Dynamic Models of Choice (DMC) is R software that fits and evaluates evidence accumulation models to data from factorial designs using Bayesian methods (DEMCMC sampling, Turner, Sederberg, Brown & Steyvers, 2013). Currently supported models include the linear ballistic accumulator (LBA: Brown & Heathcote, 2008), lognormal race (LNR, Heathcote & Love, 2012) and drift-diffusion model (DDM, Ratcliff & McKoon, 2008), using distribution functions provided by the rtdists package (Singmann, Gretton, Brown & Heathcote, 2015).
DMC is documented through a series of lessons guiding users through example analyses. We have offered a series of workshops on DMC; access to the DMC software and ongoing updates is given to workshop attendees.
The DMC software can now be downloaded from its OSF page here.
Please contact matthew.gretton@utas.edu.au if you have any questions.
DMC is documented through a series of lessons guiding users through example analyses. We have offered a series of workshops on DMC; access to the DMC software and ongoing updates is given to workshop attendees.
The DMC software can now be downloaded from its OSF page here.
Please contact matthew.gretton@utas.edu.au if you have any questions.
Testimonial: Model-based Neuroscience Summer School in Amsterdam (June, 2015)
I attended a one-day DMC workshop by Prof. Andrew Heathcote during the model-based neuroscience summer school in Amsterdam. Prof. Heathcote started with a theoretical introduction but dedicated most of the time to the practical application of the rtdists toolbox. We learned how to setup LBA models and had time to explore model predictions about RTs and accuracies in simulation exercises. We also fit these simulated data to see how well parameters could be recaptured and to get an impression of how the goodness of fit can be assessed. I am now able to specify my own models to 1) derive model-based hypotheses about participant behavior and to 2) fit the models to real datasets. I can highly recommend the DMC workshop and would love to learn more about the more complex applications of DMC in the future. |
Testimonial: Cognitive Science Conference Tutorial in Pasadena (July, 2015)
I have attended a lot of workshops, but none that integrated theory and practice in such a seamless and efficient way. The introduction of the different core theoretical notions (e.g., classes of evidence-accumulation models, differential evolution MCMC methods) was accompanied by timely placed examples and exercises that allowed participants to quickly acquire a good understanding. Given the sophistication of some of the topics being discussed, I was impressed by how much I actually learned in just a few hours. One of the reasons this workshop worked so well was that the organizers invested considerable efforts in the development of (open-source) software tools that really improved the learning experience: Instead of being swamped with "implementation issues" right from the start, participants were provided with a framework that allowed them to easily express models, predictions, and their relation with data in a clear and intuitive manner. |
