3. | Boehm, Udo; Annis, Jeff; Frank, Michael; Hawkins, Guy; Heathcote, Andrew; Kellen, David; Krypotos, Angelos-Miltiadis; Lerche, Veronika; Logan, Gordon D; Palmeri, Thomas; van Ravenzwaaij, Don; Servant, Mathieu; Singmann, Henrik; Starns, Jeffrey; Voss, Andreas; Wiecki, Thomas; Matzke, Dora; Wagenmakers, Eric-Jan: Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations. In: Journal of Mathematical Psychology, vol. 87, pp. 46-75, 2018. @article{Boehm2018,
title = {Estimating Across-Trial Variability Parameters of the Diffusion Decision Model: Expert Advice and Recommendations},
author = {Udo Boehm and Jeff Annis and Michael Frank and Guy Hawkins and Andrew Heathcote and David Kellen and Angelos-Miltiadis Krypotos and Veronika Lerche and Gordon D Logan and Thomas Palmeri and Don van Ravenzwaaij and Mathieu Servant and Henrik Singmann and Jeffrey Starns and Andreas Voss and Thomas Wiecki and Dora Matzke and Eric-Jan Wagenmakers},
url = {https://psyarxiv.com/km28u/, preprint on OSF},
doi = {10.31234/osf.io/km28u},
year = {2018},
date = {2018-12-01},
urldate = {2018-09-25},
journal = {Journal of Mathematical Psychology},
volume = {87},
pages = {46-75},
abstract = {For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM’s success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM’s between-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM’s across-trial variability parameters.},
keywords = {Diffusion model, mathematical modeling, measurement models, Software},
pubstate = {published},
tppubtype = {article}
}
For many years the Diffusion Decision Model (DDM) has successfully accounted for behavioral data from a wide range of domains. Important contributors to the DDM’s success are the across-trial variability parameters, which allow the model to account for the various shapes of response time distributions encountered in practice. However, several researchers have pointed out that estimating the variability parameters can be a challenging task. Moreover, the numerous fitting methods for the DDM each come with their own associated problems and solutions. This often leaves users in a difficult position. In this collaborative project we invited researchers from the DDM community to apply their various fitting methods to simulated data and provide advice and expert guidance on estimating the DDM’s between-trial variability parameters using these methods. Our study establishes a comprehensive reference resource and describes methods that can help to overcome the challenges associated with estimating the DDM’s across-trial variability parameters. |
2. | Mizrak, Eda; Singmann, Henrik; Öztekin, Ilke: Forgetting Emotional Material in Working Memory. In: Social Cognitive and Affective Neuroscience, vol. 13, no. 3, pp. 331–340, 2018. @article{Mizrak2018,
title = {Forgetting Emotional Material in Working Memory},
author = {Eda Mizrak and Henrik Singmann and Ilke Öztekin},
url = {https://academic.oup.com/scan/article-pdf/13/3/331/24238824/nsx145.pdf, free publisher pdf
https://academic.oup.com/scan/article/13/3/331/4767720, publisher website},
year = {2018},
date = {2018-03-01},
journal = {Social Cognitive and Affective Neuroscience},
volume = {13},
number = {3},
pages = {331–340},
keywords = {Diffusion model, emotion, measurement models, memory, neuro, working memory},
pubstate = {published},
tppubtype = {article}
}
|
1. | Merkt, Julia; Singmann, Henrik; Bodenburg, Sebastian; Goossens-Merkt, Heinrich; Kappes, Andreas; Wendt, Mike; Gawrilow, Caterina: Flanker performance in female college students with ADHD: a diffusion model analysis. In: ADHD Attention Deficit and Hyperactivity Disorders, vol. 5, no. 4, pp. 321–341, 2013, ISSN: 1866-6116, 1866-6647. @article{merkt_flanker_2013,
title = {Flanker performance in female college students with ADHD: a diffusion model analysis},
author = {Merkt, Julia and Singmann, Henrik and Bodenburg, Sebastian and Goossens-Merkt, Heinrich and Kappes, Andreas and Wendt, Mike and Gawrilow, Caterina},
url = {http://singmann.org/download/publications/Merkt%20et%20al.%20(2013)%20-%20ADHD.pdf, published article},
issn = {1866-6116, 1866-6647},
year = {2013},
date = {2013-01-01},
journal = {ADHD Attention Deficit and Hyperactivity Disorders},
volume = {5},
number = {4},
pages = {321--341},
keywords = {ADHD, Diffusion model, Flanker task, mathematical modeling, measurement models},
pubstate = {published},
tppubtype = {article}
}
|