences on the observed data level, and do not make assumptions about the underlying cognitive process. Process models may require more observations than descriptive models however, and as a consequence, usually fewer conditions can be simultaneously modeled with them. Unfortunately, it is known that the predictive validity of a model may be compromised when fewer experimental conditions are jointly accounted for (e.g., overestimation of predictor effects, or their incorrect assignment). We develop a hierarchical and covaried multiple regression approach to address this problem. Specifically, we show how to map the recurrences of all conditions, participants, items, and/or traits across experimental design cells to the process model parameters. This systematic pooling of information can facilitate parameter estimation. The proposed approach is particularly relevant for multi-factor experimental designs, and for mixture models that parameterize per cell to assess predictor effects. This hierarchical framework provides the capacity to model more conditions jointly to improve parameter recovery at low observation numbers (e.g., using only 1/6 of trials, recovering as well as standard hierarchical Bayesian methods), and to directly model predictor and covariate effects on the process parameters, without the need for post hoc analyses (e.g., ANOVA). An example application to real data is also provided.

show abstract

Elementary Catastrophe Theory

Cited by 8 publications

References 41 publications

Lying on the Dissection Table: Anatomizing Faked Responses

Lying on the Dissection Table: Anatomizing Faked Responses

Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model

Improved information pooling for hierarchical cognitive models through multiple and covaried regression

Contact Info

Product

Resources

About