Fast-dm: A free program for efficient diffusion model analysis

By A. Voss, Jochen Voss. Behavior Research Methods, 2007

375 citations recorded by the source corpus.

Subfields: Nuclear and High Energy Physics, Radiology, Nuclear Medicine and Imaging, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/5d6d394001f9da8457d5a00e1d7378f55c34221a

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Citations in the corpus

Combining speed and accuracy to control for speed-accuracy trade-offs(?) (2018, positive): “…speed– accuracy measures on data artificially generated by the well-established diffusion model (Ratcliff, 1978; Ratcliff, Smith, Brown, & McKoon, 2016; Ulrich, Schröter, Leuthold, & Birngruber, 2015; Vandekerckhove & Tuerlinckx, 2007; Voss & Voss, 2007; Wagenmakers, van der Maas, & Grasman, 2007).”
Diffusion Decision Model: Current Issues and History (2016, background): “Fitting packages are available (but subject to misuse if not understood): fast-dm [124], DMAT [125], HDDM [126], as well as alternative fitting methods [127].”
How to use the diffusion model: Parameter recovery of three methods: EZ, fast-dm, and DMAT (2009, methodology): “Code is provided in the Appendix of the paper byWagenmakers et al. (2007), which runs on the open-source statistical package R (R Development Core Team, 2004).2 The second method is the fast-dm software package,3 which is based on a Kolmogorov–Smirnov fitting routine (Voss & Voss, 2008, 2007).”
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python (2013, methodology): “This way of deﬁning parameters to be split by condition is directly inspired by the fast-dm toolbox (Voss and Voss, 2007). m_stim = hddm.HDDM(data, depends_on={’v’: ’stim’}) m_stim.find_starting_values() m_stim.sample( 2000,burn=20) Note that while every subject was tested on each condition in this…”
Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy (2009, methodology): “The fast-dm program is a platform-independent command line tool developed by Andreas and Jochen Voss (Voss & Voss, 2007, 2008).9 Just as the DMAT program, fast-dm is able to estimate all of the Ratcliff diffusion model parameters, including h, sz, and st. Fast-dm’s parameter estimation routine first”
Inferring Causality from Noninvasive Brain Stimulation in Cognitive Neuroscience (2020, background): “Such models are particularly sensitive toward slight adaptations of response strategies that may be overlooked when relying on composite measures (Ratcliff & McKoon, 2008; Voss & Voss, 2007), especially in NIBS studies suffering from relatively small effect sizes (see also Hartwigsen et al., 2015).”